health data research uk office

Health Data Research UK

By harnessing health and biomedical data in the UK, Health Data Research UK (HDR UK) will develop and apply cutting edge data science approaches in order to address the most pressing health research challenges facing the public. HDR UK is a joint investment led by the Medical Research Council (MRC), together with the National Institute for Health Research (England), the Chief Scientist Office (Scotland), Health and Care Research Wales, Health and Social Care Research and Development Division (Public Health Agency, Northern Ireland), the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the British Heart Foundation and Wellcome.

Find out more about Health Data Research UK .

Last updated: 31 March 2022

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Health Data Research UK (HDR-UK)

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Scotland hosts one of the six Health Data Research UK (HDR UK) sites.

HDR UK was set up in the latter part of 2017 to transform health research by applying cutting-edge computational techniques to dynamic, multidimensional health-relevant data. While the head office is based in London, there are currently six collaborative sites across the UK, based in Wales & Northern Ireland, the Midlands, Cambridge, Oxford, London, and Scotland.

The Scottish site capitalises on our world-leading health and informatics research capabilities and exceptional data assets from Scotland’s population of 5.4 million people.

The site is coordinated by The University of Edinburgh, with key partners in the other five prestigious medical schools – the University of Glasgow, University of Dundee, University of St. Andrews, and the University of Aberdeen) as well as the leading school of pharmacy at Strathclyde University.

It encompasses the UK’s most powerful hub for informatics and computational science research (Edinburgh, REF 2014) and brings multidisciplinary expertise in epidemiology, learning health systems, clinical phenotyping, precision medicine and therapeutics, clinical trials, public health, genomics, molecular pathology, informatics, supercomputing, data systems, software architecture, and advanced, scalable analysis methods including machine learning, artificial intelligence (AI) and natural language processing.

In Scotland, we have access to:

• The UK’s only national prescribing/dispensing and hospital imaging datasets
• Primary care data linkage being developed through the national SPIRE programme
• Multiple disease-specific registries
• A network of tissue bank repositories .

We can also access consented, accessible research cohorts with bio-samples, linkable to these routine data and tissue resources. Examples include:

• Generation Scotland (25,000 adults)
• UK Biobank (500,000 adults [36,000 in Scotland])
• SHARE (a rapidly growing research register of >200,000 people, with consent for recontact and for storage and analysis of spare blood from routine NHS laboratory testing).

We also have the capacity to link health data to diverse, cross-sectoral, national datasets – such as census, education, and crime and justice data. Scotland’s electronic Data Research and Innovation Service (eDRIS) enables linkage and secure data transfer or access for analysis in our national data safe haven, established through a collaboration between the Edinburgh Parallel Computing Centre (EPCC) and NHS Scotland.

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Health data research uk.

HSC R&D Division contributes funding to Health Data Research UK (HDR UK).

By harnessing health and biomedical data in the UK, HDR UK will develop and apply cutting edge data science approaches in order to address the most pressing health research challenges facing the public.

HDR UK is a joint investment led by the Medical Research Council, together with the National Institute for Health Research (England), the Chief Scientist Office (Scotland), Health and Care Research Wales, Health and Social Care Research and Development Division (Public Health Agency, Northern Ireland), the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the British Heart Foundation and Wellcome.

HDR UK’s mission is to make game-changing improvements in the health of patients and populations through data science research and innovation. Click here to  go to the HDR UK website>

To achieve this a network of inter-disciplinary research expertise has been created across six sites. These HDR UK Sites will develop secure and controlled environments within the highest standards of data security, privacy and ethical approval, to provide expert research data services and enable the ethical analysis and sharing of health care, clinical, genomic, biological and other multi-dimensional data. They will also enable the application of new cutting-edge technologies to enhance decision making and improve healthcare, as science heads into the fourth industrial revolution.

One of the sites is  HDR UK Wales and Northern Ireland Click here for more information>

New funding awarded to improve transparency of health data access processes for researchers and the public

30 October 2023

A new initiative launched by Health Data Research UK (HDR UK) and funded by the Medical Research Council (MRC) will enable members of the UK Health Data Research Alliance and other custodians of health data to improve the transparency of processes for accessing health datasets for research.

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Researchers and members of the public alike are set to benefit from improved transparency of data access processes, as a result of newly funded projects that will enhance available information on how to access data. The projects will implement the Alliance Transparency Standards, which were co-developed by the Pan-UK Data Governance Steering Group and HDR UK’s Public Advisory Board (PAB ).

This round of funding attracted an impressive number of high calibre applications, especially in light of the short deadline that was provided. HDR UK believes these efforts reflect a genuine interest among data custodians to make the information that is available to the public and researchers accessible, transparent and trustworthy.

Nineteen projects have been awarded funding, with the aim of improving the clarity and accessibility of information about data access processes for the public and researchers. The projects cover a range of approaches to increase transparency, including:

• Website updates that present data access steps in an accessible format. Improvements range from British Sign Language videos and text-only versions for screen readers to audio versions.
• Creation of dedicated webpages targeting different audiences, ensuring information is tailored appropriately.
• Publication of updated open-access application forms and guidance notes, co-created with patients and carers.
• Integration of the Five Safes Framework to explain the data access processes in a transparent and accessible manner, with active involvement from public members.

These quick-fire projects are designed to deliver benefits for researchers and patients in just a few short months.  After being selected for funding in October 2023, projects are expected to be completed by the end of March 2024.

Jan Speechley, Public Advisory Board Member (PAB) said:

“The Transparency Standards were developed with HDR UK’s Public Advisory Board, and the board members played an integral role in the scoring and decision-making process for this funding call. It was so interesting and exciting to see the innovative ways Alliance members plan to promote the standards and improve transparency via their websites and other platforms. I look forward to learning about their progress and seeing the improvements this funding call will create.”

Cassie Smith, Head of Legal, Trust and Ethics for HDR UK said:

“ Increasing the transparency of data access procedures is a top priority for HDR UK and Alliance members. Transparent information not only enables researchers to navigate data access processes more easily for vital life-saving research but also helps to build and maintain public trust in the safe and secure access of their data.”

Andrew Morris, Director of HDR UK said:

“This is an important UK-wide step in the transparency of the use of health data for research.  Transparency promotes accountability by providing information about what organisations are doing with data they hold – and essential for demonstrating trustworthiness.”

