new technology related to drug administration essay

MINI REVIEW article

The impact of technology on safe medicines use and pharmacy practice in the us.

• 1 MediHealthInsight, Scottsdale, AZ, United States
• 2 Division of Pharmacy Practice and Science, College of Pharmacy, The Ohio State University, Columbus, OH, United States

For decades it has been suggested that pharmacists are under-utilized and could better use their knowledge and experience to improve the use of medicines. The traditional roles for pharmacists have been preparing and distributing medicines, but this has limited both the location where they work and the available time to work more closely with other healthcare professionals to improve both the effectiveness and safety of medicines. Newly emerging technologies have made this possible. Examples include robotics that automate preparation and distribution of medicines, electronic health information, clinical decision support systems, and machine readable coding on medicine packaged. As a result of the use of these technologies, pharmacists in hospitals are working outside the hospital pharmacy and spending more time in medication therapy management activities compared to traditional distribution roles.

Introduction

The adoption of innovative ideas can be painfully slow, even when an innovation has well-demonstrated positive impact. In his work Diffusion of Innovation, Rogers concluded that based on a study of the adoption of new ideas, it takes decades for an innovation to be widely accepted ( Rogers, 2003 ). One would like to think that when the public clearly benefits from a technology that the adoption rate would be quicker. In health care, where innovations have the potential to improve the effectiveness, safety, and efficiency of care, the imperative for change would seem to be clear. As we will see, this is not always the case.

What We Know About Medicines Use

The American Society of Health-System Pharmacy (ASHP) is an organization representing pharmacists practicing in hospitals and organized health care settings. For more than 50 years, ASHP has surveyed hospital pharmacy directors in US hospitals beginning with the landmark publication Mirror to Hospital Pharmacy in 1965 ( Francke et al., 1964 ). This report assessed the practice of pharmacy in US hospitals. This audit revealed a scope of service that was limited to drug preparation and distribution. The authors challenged the profession to aspire to a more professional role by strengthening the relationship between physicians and pharmacists to improve the use of medicines.

Since then, more than 20 such surveys have been conduced. Since 1998, this survey has been conduced annually and the results provide the opportunity to identify, track, and trend changes in medicines use practices and the role of pharmacists in hospitals ( Ringold et al., 1999 , 2000 ; Pedersen et al., 2001 , 2003 , 2004 , 2005 , 2006 , 2007 , 2008 , 2009 , 2010 , 2011 , 2012 , 2013 , 2014 , 2015 , 2016 , 2017 ; Schneider et al., 2018 ). Through these surveys, progress toward the vision described in Mirror to Hospital Pharmacy has been largely realized, albeit in a time frame that supports Rogers’ theory of the diffusion of innovations. Much of this progress has been through the use of technology.

At the inception, these surveys were intended to determine what pharmacists were doing in hospitals and to assess the scope of pharmacy services in the US. In 1998, the surveys were reformatted to focus on the steps in the system of medicines-use, not just pharmacy practice. It acknowledged that this system includes many participants, not just pharmacists. At the time, a widely published report from the Institute of Medicine titled To Err is Human called attention to harm resulting from care intended to help patients ( Institute of Medicine, 2000 ). This report focused on problems with the system of care as being more significant than the performance of individual health care professionals. That was the rationale for changing the format of the survey.

The Epidemiology of Problems With Medicines Use

The medicines use system is at least multidisciplinary but ideally an interdisciplinary system. While the roles of health care professionals overlap and can vary, typically a physician prescribes a medicine, a pharmacist prepares and dispenses the dose, and the nurse, patient or family member administers the drug. The performance of each member of this team can affect the effectiveness, safety, and efficiency of a medicine and its use, but so can the interaction of these participants in the system, each of whom functions at different places and times in the process. Problems with handoffs and communication often contribute to errors, adverse events and a loss of efficiency. It would seem obvious that technology could improve the performance of the system if medicines use to the benefit of patients, health care professionals, and the institution in which the patients are cared.

Problems with the performance of the medicines use system have been studied. A seminal study that called attention to problems with the use of medicines was the Harvard Medical Practice study. The results of a review of more than 30,000 patient records found that 3.7% of hospitalized patients were injured from the care that they received ( Brennan et al., 1991 ). The most common injury was “drug complications,” which accounted for 19% of all events detected ( Leape et al., 1991 )In a follow up study of medication errors in hospitalized patients, it was found that mistakes resulting in harm to patient occurred by all participants in the medicines use system and at all steps in the system. The most common step where errors occurred was when medicines were prescribed. Fully19% of the mistakes that resulted in harm to patients were detected at this step. The next most common step was when a nurse administered a medicine to a patient, where 17% of the errors occurred. Less common steps where errors occurred was during the transcribing of orders for medicines and when doses were prepared ( Bates et al., 1995 ). These investigators looked further to determine what “system failures” contributed to the medication errors. The two most common underlying causes were a lack of information about the patient (e.g., unknown allergy to a drug) and a lack of information about the drug (e.g., an need to adjust the dose in certain patients) ( Lucian et al., 1995 ). It is easy to see how technology can be used to address these system problems.

Safe Practices for Medicines Use

Well-before the Harvard Medical Practice study, problems with drug administration errors in by nurses to hospitalized patients were documented. Studies showed error rates of up to 10% occurred when comparing what was prescribed to what was actually administered to the patient ( Barker, 1969 ). The “system” of medicines use in place at the time this study was conducted was one where all drugs were stocked in bulk containers on nursing units in the hospital. A “medication nurse” would prepare a medication tray by taking individual doses from the bulk supply, placing them in individual containers for each patient on the unit, and going from room to room to administer the doses at the scheduled time. With this method, the nurse performed both a dispensing and drug administration role and was expected to administer what the physician ordered with out questioning it. A new system that included more double checks and safeguards called the “unit dose” system was shown to reduce medication errors by as much as 50%. This system transferred responsibility for dispending medicines from the nurse to the pharmacist, providing an additional double check by the pharmacist in the process. It also provided a limited supply (24 h or less) of individually packaged and labeled medicines in a patient-specific container for the nurse to use when administering medicines ( Barker, 1969 ). This made it less likely that the wrong dose or wrong drug would be administered or the medicine administered to the wrong patient or at the wrong time.

While evidence supporting the safety of unit dose drug distribution system was published in 1968, the adoption of this innovation followed the timeline suggested by Rogers in Diffusion of Innovation. For example, in 1975, unit dose drug distribution systems were in place in only 18% of US hospitals. It took until 1995; 20 years for this system to be adopted in 92% of hospitals – consistent with the timeline of Rogers ( Schneider et al., 2018 ) (Figures 1 , 2 ).

FIGURE 1. Trends in drug dispensing.

FIGURE 2. Twenty Four-hour review of medication orders by pharmacists.

Technology Enabled Changes

There are opportunities to harness new technologies to improve the use of medicines and transform pharmacy practice. Baines and colleagues has presented a conceptual framework for analyzing production methods, productivity and technology in pharmacy practice that differentiates between dispensing and pharmaceutical care services. They outline a framework to study the relationship between pharmacy practice and productivity, shaped by educational and technological inputs ( Baines D. et al., 2018 ).

While the transfer of responsibility for dispensing of medicines from nursing to pharmacy had a positive impact on patient safety, it did not improve operational efficiency. Preparing patient-specific medication bins for each patient every day (if not many times per day) was much more labor intensive and expensive. It also created a lag between when medications were prescribed and when they were available to the nurse for administration to the patient. Medication orders change during the day, making the unit dose bins from the pharmacy outdated. This became worse as acuity increased and length of stay decreased. Two technology enabled changes resulted: robotic filling of unit dose bins and stocking medications in patient care areas using automated dispensing cabinets.

While robotic enabled centralized unit dose cart filling systems solved the workforce issue and improved accuracy in dispensing, it did not solve the responsiveness to order changes. Moreover, robotic systems were very expensive and their use limited to very large hospitals. What became a more popular option was re-locating medications to the patient care areas using automated dispensing cabinets. To illustrate this, only 7% of US hospitals employed robotic technology in 2002 for centralized unit dose drug distribution systems. This remained steady over time and only 8% had this technology by 2017 ( Schneider et al., 2018 ). In contrast, 22% of US hospitals used automated dispensing cabinets in patient care areas for drug dispensing in 2002, but this rose to 70% by 2017. A summary of these trends is shown in Figure 1 . Robotic technology could also used to compounding sterile preparations in the pharmacy. This technology is quite expensive and does not enjoy widespread use in US hospitals. The percentage using this technology has remained steady at less than 3% in the 6 years that this has been surveyed ( Schneider et al., 2018 ). Robotic technology is more commonly used to compound more complex nutrition support formulations. These preparations are not only time consuming to compound, but errors are more likely to result in harm to patients. In 2017, 14.3% of hospitals used robotic technology to compound nutrition support formulations ( Schneider et al., 2018 ).

Reverting back to a floor stock system, albeit a technology enabled one created the potential to risk an increase in medication errors comparable to the rate documented with the traditional floor stock system. Again – technology to the rescue. Two innovations emerged to prevent a return to unacceptable medication error rates. The first was the “profiling system” where a pharmacist review and approval of the prescribed therapy was necessary before a nurse could access a medicine from an automate dispensing cabinet. The second was “lidded pockets” where access to any container within the automated dispensing cabinet is restricted so that a nurse does not have free access to all medicines in the automated dispensing cabinet; only the pocket that has the medicine ordered for that patient. The use of lidded pockets has increased from 51.8% in 2008 to 70.1% in 2017 ( Schneider et al., 2018 ).

A review of prescription orders by a pharmacist is considered a safe medication practice. Lesar, at all found 3 errors per 1000 prescriptions detected by hospital pharmacists ( Lesar et al., 1997 ). A double check by pharmacist is important to detect and prevent errors in prescribing causing an adverse drug event. Unit dose and decentralized automated dispensing cabinet- based systems with profiling offer this double check before a dose is made available for administration to the patient. Historically, this practice required a pharmacist to be physically present in the hospital to review prescriptions and a 24-h pharmacy service, which was expensive and not always possible in smaller hospital. The advent of technologies including the electronic health record and properly configured automated dispensing cabinets has made it possible for a pharmacist to review and approve prescriptions remotely before a nurse can obtain and administer a medicine to a patient. As a result, the percentage of hospitals where a pharmacist does not review prescriptions before a medicine is available for administration to a patient has continued to decline from 60% in 2005 to 11% in 2017 as shown in Figure 2 ( Schneider et al., 2018 ).

Computer prescriber order entry systems were thought to reduce the need for pharmacist to review medication orders before doses were available for administration to the patient. These systems employ clinical decision support, which alert prescribers to potential dosing errors, drug allergies and drug interactions. Early investigators considered this a “systems solution” to address some of the more common underlying causes of prescribing errors; namely lack of information about the patient and the drug prescribed ( Bates et al., 1995 ; Lucian et al., 1995 ). Between 2003 and 2016, the percentage of US hospitals with computer prescriber order entry systems with clinical decision support increased from 2.5 to 95.6% ( Pedersen et al., 2017 ). While electronic prescribing has become almost universal, problems with alert fatigue and the low positive predictive value of alerts has limited the impact of these systems on error rates and has not eliminated the value of a pharmacist review of medication orders.

Another technology that has been show to improve safety and efficiency is machine-readable bar coding of medication packages. This technology has been used in many industries to more accurately reconcile and verify the identity of objects and would have logical application in verifying the identity of medicines, the persons handling them, and the patient to whom they are administered. This technology has been used in both the pharmacy to improve the safety and efficiency of drug storage, preparation, and dispensing, and by nursing to improve the safety and efficiency of drug administration and documentation in the medication administration record. Barcode scanning is also used to verify ingredients when sterile preparations are compounded in the pharmacy. The percentage of hospitals that are doing this has increased from 11.9% in 2011 to 26.9% in 2017 ( Schneider et al., 2018 ). The use of machine-readable coding to verify the accuracy of drug dispensing has increased from 5.7% in 2002 to 61.9% in 2017 ( Schneider et al., 2018 ). This technology is also used to verify the accuracy of restocking automated dispensing cabinets in patient care areas. The percentage of hospitals doing this has increased from 43.3% in 2011 to 74.7% in 2017 ( Schneider et al., 2018 ). Bedside bar code reconciliation of doses during drug administration by nurses enjoys widespread use. In 2016, 92.6% of US hospitals used this technology; an increase from only 1.5% in 2002 ( Pedersen et al., 2017 ).

How Has This Transformed Pharmacy Practice in Hospitals?

Dating back to Mirror of Hospital Pharmacy, there has been a commitment to advancing the role of pharmacists to improve the use of medicines in hospitals ( Rogers, 2003 ). To that end, ASHP and the ASHP Research and Education Foundation sponsor the Practice Advancement Initiative (PAI) ( American Society of Health-System Pharmacists, 2018 ). The goal of this initiative is to significantly advance the health and well-being of patients by supporting futuristic practice models that support the most effective use of pharmacists as direct patient care providers ( Baines D.L. et al., 2018 ).

A newer role for pharmacists is medication therapy management either by standing protocol or prescriber order/delegation or pharmacists have responsibility for writing medication orders, selecting doses, ordering appropriate laboratory tests, and monitoring patient response to therapy. In 2016, pharmacists managed the following therapies: vancomycin (94% of hospitals), renal dosing of antibiotics (83.9%), aminoglycosides (83.8%), anticoagulants (71.1%), nutrition support (46.9%), selection of antibiotics (19.6%), and pain management (6.2%). These percentages were higher than they were in 2013 ( Pedersen et al., 2017 ). The impact of pharmacist medication management services is measured by the following indicators: cost saving (61.5% of hospitals), patient outcomes (36.5%), federal quality of care indicators (23.7%), readmission rates (16.6%), and patient satisfaction scores (15.8%), decreases in length of stay (8.3%) ( Pedersen et al., 2017 ).

The transition of the pharmacist role from drug preparation and distribution to medication therapy management has resulted in their practice moving from a central pharmacy to the patient care areas. The following clinical areas commonly have pharmacists routinely assigned to manage therapies to a majority of patients at least 8 h/day, 5 days/week: inpatient medical-surgical (43.5% of hospitals). critical care (43.5%), oncology (37.5%), cardiology (32.9%), pediatrics (24.1%), and the emergency department (21.0%) ( Pedersen et al., 2016 ).

Besides enabling a transition in drug preparation and dispensing, technology also enables medication therapy management by pharmacists. Not all patients can or need medication therapy monitoring by pharmacists. A total of 43.4% of hospitals use computerized data mining to identify patients in need of monitoring. Some electronic health records have data mining functionality (58.6%), and others use proprietary clinical surveillance software (28.4%) to compile data needed to identify patients for daily monitoring by pharmacists ( Pedersen et al., 2016 ).

The transition of the patient from the hospital to the community (and back) is a step in health care where handoffs are missed and miscommunication occurs. Pharmacists are also becoming increasingly involved in transitions of care programs to reduce errors and improve care. Some examples of transitions of care activities by pharmacists include: use of medication histories at admission (74.9% of hospitals; in 2016 up from 54.3% in 2002), discharge medication counseling by pharmacists (46.4% from 21.7%), participation in discharge planning (35.8% up from 23.7%), handoff to community pharmacy at discharge (18.3% up from 917%), and designing a patient-specific medication-related action plan (11.2% up from 5.3%) ( Pedersen et al., 2017 ).

As a result of this change in the role of pharmacists, the percentage of them that they spend in drug distribution is now less than 20%. They spend more than 40% of their time reviewing and verifying prescription orders and almost 25% of their time on other clinical activities, including medication therapy management. Besides changes enabled by technology, pharmacy technicians are widely used in hospital pharmacy departments to support that role and activities of pharmacists. Pharmacy technicians spend almost 80% of their time in traditional drug preparation and distribution ( Pedersen et al., 2016 ).

Transitioning from a traditional drug preparation and dispensing role to a clinical role in medication therapy management has had implications for developing the hospital pharmacy workforce. Beginning in 2000, all US pharmacy graduates receive the Doctor of Pharmacy (PharmD) degree that prepares them for the increasing clinical roles that all pharmacists are realizing. These clinical roles are more common and often more advanced in the hospital setting, and additional training is available and increasingly required. These include post-graduate pharmacy residency training; both pharmacy practice (PGY1) and specialty (PGY2) programs. Board certification is also available to assess the competency of pharmacists in selected specialty practice areas through the Board of Pharmaceutical Specialties. Board certification is also available for pharmacy technicians through the Pharmacy Technician Certification Board. At present, 29.4% of hospital pharmacists have completed a PGY1 residency, 8.1% a PGY2 residency, and 23.1% are board certified. For pharmacy technicians the percentage that are board certified is much higher (77.8%) because there is no standard degree awarding program to prepare pharmacy technicians ( Schneider et al., 2018 ).

There is currently a need for high quality evaluation of new technologies undertaken in a pharmacy-related setting. We aim to evaluate the use of these monitoring technologies performed in this setting. Worldwide, few evaluations of mobile health, telehealth, smart pump, and monitoring technologies in pharmacy-related setting have been published. Their quality is often below the standard necessary for inclusion in a systematic review mainly due to inadequate study design. Despite the improvements in technology, there is limited evidence on how this translates to real settings and to consumer satisfaction. Most technology driven systems required significant funding and support, particularly those involving latest technology. Rigorous comparative studies are needed to evaluate the effectiveness of different technologies ( Baines D.L. et al., 2018 ).

Nevertheless, voices within the profession of pharmacy have long called for a more important role for the pharmacist. More recently, the public began to call for improvements in the quality of health care, particularly patient safety. New systems of care, many enabled by new technologies have the potential to improve the effectiveness, safety and efficiency of health care, and transform the roles of health care professionals including pharmacists. Unfortunately, the adoption of change is slow, and even though the health of the public is at stake, change in health care is no exception. Over the past decades, however new technologies have enabled the pharmacist to devote more time to working with other health care professionals to improve the use of medicines. Since virtually every patient in the health care system receives medicines, and there is ample evidence that the use of medicines needs to improve, this is a good thing.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

PS was employed by the company MediHealthInsight. The author declares no competing interests.

American Society of Health-System Pharmacists (2018). Available at: http://www.ashpmedia.org/pai/index.html [accessed June 5, 2018].

American Society of Hospital Pharmacists (1989). ASHP statement on unit dose drug distribution. Am. J. Hosp. Pharm. 46:2346.

PubMed Abstract | Google Scholar

Baines, D., Bates, I., Bader, L., Hale, C., and Schneider, P. (2018). Conceptualising production, productivity and technology in pharmacy practice: a novel framework for policy, education and research. Hum. Resour. Health 16:51. doi: 10.1186/s12960-018-0317-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Baines, D. L., Gahir, I. K., Hussain, A., Khan, A. J., Schneider, P., Hasan, S. S., et al. (2018). A scoping review of the quality and the design of evaluations of mobile health, telehealth, smart pump and monitoring technologies performed in a pharmacy-related setting. Front. Pharmacol. 9:678. doi: 10.3389/fphar.2018.00678

Barker, K. N. (1969). The effects of an experimental medication system on medication errors and costs. I. Introduction and errors study. Am. J. Hosp. Pharm. 26, 324–333.

Bates, D. W., Cullen, D. J., Laird, N., Petersen, L. A., Small, S. D., and Servi, D. (1995). Incidence of adverse drug events and potential adverse drug events: implication for prevention. JAMA 274, 29–34. doi: 10.1001/jama.1995.03530010043033

Brennan, T. A., Leape, L. L., Laird, N. M., Localio, A. R., Lawthers, A. G., et al. (1991). Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. N. Engl. J. Med. 324, 370–376. doi: 10.1056/NEJM199102073240604

Francke, D. E., Latiolais, C. J., Francke, G. N., and Ho, N. F. H. (1964). Mirror to Hospital Pharmacy; A Report of the Audit of Pharmaceutical Service in Hospitals. Washington, DC: American Society of Hospital Pharmacists.

Google Scholar

Institute of Medicine (2000). To Err Is Human: Building a Safer Health System. Washington, DC: The National Academies Press.

Leape, L. L., Brennan, T. A., Laird, N., Lawthers, A. G., Localio, A. R., Barnes, B. A., et al. (1991). The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II. N. Engl. J. Med. 324, 377–384. doi: 10.1056/NEJM199102073240605

Lesar, T. S., Lomaestro, B. M., and Pohl, H. (1997). Medication-prescribing errors in a teaching hospital - A 9-year experience. Arch. Intern. Med. 157, 1569–1576. doi: 10.1001/archinte.1997.00440350075007

Lucian, L. L., Bates, D. W., Cullen, D. J., Cooper, J., Demonoco, H. J., Gallivan, T., et al. (1995). Systems analysis of adverse drug events. JAMA 274, 35–43. doi: 10.1001/jama.1995.03530010049034

CrossRef Full Text | Google Scholar

Pedersen, C. A., Schneider, P. J., and Santell, J. P. (2001). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2001. Am. J. Health Syst. Pharm. 58, 2251–2266.

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2003). ASHP national survey of hospital pharmacy practice in hospital settings: dispensing and administration – 2002. Am. J. Health Syst. Pharm. 60, 52–68.

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2004). ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education - 2003. Am. J. Health Syst. Pharm. 61, 457–471.

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2005). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2004. Am. J. Health Syst. Pharm. 62, 378–390.