Full list of funded organisations:

• HSC Business Services Organisation – Honest Broker Service (HSCNI)
• University College London (ECHILD)
• SAIL Databank (Swansea University Medical School)
• UK Cystic Fibrosis Registry, Cystic Fibrosis Trust
• Healthcare Quality Improvement Partnership (HQIP)
• UCL- MRC Unit – Lifelong Health and Ageing
• Royal Marsden NHS Foundation Trust
• Manchester University NHS Foundation Trust
• Imperial College Healthcare NHS Trust ICHT iCARE
• Generation Scotland – University of Edinburgh
• South London and Maudsley NHS Foundation Trust
• UK Longitudinal Linkage Collaboration (UK LLC)
• “ Cambridge University Hospitals NHS Foundation Trust and NIHR- Cambridge Biomedical Research Centre”
• DATAMIND The Health Data Research Hub for Mental Health
• University of Aberdeen – Grampian Data Safe Haven (DaSH)
• Research Data Scotland
• Intensive Care National Audit and Research Centre (ICNARC)
• Optimum Patient Care Limited
• Our Future Health

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HEALTH DATA RESEARCH UK

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Uk kidney association welcomes junior developer as part of health data research uk's black internship programme 2024.

The UK Kidney Association is proud to announce its collaboration with Health Data Research UK's Black Internship Programme 2024 , as it hosts talented intern Billal Bah. This initiative, carried out in partnership with 10,000 Black Interns , is dedicated to addressing the underrepresentation of Black individuals in the health data science sector.

At the forefront of its mission is the ambition to combat racial bias in healthcare and challenge racism concerning the utilisation of health data. The programme is committed to providing opportunities for talented individuals like Billal, who is working within the UK Renal Registry as a Junior Developer.

Billal Bah is currently a first-year aerospace engineering student at the University of Bristol. He has a passion for engineering and technology and is particularly interested in their applications within the health industry. Eager to apply the knowledge gained from his academic studies to real-life problems, Billal views this internship as a crucial first step towards a career in the technology and data industry.

"I’m keen to put the knowledge I’ve gained from academics into real-life problems throughout my internship. My goal is to break into the technology and data industry, and this internship provides me with the first steps into this, which I am incredibly grateful for," says Billal.

During his internship, Billal has been working on 'Steve', a UKRR web application that collects data from 70 hospitals and 130 laboratories. The web application was originally created with Python 2.7 and Django 1.8, which are now outdated versions. Billal's project has been to update this to the newest version of Django, which is 5.0. This task has been an invaluable learning experience, particularly since he had no prior experience with Django. Upgrading Django also necessitated upgrading Python, allowing Billal to observe the significant syntax changes from Python 2 to 3.

"My coding skills have really improved over the short time I’ve been here, and as an aerospace engineering student, it's been great working on programming that goes beyond what I would do in my university course. I've learnt so much and I have the team to thank for their overwhelming support whenever I have a problem," he adds.

Billal has greatly enjoyed the internship so far, which has provided him with the experience of working both remotely and in a professional environment. In addition to his work with the UK Kidney Association, he has been participating in a technical challenge with other interns from different organisations, focusing on a data visualisation project to create a dashboard.

The Black Internship Programme, established in partnership with the UK Health Data Research Alliance and 10,000 Black Interns, addresses the underrepresentation of Black individuals within science, technology, engineering, and mathematics (STEM) careers. It accomplishes this by providing opportunities for them to engage in health data science projects with some of the UK's leading health, research, and academic organizations.

As of Monday, 1 July 2024, an impressive cohort of interns has commenced their placements with a range of host organisations. The UK Kidney Association is honoured to be part of this transformative initiative and remains steadfast in its commitment to fostering diversity, inclusivity, and representation within the health data science sector.

NICE accredited clinical practice guidelines 

Available here

25th Annual Report

Analyses about the care provided to patients at UK renal centres.

Read the report

2022 UKRR AKI Report

A report on the nationwide collection of AKI warning test scores. 

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How Secure Data Environments can help drive advances in health data research

8 August 2024

NHS England (NHSE) is changing the way that researchers access health data in England by moving to a network of Secure Data Environments (SDEs).

We are broadly supportive of the direction of the changes, but for the SDE Network to deliver all the benefits it promises, the new UK Government must guarantee sufficient and sustained funding beyond 2025 in its next spending review. This funding needs to be commensurate with the size of the challenge and the opportunity presented by NHS health data . As such, it must allow for meaningful and continuous patient and public involvement in the creation and governance of the SDE network, ensuring that patients’ data is used with their consent and endorsement.

With th at level of   support and investment , the SDE N etwork could greatly improv e how researchers access and use health data , which could have profound impacts on public health for years to come .

Secure access to health data is driving significant advances in the ways that we prevent, diagnose and treat cancer. The NHS Research SDE Network has the potential to make those advances happen faster, so we're looking forward to continuing to work with it to deliver research powered by NHS data. Now, to safeguard the SDE Network's success, the new Government must commit to funding it beyond 2025 - ensuring that our research can help more people live longer, better lives.

Using health data to create better health

In our Longer, Better Lives programme for government , we laid out how government can unlock the potential of data as a driver for change within research and health. The opportunities presented by the depth of UK health data are almost unparalleled. This is because the UK has very detailed nationwide, life-long health datasets – datasets that researchers can harness to improve our ability to prevent, detect, diagnose, and treat cancer.

However, there are currently barriers preventing researchers from using this data to its full life saving potential.

Historically, the fragmented nature of NHS systems has made it difficult for researchers to access NHS health data. This has delayed or even stopped research, as well as raising some security concerns. Encouragingly, following on from the Data Saves Lives strategy, NHSE is now looking to change this through its Data for R&D Programme. That will involve moving from a “data-dissemination” model, where most data is sent to researchers who request it, to an SDE-based “data-access” model, where researchers request access to data that always remains within NHS systems. 

We believe that this is an exciting shift. If the new network is adequately financed and executed correctly, with patients and the public at the centre of its creation and governance, it has the potential to help researchers access health data more efficiently while providing greater safety and security.

How does data save lives?   

High quality health data is critical for multiple strands of traditional research, including clinical trial recruitment and health system examination. Previously, the cancer research we focus on mostly involved relatively small-scale studies of essential pathways and genes. But now , the rise of modern computing and the proliferation of large-scale detailed data has also put data-driven research at the heart of lots of the most cutting-edge cancer science . For instance, for over 10 years, Professor Rebecca Fitzgerald at the University of Cambridge has been spearheading a huge project that brings together scientists, doctors and nurses from across the UK with the goal of better understanding oesophageal cancer. This impressive programme of work has seen researchers collecting tumour and blood samples from people with oesophageal cancer and decoding the cancer’s genetic sequence so that they have a complete map of the cancer’s DNA.

We need a lot of data. We can’t find the needle in the haystack by just using data from a few people.

Our Research Data Strategy reflects just how important and impactful health data has been for cancer research. It has enabled us to develop our data science community while ensuring the trust and involvement of patients and the public. We have also re cen t ly launched the Cancer Data Collaborative , a forum bringing NHSE, C ancer R esearch UK and other charities together with patient representatives to tackle the biggest challenges in cancer data.   

So, how is health data currently stored and accessed in England?

At present, research and analysis using health data in England predominantly happens according to the data dissemination model. This involves the NHS transferring de-identified data to third parties via data sharing agreements. Researchers submit applications to NHS data controllers, who assess both the application and the intended data usage. Upon approval, the data is transmitted directly to the researchers, who can then analyse the data with any tools they have at their disposal.

Researchers are legally bound to use the data the NHS provides them in the ways specified in their initial application, but this can be difficult to monitor under the current system. For example, researchers are required to delete the data themselves once their access rights elapse, but the NHS doesn‘t have an easy way to verify whether this has happened. There are also other issues, such as the creation of many copies of datasets, which may be error prone, and require significant amounts of computer memory.

Currently, more than half my time is spent trying to get data in the first place.

Data dissemination essentially means that the NHS acts as a lending library. Researchers come to NHS data controllers, are verified, and are then sent health data to use outside of the NHS’s ecosystem. However, because of the size and complexity of the NHS, there is not just one point of contact. Instead, estimates suggest there are around 7,000. This means researchers need to spend a lot of time and effort to get the information they need. Imagine if reading a book involved having to apply to multiple different libraries, each with their own complex lending policies, one chapter at a time. That’s often how gathering health data fo r research works in England today. Simplifying access routes is necessary to make sure researchers can focus on research .

What is the new system?   

NHSE is currently setting up a new network of SDEs as part of the Data for R&D Programme, with £175m initially allocated for this and another programme called NHS DigiTrials. The SDE Network will be formed of two parts: one large SDE covering the whole of England, containing high level national datasets, and 11 smaller subnational ones covering areas like London or the north-east, containing more detailed datasets. This should reduce the 7,000 points of data access to 12.

If the new network is properly implemented, with more effective methods of data application and granting processes, researchers will be able to obtain the data needed for their research quicker and more efficiently. To ensure this happens, the Data for R&D Programme must deliver a single front door researchers can use to access the diff ere nt S DEs .

But what is a Secure Data Environment?  

An SDE is a protected space for sensitive data that can only be accessed by authorised researchers remotely. This approach ensures that patient data remains confined within the environment: while users can extract results such as tables or graphs, the raw data itself can never leave the host system. This setup grants custodians of the data more control over its usage and increases safeguards preventing misuse of data. It also allows for improvements to the data to be more easily implemented, as everyone is working from the same dataset and not multiple copies.

‘Data access by default’?

The SDE approach is sometimes referred to as ‘data access by default ’. Some media reports have implied that this means that anyone will be able to access health data on command. This is not true. Data access by default refers to the fact that the default model is based on applying to access relevant data on NHS systems rather than applying to export it elsewhere.

A simple way to think about this is to picture an SDE as a reference library for health data, rather than a lending library. After appropriate checks have been made, researchers can (remotely) enter the library to read and analyse specific data, but they can’t take it away with them. Instead, they’re only able to export the results they get from using the data. In short, the SDE approach means data remains within NHS systems, making it fundamentally more secure. This also protects the quality of the data by ensuring it remains in a single dataset, which is easier to maintain and manage.

What a re the potential benefits of the new system ? 

The main potential benefits are:    

• Efficiency – P roperly implemented , a reduction in access points should make it easier and quicker for researchers to access health data.
• Security – Because data remains on NHS systems, it is easier to monitor what researchers are doing with it.

These benefits are reflected in the fact that there is great deal of support for SDE-like systems, often called Trusted Research Environments (TREs), across the sector and in UK research communities. Examples of TREs include Scotland’s Data Safe Haven Programme , the SAIL databank in Wales and Genomics England’s Research Environment .

Does Cancer Research UK have an SDE-like platform?

Yes! Our Trusted Research Programme provides a Trusted Research Environment (TRE) , an SDE-like system, alongside advisory services to support researchers dealing with sensitive health and related data.

Our TRE also provides access to high-performance computing facilities for advanced analytics, machine learning, and AI development for researchers across the UK who don’t have a suitable and safe set up to store and analyse patient data. Our Trusted Research Programme is currently onboarding two pilot projects and it is helping maximise the safe and effective collection and re-use of cancer related data.

Our ambition is to develop a portfolio of research projects and data that can be used to advance research across multiple different areas and help us better understand cancer. We want to harness emerging technologies, including AI, to achieve our cancer-beating goals and our TRE will help us do this.

If you are a researcher and think that your research would benefit from using our TRE, please contact [email protected] .

Sounds good! But what’s needed for the NHS Research SDE Network to be a success?

There are a few things that NHSE and the Data for R&D Programme need to do to take full advantage of this opportunity to improve data access and data security. We’re currently developing a policy paper that will outline some of the specifics, including points on pricing, avoiding further fragmentation, and ensuring patients and the public can be meaningfully involved in the creation and governance of the network.

However, most importantly, the new UK Government needs to use its next spending review to ensure that the central SDE and all subnational SDEs are on a sustainable footing beyond 2025 . This includes providing the Data for R&D Programme with the resources it needs to make sure that access to data is more timely and less complicated than it is today, and that it can be delivered at a price that doesn’t stop researchers from carrying out their vital work . The SDE N etwork also needs resources and support to meaningfully involve patients and the public .

The guaranteed funding must be commensurate with the size of this challenge. The SDE Network has the potential to improve data access for a generation, but only if it can secure sustained, significant long-term support and investment.

Ben is a policy advisor in our policy development team.

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Market in minutes: leeds occupational office data – h1 2024, contacts & related research.

Leeds office market H1 roundup

Take-up in Leeds during Q2 2024 totalled 99,000 sq ft across 23 deals done. Although take-up was down year on year, the number of deals was 15% higher than the five-year, Q2 average number of deals. This brought the half-year total to 350,000 sq ft, which was 14% and 23% above the five- and ten-year H1 averages, respectively. 

Grade A and Prime take-up during H1 totalled 241,000 sq ft and accounted for 69% of the total. This was also 22% higher than the five-year H1 average of 197,000 sq ft. 

Leeds availability at the end of H1 totals just over 1 million sq ft, a 6% decrease on the previous quarter. This means that the total vacancy rate now stands at 8.7%, a decline of 50 basis points.

Grade A and Prime supply accounted for 52% of this totalling 558,000 sq ft. Prime supply has increased in Q2 2024 due to Aire Park Phase One completing, providing 210,000 sq ft of best-in-class space for occupiers in Leeds.

Take-up by business sector

The most active sector during H1 was the 'Public Services, Education & Health sector', which leased a combined total of 90,000 sq ft, consequently accounting for 26% of the total. The largest deal of H1 in the sector was 44,000 sq ft, which was leased by Leeds Teaching Hospitals NHS Trust, at Joseph's Well. 