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2006). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2005. Am. J. Health Syst. Pharm. 63, 327–345. doi: 10.2146/ajhp050538

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2007). ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education – 2006. Am. J. Health Syst. Pharm. 64, 507–520. doi: 10.2146/ajhp060669

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2008). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2007. Am. J. Health Syst. Pharm. 65, 827–843. doi: 10.2146/ajhp080029

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2009). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2008. Am. J. Health Syst. Pharm. 66, 926–946. doi: 10.2146/ajhp080715

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2010). ASHP national survey of pharmacy practice in hospital settings: monitoring and Patient Education – 2009. Am. J. Health Syst. Pharm. 67, 542–558. doi: 10.2146/ajhp090596

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2011). ASHP national survey of pharmacy practice in hospital settings: prescribing and Transcribing – 2010. Am. J. Health Syst. Pharm. 68, 669–688. doi: 10.2146/ajhp100711

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2012). ASHP national survey of pharmacy practice in hospital settings: dispensing and Administration – 2011. Am. J. Health Syst. Pharm. 69, 768–785. doi: 10.2146/ajhp110735

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2013). ASHP national survey of pharmacy practice in hospital settings: monitoring and Patient Education – 2012. Am. J. Health Syst. Pharm. 70, 787–803. doi: 10.2146/ajhp120777

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2014). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2013. Am. J. Health Syst. Pharm. 71, 924–942. doi: 10.2146/ajhp140032

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2015). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2014. Am. J. Health Syst. Pharm. 72, 1119–1137. doi: 10.2146/ajhp150032

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2016). ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education – 2015. Am. J. Health Syst. Pharm. 73, 1307–1330. doi: 10.2146/ajhp160081

Pedersen, C. A., Schneider, P. J., and Scheckelhoff, D. J. (2017). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2016. Am. J. Health Syst. Pharm. 74, 1336–1352. doi: 10.2146/ajhp170228

Ringold, D. J., Santell, J. P., Schneider, P. J., and Arenberg, S. (1999). ASHP national survey of pharmacy practice in acute care settings, part 1: prescribing and transcribing. Am. J. Health Syst. Pharm. 56, 142–157.

Ringold, D. J., Santell, J. P., Schneider, P. J., and Kelly, E. J. (2000). ASHP national survey of pharmacy practice in acute care settings, part 2: dispensing and administration. Am. J. Health Syst. Pharm. 57, 1759–1775.

Rogers, E. M. (2003). Diffusion of Innovations. New York, NY: Free Press.

Schneider, P. J., Pedersen, C. A., and Scheckelhoff, D. J. (2018). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2017. Am. J. Health Syst. Pharm. 75, 1203–1226. doi: 10.2146/ajhp180151

Keywords : hospital pharmacy, medication therapy management, clinical pharmacy, medication technology, patient safety

Citation: Schneider PJ (2018) The Impact of Technology on Safe Medicines Use and Pharmacy Practice in the US. Front. Pharmacol. 9:1361. doi: 10.3389/fphar.2018.01361

Received: 27 June 2018; Accepted: 05 November 2018; Published: 20 November 2018.

Reviewed by:

Copyright © 2018 Schneider. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Philip J. Schneider, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Five Technology Trends: Changing Pharmacy Practice Today and Tomorrow

HIT creates value for pharmacies and opportunities for pharmacists to use their professional skills in innovative ways in today's evolving value-based care systems.

The ways in which medications are prescribed, dispensed, and administered in the United States are rapidly changing. Health information technology (HIT) is central to this transformation and includes a broad array of tools used in managing and sharing patient information electronically, rather than through paper records and traditional phone and fax methods.

Reaching beyond real-time claims adjudication, HIT creates value for pharmacies and opportunities for pharmacists to use their professional skills in innovative ways in today’s evolving value-based care systems. The result: pharmacists will use HIT to positively affect patient care, improve patient outcomes, and reduce the cost of care. With that in mind, here are 5 technology-dependent trends that are increasingly making an impact on pharmacy.

Electronic Prior Authorization

Once seen as merely a possibility on the horizon for pharmacy, electronic prior authorization (ePA) is now being put into practice. According to CoverMyMeds’ National Adoption Scorecard , released in March 2015, 67% of pharmacies have a live solution for ePA. 1 There are 2 types of prior authorization (PA) work flow, retrospective and prospective, which affect pharmacy in profound but different ways.

In the retrospective model, a pharmacy can obtain PA after an initial rejection of a claim. For example, if a pharmacy does not know that PA is required, it might submit the claim to the payer only to have it subsequently rejected. Using the pharmacy software system, the pharmacist can then either enter a code or hit an “easy” button that will result in a form being forwarded to the provider to obtain PA. The transaction, based on standards developed and maintained by the National Council for Prescription Drug Programs (NCPDP), results in a claim being subsequently submitted by the pharmacy and paid without getting a PA edit back from the payer. This model leverages the NCPDP Telecommunication and SCRIPT standards.

In the prospective model, ePA is prescriber-initiated. In this model, the prescriber is alerted by the software—either in the stand-alone electronic prescribing (e-prescribing) application or within the electronic health record (EHR)—that PA is required. The prescription is temporarily pended and a request is sent to the payer for the criteria. With some payers, that request might result in an immediate approval. In the vast majority of cases, however, questions are returned to the prescriber who, in turn, completes the request and may subsequently be given an approval. If the submitted claim goes through without edit, the pharmacy may not even know that the prescriber requested and received approval from the payer. This model also enables the pharmacy to submit a claim without receiving a PA edit in response. The prospective model leverages the NCPDP formulary and benefit flat file and SCRIPT transaction standard.

Whether prospective or retrospective, the new ePA framework allows questions to be customized, depending on the patient and the medication involved, and supports clinical attachments, such as subsets of the medical record. With the widespread adoption of EHR technology, much of the information to be exchanged can be system-driven, reducing the burden of manually entering and reviewing the data.

Ultimately, ePA at the point of prescribing will create value for pharmacies by eliminating many PAs from their work queues and freeing up valuable time, which will allow pharmacists to focus on patients and revenue-generating activities. This is borne out by the latest data from Surescripts, which estimates ePA can save 4 hours per pharmacist per week (or $11,000 per pharmacist per year). 2 In addition, ePA helps speed the approval process, thus aiding in reducing abandoned prescriptions.

The new standard was created to address a need for a more efficient process because of the rise in PAs. At the same time, several states are mandating prior-authorization procedures, creating the potential for separate processes and standards for each state.

Implementing the new NCPDP ePA standard will add clarity and consistency across the health care continuum and the state regulatory process. It also represents an opportunity to improve the perceptions of lawmakers, patients, and physicians concerning important pharmacy outcomes management strategies, such as PA programs. ePAs have the potential to reduce costs, improve access, and improve outcomes by eliminating delays in treatment or prescription abandonment, which often occur with current processes and systems.

Medication Therapy Management

Pharmacists play a vital role in their organizations by leveraging technology to provide patient care services and medication therapy management (MTM). MTM is used to describe the broad range of health care services provided by pharmacists. These services include comprehensive medication reviews, medication reconciliation, drug use review, the ordering and review of lab tests, immunizations, drug dosage adjustments, and identification of gaps in care. Integrated systems of care, such as accountable care organizations (ACOs), already view MTM as essential to care delivery and to meeting ACO quality and cost targets. Such organizations also are heavily invested in HIT, including e-prescribing and EHRs. MTM can improve medication adherence and patient outcomes among patients suffering from chronic diseases, thus cutting costs and improving the quality of care and patient safety.

Pharmacists have been providing MTM services for Medicare patients for nearly a decade due to the requirement that Medicare Part D sponsors must have an established MTM program. Now many states require similar services for Medicaid patients and, in some cases, for those in institutional care. Commercial payers and drug chains also have come on board. The role of the pharmacist in MTM is broadening to include helping patients get involved in their health care decisions. The latter is crucial in ACOs and integrated delivery networks because it is a payment metric for Medicare and other payers. Pharmacists are also taking advantage of new technologies, such as telehealth and mobile health applications, to deliver personalized care and monitor outcomes and patient adherence.

As advanced MTM becomes more widespread, the MTM activities and interventions will require information obtained through health information exchanges (HIEs). HIEs will also provide the means to communicate pharmacy interventions to physicians and other members of the patient care team. For example, community pharmacists with HIE access could provide MTM and participate in ACO-like arrangements where they are also incentivized to improve population management and care quality. Instead of simply checking a box indicating that MTM was performed, they could contract to be measured and incented based upon cost and quality metrics achieved across certain patient panels, such as those with diabetes, high cholesterol, and hypertension.

Waging the War Against Substance Abuse

Prescription drug abuse—especially of opioids—is at epidemic levels. In 2010, 38,300 people in the United States died of drug overdose; 22,000 of these deaths were due to overdose from prescription medications, and 16,600 were from opioid pain relievers. 3

State and the federal government agencies have recognized that technology has a role in addressing this serious problem. Electronic prescribing of controlled substances (EPCS) and prescription drug monitoring programs (PDMPs) are technologies currently being used in pharmacy, and their use will accelerate in the near future (see Sidebar).

EPCS now is gaining traction and should increase rapidly due to federal and state requirements. Some 1.6 million controlled-substances prescriptions were sent electronically in 2014 through the Surescripts network. Roughly three-quarters of pharmacies can receive electronic controlled-substance prescriptions, and 1.4% of providers are enabled to send them. 2 EPCS is now legal in 49 states and the District of Columbia. 2

Regular e-prescribing and EPCS can help clinicians recognize substance abuse through medication history checks, which show both controlled and noncontrolled medications that were paid for through the patient’s insurance. E-prescribing systems and pharmacy systems can also flag potentially deadly prescription errors and drug interactions related to opioid use, thus preventing accidental deaths and overdoses. Refill request monitoring can be used to help flag abuse and diversion.

Pharmacies should expect to see EPCS transaction volume start to take off due to mandatory use of EPCS by such programs as New York’s Internet System for Tracking Over-Prescribing (I-STOP), which requires e-prescribing for all prescriptions (the implementation date was moved back to March 2016). Physicians also will have to prescribe controlled substances electronically if they are to meet the federal government’s proposed higher e-prescribing threshold of 80%, which will be required for Meaningful Use (MU) stage 3, the federal incentive program that reduces Medicare payments for noncompliant physicians.

PDMPs, which collect state-specific controlled-substance prescription data, are operational or under development in most states. Checking the PDMP database before filling a controlled substance prescription is optional in most states, but highly encouraged. It is likely to become mandatory nationwide due to pressures to combat opioid abuse now that New York’s I-STOP has paved the way on the legislative front. The federal government and other entities are working to make PDMPs more accurate, interoperable, and easier to use. Real-time PDMP checks are on the horizon.

The Rise of Specialty Prescribing

Prescriptions are rapidly increasing for specialty medications, which are high-cost, complex therapies that require special handling, administration, and monitoring. Specialty medications account for less than 1% of prescriptions but more than 25% of prescription spending, which is expected to reach 50% by 2018. 4 Outlays are expected to quadruple to $402 billion by 2020. 5

The skyrocketing use and costs of specialty medications are due to several factors: increasing numbers of elderly and chronically ill patients need specialty medications. In addition, the federal government has a program encouraging development of “orphan drugs” for rare diseases or conditions. New specialty therapies are regularly coming onto the market, as well, including biosimilars (which are not to be confused with generics) and more effective drugs for such conditions as hepatitis C and cancer. As a result, specialty prescribing is on the radar of pharmacy stakeholders, who are looking to technology to help balance the high costs of specialty drugs versus their benefits. Moreover, specialty pharmacy is ripe for automation. Rather than a single transaction, specialty prescribing requires a series of transactions, which currently are done mostly by outmoded paper, phone, and fax. E-prescribing standards and infrastructure, however, are already available to handle the basic prescription process. Other necessary elements, such as ePA, are emerging and will facilitate increased automation of the specialty prescribing processes.

In the meantime, NCPDP has published guidance to improve the use of fields supported in the current version of the SCRIPT standard that would be of value to specialty pharmacies. NCPDP members recently voted to add fields to the SCRIPT standard to accommodate other information that will greatly enhance the utility and usability of specialty e-prescribing. This includes agency and service information, which will allow the provider to indicate the preferred agency and type of service; hospice eligibility indicators; IV administration information; additional patient demographic and clinical information; order-specific clinical information; and instructions related to delivery of the medication.

Real-Time Pharmacy Benefit

Real-time pharmacy benefit (RTPB) will not be widely available in the near future, although it is being pilot-tested today. There are 2 models: one using the NCPDP telecommunication (pharmacy claim) standard and the other using the NCPDP SCRIPT standard. Both hold promise to improve accuracy and clarity to the group-level formulary and benefit paradigm that is in use today, and have the potential to curb costs and, arguably, improve health care by increasing formulary compliance and medication adherence.

RTPB would replace or enhance the current process of linking an eligibility response with downloaded data files, which have limitations because of the latency of the update process and the inconsistent quantity and quality of the data. Real-time benefit verification will greatly improve the breadth, accuracy, and effectiveness of formulary data available to the prescriber at the point of care. This will address many prescribers’ perceptions that currently available formulary and benefit data are neither correct nor complete. Moreover, having real-time benefit information in the EHR will allow the prescriber to see other desired decision factors, such as co-pay amounts for individual patients at the point of prescribing. This will help with formulary compliance and medication adherence. Research has shown that high out-of-pocket costs are a main reason why patients abandon prescriptions. 6

HIT has become essential to the pharmacy industry, dramatically changing the way medications are ordered and dispensed while creating value. It enables and supports transformational changes in pharmacists’ roles, both in traditional pharmacies and those associated with value-based care organizations. HIT facilitates increase pharmacist involvement for patient care, which will cut costs and improve outcomes. All of us—as pharmacists and as patients—can look forward to a brighter future enabled by technology.

Anthony Schueth, MS, is managing partner and CEO of Point-of-Care Partners, LLC. William Hein is a former medication therapy management executive and the payer/provider executive lead at Point-of-Care Partners, LLC. Jeffrey Hull, RPh, is a practicing retail pharmacist and a senior consultant at Point-of-Care Partners, LLC.

• National adoption scorecard: electronic prior authorization (ePA). CoverMyMeds website. https://epascorecard.covermymeds.com/. Published March 2015.
• 2014 national progress report: more connected than ever before. Surescripts website. http://surescripts.com/docs/default-source/national-progress-reports/surescripts-2014-national-progress-report.pdf. Published 2014.
• Centers for Disease Control and Prevention. National Vital Statistics System. 2010 Multiple Cause of Death File. Hyattsville, MD: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2012.
• Hirsch BR, Balu S, Schulman KA. The impact of specialty pharmaceuticals as drivers of health care costs. Health Affairs (Millwood). 2014;33(10):1714-1720. doi: 10.1377/hlthaff.2014.0558.
• The growth of specialty pharmacy: current trends and future opportunities. UnitedHealth Group website. www.unitedhealthgroup.com/~/media/UHG/PDF/2014/UNH-The-Growth-Of-Specialty-Pharmacy.ashx. Published April 2014.
• Gleason PP, Starner CI, Gunderson BW, Schafer JA, Sarran AS. Association of prescription abandonment with cost share for high-cost specialty medications. J Manag Care Pharm. 2009;15(8):648-658.

Sidebar: Electronic Prescribing of Controlled Substances: Ready to Take Off

Anthony Schueth, MS

Fasten your seat belts. Five years after becoming legal, electronic prescribing of controlled substances (EPCS) is on the runway, ready to take off. It has taken a massive industry effort to get EPCS to this point, especially considering that electronic prescribing (e-prescribing) has been available for decades. (The first such solution was created for the Veteran’s Administration in the late 1980s.) E-prescribing, one of the core capabilities of today’s electronic health records (EHRs), was initially deployed in acute care settings, 1 but in the last 7 years, US physicians and hospitals have rapidly moved toward its adoption, in large part due to the EHR Incentive Program created and funded by the Health Information Technology for Economic and Clinical Health Act of 2009.

It is exciting to have witnessed and contributed to the metamorphosis of e-prescribing. Nearly 80% of ambulatory providers are e-prescribing, and most pharmacies accept electronic prescriptions. In comparison, almost 75% of pharmacies are capable of receiving controlled substances prescriptions while only 1.4% of prescribers can receive them—although that number is quickly growing. 2 EPCS volume increased by 400% in 2014. 2

The Legislative Landscape

Although the Interim Final Rule for Electronic Prescriptions made EPCS for schedules II through V controlled substances legal from a federal perspective on June 1, 2010, each state had to enact rules and legislation to make EPCS legal according to state law and in accordance with federal law. EPCS will soon be legal in all 50 states and Washington, DC. Missouri, the last state to allow EPCS, is expected to publish a rule revision this month.

Now that technology and infrastructure have advanced, state policy makers are taking advantage of e-prescribing’s potential to help address the nation’s drug abuse epidemic through such programs as New York’s State’s Internet System for Tracking Over-Prescribing (I-STOP). It mandates that all prescriptions be sent electronically by March 27, 2016, a 1-year delay from the original implementation date. I-STOP may be the biggest health information technology game changer of all because other states are likely to emulate New York. 3

EPCS will also help physicians who prescribe controlled substances to meet the higher e-prescribing thresholds that will be required for meaningful use stage 3 of the EHR Incentive Program; the current proposal is 80%. Meeting this threshold will be of key importance for providers in certain specialties, such as oncology, who have many patients using controlled substances.

Role of EHRs

Most e-prescribing occurs within EHRs. Before EHR and e-prescribing vendors can extend EPCS to their users, a third-party certifying organization must audit their EHR application for compliance with Drug Enforcement Administration requirements Code of Federal Regulations 21 Part 1311. These requirements are involved, but many leading EHRs have already enabled their systems to handle EPCS. In the last 2 years, there has been a “popcorn effect” of EHR vendor solutions becoming certified for EPCS: In New York, for example, where I-STOP has mandated EPCS, 8 of the top 10 EHRs are certified for EPCS. 4 As could be expected, I-STOP is also driving adoption of EPCS in new sectors, such as long-term care and the dental market.

Once certified for EPCS, EHR vendors must invest time to help their prescribers establish processes to meet identity proofing, access control, dual authentication, and digital signature requirements. Pharmacies must also verify their systems are certified for EPCS, set up access controls, create an e-prescription audit process, and adhere to record-keeping requirements.

Although no technology can take the place of health care providers’ professional judgment, EPCS has tremendous potential to help improve patients’ health due to the critical information it delivers to the point of care. It has the potential to reduce prescription drug abuse, unintended or intended, by giving prescribers access to medication history at the point of prescribing and by eliminating paper prescriptions that are prone to fraud. Now, it is a matter of equipping physicians for EPCS to match the capabilities of today’s pharmacies. Most of this responsibility is in the hands of EHR and e-prescribing vendors who undoubtedly have users lining up for EPCS.

• Hillblom D, Schueth A, Robertson SM, Topor L, Low G. The impact of information technology on managed care pharmacy: today and tomorrow. J Manag Care Spec Pharm . 2014;20(11):1073-1079.
• 2014 national progress report. Surescripts website. http://surescripts.com/docs/default-source/national-progress-reports/surescripts-2014-national-progress-report.pdf. Published 2014.
• Schueth T. Readers write: I-STOP may be the biggest health IT game-changer of all. HIStalk website. http://histalk2.com/2014/10/08/readers-write-i-stop-may-be-the-biggest-health-it-game-changer-of-all/. Published October 8, 2014.
• I-STOP deadline: March 27, 2016. CVS/caremark website. http://info.caremark.com/istop-compliance/are-you-compliant. Published 2014. Accessed July 6, 2015.

Baby Steps to a Healthier You: Prioritizing Lifestyle Changes Over Medication

Counsel Patients on Lifestyle Changes to Manage Heart Failure

Strategies for Conducting Strong, Efficient Peer Reviews of Papers

Empowering Pharmacy Technicians: A Comprehensive Apprenticeship Program

Focus on Women’s Health, From Contraception to Well-Being

Enhancing Outpatient Antimicrobial Stewardship: Strategies and Core Elements

2 Commerce Drive Cranbury, NJ 08512

609-716-7777

This site is intended for health professionals only

• Reviews & Research
• Cardiovascular
• Dermatology
• Gastroenterology
• Medicines safety
• Mental health
• Paediatrics
• Respiratory
• Rheumatology

Home > News > Editor's pick > Pharmacy automation: new technologies applied to medication use

Pharmacy automation: new technologies applied to medication use

teaser Computerised automated unit dose distribution systems allows an improvement of drug distribution in the clinical ward and pharmacists need to be prepared to get the most out of them Mª Esther Gómez de Salazar MSc Clinical Pharmacist

Teresa Bermejo PhD Chief Pharmacist

Rosario Pintor MSc Clinical Pharmacist Hospital Ramón y Cajal Madrid Spain

In the 1960s, unit-dose dispensing systems (UDDS) were developed in the USA as an effective way to decrease the existing error rates affecting drug prescription, dispensing and administration. However, some problems related to drug distribution still needed to be solved: delays in the arrival of the prescriptions, slowness in the response to the needs generated by new medical orders, frequent prescription changes necessitating the repetition of work, missed doses, increases in the number and amount of drugs kept on the ward,  and communication breakdowns between the pharmacy and the clinical units.[1] As more effective alternatives for the institutions were needed, both in clinical and economic terms, robotics, informatics and automation have been gaining ground and nowadays play an important role in the activities and services of pharmacy departments. Medication use in hospitals is inherently complex, involving numerous steps from prescription to drug administration. Technology has the potential to reduce medication errors by reducing complexity, avoiding over-reliance on memory, simplifying key processes, and, if designed and implemented properly, increasing efficiency. It can also be a cost-effective tool for improving quality.[2] Hospital pharmacy practice around Europe differs between countries and so does the degree of implementation of automation and the systems in use. Focusing on Spain, in October 2004 the Working Group on New Technologies (TECNO) was set up within the Spanish Society of Hospital Pharmacy (SEFH). Its main objective is to establish the technical criteria to be met by the software and hardware applied to the safe use of medication in hospital practice. So far it has published documents dealing with automated storing, computerised prescribing, total parenteral nutrition, chemotherapy, outpatients, automated unit-dose dispensing, drug distribution and registration of pharmaceutical interventions. New technologies allow us to improve patient care and they apply to the whole pharmaceutical scope of activity:

• Medicines information and drug selection are unimaginable these days without tools like the internet.
• Clinical decision support systems and computerised physician order entry (CPOE) facilitate and optimise medical prescription and pharmaceutical validation.
• Storage and dispensing performance is increased with automation.
• Drug administration safety is improved with smart pumps and barcoding.
• Comprehensive information systems able to compile and analyse data generated in the different activities mentioned above are powerful and valuable tools in medicines management.