Another active sector in Leeds during H1 was the 'Insurance & Financial Services' sector, which leased a combined 60,000 sq ft and therefore accounting for 17% of the total. The largest deal in the sector was acquired by QBE Management at West Village, totalling 38,000 sq ft.

There was a new headline rent achieved in 2023, which reached £38 per sq ft, 6% higher than 2022. This has remained unchanged in H1 2024; however, Savills is aware of a space under offer at rental levels above £38 per sq ft. Savills latest rental forecasts expect headline rents to grow further to reach £42 per sq ft by the end of 2024, representing an 11% increase on the current headline rent. Revised rental forecasts are predicting headline rent to grow by 24% and reach a market high of £47 per sq ft by 2028. 

Interested in other areas of the UK? View all of our latest H1 2024 Occupational Office Data research here.

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Overarching data protection impact assessment (DPIA) for the Federated Data Platform (FDP)

Purpose of this document.

A data protection impact assessment (DPIA) is a useful tool to help organisations demonstrate how they comply with data protection law.

DPIAs are also a legal requirement where the processing of personal data is “likely to result in a high risk to the rights and freedoms of individuals”.

Definitions of specific terms used in this DPIA are included at Section 25.

NHS England (in this DPIA “we” and “our” refers to NHS England) has prepared and published this DPIA to describe generally the processing of personal data by the Federated Data Platform. NHS England reviews and maintains this DPIA regularly, and may update it periodically.

The Federated Data Platform (FDP) is a series of separate data platforms which we call “instances”. There are data platform Instances which are operated by NHS England, called “national instances”. There are separate data platforms instances which are operated by an NHS trust or an integrated care board in a local area, which we call “local instances”. These national and local instances of the Federated Data Platform work alongside privacy enhancing technology, which we call “NHS-PET”. NHS-PET records the data which is used in data platform Instances and can de-identify data where this is needed.

Each Instance of the Federated Data Platform uses the same underlying technology and software and has the same basic technical functionality. However, the Federated Data Platform uses the technology, software, and functionality in different ways for different purposes through what we call “products”. Some products are only designed for the national instances, some are only designed for the local instances, and some are designed to be used in both types of Instance.

Information about the FDP, including high-level deployment plans is made publicly available on the NHS England website .

1. Consultation with stakeholders

Seeking and understanding the views of stakeholders and the public and patients is an integral part of the NHS Federated Data Programme (FDP Programme). There is a regular programme of engagement supported by a number of formal advisory groups that form part of the programme governance. These include:  

• FDP check and Challenge Group . This group provides strategic advice to the FDP Programme on communications, engagement, and transparency. It considers patient, public, professional, and ethical context, and complements the Health Data Patient and Public Engagement and Communications Advisory Panel (PPECAP) .  
• Health Data Public and Patient Engagement and Communications Advisory Panel . A panel consisting of public and patient members and representatives from national organisations who represent the views of the public. It supports the FDP FDP Programme to develop meaningful and accessible public communications.   
• External Information Governance (IG) Advisory Group. A group of external stakeholders with subject matter expertise in data and information governance. 
• The Data Governance Group. A national group established by NHS England to provide oversight to the approach to data processing and sharing across all Instances of the Federated Data Platform and NHS-PET which will include membership from across FDP User Organisations

Additionally, the FDP engagement portal , which is hosted on NHS England’s website, is a live tool to support the public to seek answers to their questions, provide feedback on the FDP Programme and to register their interest in future engagement activity.  

NHS England is committed to communicate and engaging with key stakeholders, the public, and patients in a meaningful way throughout the life of the FDP Programme. 

2. Data flow diagrams

The FDP allows organisations to hold and process data separately, while using common functionality, as shown below:

The flows of data into the FDP are shown below, with separate diagrams for national instances (NHS England) and local instances:

National instances

Local instances

Detailed data flow diagrams at each product level are contained within the separate product DPIAs.

All FDP instances are hosted in the UK and all dataflows in and out of the FDP start and finish in the UK.

3. Purpose of the processing

The Federated Data Platform (FDP) is a technology solution to support a variety of NHS organisations in the efficient delivery of their statutory functions, including delivering and supporting direct care. The FDP is made available for NHS organisations to use if they choose to do so. This is the purpose for which FDP user organisations process personal data in FDP (and for which commissioned health service Oorganisations may use FDP, if permitted).

The FDP is designed to be a common system that can be used by many different organisations. Each organisation has a separate space (called an “instance”), where their data is held securely, and separate from data belonging to other organisations.  The separate organisations are referred to as “FDP user organisations”, with each of those FDP user organisations retaining autonomy (and control over the identification of purpose) over the use of any data they choose to put in their Instance. 

As set out in Section 0, where NHS England is the FDP user organisation, this may be referred to as the “national instance” of FDP.  For all other FDP user organisations, the instances may be referred to as “local instances” of FDP.

The FDP enables FDP user organisations with an Instance to process data for the following five use cases:​

• elective recovery – to get patients treated as quickly as possible, reducing the backlog of people waiting for appointments or treatments, including maximising capacity, supporting patient readiness and using innovation to streamline care
• care coordination (joining up care) – to ensure that health and care organisations all have access to the information they need to support the patient, enabling care to be coordinated across NHS services
• vaccination and immunisation – to ensure that there is fair and equal access, and uptake of vaccinations across different communities
• population health management (planning NHS services) – to help local trusts, Integrated Care Boards (on behalf of the integrated care systems) and NHS England proactively plan services that meet the needs of their population
• supply chain management (getting the best value for the NHS) – to help the NHS put resources where they are needed most and buy smarter so that we get the best value for money

FDP user organisations will use the FDP initially through products, each product being linked to one or more of the use cases. The FDP will help provide NHS staff (frontline clinicians, operational staff, and planners) with timely information and insight, promoting the efficient use of resources to support the delivery and planning of patient care.

FDP functionality is classified as 13 core capabilities which are:

• distribution
• citizens Invite
• load balancing
• remote monitoring interface
• patient comms interface
• pathway management
• medicines and equipment ordering
• supply chain management
• forecasting, monitoring and evaluation
• cata enrichment
• cata cleansing

Where these capabilities are used in a product, this will be described in the relevant product DPIA.

For trusts, the ambition is that the FDP will enable users to undertake data analysis and access applications designed to support and enable planning, pathway management and direct care. ​

At ICB level, the FDP will support population health management, tackling health inequalities and care coordination, enabling integrated care systems (ICSs) to respond to a more comprehensive and detailed understanding of their populations, supporting a targeted, more effective use of resources and planning services around the needs of their population.

The national instance of FDP will improve the flow and analysis of reporting data. As a consequence, there will be a reduction in burden through the change from multiple systems using aged technology (e.g. Excel), and enhanced security and transparency. This will give NHS England teams more accurate and near-real-time data to undertake strategic and operational planning.​

The FDP information governance (IG) framework has been created to enable the management of the IG workflow for FDP.