Prescription CPOE simplifies the medication circuit in the hospital. Direct input of information makes it available in real time for validation, eliminating intermediate processes such as transcription and collection and reception of medical orders. More advanced systems allow nursing staff to register drug administration online once it has been validated. All this, together with clinical decision support tools (alerts, protocols, dose adjustments, formulary restrictions and laboratory results) as well as informatics data records, are meaningful advantages compared to traditional prescribing. Moreover, CPOE brings pharmacy the opportunity to maintain a key role coordinating the process and actively participating in patient care. The benefits of CPOE on safety, prescription quality and institutional budgets have been confirmed in several publications, although evidence of new types of errors is emerging. A descriptive study carried out in Spain in 2007 showed that CPOE was implemented in 22.4% of the hospitals that answered the survey, reaching 25% in hospitals with 400 or more beds. The fields with higher rate of CPOE implementation were medical (52%), oncology/chemotherapy and surgical (31.5% each).[3]

Automated storage and dispensing devices These concepts include heterogeneous technology. On the one hand, there are automated systems that simplify drug delivery and storage processes, and on the other,  robotised systems that replace human activity in those procedures they have been especially designed for, mainly restocking and dispensing. The USA National Association of Boards of Pharmacy (NBPA) in the Model State Pharmacy Act and Model Rules states that: ‘Automated Pharmacy Systems include, but are not limited to, mechanical systems that perform operations or activities, other than compounding or administration, relative to the storage, packaging, dispensing or distribution of medications, and which collect, control and maintain all transaction information.’[4] Finding the most suitable system for an institution is a major consideration. There are different systems that cover different areas and needs in the drug-use process and their efficiency and acceptance will depend on how well they fit into the current workflow and also on the ability of the institution and its members to achieve the cultural change required. Horizontal and vertical carousels for storage and dispensing are more popular in some European countries such as Spain because they suit the unit-dose dispensing model prevailing there, compared to robotic systems, commoner in countries like the UK where ‘original pack dispensing’ is customary. Horizontal carousels are automated storage and retrieval, computer-controlled devices with high storage capacity centralised in the pharmacy department, consisting of a series of shelving sections (bins) mounted on a horizontal, closed-loop oval track. Once activated, the bins rotate to bring requested items to the operator or automated picker. Refrigeration equipment can be integrated so that drugs requiring controlled temperature storage conditions can be managed by these systems. All the information generated by their activity is registered and with appropriate software it can be sent to the Pharmacy Information System in order to generate purchasing orders based on stock levels or to manage incoming ward ‘top-up’ orders. When interconnected with vertical carousels or automated dispensing machines they control and simplify the whole process of drug storage and distribution. Despite the initial investment required for their setting up, horizontal carousels increase efficiency and accuracy in stock and inventory management, improve productivity and manage space in an efficient manner. Similar to horizontal, vertical carousels are automated storage and retrieval devices that consist of a series of carriers (pans) mounted on a vertical closed-loop oval track, inside a metal enclosure, that can be either refrigerated or non-refrigerated. Pans rotate to bring requested items to the operator, enabling a relatively large inventory to be directed to a picker at one fixed location. This results in a more efficient ‘parts to picker’ instead of ‘picker to parts’ workflow. Vertical carousels increase storage density, throughput and material handling while reducing picking, restocking and expiry date errors. These systems include software that keeps records of all the operations performed and which is able to retrieve data from other devices such as horizontal carousels and from processes such as CPOE, unit-dose dispensing and ward stock orders. They are used mostly to fill in unit-dose carts and to restock automated cabinets. Other centralised storage systems are dispensary-based robots designed to eliminate pick errors, speed dispensing and increase storage capacity. Their primary functions are to dispense, pack labelled boxes and restock the robot inventory. There are two broad types of pharmacy robots: channel storage robots (less complex, predominantly used in community pharmacies where stock is manually loaded into predetermined channels); and robotic random storage devices (the most common type of robots used in hospital pharmacies across the globe). They can restock automated cabinets and fill in unit-dose trolleys similarly to carousels, however, their benefits are primarily with full pack dispensing and whether they will benefit individual patient dispensing remains to be seen.[5] The advantages regarding stock and inventory management, space sparing, productivity and software solutions are overall the same compared to the other pharmacy-based systems. Decentralised systems, like automated dispensing machines (ADMs), appeared on the scene in the 1980s in the USA, reaching Europe at the end of the 1990s. Ward ADMs are drug storage devices or cabinets that electronically dispense medications in a controlled fashion and track medication use. On the one hand, they permit registered nurses (most systems require user identifiers and passwords) to obtain medications for inpatients at the point of use when they are needed and on the other, internal electronic devices track nurses accessing the system together with the patients for whom medications are administered, providing useful data about how medications are used and their cost. More advanced systems provide additional information support aimed at enhancing patient safety through integration into other external systems, databases and the internet. Some models use machine-readable code for medication dispensing and administration.[6] There is the potential to reduce time to administration of first dose, particularly when interfaced with a CPOE system: however, as each machine can only have one user at a time, insufficient numbers of devices lead to lines in front of the machines and there is an increased likelihood that nurses will take medications for more than one patient at a time, or bypass the ADM.[5] ‘Vending-machine’ style inventory storage and ADMs are becoming popular not only for ward storage, but also for pharmacy-controlled drug (CD) management. These systems of automated storage and electronic recording facilitate paperless CD management and remove the need for pharmacy CD registers, and potentially requisition books (if interfaced to ward CD ADM). They have a high level of accuracy, improving the security of CD management and reducing or eliminating CD discrepancies.[5] Some hospitals are provided with smart medication trolleys. These are electronic medication trolleys for temporary storage of medications and recording of medication administration to a specific patient on a nurse medication round. When connected to CPOE and ADM they can be used in an integrated manner closing the drug-use system and reducing medication errors. The nurse selects patient and medications to be given from the smart trolley computer, and the prescribed medication for a specific patient is transferred from the ward ADM to a specific drawer in the smart trolley. As soon as the patient’s barcode is scanned, the specific patient treatment drawer on the smart trolley opens. Administration can be recorded either manually in the computer or via barcode reading. As mentioned previously, insufficient smart trolleys can lead to queues and an increase in the likelihood of bypassing the smart trolley and reverting to conventional manual administration of medications.[5] The study by Bermejo et al[3] showed that 93.4% of the Spanish hospitals that completed the survey had a centralised unit dose model, but only 18.4% of them were using automated cart filling. Unit dose based on automated dispensing systems (decentralised) was available in only 13.3% of the institutions that responded.

Compounding Automation of sterile and non-sterile compounding can benefit hospitals by creating a proper environment in which the pharmacy staff can be proficient, safe and efficient. Integration of automation into sterile compounding achieves greater efficiency, security and safety in handling and storage, and facilitates compliance with legislation. New devices and software for small-scale non-sterile compounding are being developed to integrate compounding, conditioning, labelling, weight and volume control, and recording. Interconnection with precision digital scales and different databases providing information about patients, drugs, excipients, dose calculation and stability can be incorporated. This allows printing of individual manufacturing standard operating procedures (SOPs), labels and records. These records, if properly filled, could eventually replace the compounding record book. Sterile compounding ranges from relatively simple filling devices to complex robotic systems. The former are volumetric peristaltic compounding devices designed for use in laminar flow cabinets and isolators, which accurately deliver a required volume of a solution to a final container. There are two main kinds: single ingredient (more affordable but does not allow interface with other systems); and multi-ingredient devices. These are used to prepare TPN and complex fluid admixtures and contain software that calculates quantities of ingredients and compatibilities. Automated robotic systems are generally ISO Class 5 classified manufacturing cabinets, interfaced with a pharmacy information system with potential to be interfaced with CPOE software. These devices are able to perform almost all of the actions carried out by a pharmacy technician in the sterile or cytotoxic compounding process: product recognition, reconstitution, extraction from vial into syringe, loading volume into the final container, volume check, waste disposal and cleaning. However, there are still some functions that must be performed by technicians such as loading drugs and consumables into the robot, and removing and labelling completed products. Each product for a specific patient is identified with a barcode sticker – when scanned, it will indicate if the product has passed the quality assurance restrictions. If passed, then a second label is generated with patient- and product-specific information. If it fails, a second request will be sent to the robot. These robots reduce error and increase operator safety by minimising exposure to cytotoxic and hazardous medications, and reducing needle stick injuries, stress and excess workload and occupational overuse syndrome. They also have the potential to reduce drug wastage through the ability to store and reuse vials internally.[5]

Administration The five rights of medication administration (right patient, right drug, right dose, right route and right time) are principles taught to nurses as part of their education. However, they may not always adhere to them and they may also lack knowledge about the medication (indication, usual dose, route, actions, adverse effects, contraindications and interactions).[7] Barcode medication administration (BCMA) at the most basic level helps to verify that the right drug is being administered to the right patient at the right dose by the right route and at the right time. Some systems can also create an online medical administration record (MAR). Smart systems can also facilitate drug reference information and various alerts and reminders. Finally, data capture allows for retrospective analysis of administration records.[8] Delivery of i.v. medications via infusion devices has traditionally not been a major concern for pharmacists. The introduction of smart infusion technology has changed that paradigm by requiring pharmacist involvement in defining minimum and maximum doses for continuous and bolus infusions used within a health-care facility. This technology provides a software filter to prevent key-stroke errors in programming infusion devices for delivery of i.v. drugs, as well as a new source of data with which to measure medication errors at the bedside. Computerised i.v. infusion devices, known as ‘smart pumps’, include software that incorporates institution-established dosage limits, warnings to the clinician when dosage limits are exceeded, configurable settings by patient type and access to transaction data by direct cable downloads to a desktop computer. Smart infusion systems can also integrate barcode technology to provide additional checks and balances in the drug administration process. The involvement of pharmacists is essential for needs assessment, evaluation, selection, customisation and quality control.[9] When interfaced with the pharmacy department management software, both BCMA and smart pumps provide a closed-loop medication use process and an improved inventory control. In 2007 only 5.4% of the hospitals were recording administration electronically and patient/drug identification via barcoding prior to drug administration was present in just 1.4% of the hospitals surveyed in Spain.[3] The same study revealed that 63.7% of hospitals expected to implement new technologies in a short time period, principally CPOE, automated dispensing, vertical storage, electronic recording of drug administration and administration by barcode.[3] Although the authors recognise that they cannot assess whether the sample obtained is representative of the Spanish hospitals, the results reflect the interest of Spanish institutions in modernising and in getting up-to-date with new technologies, as these can improve drug safety and management. From the pharmacy department point of view, re-engineering some processes will enable pharmacists to devote more time to pharmaceutical care.[3] The institutional cultural change and the initial high investment needed to carry out and succeed in the implementation of new technologies are the main drawbacks we presently face. There are various systems and providers available, so the prioritisation of which technology or system to implement first is crucial, especially in times of economic restrictions. The decision will be based on factors such as feasibility, institution characteristics, timelines and economics. A good strategy is to focus on those parts of the medication use process where more medical errors occur and where the risk is higher, or those whose complexity makes them susceptible to be simplified in order to be more effective. Although automation decreases the number and frequency of errors, they cannot be completely eradicated and even new types of errors can emerge. A six-month prospective study was carried out during 2008 in Ramón y Cajal Hospital, a third level, 1070-bed hospital in Madrid, to analyse errors and their contributing factors in the coexisting dispensing systems.[10] Error rates were as follows: UDDS without CPOE, 3.7%; UDDS with CPOE, 2.2%; automated dispensing systems (ADS) without CPOE, 20.7%; and ADS with CPOE, 2.9%. Discrepancies when filling the drug carts accounted for most errors in UDDS, whereas ADS errors were mostly related to the ADMs filling. We found out that stock out and supply problems contributed mainly to ADS errors. Prime factors for errors in UDDS were inexperience and deficient communication between professionals. When calculating error rates in ADS without CPOE only the restocking lines were considered as error opportunities, thus explaining the higher value compared to ADS with CPOE, where the lines of drugs prescribed and validated were also included. These results are similar to those reported in the literature. Provider(s) support is indispensable, above all at the early stages of implementation of a new technology, and this should not be neglected when contracting their services. Fluent communication between the hospital pharmacy and IT departments with the provider ensures a smoother performance. Not less important is the development of contingency plans for downtime that guarantee the working processes can continue safely and prevent data loss. A ‘closed-circuit’ seamless system seems to be the future trend. This kind of system  understands and records all data generated in the whole medication use process (from electronic prescribing to smart administration) and would be the ideal one to integrate solutions for issues such as patient safety and management needs. New technologies are here to stay and pharmacists need to be prepared to get the most out of them.

References 1. Bonal J and Gamundi MC. Automated drug dispensing systems SEFH 2001. Available online at:  www.sefh.es/bibliotecavirtual/Monografias/dispensacion.pdf (Accessed June 2010). 2. AHA; ASHP, HHN. Hosp Health Netw 2001;75:33–34. Available online at: www.ashp.org/Import/PRACTICEANDPOLICY/PracticeResourceCenters/PatientSafety/MedicationSafetyIssueBriefs_1/UsingAutomationtoReduceErrors.aspx (Accessed June 2010). 3. Bermejo V and Pérez Menéndez C. Farm Hosp 2007;31:17–22. Available online at:  www.grupoaran.com/mrmUpdate/lecturaPDFfromXML.asp?IdArt=458682&TO=RVN&Eng=1 (Accessed June 2010). 4. Article 1. Model State Pharmacy Act and Model Rules of the National Association of Boards of Pharmacy 2009, National Association of Boards of Pharmacy Act, article 1, Section 105. Definitions (h), p. 3. Available online at: www.nabp.net/publications/model-act/ (Accessed June 2010). 5. Bula N. IT and Automation Solutions for Medicines Management. Pharmacy services. Canberra Hospital. Available online at: www.shpa.org.au/lib/pdf/grants/Bula_oct2009.pdf (Accessed June 2010). 6. Shojania K et al. Evid Rep Technol Assess (Summ) 2001;(43):i-x:117–23. Available online at: www.premierinc.com/safety/topics/patient_safety/downloads/23_AHRQ_evidence_report_43.pdf (Accessed: May 2010). 7. Shane R. Am J Health-Syst Pharm 2009;66(Suppl 3):S42–48. Available online at:  www.ajhp.org/cgi/reprint/66/5_Supplement_3/s42 (Accessed June 2010). 8. Pathways for Medication Safety: Assessing Bedside Bar-Coding Readiness 2002 American Hospital Association, Health Research & Educational Trust, and the Institute for Safe Medication Practices. Available online at:  www.ismp.org/selfassessments/PathwaySection3.pdf (Accessed June 2010). 9. Wilson K and Sullivan M. Am J Health-Syst Pharm 2004;61:177–83. Available online at: www.medscape.com/viewarticle/467525 (Accessed June 2010). 10. Álvarez Díaz A et al. Farm Hosp 2010;34(2):59–67. Available online at: www.sefh.es/fh/105_121v34n02pdf003.pdf (Accessed June 2010).

• Privacy Policy
• Terms and conditions
• Yellow Card reporting

Join in the discussion and be a part of Hospital Pharmacy Europe

Download Hospital Pharmacy Europe free app

© Cogora 2024 Cogora Limited. 1 Giltspur Street, London EC1A 9DD Registered in the United Kingdom. Reg. No. 2147432

Hospital Pharmacy Europe newsletters

Sign up today to receive the latest clinical news, analysis, expert opinion, and research for hospital pharmacists across Europe.

• Search Menu
• Sign in through your institution
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

New benzodiazepine receptor agonists, etomidate derivatives, other novel anaesthetic agents, using technology to support drug administration, outstanding questions, using new drugs to tackle new tasks, conflict of interest.

• < Previous

New drugs and technologies, intravenous anaesthesia is on the move (again)

• Article contents
• Figures & tables
• Supplementary Data

J. R. Sneyd, A. E. Rigby-Jones, New drugs and technologies, intravenous anaesthesia is on the move (again), BJA: British Journal of Anaesthesia , Volume 105, Issue 3, September 2010, Pages 246–254, https://doi.org/10.1093/bja/aeq190

Although well established in clinical practice, both propofol and midazolam have limitations. New hypnotics with different and potentially superior pharmacokinetics and pharmacodynamics are under development. These include the benzodiazepine receptor agonists CNS7056 and JM-1232 (−), the etomidate-based methoxycarbonyl-etomidate and carboetomidate, the propofol-related structures PF0713 and fospropofol, and THRX-918661/AZD3043. The basic pharmacology and the initial anaesthesia studies for each of these agents are reviewed. Several of the agents (CNS7056, THRX-918661/AZD3043, and fospropofol) have reached the stage of clinical trials. To be successful, novel compounds need to establish clear clinical advantages over existing agents and where possible the new agents are discussed in this context. Computer-controlled drug administration offers the ability to automatically implement infusion schemes too complex for manual use and the possibility of linking patient monitoring to administration to enhance patient safety.

Current i.v. anaesthetic agents have some limitations.

New agents based on benzodiazepine, etomidate, and propofol structures are being developed.

Some are reaching the stage of clinical trials.

Improved methods of drug delivery are also being developed.

Current clinical practice in i.v. anaesthesia and sedation is focused around two compounds, midazolam and propofol. Both are widely used and available as generic preparations and are therefore well understood and inexpensive. Why then look for new compounds? The answer is that both have limitations.

Propofol causes pain on injection which can be attenuated by co-administration of lidocaine 1 and by formulation in medium chain rather than long chain triglyceride. 2 Propofol also supports bacterial growth 3 and causes haemodynamic depression which may be problematic for compromised patients if dosing is inappropriate. Lipid accumulation during infusion has been associated with serious syndromes in both paediatric and adult practice. 4 , 5 In addition, propofol occasionally causes excitation. 6

Midazolam has a slow onset of action, that is, time-to-peak effect, with the consequence that patients need to be dosed by titration over several minutes (Fig.  1 ). Further, over-rapid titration may lead to inadvertent overdosing. Midazolam has an active metabolite, α1-hydroxymidazolam. 7 , 8 Both propofol and midazolam have relatively steep dose–response curves meaning that dosing for anything other than maximum effect requires careful titration (Fig.  2 ).

Simulations of arterial concentration and predicted effect-site concentration after bolus doses of propofol 2 mg kg −1 (Diprifusor ® model) 61 and midazolam 0.05 mg kg −1 (Greenblatt model, 62 70 kg). Simulations were made using STANPUMP software from www.opentci.org . Note that the time-to-peak effect-site concentration for midazolam (10.4 min) is considerably longer than for propofol (2.4 min).

Propofol and midazolam dose–response curves for hypnosis (failure to open eyes on command). Both agents have relatively steep dose–response curves, implying that a small increase in dose results in a large increase in response. Gamma, the slope of the dose–response curve is 2.04 for midazolam and 5.84 for propofol. Data taken from Short and Chui. 63

CNS7056 is a new esterase-hydrolysed benzodiazepine 9 with rapid onset, short duration of action, and a fast recovery profile in animal models (Fig.  3 ). To date, it has been tested in volunteers and, to a limited degree, in patients. When 1 min infusions of CNS7056 were administered to healthy male volunteers, a dose-related depression of bispectral index (BIS) and a change in the sedation state occurred. 10 CNS7056 0.75 mg kg −1 reduced the Modified Observers Assessment of Anaesthesia and Sedation (MOAA/S) score 11 from 5 (responds readily to name spoken in normal tone) to 1 (does not respond to mild prodding or shaking). The sedative effect was of short duration with recovery within 10 min compared with ∼40 min for midazolam 0.075 mg kg −1 , which was used as an active control producing equivalent depression of consciousness. Early patient studies are in short diagnostic procedures. In a randomized, double-blind, study of 100 patients undergoing upper gastrointestinal endoscopy, the procedure was completed without assisted ventilation or supplementary sedation in 32%, 56%, and 64% of patients receiving CNS7056 0.1, 0.15, and 0.2 mg kg −1 , respectively, compared with 44% of patients receiving midazolam 0.075 mg kg −1 . 12

CNS7056 is a novel benzodiazepine which is rapidly broken down by tissue esterases.

JM-1232 (−)

JM-1232 (−) is a novel isoindoline derivative benzodiazepine receptor site agonist which is chemically different from benzodiazepines (Fig.  4 ) but acts as a full agonist at the benzodiazepine receptor and is fully reversible by flumazenil. 13

Structure of JM-1232 (−) (upper), midazolam (lower left), and diazepam (lower right). JM-1232 (−) is (−)-3-[2-(4-methyl-1-piperazinyl)-2-oxoethyl]-2-phenyl-3,5,6,7-tetrahydrocyclopenta[ f ]isoindol-1( 2H )-one and is not a benzodiazepine. The closed-ring (lipid-soluble) structure of midazolam, as shown, is formed at physiological pH.

JM-1232 (−) has been evaluated in man as MR04A3, a 1% aqueous preparation of JM-1232 (−). When volunteers were given 10 and 1 min infusions of MR04A3, a dose-related depression of BIS and sedation score occurred. 14 Both onset and offset times were faster than literature values for midazolam and there was an estimated value of t 1/2 K e0 of 4.2 min [95% confidence interval (CI) 3.1–6.7]. This t 1/2 K e0 value compares with published estimates of 0.9–5.6 min for midazolam and 1.2–3.3 min for propofol. 14 Early animal work also suggests a possible analgesic effect for this compound; 15 however, this has not yet been evaluated in man.

Etomidate was widely used for induction and occasionally for maintenance of anaesthesia because of its brief duration of action and haemodynamic stability. These beneficial characteristics were perceived to outweigh its propensity to cause myoclonus 16 and postoperative nausea and vomiting (PONV). The recognition of serious risks caused by etomidate-induced adrenocortical depression 17 led to its abandonment as an intensive care unit (ICU) sedative, and recently, it has fallen out of favour as an induction agent for similar reasons, although the importance of these effects after a single dose remains disputed. 18 , 19 A retrospective analysis of data from 176 patients admitted to an ICU after emergency laparotomy showed that in 52 patients, anaesthesia had been induced using etomidate. 18 However, there was no association between receiving etomidate and dying in hospital. The risk of developing hypotension at induction or of receiving vasopressor to treat hypotension was lowest with etomidate. 18 In contrast, when the use and timing of etomidate administration were evaluated in a substudy of the CORTICUS trial of hydrocortisone in septic shock, the 19% ( n =96) of ICU patients who had received etomidate were less likely to respond to corticotrophin and more likely to die. 20 A randomized controlled trial comparing single-dose etomidate and ketamine in the critically ill found that the percentage of patients with adrenal insufficiency was significantly higher in the etomidate group than in the ketamine group (odds ratio 6.7, 95% CI 3.5–12.7). 21 The issue of etomidate and outcome after single bolus doses remains unclear and there are no data to suggest that it is associated with adverse outcome in proper randomized trials. What is now needed is an outcome study in non-septic patients.

Attempts to improve on the clinical pharmacology of etomidate are based on improved understanding of the drug's action at a molecular level and chemical modification of the molecule to allow esterase hydrolysis. 22

Etomidate potentiates GABA A receptor activation. GABA A receptors are pentameric with two etomidate-binding sites per GABA A receptor. Methanethiosulphate-etomidate (MTS-etomidate) (Fig.  5 ) is an etomidate analogue which forms a covalent bond to one of these binding sites but not to the other. 23 , 24 This provides information about the orientation of etomidate within its binding site on the GABA A receptor and identifies the residues with which it interacts. The hypnotic properties of MTS-etomidate have been demonstrated on tadpoles. 24 However, its duration of hypnotic effect and its impact on adrenocorticosteroidogenesis are currently unclear. Because it binds with a covalent bond to the receptor site, MTS-etomidate is not a candidate for clinical development and should be considered a molecular probe which can be used to interrogate the etomidate-binding site.

R (+)Methanethiosulfate etomidate, MTS-etomidate.

Methoxycarbonyl-etomidate , also known as MOC-etomidate, is a rapidly metabolized etomidate analogue which only briefly depresses adrenocortical function after single administration in rats (Fig.  6 ). 25 Published data comparing the effects of etomidate and MOC-etomidate on adrenocortical function in rats were based on adrenocorticotropic hormone injections 15 min after bolus administration of the anaesthetic agents. 25 At this time, MOC-etomidate will have been almost entirely eliminated, whereas etomidate would have been present in significant concentration. Thus, although adrenocortical function has been shown to recover quickly after single doses of MOC-etomidate, it is likely to be depressed during infusions even if this effect terminates promptly at the end of infusion as the drug is metabolized.

Etomidate, MOC-etomidate, and its inactive metabolite MOC-etomidate carboxylic acid.

Bolus injection of MOC-etomidate induced haemodynamically stable anaesthesia of brief duration in rats. 25 Although the degree and duration of arterial pressure reduction induced by MOC-etomidate were less than those seen in animals receiving etomidate or propofol, these data must be interpreted with caution. The duration of hypnosis after bolus injection of etomidate and propofol was very sensitive to the administered dose, whereas the short period of anaesthesia induced by MOC-etomidate is almost dose-independent. 25

Etomidate, propofol, and MOC-etomidate produced loss of righting reflex in rats with ED 50 s of 1.0, 4.1, and 5.2 mg kg −1 , respectively. Thus, MOC-etomidate is less potent than the older agents. Further, given its very brief duration of action, maintaining anaesthesia by infusion of MOC-etomidate will require a substantial mass of drug with subsequent metabolism to carboxylic acid and methanol (Fig.  6 ). The safety of these metabolites in the amounts likely to accumulate after prolonged infusion of MOC-etomidate is currently unclear. By way of example, we know that after prolonged remifentanil infusion, the arterial concentration of the metabolite remifentanil acid is many times greater than that of the parent drug and this is exaggerated in patients with renal impairment. 26 Fortunately, remifentanil acid is well tolerated but the same cannot be automatically assumed for the breakdown products of every new ‘soft’ 22 drug. Although preliminary data describing MOC-etomidate are attractive, its proper evaluation requires human exposure.

Carboetomidate

Carboetomidate (Fig.  7 ) represents an alternative solution to the problem of adrenocortical suppression by etomidate. Etomidate binds to the P450 enzyme 11-β-hydroxylase and thereby suppresses steroidogenesis. This effect is due to binding of a nitrogen atom in etomidate's imidazole ring to haem iron within the 11-β-hydroxylase enzyme's active site. Removal of the binding nitrogen atom from etomidate reduced adrenocortical inhibitory potency by three orders of magnitude, whereas the molecule remains anaesthetic. 27 Carboetomidate is the lead compound in a class of etomidate analogues designed not to inhibit adrenocortical function at pharmacologically relevant doses. When tested in human adrenocortical cells, the half maximal concentration for inhibition of cortisol synthesis by carboetomidate and etomidate was 2.6 ( sd 1.5) µM and 1.3 (0.2) nM, respectively, a difference of three orders of magnitude. 27 Serum corticosterone concentrations in rats given carboetomidate were not significantly different from those given vehicle alone. 27

Carboetomidate.

Carboetomidate is anaesthetic in tadpoles and rats. Hypnotic bolus doses of 14 mg kg −1 produced minimal changes in arterial pressure in rats. 27 Because carboetomidate should not suppress adrenocortical function during administration, it might be suitable for the maintenance of anaesthesia or sedation. 28

MOC-etomidate and carboetomidate represent two distinct approaches to the adrenocortical effects of etomidate. The rapid metabolism of MOC-etomidate may be considered a pharmacokinetic solution—swift esterase metabolism rapidly removes the cause of adrenocortical depression. In contrast, carboetomidate offers a pharmacodynamic solution—removing the nitrogen atom from etomidate massively decreases adrenocortical depression.

Both drugs appear to offer the principal advantages of etomidate—they are potent anaesthetic agents with minimal haemodynamic effects. What is unclear is whether the unwelcome effects of etomidate–myoclonus and nausea and vomiting remain unchanged.

THRX-918661/AZD3043

This allosteric modulator of the GABA A receptor has a very similar structure to that of propanidid (Fig.  8 ). AZD3043 is hydrolysed by esterases to an inactive metabolite. THRX-918661appeared very attractive when tested in a pig model, 29 although concern was expressed that it might actually be too short acting and insufficiently potent for human use. 30 Subsequent development was delayed by formulation difficulties. Now renamed AZD3043, the compound has been tested in man by bolus injection and infusion with four studies registered at www.clinicaltrials.gov , although the data are currently unpublished.

The structure of AZD3043 is similar to that of propanidid.

PF0713, ( R , R )-2,6-di- sec -butylphenol, is a compound very similar to propofol but with larger side groups. ( R , R )-2,6-di- sec -butylphenol is a single diastereomer containing two defined chiral centres of the R- configuration (Fig.  9 ). PF0713 is a potent GABA A receptor agonist and behaved similarly to propofol in the hippocampal brain slice assay and fully potentiated the muscimol-mediated response at the GABA A receptor. 31

PF0713 is ( R , R )-2,6-di- sec -butylphenol, a compound very similar to propofol. Both the two and the six groups on the phenol ring are optically active and it is the R , R form that has been evaluated in man.

Interestingly, several similar molecules including 2,6-di- sec -butylphenol were considered during the chemical development of propofol. 32 2,6-Di- sec -butylphenol (then known as Compound 31 ) was formulated in the surfactant Cremophor EL and tested in mice and rabbits. Hypnotic potency was similar to that of propofol (then known as Compound 25 ) but onset of action was slower and duration of action was longer than propofol. 32 Thus, it is clear that the anaesthetic properties of this molecule have been known for some time. What is not clear is whether the original experiments used a racemic mixture or, as recently tested in man, a single enantiomer. Individual enantiomers of molecules often have strikingly different pharmacological properties to racemic mixtures—consider cisatracurium and l -bupivacaine in comparison with the racemates atracurium and bupivacaine. Accordingly, the difference in performance of (presumably racemic) 2,6-di- sec -butylphenol and propofol in small animals may be irrelevant to the as yet untried comparison between PF0713 and propofol in man.

Like propofol, PF0713 is minimally soluble in water and has been investigated as a 1% oil in water emulsion. PF0713 induces brief propofol-like anaesthesia without pain on injection. 33

The aqueous phase concentration of ( R , R )-2,6-di- sec -butylphenol in a 1% formulation is 0.38 (0.02) µg ml −1 , whereas the aqueous phase concentration of propofol in Diprivan ® was 4.1 µg ml −1 . 31 This reduction in the aqueous phase concentration may be the explanation for the reported lack of pain on injection. PF0713 is antiemetic in ferrets (an established laboratory model which also demonstrates the antiemetic effect of propofol). 31 PF0713 produces similar anaesthesia to propofol in man; 33 however, no effect-site model has yet been described and it is possible that its onset and time-to-peak effect may differ from those of propofol. Although, in man, recovery from bolus injections appears satisfactory, its performance during and after infusion requires further investigation.

Fospropofol

Fospropofol is a phosphate pro-drug for propofol which is converted to propofol within a few minutes of i.v. injection. Inevitably, time-to-peak effect is longer than when propofol is used and recovery is correspondingly slower. These apparent disadvantages are being developed as ‘advantages’ with US Food and Drug Administration (FDA) approval recently given for ‘moderate sedation’. However, an application for the drug to be licensed for more profound sedation has been refused. The FDA required that fospropofol be used only by persons trained in the administration of general anaesthesia and that all patients should be continuously monitored by persons not involved in the conduct of the procedure (see www.fda.gov ). These restrictions effectively preclude operator–sedation by non-anaesthetists during endoscopy. Fospropofol does not cause pain on injection and is water soluble. However, it does have some side-effects not described with propofol, specifically perineal pain or paraesthesia. Whether the compound is clinically useful will require evaluation in comparative studies and the development of clinician understanding of how to best use a drug which when injected i.v. only works after a significant delay.

Target-controlled infusion (TCI) of propofol has been adopted worldwide with the exception of the USA. Initial scepticism has given way to general use, aided by the development by pump manufacturers of systems capable of TCI without using expensive proprietary pre-filled Diprivan ® syringes. However, the availability of alternative pharmacokinetic models is potentially confusing for clinicians. 34 The availability of generic or ‘open’ TCI also allows infusion of other anaesthetic drugs notably remifentanil. Open TCI systems implement published pharmacokinetic and pharmacodynamic data sets. Although the equipment manufacturers are responsible for the electronic and mechanical systems which deliver drug at the infusion rates dictated by the mathematical models, the choice of model and the question of its appropriateness to the individual patient remain the responsibility of the anaesthetist. Only remifentanil and propofol have been specifically licensed for TCI. The Open TCI initiative, www.opentci.org , brings together academics and manufacturers to share data and develop best practice in TCI. Currently available TCI remains strictly ‘open loop’, that is, there is no feedback of an effect measure from the patient to the TCI system. Closed-loop anaesthesia has been the subject of considerable research but has not been developed commercially, possible for fear of liability issues and the possibility of litigation.

Sedasys ® (Ethicon Endo Surgery, Inc., Cincinnati, OH, USA) is a new proprietary system developed for automated sedation with propofol in conjunction with comprehensive patient monitoring and sophisticated software. Described as computer-assisted personalized sedation (CAPS), the apparatus monitors ECG, S a o 2 , exhaled CO 2 , and patient responsiveness to auditory commands. Subject to satisfactory monitoring values and various safety interlocks, the system administers propofol sedation in accordance with the prescribing information, that is, slow titration in small increments. Operation of the sedation protocol is contingent on patient responsiveness and the absence of apnoea or desaturation. Sedasys ® may stop or decrease the rate of propofol administration but cannot increase it. Sedasys ® is therefore a hybrid system—more complex than an open-loop system such as Diprifusor ® yet not a true closed-loop anaesthesia system which titrates drug against an effect measure to achieve a hypnotic endpoint. Instead, Sedasys ® delivers a fixed sedation protocol or a subset of it and reacts to protect patient safety. When used with patients undergoing endoscopy, Sedasys ® provided satisfactory sedation without device-related adverse events. 35

In Europe, Sedasys ® has received a CE mark for routine colonoscopy and screening of the upper gastrointestinal tract and the Canadian regulator, Health Canada, has approved the device for use during routine colonoscopy. Approval has been refused in the USA.

It is also unclear to what extent machine supervised propofol sedation will meet clinical needs. One commentator observed that ‘The intent of Sedasys ® is to permit practitioners to use propofol at the low end of the sedation continuum. Although we have little doubt that this is useful, practitioners expecting the performance of anaesthetist-administered propofol may be disappointed’. 36

Certainly, Diprifusor ® and Sedasys ® represent impressive examples of integration of computer technology and clinical pharmacology and we can expect to see further such cross boundary devices in the future.

Almost 50 yr ago, Barron and Dundee 37 signposted the track for anaesthetic drug discovery by identifying cardiovascular and respiratory depression, accumulation, and slow recovery and also tissue irritation as undesirable characteristics of thiopental. Arguably, short-onset, rapid offset hypnosis with modest haemodynamic perturbation by drugs without active metabolites has already been achieved. Even patients with very severe circulatory compromise may be safely anaesthetized with propofol, provided that the dosage is judiciously adjusted. 38

Times change, however, and several new goal criteria are emerging. Is it possible for an i.v. hypnotic to be too short acting? For AZD3043 and MOC-etomidate, esterase hydrolysis offers ultra-fast recovery from anaesthetic drug effect. If translated into humans, this might allow swift and clear-headed recovery of consciousness and perhaps early home readiness. Recently, concern has mounted about perioperative awareness due to inadequate delivery of i.v. anaesthesia. 39 Anaesthetic techniques based on ultra-short offset hypnotics will be especially vulnerable to interruptions in drug delivery and offer the alarming prospect of a patient inadvertently going rapidly from the anaesthetized state to full wakefulness at an inappropriate time.

In addition, ultra-short-acting drugs offer a pricing challenge to the pharmaceutical industry: if a reasonable price is achieved for a single bolus injection, then maintenance by infusion becomes hugely expensive. Alternatively, if such a compound is priced so that maintenance is affordable, then an induction dose will be something of a bargain! One approach to containing drug costs for i.v. anaesthesia is to use a cheap agent for induction and for the maintenance of anaesthesia and then to switch to a more expensive short-acting agent towards the end of surgery in the hope of achieving rapid recovery without excessive cost. This technique has been demonstrated for the opioid component of i.v. anaesthesia by sequential use of alfentanil (infused for most of the anaesthetic) and remifentanil (used only for the last part of the anaesthetic) in neurosurgery. 40 The technique may be applicable to new ultra-short-acting hypnotics by sequential use at the end of a propofol anaesthetic. Further, the antiemetic effect of residual subanaesthetic concentrations of propofol might attenuate PONV induced by a novel hypnotic.

Obvious goals for the development of new hypnotics are compounds with superior performance compared with midazolam and propofol in current clinical applications. Less obvious is the opportunity to tackle new tasks. By way of illustration, consider the clinical development of opioids. Clinicians choose combinations of hypnotic and opioid to achieve a clinical endpoint with reasonably prompt recovery at the end of anaesthesia. If anaesthesia is provided with a combination of alfentanil and propofol, there are an infinite number of possible equipotent drug combinations. Thus, a 50% probability of suppression of response to lower abdominal surgery, EC 50 , is achieved by a combination of propofol 2 µg ml −1 with alfentanil 209 ng ml −1 or propofol 10 µg ml −1 with alfentanil 16 ng ml −1 or a range of intermediate combinations. 41 In practice, both of the above combinations cause slow awakening and the optimum mixture for swift recovery is propofol 3.5 µg ml −1 with alfentanil 85 ng ml −1 . 41 High-dose opioid anaesthesia with alfentanil offers haemodynamic stability but carries a time penalty for recovery of consciousness at the end of surgery. The newer opioid remifentanil is rapidly metabolized and eliminated; this allows clinicians to offer their patients the benefit of intense opioid anaesthesia without the penalty of prolonged respiratory depression and delayed recovery 42 —something that was previously unavailable. Mindful of remifentanil, new hypnotics need to be evaluated for the possibility that they might open new therapeutic options and improve existing ones.

What might we be able to do with new benzodiazepine receptor agonists that we cannot do already? A drug with faster onset than midazolam might simplify titration to clinical effect and decrease the risk of inadvertent over-sedation by repeated dosage with insufficient interval between consecutive doses. Midazolam sedation has been combined with flumazenil reversal in short procedures 43 and after extended infusion on intensive care. 44 In addition, the same approach has been used for surgical anaesthesia. 45 , 46 Nevertheless, the technique has never been widely used, perhaps reflecting concerns about excitation and cardiovascular response after flumazenil administration and the possibility of resedation. 47 Perhaps, a truly short-acting benzodiazepine agonist, or one with a faster onset than midazolam, might allow benzodiazepine anaesthesia to be revisited.

At this point, it may be appropriate to consider whether benzodiazepines can produce anaesthesia at all. Typically, even large doses of benzodiazepines depress the BIS only to values of 40–60 and the compounds do not easily produce brain electrical silence (BIS=0). When the auditory-evoked potential (AEP) is recorded during progressive anaesthesia with inhalation agents or propofol, the second negative wave n b is delayed (increased latency) and reduced in size (decreased amplitude). 48 , 49 In contrast, patients ‘anaesthetized’ with flunitrazepam and fentanyl showed minimal changes in the AEP, despite being apparently unconscious. 50 Perhaps, patients anaesthetized with a benzodiazepine/opioid combination are in some way aware but amnesic because of the interference by benzodiazepines with the formation of new memories? Certainly, when ‘anaesthesia’ was maintained with a combination of midazolam and alfentanil, and consciousness monitored by the isolated forearm technique, 72% of 32 patients responded with purposeful movements to verbal commands during surgery, but none had spontaneous, unprompted postoperative recall for the event. 51 The response to auditory commands implies functioning auditory and motor pathways and sufficient cortical function to interpret the command and develop an appropriate movement. If the new benzodiazepine receptor agonists are used to induce and maintain ‘anaesthesia’, then this issue needs to be considered.

In the past, we evaluated new hypnotics by surrogate endpoints such as time to eye opening and discharge from recovery. Subsequently, the focus of perioperative research shifted to patient satisfaction, 52 PONV, and discharge home with attempts to use health economics to offset increased drug acquisition costs against decreased hospital stay. 53 Recently, we started looking at major outcomes including cardiovascular morbidity, other major complications, and 30 day mortality. 54 , 55

As we approach the second decade of the twenty-first century, several new issues are emerging.

Preliminary data suggest that anaesthetics may be neuro-protective, 56 effect (or not) preconditioning, 57 alter cell-mediated immunity 58 and cell proliferation, 59 and perhaps cause neurotoxicity in the developing infant brain. 60 Currently, researchers are working to clarify these preliminary data and identify how we should adjust our practice to exploit the beneficial effects and moderate or avoid the harmful ones. New i.v. agents must be positioned in this complex context. For a new i.v. hypnotic to be successful, it will not be sufficient for it to be ‘quick’ and ‘clean’, clinicians and their patients, and commissioners of healthcare and local budget holders will expect clarity about the place of any new agents against this body of emerging anaesthetic science.

This is an exciting time for anaesthetic drug development with a plethora of new hypnotics under development. Few of them will survive the rigours of commercialization in a cost-constrained environment and to be successful, compounds will need to address the broadening agenda set out above.

Propofol—Diprivan—no current conflicts. J.R.S. worked for ICI pharmaceuticals over 18 yr ago and has 18 months of contributions in the AstraZeneca pension fund. Propofol-RM—not mentioned but a competitor. J.R.S. and A.E.R.-J. have published on this and J.R.S. has consulted for LaboPharm. Remifentanil—J.R.S. did contract research for its manufacturer years ago and lectured (paid) on remifentanil study days last year (2009). CNS7056—J.R.S. consulted for CeNes until 2 yr ago. No involvement in the human studies. JM-1232 (−)/MR04A3—J.R.S. consulted for Maruishi in 2008/9 and led clinical investigations of the compound. A.E.R.-J. did contracted computer modelling for Maruishi. AZD3043/THRX-918661—J.R.S. has given some paid advice to AstraZeneca in 2010. MOC-etomidate, MTS-etomidate, PF0713, Fospropofol, and Sedasys ® —no conflicts for any of these.