The FDP IG Framework sets out minimum IG requirements to be applied in the implementation and operation of FDP, with the aim of ensuring a consistent approach and high standard of IG and transparency across the FDP User Organisation community. This framework includes: 

• working within the contractual documentation associated with the FDP Programme. The FDP IG Framework identifies these and sets out how they work together.
• clearly identifying the various parties involved in delivering the FDP Programme, explaining their data protection roles and setting out their IG responsibilities.
• laying out the core IG principles of the FDP Programme.
• identifying the IG documentation that will be required to be put in place and who is responsible for producing and supporting the production of the documentation.
• setting out the procedure for reporting Security Breaches and Personal Data Breaches relating to data processed in the FDP.
• setting out how requests under the Freedom of Information Act 2000 will be handled.
• setting out the governance arrangements relating to how the parties will work together and the various governance groups to be established to facilitate those arrangements. This includes the Data Governance Group and the NHS FDP System IG Group referred to above. More details about the group can be found within the FDP IG framework
• identifying the supporting IG documentation for the FDP Programme and where it can be accessed
• explaining how the framework will be reviewed, changed and published to provide transparency over the FDP IG Framework

4. Identification of risks

NHS England has in this section identified inherent risks of FDP data processing and potential harm or damage that it might cause to individuals whether physical, emotional, moral, material or non-material e.g. inability to exercise rights; discrimination; loss of confidentiality; re-identification of pseudonymised data, etc.

This section is used to detail the risks arising from the proposed processing data if there are no steps in place to mitigate the risks. The sections below then set out steps taken to mitigate the risks followed by a second risk assessment which considers the residual risk once the mitigation steps are in place. 

5. Approach to risk

Documentation.

Where data is processed in FDP, the data will be processed in products, inside an instance of the Federated Data Platform. The data protection risks associated with processing data are considered through a suite of risk assessments (DPIAs).

This DPIA is the overarching DPIA for the FDP,  including an overview of the generic system functionality required for integrating, managing, and operationalising data, describing the risks and mitigations associated with the Federated Data Platform which are relevant across any FDP instances. This approach should mean that common elements do not have to be described in the ontology DPIA and separate product DPIAs to be carried out.

The FDP uses a data model, referred to as an ontology. This model defines the tables, schemas and definitions of various NHS concepts such as “patient” and “encounter”. When the model is deployed in an instance, an ontology DPIA or product DPIA will assess the particular data processing to populate the model, as further described in section 7.

Each FDP user organisation should also consider risks through the use of products. Product DPIAs assess the risk in relation to specific processing activities. Each product will have its own DPIA, and when a product is deployed to an instance the FDP user organisation should consider the associated risks as they relate to their use of the product in their own Instance.

DPIAs will evolve over time to reflect the enhancements and development in core functionality, development of products and additional FDP user organisations. They are not static. This DPIA will therefore also evolve over time.

6. Description of the processing

The processing of data within FDP will be described in seven broad categories:

• data ingress (data arriving into the FDP)
• data transfer (data flowing between FDP instances)
• data egress (data leaving the FDP)
• data platform services (tools provided to enable data processing within the FDP)
• data linkage and analysis (data used within the FDP)
• detailed processing

Data ingress (data landing into the FDP)

Personal data (including confidential patient information) can arrive in FDP instances via direct connection to source systems via REST APIs, HL7 streams, ODBC/JDBC connections, S3/ABFS sources, connections made to data warehouses, Secure FTP (SFTP) or MESH. FDP user organisations as controllers approve data ingress to FDP further to the FDP IG framework. All instances are hosted within cloud infrastructure. This aligns to the ‘cloud first’ policy for public sector IT introduced in 2013, endorsed by the National Information Board’s personalised health and care 2020 framework .

All personal data brought into the FDP will be registered through NHS-PET (there is a separate DPIA assessing risks in relation to NHS-PET). This registration process is documented elsewhere, but in summary the purpose is to provide transparency of what data is held within FDP. In future (but not in the initial deployment of FDP and NHS-PET), the NHS-PET will also “treat” data, de-identifying it as required (this DPIA will be updated before this occurs).

Any data landing in the FDP is limited to a specific instance until such time as it is transferred to another Instance (which in all cases is only with the approval of the relevant controller). Subject to the limitations of data use in the FDP (set out in the FDP IG framework). FDP user organisations are responsible for the data entering and leaving their instance.

Data transfer (data flowing between FDP instances)

Data, including personal data (including confidential patient information) can transfer between FDP instances without leaving the secure platform boundaries when authorised by the FDP user organisation (a copy of the data is created, this is not multi-user access to a single copy of data). 

Authorisation by the FDP user organisation is also subject to the appropriate governance arrangements being put in place for the transfer of any personal data (including confidential patient information), which includes data sharing agreements required under the FDP IG framework and any transfer being lawful under UK General Data Protection Regulation (GDPR) and the Common Law Duty of Confidentiality. Any transfer is subject to registration through NHS-PET, for transparency.

Data egress (data leaving the FDP)

Personal data (including confidential patient information) may leave an FDP instance via the same protocols and mechanism as ingress, subject to the FDP user organisation’s authorisation. In addition, industry standard APIs exist in FDP Instances to allow for egress of data by external systems when authorised. When authorised, external analytics tools used by FDP user organisations or with their permission can integrate with data in FDP, such as Tableau, Power BI, Excel and more. Any such use of data held within FDP but accessed from outside the Federated Data Platform must occur only where that processing is described by a product DPIA.

As described above, authorisation by the FDP user organisation is also subject to the appropriate governance arrangements being put in place for the transfer of any personal data (including confidential patient information), which includes data sharing agreements required under the FDP IG framework and any transfer being lawful under UK GDPR and the Common Law Duty of Confidentiality.

Data platform services (tools provided to enable data processing within the FDP)

The FDP provides tools to enable FDP user organisations to have access to their own individual Instance, and specific canonical data model (“CDM”), through the use of the data platform services (which include pipeline management; modelling and branching; and data and code versioning) to customise the data model via extensions appropriate to their deployment profile to enable the integration, management and operationalising of data.

A canonical model is a design pattern used to communicate between different data formats. Essentially: create a data model which is a superset of all the others (“canonical”) and create a “translator” module or layer to/from which all existing modules exchange data with other modules.

The FDP instance has a CDM, also referred to as the shared healthcare ontology, this model defines the tables, schemas and definitions of a various NHS concepts such as patient and encounter. When the model is deployed in the national instance, an ontology DPIA which will be published, will assess the particular data processing to populate the model. Local product DPIAs will describe the processing to populate local ontologies.

When the instance is configured, FDP user organisations can leverage the data platform services together with object layer services (including ontology manager, object monitoring, scenarios, actions and functions) to integrate data from third-party sources to a CDM which enables the systematic mapping of data to intuitive, operational NHS or FDP user organisation specific concepts.

Additionally, different types of data may be visible to individual users of the FDP, who may view that data, subject to role based access controls and purpose based access controls put in place by the FDP user organisation, for a specific purpose under one of the five use cases as detailed within a product DPIA. 

In line with the strict access controls in place, the information being displayed may be personal data which is directly identifiable data, pseudonymised data, anonymised data, aggregated data or operational data.

Data linkage and analysis (data used within the FDP)

Directly identifiable personal data may be linked with other directly identifiable personal data through the use of patient identifiers such as the NHS number, date of birth and postcode.

Pseudonymised data may be linked based on common pseudonyms, i.e. the direct identifiers have been replaced in a consistent manner across datasets, to allow linkage.

Specific analysis (including ad hoc analysis and reporting) will be detailed within product DPIAs, and must adhere to data protection principles, including using the minimum data necessary.

Use of all FDP Instances will be audited, and logs will be held within the system.  The logs will enable troubleshooting activities and may be used to detect unusual activity within the system, or in the event of a suspected cyber incident or unlawful activity.

Audit logs containing details about user activities and data in the platform can be shared with the NHS Cyber Security Operations Centre (CSOC) operated by NHS England to enable monitoring and responses to unusual activity in the platform.

Users have access to their respective audit logs to enable monitoring of activities.

Detailed Processing

The canonical data model (CDM) is deployed to each FDP instance. As this is purely a model, there is no data at this point.

Products are developed using this model (to ensure consistency and ‘deployability’ between products).  Where a product requires data, the data must be present in the ontology. These products provide a wide range of capabilities ranging from dashboards and command centres for staff, alerting, data analysis tools, operational scheduling and data cleaning tools.

Any data ingested into FDP can be fully traced back from the product and canonical data model back to the original ingestion via the data lineage tool. Purpose based access control enforces that data ingested for a particular purpose, such as direct care, is only used for this approved purpose.

7. Compliance with the data protection principles

Compliance with the data protection principles, as set out in Article 5 of the UK General Data Protection Regulation, are addressed in this DPIA in the following sections: 

8. Describe the legal basis for the processing (collection, analysis or disclosure) of personal data?

The FDP is designed to be a common system that can be used by many different organisations, with each of those organisations retaining autonomy over the use of any data they choose to put there. Each organisation has a separate space (called an “instance”), where their data is held securely, and separate from data belonging to other organisations.  The separate organisations are referred to as “FDP user organisations”.

Data controllership in relation to local and national instances

• NHS England is the sole controller of the personal data which flows into and is processed within any approved products it chooses to use in the National Instance of FDP.
• Local FDP user organisations are the sole controllers of the personal data which flows into and is processed within any approved products they chose to use in their own local instance of the FDP, subject to what is said below regarding joint controllership.
• NHS England has procured, funds, broadly determines the parameters for use, and manages the security, of the Data Platform.
• Local FDP User Organisations decide whether to use FDP, what Products to use, what data to commit to FDP and how to use it within these parameters.

NHS England and each Local FDP User Organisation are therefore Controllers for different aspects of how FDP operates at a national and local level, including in relation to their own FDP Instances. 

Controllership responsibilities between NHS England and FDP User Organisations are set out clearly in a Joint Controller table (at the end of this DPIA, and in the FDP IG Framework) and are agreed between NHS England and each Local FDP User Organisation further to the terms of the MoU through the contractual documentation they enter into. The essence of this arrangement will also be made publicly available in privacy material relating to the FDP so that this is readily and easily apparent to the public in line with Article 26 of UK GDPR.

The following explains firstly NHS England’s legal basis to procure and provide the FDP system, and then explains the likely legal basis for the processing of Personal Data within an Instance. In each case this will be specifically documented in the separate Product DPIAs and reflect the Processing of Personal Data within a Product.

NHS England’s legal basis to procure and provide the FDP

1. Statutory authority

NHS England has various statutory functions that enable it to procure and provide FDP for itself and for other FDP User Organisations. These include:

• Section 270 of the Health and Social Care Act 2012 (2012 Act), to establish and provide FDP for as a service to NHS Trusts and ICBs pursuant to NHS England’s power to supply services to any person and provide new services. The supply of FDP involves, and is connected with, the collection, analysis, publication or other dissemination of information.
• Section 13D of the National Health Service Act 2006 (NHS Act), as part of its duty as to effectiveness, efficiency.
• Section 13K of the NHS Act, as part of its duty to promote innovation.
• Section 1H(2) of the NHS Act as part of its duty under Section 1(1) to promote a comprehensive health service.
• Section 2(2) to do anything which is calculated to facilitate, or is conducive or incidental to, the discharge of any of its functions. Under Section 13Y of the NHS Act this expressly includes the power to enter into agreements.
• The duty under Section 253(1)(ca) to have regard, in the exercise of its functions, to the need to respect and promote the privacy of recipients of health services and of adult social care in England

1.2 Legal basis – NHS England

In relation to the procurement and provision of FDP for itself and for other FDP User Organisations, NHS England relies on the following legal basis:

Article 6 – personal data

Article 6 (1)(e): processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller by virtue of the statutory functions referred to above (public task);

Article 9 – special category personal data

Article 9(2)(g): processing is necessary for reasons of substantial public interest (public interest). Under section 10(3) of the Data Protection Act 2018 (DPA), this requires a condition in Part 2 of Schedule 1 of the DPA. NHS England relies on paragraph 6 (statutory purpose), as the processing—

• is necessary for the exercise of a function conferred on a person by an enactment or rule of law. Processing is necessary to discharge NHS England’s statutory functions set out above, and
• is necessary for reasons of substantial public interest. This is to enable the safe, secure, efficient processing of patient data to deliver more effective and efficient healthcare services.

Legal Basis for Processing Personal Data in each Instance of FDP

Where NHS England or an FDP User Organisation is Processing Personal Data and Special Categories of Personal Data, they will each separately as Controllers identify:

• a relevant condition for Processing under Articles 6 and 9 of UK GDPR and Schedule 1 of the DPA, and
• in relation to Confidential Patient Data, a legal basis under the Common Law Duty of Confidentiality.

This will be determined at a Product level for the Personal Data being processed through FDP and reflected in the relevant national or local Product DPIA and Product Privacy Notice. The likely legal basis are set out below:

1.3 Legal basis – FDP user organisation

Under Article 6, it is expected that the legal basis for Processing Personal Data in FDP would include:

• Article 6(1)(c) Legal Obligation, for example where NHS England collects and analyses data under a Direction.
• Article 6(1)(e) Public Task, for example where an FDP User Organisations Processes Personal Data for the purposes of providing an individual with care and treatment. Also where NHS England shares data with NHS Trusts or ICBs through the Platform relying on its powers to disseminate data under Section 261 of the 2012 Act.