Google Scholar

Citing articles via

Affiliations.

• Online ISSN 1471-6771
• Print ISSN 0007-0912
• Copyright © 2024 The British Journal of Anaesthesia Ltd
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Pharmacol

The Impact of Technology on Safe Medicines Use and Pharmacy Practice in the US

Philip j. schneider.

1 MediHealthInsight, Scottsdale, AZ, United States

2 Division of Pharmacy Practice and Science, College of Pharmacy, The Ohio State University, Columbus, OH, United States

For decades it has been suggested that pharmacists are under-utilized and could better use their knowledge and experience to improve the use of medicines. The traditional roles for pharmacists have been preparing and distributing medicines, but this has limited both the location where they work and the available time to work more closely with other healthcare professionals to improve both the effectiveness and safety of medicines. Newly emerging technologies have made this possible. Examples include robotics that automate preparation and distribution of medicines, electronic health information, clinical decision support systems, and machine readable coding on medicine packaged. As a result of the use of these technologies, pharmacists in hospitals are working outside the hospital pharmacy and spending more time in medication therapy management activities compared to traditional distribution roles.

Introduction

The adoption of innovative ideas can be painfully slow, even when an innovation has well-demonstrated positive impact. In his work Diffusion of Innovation, Rogers concluded that based on a study of the adoption of new ideas, it takes decades for an innovation to be widely accepted ( Rogers, 2003 ). One would like to think that when the public clearly benefits from a technology that the adoption rate would be quicker. In health care, where innovations have the potential to improve the effectiveness, safety, and efficiency of care, the imperative for change would seem to be clear. As we will see, this is not always the case.

What We Know About Medicines Use

The American Society of Health-System Pharmacy (ASHP) is an organization representing pharmacists practicing in hospitals and organized health care settings. For more than 50 years, ASHP has surveyed hospital pharmacy directors in US hospitals beginning with the landmark publication Mirror to Hospital Pharmacy in 1965 ( Francke et al., 1964 ). This report assessed the practice of pharmacy in US hospitals. This audit revealed a scope of service that was limited to drug preparation and distribution. The authors challenged the profession to aspire to a more professional role by strengthening the relationship between physicians and pharmacists to improve the use of medicines.

Since then, more than 20 such surveys have been conduced. Since 1998, this survey has been conduced annually and the results provide the opportunity to identify, track, and trend changes in medicines use practices and the role of pharmacists in hospitals ( Ringold et al., 1999 , 2000 ; Pedersen et al., 2001 , 2003 , 2004 , 2005 , 2006 , 2007 , 2008 , 2009 , 2010 , 2011 , 2012 , 2013 , 2014 , 2015 , 2016 , 2017 ; Schneider et al., 2018 ). Through these surveys, progress toward the vision described in Mirror to Hospital Pharmacy has been largely realized, albeit in a time frame that supports Rogers’ theory of the diffusion of innovations. Much of this progress has been through the use of technology.

At the inception, these surveys were intended to determine what pharmacists were doing in hospitals and to assess the scope of pharmacy services in the US. In 1998, the surveys were reformatted to focus on the steps in the system of medicines-use, not just pharmacy practice. It acknowledged that this system includes many participants, not just pharmacists. At the time, a widely published report from the Institute of Medicine titled To Err is Human called attention to harm resulting from care intended to help patients ( Institute of Medicine, 2000 ). This report focused on problems with the system of care as being more significant than the performance of individual health care professionals. That was the rationale for changing the format of the survey.

The Epidemiology of Problems With Medicines Use

The medicines use system is at least multidisciplinary but ideally an interdisciplinary system. While the roles of health care professionals overlap and can vary, typically a physician prescribes a medicine, a pharmacist prepares and dispenses the dose, and the nurse, patient or family member administers the drug. The performance of each member of this team can affect the effectiveness, safety, and efficiency of a medicine and its use, but so can the interaction of these participants in the system, each of whom functions at different places and times in the process. Problems with handoffs and communication often contribute to errors, adverse events and a loss of efficiency. It would seem obvious that technology could improve the performance of the system if medicines use to the benefit of patients, health care professionals, and the institution in which the patients are cared.

Problems with the performance of the medicines use system have been studied. A seminal study that called attention to problems with the use of medicines was the Harvard Medical Practice study. The results of a review of more than 30,000 patient records found that 3.7% of hospitalized patients were injured from the care that they received ( Brennan et al., 1991 ). The most common injury was “drug complications,” which accounted for 19% of all events detected ( Leape et al., 1991 )In a follow up study of medication errors in hospitalized patients, it was found that mistakes resulting in harm to patient occurred by all participants in the medicines use system and at all steps in the system. The most common step where errors occurred was when medicines were prescribed. Fully19% of the mistakes that resulted in harm to patients were detected at this step. The next most common step was when a nurse administered a medicine to a patient, where 17% of the errors occurred. Less common steps where errors occurred was during the transcribing of orders for medicines and when doses were prepared ( Bates et al., 1995 ). These investigators looked further to determine what “system failures” contributed to the medication errors. The two most common underlying causes were a lack of information about the patient (e.g., unknown allergy to a drug) and a lack of information about the drug (e.g., an need to adjust the dose in certain patients) ( Lucian et al., 1995 ). It is easy to see how technology can be used to address these system problems.

Safe Practices for Medicines Use

Well-before the Harvard Medical Practice study, problems with drug administration errors in by nurses to hospitalized patients were documented. Studies showed error rates of up to 10% occurred when comparing what was prescribed to what was actually administered to the patient ( Barker, 1969 ). The “system” of medicines use in place at the time this study was conducted was one where all drugs were stocked in bulk containers on nursing units in the hospital. A “medication nurse” would prepare a medication tray by taking individual doses from the bulk supply, placing them in individual containers for each patient on the unit, and going from room to room to administer the doses at the scheduled time. With this method, the nurse performed both a dispensing and drug administration role and was expected to administer what the physician ordered with out questioning it. A new system that included more double checks and safeguards called the “unit dose” system was shown to reduce medication errors by as much as 50%. This system transferred responsibility for dispending medicines from the nurse to the pharmacist, providing an additional double check by the pharmacist in the process. It also provided a limited supply (24 h or less) of individually packaged and labeled medicines in a patient-specific container for the nurse to use when administering medicines ( Barker, 1969 ). This made it less likely that the wrong dose or wrong drug would be administered or the medicine administered to the wrong patient or at the wrong time.

While evidence supporting the safety of unit dose drug distribution system was published in 1968, the adoption of this innovation followed the timeline suggested by Rogers in Diffusion of Innovation. For example, in 1975, unit dose drug distribution systems were in place in only 18% of US hospitals. It took until 1995; 20 years for this system to be adopted in 92% of hospitals – consistent with the timeline of Rogers ( Schneider et al., 2018 ) (Figures ​ (Figures1, 1 , ​ ,2 2 ).

Trends in drug dispensing.

Twenty Four-hour review of medication orders by pharmacists.

Technology Enabled Changes

There are opportunities to harness new technologies to improve the use of medicines and transform pharmacy practice. Baines and colleagues has presented a conceptual framework for analyzing production methods, productivity and technology in pharmacy practice that differentiates between dispensing and pharmaceutical care services. They outline a framework to study the relationship between pharmacy practice and productivity, shaped by educational and technological inputs ( Baines D. et al., 2018 ).

While the transfer of responsibility for dispensing of medicines from nursing to pharmacy had a positive impact on patient safety, it did not improve operational efficiency. Preparing patient-specific medication bins for each patient every day (if not many times per day) was much more labor intensive and expensive. It also created a lag between when medications were prescribed and when they were available to the nurse for administration to the patient. Medication orders change during the day, making the unit dose bins from the pharmacy outdated. This became worse as acuity increased and length of stay decreased. Two technology enabled changes resulted: robotic filling of unit dose bins and stocking medications in patient care areas using automated dispensing cabinets.

While robotic enabled centralized unit dose cart filling systems solved the workforce issue and improved accuracy in dispensing, it did not solve the responsiveness to order changes. Moreover, robotic systems were very expensive and their use limited to very large hospitals. What became a more popular option was re-locating medications to the patient care areas using automated dispensing cabinets. To illustrate this, only 7% of US hospitals employed robotic technology in 2002 for centralized unit dose drug distribution systems. This remained steady over time and only 8% had this technology by 2017 ( Schneider et al., 2018 ). In contrast, 22% of US hospitals used automated dispensing cabinets in patient care areas for drug dispensing in 2002, but this rose to 70% by 2017. A summary of these trends is shown in Figure ​ Figure1. 1 . Robotic technology could also used to compounding sterile preparations in the pharmacy. This technology is quite expensive and does not enjoy widespread use in US hospitals. The percentage using this technology has remained steady at less than 3% in the 6 years that this has been surveyed ( Schneider et al., 2018 ). Robotic technology is more commonly used to compound more complex nutrition support formulations. These preparations are not only time consuming to compound, but errors are more likely to result in harm to patients. In 2017, 14.3% of hospitals used robotic technology to compound nutrition support formulations ( Schneider et al., 2018 ).

Reverting back to a floor stock system, albeit a technology enabled one created the potential to risk an increase in medication errors comparable to the rate documented with the traditional floor stock system. Again – technology to the rescue. Two innovations emerged to prevent a return to unacceptable medication error rates. The first was the “profiling system” where a pharmacist review and approval of the prescribed therapy was necessary before a nurse could access a medicine from an automate dispensing cabinet. The second was “lidded pockets” where access to any container within the automated dispensing cabinet is restricted so that a nurse does not have free access to all medicines in the automated dispensing cabinet; only the pocket that has the medicine ordered for that patient. The use of lidded pockets has increased from 51.8% in 2008 to 70.1% in 2017 ( Schneider et al., 2018 ).

A review of prescription orders by a pharmacist is considered a safe medication practice. Lesar, at all found 3 errors per 1000 prescriptions detected by hospital pharmacists ( Lesar et al., 1997 ). A double check by pharmacist is important to detect and prevent errors in prescribing causing an adverse drug event. Unit dose and decentralized automated dispensing cabinet- based systems with profiling offer this double check before a dose is made available for administration to the patient. Historically, this practice required a pharmacist to be physically present in the hospital to review prescriptions and a 24-h pharmacy service, which was expensive and not always possible in smaller hospital. The advent of technologies including the electronic health record and properly configured automated dispensing cabinets has made it possible for a pharmacist to review and approve prescriptions remotely before a nurse can obtain and administer a medicine to a patient. As a result, the percentage of hospitals where a pharmacist does not review prescriptions before a medicine is available for administration to a patient has continued to decline from 60% in 2005 to 11% in 2017 as shown in Figure ​ Figure2 2 ( Schneider et al., 2018 ).

Computer prescriber order entry systems were thought to reduce the need for pharmacist to review medication orders before doses were available for administration to the patient. These systems employ clinical decision support, which alert prescribers to potential dosing errors, drug allergies and drug interactions. Early investigators considered this a “systems solution” to address some of the more common underlying causes of prescribing errors; namely lack of information about the patient and the drug prescribed ( Bates et al., 1995 ; Lucian et al., 1995 ). Between 2003 and 2016, the percentage of US hospitals with computer prescriber order entry systems with clinical decision support increased from 2.5 to 95.6% ( Pedersen et al., 2017 ). While electronic prescribing has become almost universal, problems with alert fatigue and the low positive predictive value of alerts has limited the impact of these systems on error rates and has not eliminated the value of a pharmacist review of medication orders.

Another technology that has been show to improve safety and efficiency is machine-readable bar coding of medication packages. This technology has been used in many industries to more accurately reconcile and verify the identity of objects and would have logical application in verifying the identity of medicines, the persons handling them, and the patient to whom they are administered. This technology has been used in both the pharmacy to improve the safety and efficiency of drug storage, preparation, and dispensing, and by nursing to improve the safety and efficiency of drug administration and documentation in the medication administration record. Barcode scanning is also used to verify ingredients when sterile preparations are compounded in the pharmacy. The percentage of hospitals that are doing this has increased from 11.9% in 2011 to 26.9% in 2017 ( Schneider et al., 2018 ). The use of machine-readable coding to verify the accuracy of drug dispensing has increased from 5.7% in 2002 to 61.9% in 2017 ( Schneider et al., 2018 ). This technology is also used to verify the accuracy of restocking automated dispensing cabinets in patient care areas. The percentage of hospitals doing this has increased from 43.3% in 2011 to 74.7% in 2017 ( Schneider et al., 2018 ). Bedside bar code reconciliation of doses during drug administration by nurses enjoys widespread use. In 2016, 92.6% of US hospitals used this technology; an increase from only 1.5% in 2002 ( Pedersen et al., 2017 ).

How Has This Transformed Pharmacy Practice in Hospitals?

Dating back to Mirror of Hospital Pharmacy, there has been a commitment to advancing the role of pharmacists to improve the use of medicines in hospitals ( Rogers, 2003 ). To that end, ASHP and the ASHP Research and Education Foundation sponsor the Practice Advancement Initiative (PAI) ( American Society of Health-System Pharmacists, 2018 ). The goal of this initiative is to significantly advance the health and well-being of patients by supporting futuristic practice models that support the most effective use of pharmacists as direct patient care providers ( Baines D.L. et al., 2018 ).

A newer role for pharmacists is medication therapy management either by standing protocol or prescriber order/delegation or pharmacists have responsibility for writing medication orders, selecting doses, ordering appropriate laboratory tests, and monitoring patient response to therapy. In 2016, pharmacists managed the following therapies: vancomycin (94% of hospitals), renal dosing of antibiotics (83.9%), aminoglycosides (83.8%), anticoagulants (71.1%), nutrition support (46.9%), selection of antibiotics (19.6%), and pain management (6.2%). These percentages were higher than they were in 2013 ( Pedersen et al., 2017 ). The impact of pharmacist medication management services is measured by the following indicators: cost saving (61.5% of hospitals), patient outcomes (36.5%), federal quality of care indicators (23.7%), readmission rates (16.6%), and patient satisfaction scores (15.8%), decreases in length of stay (8.3%) ( Pedersen et al., 2017 ).

The transition of the pharmacist role from drug preparation and distribution to medication therapy management has resulted in their practice moving from a central pharmacy to the patient care areas. The following clinical areas commonly have pharmacists routinely assigned to manage therapies to a majority of patients at least 8 h/day, 5 days/week: inpatient medical-surgical (43.5% of hospitals). critical care (43.5%), oncology (37.5%), cardiology (32.9%), pediatrics (24.1%), and the emergency department (21.0%) ( Pedersen et al., 2016 ).

Besides enabling a transition in drug preparation and dispensing, technology also enables medication therapy management by pharmacists. Not all patients can or need medication therapy monitoring by pharmacists. A total of 43.4% of hospitals use computerized data mining to identify patients in need of monitoring. Some electronic health records have data mining functionality (58.6%), and others use proprietary clinical surveillance software (28.4%) to compile data needed to identify patients for daily monitoring by pharmacists ( Pedersen et al., 2016 ).

The transition of the patient from the hospital to the community (and back) is a step in health care where handoffs are missed and miscommunication occurs. Pharmacists are also becoming increasingly involved in transitions of care programs to reduce errors and improve care. Some examples of transitions of care activities by pharmacists include: use of medication histories at admission (74.9% of hospitals; in 2016 up from 54.3% in 2002), discharge medication counseling by pharmacists (46.4% from 21.7%), participation in discharge planning (35.8% up from 23.7%), handoff to community pharmacy at discharge (18.3% up from 917%), and designing a patient-specific medication-related action plan (11.2% up from 5.3%) ( Pedersen et al., 2017 ).

As a result of this change in the role of pharmacists, the percentage of them that they spend in drug distribution is now less than 20%. They spend more than 40% of their time reviewing and verifying prescription orders and almost 25% of their time on other clinical activities, including medication therapy management. Besides changes enabled by technology, pharmacy technicians are widely used in hospital pharmacy departments to support that role and activities of pharmacists. Pharmacy technicians spend almost 80% of their time in traditional drug preparation and distribution ( Pedersen et al., 2016 ).

Transitioning from a traditional drug preparation and dispensing role to a clinical role in medication therapy management has had implications for developing the hospital pharmacy workforce. Beginning in 2000, all US pharmacy graduates receive the Doctor of Pharmacy (PharmD) degree that prepares them for the increasing clinical roles that all pharmacists are realizing. These clinical roles are more common and often more advanced in the hospital setting, and additional training is available and increasingly required. These include post-graduate pharmacy residency training; both pharmacy practice (PGY1) and specialty (PGY2) programs. Board certification is also available to assess the competency of pharmacists in selected specialty practice areas through the Board of Pharmaceutical Specialties. Board certification is also available for pharmacy technicians through the Pharmacy Technician Certification Board. At present, 29.4% of hospital pharmacists have completed a PGY1 residency, 8.1% a PGY2 residency, and 23.1% are board certified. For pharmacy technicians the percentage that are board certified is much higher (77.8%) because there is no standard degree awarding program to prepare pharmacy technicians ( Schneider et al., 2018 ).

There is currently a need for high quality evaluation of new technologies undertaken in a pharmacy-related setting. We aim to evaluate the use of these monitoring technologies performed in this setting. Worldwide, few evaluations of mobile health, telehealth, smart pump, and monitoring technologies in pharmacy-related setting have been published. Their quality is often below the standard necessary for inclusion in a systematic review mainly due to inadequate study design. Despite the improvements in technology, there is limited evidence on how this translates to real settings and to consumer satisfaction. Most technology driven systems required significant funding and support, particularly those involving latest technology. Rigorous comparative studies are needed to evaluate the effectiveness of different technologies ( Baines D.L. et al., 2018 ).

Nevertheless, voices within the profession of pharmacy have long called for a more important role for the pharmacist. More recently, the public began to call for improvements in the quality of health care, particularly patient safety. New systems of care, many enabled by new technologies have the potential to improve the effectiveness, safety and efficiency of health care, and transform the roles of health care professionals including pharmacists. Unfortunately, the adoption of change is slow, and even though the health of the public is at stake, change in health care is no exception. Over the past decades, however new technologies have enabled the pharmacist to devote more time to working with other health care professionals to improve the use of medicines. Since virtually every patient in the health care system receives medicines, and there is ample evidence that the use of medicines needs to improve, this is a good thing.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

PS was employed by the company MediHealthInsight. The author declares no competing interests.

• American Society of Health-System Pharmacists (2018). Available at: http://www.ashpmedia.org/pai/index.html [accessed June 5 2018]. [ Google Scholar ]
• American Society of Hospital Pharmacists (1989). ASHP statement on unit dose drug distribution. Am. J. Hosp. Pharm. 46 : 2346 . [ PubMed ] [ Google Scholar ]
• Baines D., Bates I., Bader L., Hale C., Schneider P. (2018). Conceptualising production, productivity and technology in pharmacy practice: a novel framework for policy, education and research. Hum. Resour. Health 16 : 51 . 10.1186/s12960-018-0317-5 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baines D. L., Gahir I. K., Hussain A., Khan A. J., Schneider P., Hasan S. S., et al. (2018). A scoping review of the quality and the design of evaluations of mobile health, telehealth, smart pump and monitoring technologies performed in a pharmacy-related setting. Front. Pharmacol. 9 : 678 . 10.3389/fphar.2018.00678 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barker K. N. (1969). The effects of an experimental medication system on medication errors and costs. I. Introduction and errors study. Am. J. Hosp. Pharm. 26 324–333. [ PubMed ] [ Google Scholar ]
• Bates D. W., Cullen D. J., Laird N., Petersen L. A., Small S. D., Servi D. (1995). Incidence of adverse drug events and potential adverse drug events: implication for prevention. JAMA 274 29–34. 10.1001/jama.1995.03530010043033 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brennan T. A., Leape L. L., Laird N. M., Localio A. R., Lawthers A. G., et al. (1991). Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. N. Engl. J. Med. 324 370–376. 10.1056/NEJM199102073240604 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Francke D. E., Latiolais C. J., Francke G. N., Ho N. F. H. (1964). Mirror to Hospital Pharmacy; A Report of the Audit of Pharmaceutical Service in Hospitals. Washington, DC: American Society of Hospital Pharmacists. [ Google Scholar ]
• Institute of Medicine (2000). To Err Is Human: Building a Safer Health System. Washington, DC: The National Academies Press. [ PubMed ] [ Google Scholar ]
• Leape L. L., Brennan T. A., Laird N., Lawthers A. G., Localio A. R., Barnes B. A., et al. (1991). The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II. N. Engl. J. Med. 324 377–384. 10.1056/NEJM199102073240605 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lesar T. S., Lomaestro B. M., Pohl H. (1997). Medication-prescribing errors in a teaching hospital - A 9-year experience. Arch. Intern. Med. 157 1569–1576. 10.1001/archinte.1997.00440350075007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lucian L. L., Bates D. W., Cullen D. J., Cooper J., Demonoco H. J., Gallivan T., et al. (1995). Systems analysis of adverse drug events. JAMA 274 35–43. 10.1001/jama.1995.03530010049034 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Santell J. P. (2001). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2001. Am. J. Health Syst. Pharm. 58 2251–2266. [ PubMed ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2003). ASHP national survey of hospital pharmacy practice in hospital settings: dispensing and administration – 2002. Am. J. Health Syst. Pharm. 60 52–68. [ PubMed ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2004). ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education - 2003. Am. J. Health Syst. Pharm. 61 457–471. [ PubMed ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2005). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2004. Am. J. Health Syst. Pharm. 62 378–390. [ PubMed ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2006). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2005. Am. J. Health Syst. Pharm. 63 327–345. 10.2146/ajhp050538 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2007). ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education – 2006. Am. J. Health Syst. Pharm. 64 507–520. 10.2146/ajhp060669 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2008). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2007. Am. J. Health Syst. Pharm. 65 827–843. 10.2146/ajhp080029 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2009). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2008. Am. J. Health Syst. Pharm. 66 926–946. 10.2146/ajhp080715 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2010). ASHP national survey of pharmacy practice in hospital settings: monitoring and Patient Education – 2009. Am. J. Health Syst. Pharm. 67 542–558. 10.2146/ajhp090596 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2011). ASHP national survey of pharmacy practice in hospital settings: prescribing and Transcribing – 2010. Am. J. Health Syst. Pharm. 68 669–688. 10.2146/ajhp100711 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2012). ASHP national survey of pharmacy practice in hospital settings: dispensing and Administration – 2011. Am. J. Health Syst. Pharm. 69 768–785. 10.2146/ajhp110735 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2013). ASHP national survey of pharmacy practice in hospital settings: monitoring and Patient Education – 2012. Am. J. Health Syst. Pharm. 70 787–803. 10.2146/ajhp120777 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2014). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2013. Am. J. Health Syst. Pharm. 71 924–942. 10.2146/ajhp140032 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2015). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2014. Am. J. Health Syst. Pharm. 72 1119–1137. 10.2146/ajhp150032 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2016). ASHP national survey of pharmacy practice in hospital settings: monitoring and patient education – 2015. Am. J. Health Syst. Pharm. 73 1307–1330. 10.2146/ajhp160081 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pedersen C. A., Schneider P. J., Scheckelhoff D. J. (2017). ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing – 2016. Am. J. Health Syst. Pharm. 74 1336–1352. 10.2146/ajhp170228 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ringold D. J., Santell J. P., Schneider P. J., Arenberg S. (1999). ASHP national survey of pharmacy practice in acute care settings, part 1: prescribing and transcribing. Am. J. Health Syst. Pharm. 56 142–157. [ PubMed ] [ Google Scholar ]
• Ringold D. J., Santell J. P., Schneider P. J., Kelly E. J. (2000). ASHP national survey of pharmacy practice in acute care settings, part 2: dispensing and administration. Am. J. Health Syst. Pharm. 57 1759–1775. [ PubMed ] [ Google Scholar ]
• Rogers E. M. (2003). Diffusion of Innovations. New York, NY: Free Press. [ Google Scholar ]
• Schneider P. J., Pedersen C. A., Scheckelhoff D. J. (2018). ASHP national survey of pharmacy practice in hospital settings: dispensing and administration – 2017. Am. J. Health Syst. Pharm. 75 1203–1226. 10.2146/ajhp180151 [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Subscribe to journal Subscribe
• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Technology and Safe Medication Administration

Crawford, Mark; Mullan, Janet BSN, DDS, RN, CCRN; Vanderveen, Timothy PharmD, MS

Mark Crawford is director of concept development for Becton Dickinson and Company in Sandy, UT. Contact author: [email protected] .

Janet Mullan is clinical communications manager, clinical affairs, for Baxter Healthcare Corporation in Round Lake, IL. Contact author: [email protected] .

Timothy Vanderveen is executive clinical director, the ALARIS Center for Medication Safety and Improvement, for ALARIS Medical Systems, Inc., in San Diego, CA. Contact author: [email protected] .

Representatives of three companies that manufacture medication delivery systems write about technologic innovations.

Medication administration today is highly dependent on technology. Bar codes, smart pumps, and other technologic advances are assisting with safe medication administration in hospitals.

For a better appreciation of technology used for drug delivery, the State of the Science on Safe Medication Administration invitational symposium included an industry panel. The moderator was Mary Alexander, BS, CRNI, chief executive officer of the Infusion Nurses Society. Representatives of three companies that manufacture smart pumps and other medication delivery systems spoke about the process of creating a new device, how technology aids nurses at the bedside, how technologic innovations improve patient safety, and how these innovations can provide data for facilities to use to make medication administration safer.— Laurie Lewis

How a Product Is Designed: It Starts and Ends with the Customer

The design process is driven by customer requirements. When we are designing a product that will deliver medication, such as an infusion pump, our customers include the physicians who order the medication, the pharmacists who prepare it, the nurses who administer it at the bedside, and the patients who use it. To hear the voice of the customer, we talk to all of these people and observe them at work. We also hold focus groups.

Throughout this gathering of information, we record the exact words of our customers. Then the product development team, which includes people from marketing, manufacturing, quality, and engineering, has to translate these words. And although each member of the team has his own way of interpreting, the team works together to develop a product.

The next step in the design process is to create the specifications. Everything needs to be measurable and unambiguous. We look at reliability (the product performs the same way every time it is used), safety, the interface with other products, how well the product performs, and functionality (what the device does).

After a design is suggested, we perform a design review. Team members and others, such as clinicians, review the specifications. We look at all the information collected and all the assumptions that have been made. We try to validate our assumptions against what the customer initially said.

Sometimes the design process goes well and sometimes it doesn’t, as in the example illustrated above, a design for a swing that a customer wanted. Image A shows what marketing suggested the customer wanted: a three-tier swing. Management approved something a little different, as seen in Image B. Engineering had a different view and designed the product in Image C, which is totally nonfunctional. Image D shows what manufacturing produced. It’s a swing, but it not going to be totally functional in that location. So maintenance installed it as shown in Image E. Image F shows what the customer actually wanted. It was a very simple idea, but it got lost in translation.

To avoid this type of experience, the design is verified to make sure it meets the product specification and the customer’s requirements before it’s manufactured. We simulate usage at multiple sites and involve the customer by asking if the product meets his or her needs in the particular clinical setting.

Before we are ready to go to market, we must be sure we can manufacture the product in a reliable, continuous supply at reasonable cost. We examine the supply chain to make sure the product will be available when needed.

But we can’t go to market without providing training. We try to educate everyone who is going to come into contact with the product on its use. In-services should reach people on all shifts, and education has to be ongoing.

This product cycle begins and ends with the customer. We listened to the customer before we started to design the product. We listened to the customer during the design process. And once the product is manufactured, we continue to work with the customer to be certain the product meets the user’s needs.

Technology as an Aid to the Nurse–Patient Interaction at the Bedside

Creating a safe medication management process begins at the point where an error has the potential to cause the most harm: during medication administration. The focus in error prevention has shifted to the bedside, at the point of care where the nurse interacts with the patient.

A major factor leading to medication errors is a lack of patient information critical for making correct clinical decisions. For example, many adverse drug events can be prevented with adequate knowledge about a known allergy or anaphylactic reaction. Harm can be prevented by using information and communication tools at the point of care that provide information to the physician writing the order and to the nurse administering the medication.

Another factor leading to error is the complex nature of drug preparation and administration. Simplification should be a priority, by using unit doses, premixes, bar coding, and pharmacy management and point-of-care systems. Effective product education and standardization of processes, protocols, and equipment are also important in preventing errors. The goal should be a system that makes it easy to do the right thing and hard to do the wrong thing.

Nurses need real-time monitoring capability for patient information, even when they are not at the bedside. A way to reduce the time spent gathering information is with a medication management system that sends alerts to nurses via a hand-held device, which can also include an electronic patient minichart with the complete medication administration history including allergies and real-time laboratory results.

“Smart” infusion pumps help nurses reduce the risk of errors associated with administration of iv medications by providing a warning if the program entered is outside the hospital’s predefined limits. However, pumps with medication safety software are only part of the solution because an infusion pump doesn’t recognize patients’ needs or allergies. Programming errors outside the ordered dose or rate may not be outside the pump’s predefined limits and may not be caught by the pump’s safety software alone.

A comprehensive medication management system extends beyond the pump to verify the five rights of medication administration: the right drug at the right dose by the right route given to the right patient at the right time. At the bedside, nurses can use real-time wireless technology to scan bar codes on the medication bag, the patient, and the pump channel. The bar code on the medication bag verifies the intended patient, the medication, the start time, and the bag sequence. The patient’s bar-coded wristbands may provide access to information about medical history and drug sensitivities. Nurses may electronically compare pump settings to the pharmacy-entered order to verify that they match before administering iv medications. This alerts the nurse to pump programming errors other than those related to the ordered dose or rate.

Technology can enhance safe delivery of care and provide access to information about the patient throughout the process. Decision-support tools like real-time access to laboratory results can help identify trends early and help clinicians make proactive decisions about the appropriate care for patients. Technology is a valuable adjunct to the nurse–patient interaction at the bedside.

How IV Safety Systems Have Prevented Medication Errors

Complex infusions of medications are administered using iv infusion pumps that, although designed to be easy to use, have been associated with programming errors that can result in serious underdosing and overdosing. Until recently, infusion pumps were unable to alert nurses if programming was wrong. They also could be used on all patients, from the smallest neonate to the largest adult.

These pumps are now being replaced by “smart” (computerized) iv pumps with comprehensive drug libraries, dosing limits, and best-practice guidelines that are both prescriptive and restrictive. Smart pumps can match performance characteristics to the patient being treated—for example, allowing different infusion rates and doses for infants and for the elderly. Some devices also maintain an electronic record of all programming alerts and subsequent actions taken by the nurse. This information is useful in comprehensive medication safety programs and continuous quality improvement initiatives. Some iv medication safety systems can provide a technology platform that integrates infusion and monitoring modules and can link to the hospital’s information system, allowing for real-time data evaluation.

Infusion pumps do not come from the manufacturer programmed with drug libraries and best-practice rules. Each hospital develops its own complete data set, including drug names, concentrations, dosing units, and dose limits, and loads it into each device. Some smart pumps include additional features such as drug information and weight, rate, and volume limits specific to the patient care area where the device is used.

Although smart iv systems have been available for about three years, they are rapidly adding a new level of safety to iv infusions that was not possible in the previous 30-plus-year history of iv infusion pump use. Consider these examples of how the new iv medication safety systems have prevented potentially serious errors:

• A recently published article from a university hospital documented 17 potentially fatal and 17 additional serious errors that were prevented in eight months in three special care units. 1
• Analysis of the iv safety software data from two community hospitals documented almost 600 prevented iv errors over an eight-month period. 1 These prevented errors included 10- to 50-fold overdoses or underdoses of high-risk medications.
• The iv safety system logs at a children’s hospital revealed an average of three iv reprogramming events per day because nurses were alerted to programming errors. 2 Medications included insulin, morphine, dopamine, and other drugs associated with a high risk of potential harm when administered in overdoses to children.

Smart IV systems are adding a new level of safety to IV infusions that was not possible previously.

Many of the alerts for doses below or above the established limits result in overrides, some of which can be caused by unusual requirements for a drug. For example, a dopamine infusion limit of 22 mcg/kg/min might be exceeded if a higher dosage is needed to maintain blood pressure in a critically ill patient. Other overrides occur because the smart pump’s data set does not match practice, such as the use of momentary high infusion rates to give a bolus of a sedative such as propofol. Setting an infusion device to a high rate and not limiting the volume of drug to be administered may not be the best practice. Identifying an unsafe practice such as this enables corrective actions to be taken. Event logs can be used to measure the impact of the corrective actions.

An unexpected finding is the large variation in the use of iv safety systems within and among hospitals. 3 An analysis of iv safety system data sets from 65 hospitals documented the differences in drug nomenclature, concentrations, and dosing units. Here are some examples.

• Tissue plasminogen activator was identified by 20 different names.
• There were six different dosing units for amiodarone, ranging from mcg/kg/min to mg/day.
• Hospitals used eight different dosing units for magnesium.
• In the same patient care area, 64 medications were identified with two dosing units, often one with weight (for example, units/kg/hr) and another without (for example, units/hr).

Although the impact of these variations—dosing units and drug nomenclature—on safety and medication errors has not been assessed, it seems reasonable that the lack of standardization contributes to errors. Agency, per diem, and traveling nurses, as well as nurses reassigned from one unit to another, may not be aware of variations in practice. For a 70-kg patient, programming a nitroglycerin infusion as mcg/kg/min when the order was for mcg/min would result in a 70-fold overdose.

IV medication safety systems added a new level of safety to drug infusions. “Smart” infusion technology provides a second check of pump programming, alerts caregivers when the programming falls outside the guidelines, and records both the alert and subsequent programming in a comprehensive log. Data are now available to evaluate practice and to provide a roadmap for improvement.

• + Favorites
• View in Gallery

Advertisement

Intelligent automated drug administration and therapy: future of healthcare

• Review Article
• Published: 14 January 2021
• Volume 11 , pages 1878–1902, ( 2021 )

Cite this article

• Richa Sharma 1 ,
• Dhirendra Singh 2 ,
• Prerna Gaur 3 &
• Deepak Joshi 1 , 4  

2128 Accesses

17 Citations

1 Altmetric

Explore all metrics

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review’s main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Closed-Loop Systems

Recent advances in the precision control strategy of artificial pancreas

Smart Infusion Pump Control: The Control System Perspective

Liu Y-L, Chen D, Shang P, Yin D-C. A review of magnet systems for targeted drug delivery. J Control Release. 2019;302:90–104.

Article   CAS   PubMed   Google Scholar  

Carpenter GW, Myers HG, Sherer EA, Evans KA, O’Neal DP. Closed-loop intravenous drug administration using photoplethysmography. IEEE J Transl Eng Health Med. 2018;6:1–8.

Article   Google Scholar  

Woodruff EA, Northrop RB. Closed-loop regulation of a physiological parameter by an IPFM/SDC (Integral pulse frequency modulated/smith delay compensator) controller. IEEE Trans Biomed Eng. 1987;34(8):595–602.

Li J, Liang JY, Laken SJ, Langer R, Traverso G. Clinical opportunities for continuous biosensing and closed-loop therapies. Trends in Chemistry. 2020;2(4):319–40.

Article   CAS   Google Scholar  

Davoodi P, Lee LY, Xu Q, Sunil V, Sun Y, Soh S, Wang CH. Drug delivery systems for programmed and on-demand release. Adv Drug Deliv Rev. 2018;132:104–38.

Yadav KS, Kapse-Mistry S, Peters GJ, Mayur YC. E-drug delivery: a futuristic approach. Drug Discov Today. 2019;24(4):1023–30.

Michel MMRF, Struys TD, Smet JB, Glen EM, Vereecke AR, Absalom TW. Schnider, “The history of target-controlled infusion.” Anesth Analg. 2016;122(1):56–69.

Jin X, Kim CS, Dumont GA, Ansermino JM, Hahn JO. A semi-adaptive control approach to closed-loop medication infusion. Int J Adapt Contr Signal Process. 2017;31(2):240–54.

Liu X, Kruger P, Maibach H, Colditz PB, Roberts MS. Using skin for drug delivery and diagnosis in the critically ill. Adv Drug Deliv Rev. 2014;77:40–9.

Fry A. Electronically enabled drug-delivery devices: are they part of the future? Ther Deliv. 2012;3:805–7.

Cobo A, Sheybani R, Meng E. MEMS: enabled drug delivery systems. Adv Healthc Mater. 2015;4(7):969–82.

Percival MW, Wang Y, Grosman B, Dassau E, Zisser H, Jovanovič L, Doyle FJ III. Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters. J Process Control. 2011;21(3):391–404.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Steil GM, Panteleon AE, Rebrin K. Closed-loop insulin delivery—the path to physiological glucose control. Adv Drug Deliv Rev. 2004;56(2):125–44.

Medvedev A, Zhusubaliyev ZT, Rośen O, Silva MM. Oscillations-free PID control of anesthetic drug delivery in neuromuscular blockade. Comput Meth Prog Biomed. 2019;171:119–31.

Nimri R, Muller I, Atlas E, Miller S, Kordonouri O, Bratina N, Tsioli C, Stefanija MA, Danne T, Battelino T, Phillip M. Night glucose control with MD‐Logic artificial pancreas in home setting: a single blind, randomized crossover trial—interim analysis. Pediatric Diabetes. 2014;15(2):91–9.

Article   PubMed   Google Scholar  

Nimri R, Atlas E, Ajzensztejn M, Miller S, Oron T, Phillip M. Feasibility study of automated overnight closed-loop glucose control under MD-logic artificial pancreas in patients with type 1 diabetes: the DREAM Project. Diabetes Technol Ther. 2012;14(8):728–35.

Catalogna M, Cohen E, Fishman S, Halpern Z, Nevo U, Ben-Jacob E. Artificial neural networks based controller for glucose monitoring during clamp test. PloS one. 2012;7(8):e44587.

Bothe MK, Dickens L, Reichel K, Tellmann A, Ellger B, Westphal M, Faisal AA. The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas. Expert Rev Med Devices. 2013;10(5):661–73.

Mauseth R, Hirsch IB, Bollyky J, Kircher R, Matheson D, Sanda S, Greenbaum C. Use of a “fuzzy logic” controller in a closed-loop artificial pancreas. Diabetes Technol Ther. 2013;15(8):628–33.

DeJournett L, DeJournett J. In silico testing of an artificial-intelligence based artificial pancreas designed for use in the intensive care unit setting. J Diabetes Sci Technol. 2016;10(6):1360–71.

Article   PubMed   PubMed Central   Google Scholar  

Zaouter C, Joosten A, Rinehart J, Struys MM, Hemmerling TM. Autonomous systems in anesthesia: where do we stand in 2020? A narrative review. Anesth Analg. 2020;130(5):1120–32.

Sundaravadivel P, Kougianos E, Mohanty SP, Ganapathiraju MK. Everything you wanted to know about smart health care: evaluating the different technologies and components of the Internet of Things for better health. IEEE Consumer Electronics Magazine. 2017;7(1):18–28.

Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151–152:169–90.

Article   PubMed   CAS   Google Scholar  

Zhang M, Raghunathan A, Jha NK. MedMon: securing medical devices through wireless monitoring and anomaly detection. IEEE Trans Biomed Circuits Syst. 2013;7(6):871–81.

Ince C. Intensive care medicine in 2050: the ICU in vivo. Intensive Care Med. 2017;43(11):1700–2.

Niewolny D. How the Internet of Things is revolutionizing healthcare. Freescale Semiconductor. Eindhoven: The Netherlands. WhitePaper; 2013.

Google Scholar  

Cobo A, Sheybani R, Tu H, Meng E. A wireless implantable micropump for chronic drug infusion against cancer. Sens Actuators, A. 2016;239:18–25.

Gonzales WV, Mobashsher AT, Abbosh A. The progress of glucose monitoring-a review of invasive to minimally and non-invasive techniques, devices and sensors. Sensors, MDPI. 2019;19:800.

Dutta A, Bera SC, Das K. A non-invasive microcontroller based estimation of blood glucose concentration by using a modified capacitive sensor at low frequency. AIP Adv. 2019;9(10):105027.

Juanola-Feliu E, Miribel-Català PL, Avilés CP, Colomer-Farrarons J, González-Piňero M, Samitier J. Design of a customized multipurpose nano-enabled implantable system for in-vivo theranostics. Sensors. 2014;14:19275–306.

Gray M, Meehan J, Ward C, Langdon SP, Kunkler IH, Murray A, Argyle D. Implantable biosensors and their contribution to the future of precision medicine. Vet J. 2018;239:21–9.

Nguyen QV, Caro A, Raoux M, Quotb A, Floderer J-B, Bornat Y, Renaud S, Lang J. “A novel bioelectronic glucose sensor to process distinct electrical activities of pancreatic beta-cells”, 35 th Annual International conference of IEEE EMBS Osaka. Japan. 2013;3–7(July):172–5.

Sheybani R, Cabrera-Munoz NE, Sanchez T, Meng E. Design, fabrication and characterization of an electrochemically-based dose tracking system for closed-loop drug delivery, 34 th Annual International Conference of the IEEE EMBS; 28 Aug-1 Sep; San Diego, California, USA. 2012;519–22.

Schoukroun-Barnes LR, Macazo FC, Gutierrez B, Lottermoser J, Liu J, White RJ. Reagentless, structure-switching, electrochemical aptamer-based sensors. Annual Review of Analytical Chemistry. 2016;9:163–81.

Tasca F, Tortolini C, Bollella P, Antiochia R. Microneedles-based electrochemical devices for transdermal biosensing: a review. Curr Opin Electrochem. 2019;16:42–9.

Scholten K, Meng E. A review of unplantable biosensors for closed-loop glucose control and other drug delivery applications. International Journal of Pharmaceutical. 2018;544:319–34.

Kwok E, Shah SL, Finegan BA, Kwong GK. Experience with experimental clinical evaluation of a computerized drug delivery system for regulation of mean arterial blood pressure. Proceedings of the Americal Control Conference; June 1264–1268; San Diego, California.

Anhalt H, Bohannon NJV. Insulin patch pumps: their development and future in closed-loop systems. Diabetes Technol Ther. 2010;12:52-8.

Ly TT, Layne JE, Huyett LM, Nazzaro D, O’Connor JB. Novel Bluetooth-enabled tubeless insulin pump: innovating pump therapy for patients in the digital age. Journal of diabetes science and technology. 2019;13(1):20–6.

Cengiz E, Bode B, Van Name M, Tamborlane WV. Moving toward the ideal insulin for insulin pumps. Expert Rev Med Devices. 2016;13(1):57–69.