Under Article 9, it is expected that the legal basis for Processing Special Categories of Personal Data in FDP would include:

• Article 9(2)(g) Public Interest,
• Article 9(2)(h) for medical diagnosis, the provision of health care, or the treatment or management of health care services and system (health care),
• Article 9(2)(i) for public health purposes (public health)
• Article 9(2)(j) for statistical purposes (statistical purposes)

Under Schedule 1 of the DPA it is expected the additional conditions of Processing Special Categories of Personal Data would include:

• paragraph 2 (health care),
• paragraph 3 (public health),
• paragraph 4 (statistical purposes), and
• paragraph 6 (statutory purpose).

1.4 Common Law Duty of Confidentiality

Under the Common Law Duty of Confidentiality where confidential data, including Confidential Patient Information (Confidential Patient Data) is Processed within FDP, it is expected it would be lawful because of:

• implied consent where the Processing of Confidential Patient Data in any particular circumstances is carried out for the purpose of the Direct Care of a patient.
• under section 254 of the 2012 Act in relation to data that NHS England has been directed to collect and/or analyse pursuant to a direction issued by the Secretary of State for Health and Social Care (Direction) that may be processed in the national Instances for purposes covered by a Direction.
• Under section 259 of the 2012 Act in relation to data that NHS England has required is supplied to it by an FDP User Organisation in response to a data provision notice so that it can comply with its duty to collect and analyse data under a Direction. This may apply to data shared from a local to a national Instance.
• Regulation 3 of the National Health Services (Control of Patient Information) Regulations 2002 (“COPI Regulations”)
• Regulation 5 of the COPI Regulations in relation to medical purposes approved by the Secretary of State with support from the Confidentiality Advisory Group, also known as an approval under Section 251 of the NHS Act 2006.

It is not expected that any Processing of Confidential Data within the FDP would rely on a public interest justification.

9. Demonstrate the fairness of the processing

Each FDP User Organisation is responsible for ensuring that the patient information in their FDP Instance is used fairly and transparently.  Because the specific uses of the data are determined by the FDP User Organisation, it is not possible for this DPIA to demonstrate the fairness of each specific use/ Product, this will be detailed within the specific Product DPIA’s

The high-level uses of the FDP (repeated below) have been developed through consultation with stakeholders:

• Elective recovery (reducing waiting times) – to get patients treated as quickly as possible, reducing the backlog of people waiting for appointments or treatments which has resulted from the COVID-19 pandemic alongside winter pressures on the NHS.
• Vaccination and immunisation – to ensure that there is fair and equal access, and uptake of vaccinations across different communities.
• Population health management (Planning NHS services) – to help local NHS organisations to plan the right services, in the right places, for their local communities.
• Care coordination (Joining up care) – to ensure that health and care organisations all have access to the information they need to support the patient, reducing the number of long stays in hospital and ensuring that everyone is cared for in the right place for them at the right time.
• Supply chain management (Getting the best value for the NHS) – to help the NHS put resources where they are needed most and buy smarter so that we get the best value for money.

Where Directly Identifiable Personal Data, including Confidential Patient Information is used within the FDP (until such time as new activity is developed, and this DPIA is updated), it is for Direct Care only.

Where possible, Personal Data will be protected through the use of Pseudonymisation.

In all cases, where Personal Data is used within the FDP, it is the responsibility of the FDP User Organisation to ensure that there is sufficient transparency for the public, and that processing is both fair and lawful.

To assist FDP User Organisations with fulfilling their transparency obligations, NHS England publishes some information about the FDP, including information about the Use Cases. Until further Use Cases are developed and approved, processing of Personal Data within the FDP must only be carried out for the purposes of the initial five Use Cases.

Transparency needs to be achieved through a layered approach, describing various topics at a high level (such as FDP, NHS-PET, National and Local Instances, National and Local Products, etc.), alongside a more detailed FDP Privacy Notice, and more specific Product Privacy Notices. NHS England will be publishing this information on its website and Local FDP User Organisations will also need to publish appropriate transparency information on their websites.

10. What steps have you taken to ensure individuals are informed about the ways in which their personal data is being used?

In establishing the way in which the FDP will be delivered and operate for the NHS, there has been on-going engagement with several stakeholder groups (see “Consultation with Stakeholders” section) to co-design the approach to transparency.  This has ensured that FDP has taken a privacy-by-design approach, ensuring that the views of the data subjects have been included in the design of the FDP IG Framework and associated documentation.

NHS England will publish overarching generic information about the use cases, and about the FDP, as mentioned in the previous section.  In addition, NHS England is an FDP User Organisation in its own right, and has a responsibility to be transparent about processing within the NHS England Instance of FDP. NHS England is also therefore publishing a General FDP Privacy Notice, and separate Product Privacy Notices for every national and local Product on its website.

NHS England has taken a layered approach to providing the public with transparency information and UK GDPR required privacy notice information. The diagram below describes the approach that has been taken:

FDP Programme: privacy notice and transparency information suggested approach based on user research

Additionally, each Product will be approved by the Data Governance Group (with membership from across FDP User Organisations) and template approved IG documentation will be made available to any FDP User Organisation who wants to use the Product. This Product template documentation will include a DPIA and Level 4 Product Privacy Notice.

Each FDP User Organisation is responsible for its own data protection obligations, and any generic templates (provided to improve consistency, transparency, and understanding) may be altered or the information presented in other formats by an FDP User Organisation.

NHSE FDP Programme will be issuing regular FDP communications to be clear to the public about the approach to the roll out of the FPD Programme, what data is being processed for what purposes as products evolve, how FDP works and how privacy is protected, including how NHS-PET works, particularly when PET starts treating data later in 2024.

11. Is it necessary to collect and process all data items?

12. Describe if personal datasets are to be matched, combined or linked with other datasets? (internally or for external customers)

Datasets will be matched/combined/linked within FDP. Any such use of data will be described in Product DPIAs.

In general:

• Linkage may occur through data which is not directly Personal Data, but relates to other factors (e.g. linkage of data relating to a common location).
• Directly Identifiable Personal Data may be linked with other Directly Identifiable Personal Data through the use of direct patient identifiers,g. NHS Number.
• Pseudonymised Data may be linked based on common pseudonyms, i.e. the direct patient identifiers have been replaced in a consistent manner across datasets, to allow linkage but to protect Personal Data by preventing re-identification of individuals without additional information/resources.

Specific analysis will be detailed within Product DPIAs, and must adhere to data protection principles, including using the minimum data necessary.

13. Describe if the personal data is to be shared with other organisations and the arrangements you have in place

The FDP functionality allows for data to be transferred between Instances, or to be taken away from the FDP (Egress).