Dagdeviren C, Ramadi KB, Joe P, Spencer K, Schwerdt HN, Shimazu H, Delcasso S, Amemori KI, Nunez-Lopez C, Graybiel AM, Cima MJ. Miniaturized neural system for chronic, local intracerebral drug delivery. Sci Transl Med. 2018;10(425):eaan2742.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Batmani Y, Khodakaramzadeh S. Blood glucose concentration control for type 1 diabetic patients: a multiple-model strategy. IET Syst Biol. 2020;14(1):24–30.

Chee F, Fernando TL, Savkin AV, Van Heeden V. Expert PID control system for blood glucose control in critically ill patient. IEEE Trans Inf Technol Biomed. 2003;7(4):419–25.

Bruttomesso D, Farret A, Costa S, Marescotti MC, Vettore M, Avogaro A, Tiengo A, Dalla Man C, Place J, Facchinetti A, Guerra S. Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: preliminary studies in Padova and Montpellier. 2009;1014–21.

Soru P, De Nicolao G, Toffanin C, Dalla Man C, Cobelli C, Magni L, AP@ Home Consortium. MPC based artificial pancreas: strategies for individualization and meal compensation. Annu Rev Control. 2012;36(1):118–28.

Cameron F, Bequette BW, Wilson DM, Buckingham BA, Lee H, Niemeyer G. A closed-loop artificial pancreas based on risk management. Journal of diabetes science and technology. 2011;5(2):368–79.

Daskalaki E, Diem P, Mougiakokou SG. An actor-critic based controller for glucose regulation in type 1 diabetes. Comput Methods Programs Biomed. 2013;109:116–25.

Abbod MF, Linkens DA. “Fault diagnosis for closed-loop drug infusion”, IEE Colloquium on condition monitoring, machinery, external structures and health. UK: Birmingham; April 22-23 1999.

de Canete JF, Gonzalez-Perez S, Ramos-Diaz JC. Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. Computer Methods and Programs in Medicine. 2012;106:55–66.

Kienitz KH, Yoneyama T. A robust controller for insulin pumps based on H-infinity theory. IEEE Trans Biomed Eng. 1993;40(11):1133–7.

Jin X, Zhu DD, Chen BZ, Ashfaq M, Guo XD. Insulin delivery systems combined with microneedle technology. Adv Drug Deliv Rev. 2018;127:119–37.

Shen D, Haojie Y, Wang L, Khan A, Haq F, Chen X, Huang Q, Teng L. Recent progress in design and preparation of glucose-responsive insulin delivery systems. J Controlled Release. 2020;321:236–58.

Pease A, Lo C, Earnest A, Liew D, Zoungas S. Evaluating optimal utilisation of technology in type1 diabetes mellitus from a clinical and health economic perspective: protocol for a systematic view. Systematic Reviews. 2018;7:44.

IDF Diabetic Atlas. 9 th Brussels. International Diabetes Federation: Belgium; 2019.

Forlenza GP, Rewers M. The epidemic of type1 diabetes: what is it telling us ? . Curr Opin Endocrinol Diabetes Obes. 2017;18:248–51.

Sekigami T, Shimoda S, Nishida K, Matsuo Y, Ischimori S, Ichinose K, Shichiri M, Sakakida M, Araki E. Comparison between closed-loop portal and peripheral venous insulin therapy systems for an artificial endocrine pancreas. J Artif Organs. 2004;7:91–100.

Voss GJ. Closed loop drug delivery: an industry perspective. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1990;12(3):0940.

Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectronic Medicine. 2018;4(14):1–12.

Abbes IB, et al. A closed-loop artificial pancreas using a proportional integral derivative with double phase lead controller based on a new nonlinear model of glucose metabolism. J Diabetes Sci Technol. 2013;7(3):699–707.

Chee F, Savkin AV, Fernando TL, Nahavandi S. Optimal h∞ insulin injection control for blood glucose regulation in diabetic patients. IEEE Trans Biomed Eng. 2005;52:1625–31.

Dudde R, Vering T. Advanced insulin infusion using a control loop (ADICOL) concept and realization of a control-loop application for the automated delivery of insulin, Proceedings of 4 th Annual IEEE Conference on Information Technology Applications in Biomedicine; UK. 280–87.

Abu-Rmileh A, Garcia-Gabin W, Zambrano D. Internal model sliding mode control approach for glucose regulation in type 1 diabetes. Biomed Sig Process Control. 2010;5:94-102.

Campos-Delgado DU, et al. Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route. IEEE Trans Bio-Med Eng. 2006;53(11):2201–10.

Leon BS, et al. Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients. J Franklin Ins Eng App Math. 2012;349(5):1851–70.

Asadi S, Nekoukar V. Adaptive fuzzy integral sliding mode control of blood glucose level in patients with type 1 diabetes: in silico studies. Math Biosci. 2018;305:122–32.

Kaveh P, Shtessel YB. Blood glucose regulation in diabetes using sliding mode control techniques. Proceedings of the 38 th South eastern Symposium on System Theory, Tennessee Technological University; March 5–7; Cookeville, TN, USA. 2006. 172–75.

Yasini S, Karimpour A, Naghibi-Sistani MB, Ghareh S. An automatic insulin infusion system based on H-infinity control technique. Cairo Int'l Biomed Eng Conference; 2008; Cairo, Egypt.

Borrello MA, Sun J, Bequette BW. A comparison of clinical control strategies for hyperglycemia of injury and illness. American control conference Marriott Waterfront, Baltimore, MD, USA 2010;5149–54.

Zavitsanou S, Montalaris A, Georgiadis MC, Pistikopoutos EN. In Silico closed-loop control validation studies for optimal insulin delivery in type 1 Diabetes. IEEE Trans Biomed Eng. 2015;62(10):2369–78.

Emami A, El. Youssef J, Rabasa-Lhoret R, Pineau J, Castle R, Haider A. Modeling glucagon action in patients with type 1 diabetes. IEEE J Biomedical and Health Informatics. 2017;21(4):1163–71.

Del Favero S, Bruttomesso D, Di Palma F, Lanzola G, Visentin R, Filippi A, Scotton R, Toffanin C, Messori M, Scarpellini S, Keith-Hynes P. First use of model predictive control in outpatient wearable artificial pancreas. Diabetes Care. 2014;37(5):1212–5.

Cao Z, Gondhalekar R, Dassau E, Doyle FJ III. Extreme seeking control for personalized zone adaptation in model predictive control for type1 diabetes. IEEE Trans Biomed Eng. 2018;65(8):1859–70.

Shi D, Dassau E, Doyle FJ III. Adaptive zone mode predictive control of artificial pancreas based on Glucose-and velocity-dependent control penalties. IEEE Trans Biomed Eng. 2019;66(4):1045–54.

Yasini S, Karimpour A, Sistani MBN. Knowledge-based closed-loop control of blood glucose concentration in Diabetic patients and comparison with H ∞ control technique. IETE J Res. 2012;58(4):328–36.

Wang Y, Xie H, Jiang X, Liu B. Intelligent closed-loop insulin delivery systems for ICU patients. IEEE J Biomed and Health Informatics. 2014;18(1):290–9.

Trajanoski Z, Wach P. Neural predictive controller for insulin delivery using the subcutaneous route. IEEE Trans Biomed Eng. 1998;45(9):1122–34.

Song L, Liu C, Yang W, Zhang J, Kong X, Zhang B, Chen X, Wang N, Shen D, Li Z, Jin X, Shuai Y, Wang Y. Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes. Comput Methods Programs Biomed. 2020;191:105416.

Grosman B, Llany J, Roy A, Kurtz N, Wu D, Parikh N, Voskanyan G, Konvalina N, Mylonas C, Gottlieb R, Kaufman F, Cohen O. Hybrid closed-loop insulin delivery in type1 diabetes during supervised outpatient condition. J Diabetes Sci Technol. 2016;10(3):708–13.

Kato R, Munkhjaragal M, Takahashi D, Arakawa T, Kudo H, Mitsubayashi K. An autonomous drug release system based on chemo-mechanical energy conversion “Organic Engine” for feedback control of blood glucose. Biosens Bioelectron. 2010;26:1455–9.

Patek SD, Magni L, Dassau E, Hughes-Karvetski C, Toffanin C, Nicolao GD, Favero SD, Breton M, Dolla Man C, Renard E, Zisser H, Doyle III FJ, Cobelli C, Kovtchev BP, International Artificial Pancreas (IAP) group: Modular closed-loop control of Diabetes. IEEE Trans Biomed Eng .  2012;59(11):2986–99.

Jacobs PG, El Youssef J, Castle JR, Engle JM, Branigan DL, Johnson P, Massoud R, Kamath A, Ward WK. Development of a fully automated closed loop artificial pancreas control system with dual pump delivery of insulin and glucagon, 33rd Annual International Conference of the IEEE EMBS. Boston, Massachusetts, USA. 2012;3:397–400.

Moscardό V, Herrero P, Diez JL, Giménez M, Rossetti P, Bondia J. In silico evaluation of a parallel control -based coordinated dual-hormone artificial pancreas with insulin on board limitation American Control Conference. Philadelphia, PA, USA; 2019.

Berián J, Bravo I, Gardel A, Laźaro JL, Hernańdez S. A wearable closed-loop insulin delivery system based on Low-power SoCs. Electronics. 2019;8(612):1–19.

Ruan Y, Wilinska ME, Thabit H, Hovorka R. Modeling day-to-day variability of glucose-insulin regulation over 12-week home use of closed-loop insulin delivery. IEEE Trans Biomed Eng. 2017;64(6):1412–9.

Tschaikner M, Powell K, Jungklaus M, Fritz M, Ellmerer M, Hovorka R, Lane S, Pieber TR, Regittnig W. Novel single-site device for conjoined glucose sensing and insulin infusion: performance evaluation in diabetes patients during home-use. IEEE Trans Biomed Eng. 2020;67(1):323–32.

Tauschmann M, Allen JM, Wilinska ME, Ruan Y, Thabit H, Acerini CL, Dunger DB, Hovorka R. Sensor life and overnight closed loop: a randomized clinical trial. J Diabetes Sci Technol. 2017;11(3):513–21.

Elleri D, Dunger DB, Hovorka R. Closed-loop insulin delivery for treatment of type1 diabetes. BMC Medicine. 2011;9:1–20.

Tauschmann M, Hovorka R. Insulin pump therapy in youth with type1 diabetes: towards closed-loop systems. Expert Opin Drug Deliv. 2014;11(6):943–55.

Hovorka R. Closed-loop insulin delivery: from bench to clinical practice. Endocrinology. 2011;7:385–95.

CAS   PubMed   Google Scholar  

Kropff J, DeVries JH. Continuous glucose monitoring, future products, and update on worldwide artificial pancreas projects. Diabetes Technol Ther. 2016;18(S2):S2-53.

Article   PubMed Central   Google Scholar  

Bally L, Thabit H, Hartnell S, Andereggen E, Ruan Y, Wilinska ME, Evans ML, Wertli MM, Coll AP, Stettler C, Hovorka R. Closed-loop insulin delivery for glycemic control in noncritical care. The New England J Med. 2018;379(6):547–56.

Turksoy K, Quinn LT, Littlejohn E, Cinar A. An integrated multivariable artificial pancreas control system. J Diabetes Sci Technol. 2014;8(3):498–507.

Thabit H, Hovorka R. Closed-loop insulin delivery in type 1 diabetes. Endocrinol Metab Clin. 2012;41(1):105–17.

Biswas P, Sutradhar A, Datta P. Estimation of parameters for plasma glucose regulation in type-2 diabetics in presence of meal. IET Syst Biol. 2018;12(1):18–25.

Battelino T, Omladiĉ JS, Phillip M. Closed loop insulin delivery in diabetes, Best Practice & Research clinical. Endocrinology & Metabolism. 2015;29:315–25.

CAS   Google Scholar  

Giridharan G, Skliar M. Nonlinear controller for ventricular assist devices. Artif Organs. 2002;26:980–4.

Boston J, Simaan M, Antaki J, Yu Y. Control issues in rotary heart assist devices. In: Proc. American Control Conference; 2002;3473–77.

Paden B, Ghosh J, Antaki J. Control system architectures for mechanical cardiac assist devicesl. In: Proc. American Control Conference; 2000;3478–82.

Haddad SA, Houben RP, Serdijin WA. The evolution of pacemakers. IEEE Eng Med Biol Mag. 2006;25(3):38–48.

Elnaggar MI, Ashour AS, Guo Y, El-Khobby HA, Abd Elnaby MM. An optimized Mamdani FPD controller design of cardiac pacemaker. Health Info Sci and Syst. 2019;7(1):2.

Shi WV. Advanced intelligent control of cardiac pacemaker systems using a fuzzy PID controller. Int J Intell Control Syst. 2013;18(2):28–34.

Karar ME. Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers. Int J Adapt Control Signal Process. 2018;32(7):1040–51.

Valcke CP, Chizerk HJ. Closed-loop drug infusion for control of heart-rate trajectory in pharmacological stress tests. IEEE Trans Biomed Eng. 1997;44(3):185–95.

D M Cosgrove III, J H Petre, J L Waller, J V Roth, C Shepherd L H.Cohn, Automated control of Post-operative hypertension: a prospective, randomized multi centre study, In: Twenty-Fourth Annual meeting of The Society of Thoracic Surgeons, New Orleans, LA, 1988.

J. B. Slate, L. C. Sheppard, V. C. Rideout, E. H. Blackstone, A model for design of a blood pressure controller for hypertensive patients, Proc. First Annual Conf. IEEE Engineering in Medicine and Biology Society, Denver, Colorado, October, 1979.

Arnsparger JM, Mcinnis BC, Glover JR, Normann NA. Adaptive control of blood pressure. IEEE Trans Biomed Eng. 1983;38:168–76.

Pajunen GA, Steinmetz M, Shankar R. Model reference adaptive control with constraints for postoperative blood pressure management. IEEE Trans Biomed Eng. 1990;37:679–87.

Malagutti N, Dehghani A, Kennedy R. A robust control design for automatic regulation of blood pressure. IET Control Theory Appl. 2012;7:387–96.

Ying H, McEachern M, Eddleman DW, Sheppard LC. Fuzzy control of mean arterial pressure in postsurgical patients with sodium nitroprusside infusion. IEEE Trans Biomed Eng. 1992;39:1060–70.

Lee CC. Fuzzy logic in control systems: fuzzy logic controller-Part 1. IEEE Transactions on Systems, Man and Cybernetics. 1990;20(2):404–18.

S. Isaka, On the design and architecture of adaptive fuzzy controllers and their application to a biomedical control problem, Ph.D. Thesis, Uni. California. San Diego, 1989.

Frei CW, Derighetti M, Morari M, Glattfelder AH, Zbinden AM. Improving regulation of mean arterial blood pressure during anesthesia through estimates of surgery effects. IEEE Trans Biomed Eng. 2000;47:1456–64.

Sondhi S, Hote YK. Fractional-Order PI controller with specific gain-phase margin for MABP control. IETE Journal of Research. 2015;61:142–53.

Chen C-T, Lin W-L, Kuo T-S, Wang C-Y. Adaptive control of arterial blood pressure with a learning controller based on multilayer neural network. IEEE Trans Biomed Eng. 1997;44:601–9.

Padmanabhan R, Meskin N, Haddad WM. Closed-loop control of anaesthesia and mean arterial pressure using reinforcement learning. Biomed Signal Process Control. 2015;22:54–64.

Kwok KE, Shah SL, Finegan BA, Kwong GK. An observational trail of a computerized drug delivery system on two patients. IEEE Trans Control Syst Technol. 1997;5(4):385–93.

Polycarpou MM, Conway JY. Indirect adaptive nonlinear control of drug delivery systems. IEEE Trans Autom Control. 1998;43(6):849–56.

Ying H, McEachern M, Eddleman DW, Sheppard LC. Fuzzy control of mean arterial pressure in postsurgical patients with sodium nitroprusside infusion. IEEE Trans Biomed Eng. 1992b;39(10):060–1070.

Treesatayapun C. Minimized sodium nitroprusside for mean arterial pressure regulation based on fuzzy rules emulated networks. Appl Math Model. 2010;34:1292–310.

Gao Y, Er MJ. An intelligent adaptive control scheme for postsurgical blood pressure regulation. IEEE Trans Neural Networks. 2005;16(2):475–83.

Tafreshi AS, Klamroth-Marganska V, Nussbaumer S, Riener R. Real-time closed-loop control of human heart rate and blood pressure. IEEE Trans Biomed Eng. 2015;62(5):1434–42.

Sharma R, Deepak KK, Gaur P, Joshi D. An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure. Comput Methods Programs Biomed. 2020;185:105167.

Doyle FJ, Bequette BW, Middleton R, Ogunnaike B, Paden B, Parker RS, Vidyasagar M, Control in Biological systems, in Samad, T. Annaswamy, A. M. (Eds.): The impact of control technology (IEEE Control Systems Society) 2011.

Guo X, Luo Z, Cui H, Wang J, Jiang Q. A novel and reproducible release mechanism for a drug-delivery system in the gastrointestinal tract. Biomed Microdevice. 2019;21:25.

Woods SP, Constandinou TG. Wireless capsule endoscope for targeted drug delivery: mechanics and design considerations. IEEE Trans Biomed Eng. 2013;62(4):945–53.

Meron GD. The development of the swallowable video capsule (M2A). Gastrointent Endos. 2004;52(12):1866–75.

Swain P, Fritscher-Ravens A. Role of video endoscopy in managing small bowel disease. Gut. 2004;53(12):1866–75.

Cave DR, Fleischer DE, Leighton JA, Faigel DO, Heigh RI, Sharma VK, Gostout CJ, Rajan E, Mergener K, Foley A, Lee M, Bhattacharya K. A multicenter randomized comparison of the endocapsule and pillcam SB. Gastrointestinal Endosc. 2008;68:487–94.

Le VH, Rodriguez HL, Lee C, Go G, Zhen J, Nguyen VD, Choi H, Ko SY, Park J-O, Park S. A soft magnet-based drug-delivery module for active locomotive intestinal capsule endoscopy using an electromagnetic actuation system. Sens Actuators, A. 2016;243:81–9.

Woods SP, Constandinou TG. Wireless capsule endoscope for targeted drug delivery: mechanics and Design considerations. IEEE Trans Biomed Eng. 2013b;60(4):945–53.

Menciassi P, Dario P, Ho CN, Anhoeck G, Schurr MO. Wireless therapeutic endoscopic capsule: in vivo experiment. Endoscopy. 2008;40(12):979–82.

Ciuti G, Menciassi A, Dario P. Capsule endoscopy: from current achievements to open challenges. IEEE Rev Biomed Eng. 2011;4:59–72.

Li B, Meng MQ-H. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature section. IEEE Trans Inf Technol Biomed. 2012;16(3):323–9.

Fu Y, Zhang W, Mandal M, Meng MQ-H. Computer aided bleeding detection in WCE video. IEEE J Biomed Health Inform. 2014;18(2):636–42.

Parasrampuria DA, Kanamaru T, Connor A, Wilding I, Ogata K, Shimoto Y, Kunitada S. Evaluation of regional gastrointestinal absorption of edoxaban using the enterion capsule. The J Clinical Pharmacology. 2015;55(11):1286–92.

Becker D, Zhang J, Heimbach T, Penland RC, Wanke C, Shimizu J, Kulmatycki K. Novel orally swallowable IntelliCap® device to quantify regional drug absorption in human GI tract using diltiazem as model drug. AAPS Pharm SciTech. 2014;15(6):1490–7.

Lee C, Choi H, Go G, Jeong S, Ko SY, Park J-O, Park S. Active locomotive intestinal capsule endoscope (ALICE) system: a perspective feasibility study. IEEE ASME Trans Mechatronics. 2015;20(5):2067–74.

Fontana R, Mulana F, Cavallotti C, Tortora G, Vigliar M, Vatteroni M, Menciassi A. An innovative wireless endoscopic capsule with spherical shape. IEEE Trans Biomed Circuits Syst. 2016;11(1):143–52.

Tortora G, Orsini B, Pecile P, Menciassi A, Fusi F, Romano G. An ingestible capsule for the photodynamic therapy of Helicobacter pylori infection. IEEE/ASME Trans Mechatron. 2016;21(4):1935–42.

Leung BHK, Poon CCY, Zhang R, Zhang R, Zhang Y, Zheng Y, Chan CKW, Chiu PWY, Lau JYW, Sung JJY. A therapeutic wireless capsule for treatment of gastrointestinal haemorrhage by balloon tamponade effect. IEEE Trans Biomed Eng. 2017;64(5):1106–14.

Goffredo R, Accoto D, Guglielmelli E. Swallow-able smart pills for local drug delivery: present status and future perspectives. Expert Rev Med Devices. 2015;12(5):585–99.

Gonzalez-Guillaumin JL, Sadowski DC, Kaler KV, Mintchev MP. Ingestible capsule for impedance and pH monitoring in the esophagus. IEEE Trans Biomed Eng. 2007;54(12):2231–6.

Liu L, Towfighian S, Hila A. A review of locomotion systems for capsule endoscopy. IEEE Rev Biomed Eng. 2015;8:138–51.

Gao J, Yan G, Wang Z, He S, Xu F, Jiang P, Liu D. Design and testing of a motor-based capsule robot powered by wireless power transmission. IEEE/ASME Trans Mechatron. 2016;21(2):683–93.

Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Programs Biomed. 2015;122:341–53.

Goffredo R, Pecora A, Maiola L, Ferrone A, Guglielmelli E, Accota D. A swallowable smart pill for local drug delivery. J Microelectromech Syst. 2016;25(2):362–70.

Luo H et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Oncology. 2019;20:1645–1654.

Saito H, Aoki T, Aoyama K, Kato Y, Tsuboi A, Yamada A, Fujishiro M, Oka S, Ishihara S, Matsuda T, Nakahori M, Tanaka S, Koike K, Tada T. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolution neural network. Gastrointestinal endoscopy. 2020;192:144-151.

Soffer S, Klang E, Shimon O, Nachmias N, Eliakim R, Ben-Horin S, Kopylov U, Barash Y. Deep learning for wireless capsule: a systematic review and meta-analysis. Gastrointestinal Endoscopy. 2020.

Cabo A, Sheybani R, Tu H, Meng E. A wireless implantable micropump for chronic drug infusion against cancer. Sens Actuators, A. 2016;239:18–25.

Dua P, Dua V, Pistikopoulos EN. Optimal delivery of chemotherapeutic agents in cancer. Comput Chem Eng. 2008;32(1–2):99–107.

Sharifi M, Moradi H. Nonlinear composite adaptive control of cancer therapy with online identification of uncertain parameters. Biomed Signal Process Control. 2019a;49:360–74.

Pachauri N, Yadav J, Rani A, Singh V. Modified fractional order IMC design based drug scheduling for cancer treatment. Comput Biology and Med. 2019;109:121–37.

Lobato FS, Machado VS, Steffen Jr V. Determination of an optimal control strategy for drug administration in tumor treatment using multi-objective optimization differential evolution. Comp Methods Prog Biomed. 2016;131:51–61.

Hare JI, Lammers T, Ashford MB, Puri S, Storm G, Barry ST. Challenges and strategies in anti-cancer nanomedicine development: an industry perspective. Adv Drug Deliv Rev. 2017;108:25–38.

Brannon-Peppas L, Blanchette JO. Nanoparticle and targeted systems for cancer therapy. Adv Drug Deliv Rev. 2004;56(11):1649–59.

Moradi H, Vossoughi G, Salarieh H. Optimal robust control of drug delivery in cancer chemotherapy: a comparison between three control approaches. Comput Meth Prog Biomed. 2013;112:69–83.

Martin RB. Optimal control drug scheduling of cancer chemotherapy. Automatica. 1992;28(6):1113–23.

Tan KC, Khor EF, Cai J, Heng CM, Lee TH. Automating the drug scheduling of cancer chemotherapy via: evolutionary computation. Artif Intell Med. 2002;1:908–913.

Liang Y, Leung KS, Mok TSK. A novel evolutionary drug scheduling model in cancer chemotherapy. IEEE Trans Inf Technol Biomed. 2006;10(2):237–45.

Tse S-M, Liang Y, Leung K-S, Lee K-H, Mok TS-K. A memetic algorithm for multiple-drug cancer chemotherapy schedule optimization. IEEE Transactions on Systems, Man, and Cybernetics-Part: Cybernetics. 2007;37(1):84–91.

Karar ME, El-Garawany AH, El-Brawany M. Optimal adaptive intuitionistic fuzzy logic control of anti-cancer drug delivery systems. Biomed Signal Process Control. 2020;58:101861.

Khalili P, Vatankhan R, Taghvaei S. Optimal sliding mode control of drug delivery in cancerous tumor chemotherapy considering the obesity effects. IET Syst Biol. 2018;12(4):185–9.

Bojkov B, Hansel R, Luus R. Application of direct search optimization to optimal control problems. Hung J Ind Chem. 1993;21(3):177–85.

Algoul S, Alam MS, Hossain MA, Majumder MA. Multi-objective optimal chemotherapy control model for cancer treatment. Med Biol Eng Compu. 2011;49:51–65.

El-Garawany AH, Karar ME, El-Brawany MA, Embedded drug delivery controller for cancer chemotherapy under treatment constraints. In, . Intl Conf on Advanced Control Circuits Systems (ACCS) Systems & 2017 Intl Conf on New Paradigms in Electronics & Information Technology (PEIT), 2017. Egypt: Alexandria; 2017.

Khadraoui S, Harrou F, Nounou HN, Nounou MN, Datta A, Bhattacharya SP. A measurement-based control design approach for efficient cancer chemotherapy. Inf Sci. 2016;333:108–25.

Nasiri H, Kalat AA. Adaptive fuzzy back-stepping control of drug dosage regimen in cancer treatment. Biomed Signal Process Control. 2018;42:267–76.

Padmanabhan R, Meskin N, Haddad WM. Learning-based control of cancer chemotherapy treatment. IFAC PapersOnLine. 2017;50(1):15127–32.

Sharifi M, Moradi H. Nonlinear composite adaptive control of cancer chemotherapy with online identification of uncertain parameters. Biomed Signal Process Control. 2019b;49:360–74.

Ramkumar B, Naidu DS. Closed-loop optimal control strategy for cancer chemotherapy. In Proceedings of IMECE, 2007 ASME International Mechanical Engineering Congress and Exposition; November 11–15; Seattle, Washington, USA; 2007.

Mahmoodian H, Salem S, Shojaei K, Adaptively adjusted footprint of uncertainty in interval type-2 fuzzy controller for cancer drug delivery. In,. IEEE International Symposium on Robotics and Intelligent Sensors. Procedia Computer Science. 2015;2015(76):360–7.

Florian Jr JA, Eiseman JL, Parker RS. Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model. Computers in Biology and Medicine .  2008;38:339–347.

Sharifi N, Ozgoli S, Ramezani A. Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours. Comput Meth Prog Biomed. 2017;144:13–9.

Padmanabhan R, Meskin N, Haddad WM. Reinforcement learning-based control of drug dosing for cancer chemotherapy treatment. Mathematical biosciences. 2017;293:11–20.

Dey BS, Bera MK, Roy BK. Super twisting sliding mode control of cancer chemotherapy: 15 th International Workshop on Variable Structure Systems (VSS). Graz University of Technology, Graz, Austria, 2018; 343–34.

Kovács L, Szeles A, Sápi J, Drexler DA, Rudas I, Harmati I, Sápi Z. Model-based angiogenic inhibition of tumor growth using modern robust control method. Comput Meth Prog Biomed. 2014;114(3):98–110.

Karnik R. Drug delivery: closed-loop dynamic dosing. Nat Biomed Eng. 2017;1(5):0073.

Mage PL, Ferguson BS, Maliniak D, Ploense KL, Kippin TE, Soh HT. Closed-loop control of circulating drug levels in live animals. Nat Biomed Eng. 2017;1:0070.

Rokhforoz P, Jamshidi AA, Sarvestani NN. Adaptive robust control of cancer chemotherapy with extended Kalman filter observer. Informatics in Medicine Unlocked. 2017;8:1–7.

Zhan W, Alamer M, Xu XY. Computational modelling of drug delivery to solid tumour: Understanding the interplay between chemotherapeutics and biological system for optimized delivery systems. Advanced Drug Delivery Review. 2018;132(81)1–3.

Khalili P, Vatankhah R. Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy. Comput Biol Med. 2019;109:195–206.

Shindi O, Kanesan J, Kendall G, Ramanathan A. The combined effect of optimal control and swarm intelligence on optimization of cancer chemotherapy. Comput Methods Programs Biomed. 2020;189:105327.

Pandey V, Pachauri N, Ran A, Rani V, Single V. Optimal ISA-PID-based drug concentration control in cancer chemotherapy. Book chapter in Advances in Intelligent Systems and Computing. 2018;165–171.

Panjani B, Mohan V, Rani A, Singh V. Optimal drug scheduling for cancer chemotherapy using two-degree of freedom fractional order PID scheme. J intell Fuzzy Systems. 2019;36(3):2273–84.

Karar ME, El-Brawany MA. Automated cardiac drug infusion system using adaptive fuzzy neural networks controller. Biomedical Engineering and Computational Biology. 2011;3:BECB-S6495.

Yu YN, Doctor F, Fan SZ, Shieh JS. An adaptive monitoring scheme for automatic control of anaesthesia in dynamic surgical environments based on bispectral index and blood pressure. J Med Syst. 2018;42(5):95.

Song P, Tng DJH, Hu R, Lin G, Meng E, Yong KT. An electrochemically actuated MEMS device for individualized drug delivery: an in vitro study. Adv Healthc Mater. 2013;2(8):1170–8.

Hemmerling TM, Arbeid E, Wehbe M, Cyr S, Taddei R, Zaouter C. Evaluation of a novel closed-loop total intravenous anesthesia drug delivery system: a randomized controlled trial. Br J Anaesth. 2013;110(6):1031–9.

Labbafq H, Aliyari M, Teshnehlab M. A new approach in drug delivery control in anaesthesia, In IEEE International Conference on Systems, Man and Cybernetics, 10–13, Oct 2010, Istanbul, Turkey.

Moore BL, Quasny TM, Doufas AG. Reinforcement learning versus proportional-integral-derivative control of hypnosis in a simulated intraoperative patient. Anesthesia-analgesia. 2011;112(2):350–359.

Heusden KV, Ansermino JM, Dumont GA. Closed-loop instrumental variable identification of propofol anaesthesia. In: 2017 IEEE Conference on Control Technology and Applications (CCTA); August 27–30; Kohala, Coast, Hawai, USA; 2017. pp. 1165–1170.

Yang Y, Shanechi MM. A generalizable adaptive brain-machine interface design for control of anaesthesia. In 2015 37 th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 25–29 Aug 2015; Milan, Italy.

Zhusubaliyev ZT, Silva MM, Medvedev A, Automatic recovery from nonlinear oscillations in PID-controlled anaesthetic drug delivery, . European Control Conference, July 15–17. Lenz, Australia. 2015;2015:2725–30.

West N, van Heusden K, Görges M, Brodie S, Rollinson A, Petersen CL, Dumont GA, Ansermino JM, Merchant RN. Design and evaluation of a closed-loop anesthesia system with robust control and safety system. Anesth Analg. 2018;127(4):883–94.

Myers MH, Li Y, Kivlehan F, Lindner E, Chaum E. A feedback control approach to organic drug infusion using electrochemical measurement. IEEE Trans Biomed Eng. 2016;63(3):506–11.

Ngan Kee WD, Tam YH, Khaw KS, Ng FF, Critchley LA, Karmakar MK. Closed‐loop feedback computer‐controlled infusion of phenylephrine for maintaining blood pressure during spinal anaesthesia for caesarean section: a preliminary descriptive study. Anaesthesia. 2007;62(12):1251–56.

Gentilini A, Schaniel C, Morari M, Bieniok C, Wymann R, Schnider T. A new paradigm for the closed-loop intraoperative administration of analgesics in humans. IEEE Trans Biomed Eng. 2002;49(4):289–99.

Soltesz K, Heusden KV, Hast M, Ansermino JM, Dumont GA. A synthesis method for automatic handling of inter-patient variability in closed-loop anesthesia. In American Control Conference (ACC) IEEE; 2016; 4877–82.

Agrawal D, Kumar S, Kumar A, Gombar S, Trikha A, Anand S. Design of an assistive anaesthetic drug delivery control using knowledge based systems. Knowl Based Syst. 2012;31:1–7.

Borera EC, Moore BL, Doufas AG, Pyeatt LD. An adaptive neural network filter for improved patient state estimation in closed-loop anaesthetic control. 2011 23 rd IEEE International conference on Tools with Artificial Intelligence; 2011. pp. 42–46.

Zaouter C, Hemmerling TM, Lanchon R, Valoti E, Remy A, Leuillet S, Ouattara A. The feasibility of a completely automated total IV anesthesia drug delivery system for cardiac surgery. Anesth-analg. 2016;123(4):885–93.

Caruso ALG, Bouillon TW, Schumacher PM, Zanderigo E, Morari M. Control of drug administration during monitored anesthesia care. IEEE Trans Autom Sci Eng. 2009;6(2):256–64.

Yousefi M, van Heusden K, West N, Mitchell IM, Ansermino JM, Dumont GA. A formalized safety system for closed-loop anesthesia with pharmacokinetic and pharmacodynamic constraints. Control Eng Pract. 2019;84:23–31.

Savoca A, Heusden KV, Manea D, Ansermino JM, Dumont GA. The effect of cardiac output on pharmacokinetics and pharmacodynamics of propofol during closed-loop induction of anesthesia. Comput Meth Prog Biomed. 2020;192:105406.

Ramgopal S, Thome-Souza S, Jackson M, Kadish NE, Fernández IS, Klehm J, Bosl W, Reinsberger C, Schachter S, Loddenkemper T. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 2014;37:291–307.

Israeli-Korn SD. Tsviya Fay-Karmon, Steven Tessler, Gilad Yahalom, Sandra Benizri, Hanna Strauss, Zion Zibly, Roberto Spiegelmann, and Sharon Hassin-Baer, Decreasing battery life in subthalamic deep brain stimulation for Parkinson’s disease with repeated replacements: just a matter of energy delivered? Brain stimulation. 2019;12(4):845–50.

Grahn PJ, Mallory GW, Khurram OU, Berry BM, Hachmann JT, Bieber AJ, Bennet KE, Min HK, Chang S-Y, Lee KH, Lujan JL. A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies. Frontiers in Neuroscience. 2014;8:169.

Sun FT, Morrell MJ. Closed-loop neurostimulation: the clinical experience. Neurotherapeutics. 2014;11(3):553–63.

Pravika M, Jacob J. PID controlled fully automated portable duodopa pump for Parkinson’s disease patients. Biomed Signal Process Control. 2019;50:178–87.

Contin M, Martinelli P. Pharmacokinetics of levodopa, Journal of Neurology. 2010;257: 253–261, 

 Araujo CED, Abatti PJ, Da Cunha C, Gόmez A, Dombrowski PA. In vitro evaluation of a closed-loop feedback system for dopamine concentration control. Rev Bras Eng Biomed. 2015;31:26–32.

Guebila MB, Thunberg, J. Toward a closed-loop subcutaneous delivery of L-DOPA, arXiv preprint arXiv: 1608.07232 , 2016.

Fleming JE, Dunn E, Lowery MM. Simulation of closed-loop deep brain stimulation control schemes for suppression of pathological beta oscillations in Parkinson’s disease. Front in Neurosci. 2020;14:166.

Feng XJ, Greenwald B, Rabitz H, Shea-Brown E, Kosut R. Toward closed-loop optimization of deep brain stimulation for Parkinson’s disease: concepts and lessons from a computational model. J Neural Eng. 2007;4(2):L14.

Stypulkowski PH, Stanslaski SR, Denison TJ, Giftakis JE. Chronic evaluation of a clinical system for deep brain stimulation and recording of neural network activity. Stereotact Funct Neurosurg. 2013;91(4):220–32.

Bigelow MD, Kouzani AZ. Neural stimulation systems for the control of refractory epilepsy: a review. J Neuroeng Rehabil. 2019;16(1):126.

Dümpelmann M. Early seizure detection for closed loop direct neurostimulation devices in epilepsy. J Neural Eng. 2019;16(4):041001.

Bergey GK. Neurostimulation in the treatment of epilepsy. Exp Neurol. 2013;244:87–95.

Sisterson ND, Wozny TA, Kokkinos V, Constantino A, Richardson R M. Closed-loop brain stimulation for drug-resistant epilepsy: towards an evidence-based approach to personalized medicine. Neurotherapeutics. 2019;16:119–27.

Jouny CC, Franaszczuk PJ, Bergey GK. Improving early seizure detection. Epilepsy Behav. 2011;22:S44–8.

Salam MT, Mirzaei M, Ly MS, Nguyen DK, Sawan M. An implantable closed loop asynchronous drug delivery system for the treatment of refractory epilepsy. IEEE Trans on Neural System Rehabil Eng. 2012;20(4):432–42.

Selvaraj P, Sleigh JW, Kirsch HE, Szeri AJ. Closed-loop feedback control and bifurcation analysis of epileptiform activity via optogenetic stimulation in a mathematical model of human cortex. Phys Rev E. 2016;93(1):012416.

Geller EB, Skarpaas TL, Gross RE, Goodman RR, Barkley GL, Bazil CW, Berg MJ, Bergey GK, Cash SS, Cole AJ, Duckrow RB. Brain-responsive neurostimulation in patients with medically intractable mesial temporal lobe epilepsy. Epilepsia. 2017;58(6):994–1004.

Jobst BC, Kapur R, Barkley GL, Bazil CW, Berg MJ, Bergey GK, Boggs JG, Cash SS, Cole AJ, Duchowny MS, Duckrow RB. Brain-responsive neurostimulation in patients with medically intractable seizures arising from eloquent and other neocortical areas. Epilepsia. 2017;58(6):1005–14.

Yu J, Zhang Y, Yan J, Kahkoska AR, Gu Z. Advances in bioresponsive closed-loop drug delivery systems. Int J Pharm. 2018;544(2):350–7.

Dhanvijay MM, Patil SC. Internet of Things: a survey of enabling technologies in healthcare and its applications. Comput Netw. 2019;153:113–31.

Donohoe M, Jennings B, Balasubramaniam S. Deep brain drug-delivery control using vagus nerve communications. Comput Netw. 2020;171:107137.

Wu C, Jiang P, Li W, Guo H, Wang J, Chen J, Prausnitz MR, Wang ZL. Self-powered iontophoretic transdermal drug delivery system drive and regulated by biomechanical motions. Adv Func Mater. 2020;30(3):1907378.

Hussain A, Wenbi R, da Silva AL, Nadher M, Mudhish M. Health and emergency-care platform for the elderly and disabled people in the Smart City. J Syst Softw. 2015;110:253–63.

Download references

Acknowledgements

Dr. Richa Sharma would like to thank the Indian Institute of Technology Delhi for providing her postdoctoral fellowship and excellent lab facilities.

Author information

Authors and affiliations.

Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India

Richa Sharma & Deepak Joshi

Renaissance Technology Private Ltd Delhi, New Delhi, India

Dhirendra Singh

Division of Instrumentation & Control Engineering, Netaji Subhas University of Technology, New Delhi, India

Prerna Gaur

Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, 110029, India

Deepak Joshi

You can also search for this author in PubMed   Google Scholar

Contributions

Prof. Deepak Joshi and Dr. Richa Sharma conceptualized the study; Prof. Prerna Gaur suggested control aspects; Industry perspective was introduced by Mr. Dhirendra Singh; the paper was edited and drafted by all authors.

Corresponding author

Correspondence to Deepak Joshi .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate.

It is a review paper. The paper does not contain any studies with animals or humans.

Consent for publication

All the authors gave their approval for publication.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Sharma, R., Singh, D., Gaur, P. et al. Intelligent automated drug administration and therapy: future of healthcare. Drug Deliv. and Transl. Res. 11 , 1878–1902 (2021). https://doi.org/10.1007/s13346-020-00876-4

Download citation

Accepted : 09 November 2020

Published : 14 January 2021

Issue Date : October 2021

DOI : https://doi.org/10.1007/s13346-020-00876-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Closed-loop control
• Drug delivery
• Control system
• Biological systems
• Insulin therapy
• Cancer treatment
• Neurological disorders
• Cardiac ailments
• Find a journal
• Publish with us
• Track your research

FDA rescinds ban on Juul vape products, resumes scientific review

June 6 (UPI) -- The U.S. Food and Drug Administration Thursday reversed an earlier ruling banning Juul vaping products almost two years after ordering the products off the market.

The FDA said in a statement Thursday that court decisions establishing new case law, combined with more FDA experience with various scientific issues regarding e-cigarette products, led to it rescinding the marketing denial order. Advertisement

"This action is being taken, in part, as a result of the new case law, as well as the FDA's review of information provided by the applicant," FDA's statement said. "Rescission of the MDOs is not an authorization or a denial and does not indicate whether the applications are likely to be authorized or denied."

• Juul says it is 'substantially reducing' workforce to increase profits
• E-cigarette use, sales climb dramatically, CDC says
• Young teens who vape may become heavy smokers
• Juul to pay $462 million to settle multiple state lawsuits over marketing to minors

The FDA said on July 5, 2022 the Juul MDOs were stayed because certain scientific issues warranted additional review.

Juul said in a statement, "We appreciate the FDA's decision and now look forward to re-engaging with the agency on a science- and evidence-based process to pursue a marketing authorization for JUUL products.We remain confident in the quality and substance of our applications and believe that a full review of the science and evidence will demonstrate that our products meet the statutory standard of being appropriate for the protection of public health." Advertisement

Juul said the products in question - the JUUL device and Virginia Tobacco and Menthol JUUL pods in 5.0% and 3.0% nicotine concentration - will remain on the market during the FDA review.

The FDA said Thursday that the original marketing denial orders were done because the FDA had determined that Juul's approval applications for the vaping products "lacked sufficient evidence regarding the toxicological profile of the products to demonstrate that the marketing of the products met the public health standard required by law."

The FDA said some of Juul's study findings were concerning because of insufficient and conflicting data regarding genotoxicity, as well as potentially harmful chemicals leaching from Juul's proprietary e-liquid vape pods.

The agency said rescinding of the MDOs doesn't change the requirement that all e-cigarette products must have FDA authorization to be legally marketed.

The FDA has granted approval so far to just 23 e-cigarette products manufactured by just three companies.

Juul has long claimed that the company's e-cigarette products can help people stop smoking cigarettes, But the vape products still deliver nicotine, an addictive ingredient in tobacco products.

In August 2023 Juul announced that it was laying off almost a third of its workforce to increase profits . Juul said at the time it was carrying out a restructuring specifically aimed at maximizing profitability and cash-flow by cutting workers from the payroll. Advertisement

• Food and Drug Administration
• E-cigarettes

Latest Headlines

Trending Stories