Any such transfer/flow will be subject to the FDP User Organisation’s approval, its governance processes, there being a legal basis, audit within FDP, and registration by the NHS-PET.

14. How long will the personal data be retained?

Retention of Personal Data within an Instance is subject to the FDP User Organisation’s policies.  Each Product may have a unique data retention period, this will be set by the FDP User Organisation and articulated within the Product DPIA.  

Each FDP User Organisation must abide by their own Records Management Policies and Retention Schedules, in compliance with the NHS Records Management Code of Practice 2021.

15. Where you are collecting personal data from the individual, describe how you will ensure it is accurate and if necessary, kept up to date

The FDP will not be directly collecting data from patients.

All FDP User Organisations are responsible for adhering to data protection law requirements such as transparency and maintaining accurate records.  There may be additional requirements around clinical record-keeping.

16. How are individuals made aware of their rights and what processes do you have in place to manage such requests?

All FDP User Organisations must:

• have the relevant policies and procedures to ensure that data subjects understand their rights in relation to their data.
• have the policies and procedures in place to be able to answer any subject rights requests.

All individual rights requests should be considered by the relevant Controller, i.e. NHS England do not have a central co-ordinating role.  If a request is received by the FDP Platform Contractor, it will be passed to the relevant FDP User Organisation(s).

FDP User Organisations will follow their own internal processes for determining how to progress with any rights request.  If this results in action being required within the FDP, then the FDP User Organisation will inform the FDP Platform Contractor to take the appropriate action (such as provision, amendment or deletion of information).

The general (Level 2) FDP Privacy Notice provides general information about an individual’s rights under UK GDPR and the specific (Level 4) Product Privacy Notices identify which rights apply to the data processed in the Product.  

17. What technical and organisational controls for “information security” have been put in place?

Encryption and security:

All data stored in the FDP will be protected via industry good practice layers of protection including encryption at rest and transit, regular penetration testing, firewall, anti-virus and intrusion protection.

The central data platforms are penetration tested to provide assurance and confirmation that all data is secure in accordance with an agreed schedule.

Both the FDP Platform Contractor and the national Cyber Security team will monitor technical systems for signs of suspicious activity.

All access to the FDP must be authenticated using Multi-Factor Authentication (MFA). Each FDP Instance can integrate with the FDP User Organisation’s chosen Single Sign-On (SSO) provider, commonly NHSmail. MFA must be enforced by the SSO provider via either smartcard, application based, hardware token, or phone based.

FDP utilises Role Based Access Controls and Purpose-Based Access Control to ensure all access to data for users is approved and with justification. Access of individual users and groups of users can be audited at any time by the FDP User Organisation to view when, why and how access was approved. The FDP User Organisation remains fully in control of data access and must approve any requests for access to data to enable the relevant processing or support.

Purpose-Based Access Control enforces that data ingested for a particular purpose, such as Direct Care, is only used for this approved purpose. Information on what data is being ingested, and the associated purpose, can be viewed in FDP and NHS-PET by the FDP User Organisation.

More detail on Purpose-Based Access Controls is available within Part 2 of Schedule 3 of the FDP IG Framework.

Further detailed system and technical level security policies apply, which are not published.

18. In which country/territory will personal data be stored or processed?

All processing of patient information will be within the UK only. This is a contractual requirement and one of the key principles of the FDP IG Framework. This is enforced through technical controls within FDP.

19. Do Opt Outs apply to the processing?

There will be a wide range of Products available for use within the FDP by FDP User Organisations.

National Data Opt Out

The National Data Opt-Out provides an individual with a right to opt out of their Confidential Patient Information being used for purposes beyond their Direct Care, unless an exemption applies under the National Data Opt-Out Operational Policy Guidance .

At the start of the Transition Phase of the FDP Programme there are no existing Products where the National Data Opt Out would be applicable because:

• No Confidential Patient Information is being processed by a Product in the National Instances of FDP to which the National Data Opt-Out would apply.
• Confidential Patient Information that is being used in the FDP in a Product in a Local Instance is only being used for the purposes of Direct Care and therefore the National Data Opt-Out does not apply.

Product DPIAs and (Level 4) Product Privacy Notices will explain why the National Data Opt Out does not apply.

Type 1 Opt Outs

A Type 1 opt-outs registered with a GP Practice prevents an individual’s confidential patient information from being shared outside of their GP Practice except when it is being used for the purposes of their individual care.

At the start of the Transition Phase of the FDP Programme there are no existing Products where Type 1 Opt Outs would be applicable because:

• No Confidential Patient Information that has come from a GP Practice is being processed by a Product in the National Instances of FDP.
• Any Confidential Patient Information that has come from a GP Practice which is being used in the FDP in a Product in a Local Instance is only being used for the purposes of individual care.

Product DPIAs and (Level 4) Product Privacy Notices will explain why the Type 1 Opt Out does not apply.

Future changes

If this changes in the future because a new Product processes Confidential Patient Information in a way which would mean that one of the above opt-outs would apply, the relevant FDP User Organisation would be responsible for ensuring that the opt-out was applied, the relevant (Level 4) Product Privacy Notice would identify this and the general (Level 2) FPD Privacy Notice and general (Level 1) transparency information would be updated to make this clear.

It is a core principle of the FDP IG Framework and also a contractual obligation of the suppliers of both FDP and NHS-PET that National Data Opt-Outs and Type 1 Opt Outs are respected and applied where they should be applied.

20. Risk mitigation and residual risks

The “identification of risks” section of this DPIA sets out the inherent risks arising from the proposed data processing. This section summarises the steps to mitigate those risks (which are explained in detail above) and assesses the residual risks, i.e. the level of risk which remains once the mitigations are in place.

Against each risk that have been identified, record the options/controls you have put in place to mitigate the risk and what impact this has had on the risk. Make an assessment as to the residual risk.

Also indicate who has approved the measure and confirm that responsibility and timescales for completion have been integrated back into the project plan.

Download a copy of the risk mitigation table .

21. Actions

NHS England through FDP Programme governance and management arrangements regularly reviews this DPIA and identifies and manages to completion actions to ensure it is accurate and updated.

22. Definitions

Definitions which may be useful in the review of this document.

• Aggregated data: Counts of data presented as statistics so that data cannot directly or indirectly identify an individual.
• Anonymisation anonymisation : involves the application of one or more anonymisation techniques to personal data. When done effectively, the anonymised information cannot be used by the user or recipient to identify an individual either directly or indirectly, taking into account all the means reasonably likely to be used by them. This is otherwise known as a state of being rendered anonymous in the hands of the user or recipient.
• Anonymised data: Personal data that has undergone anonymisation.
• Anonymous data : Anonymised data, aggregated data and operational data.
• Approved use cases : Means one of the five initial broad purposes for which Products in the Data Platform can be used as outlined in the FDP IG Framework (Part 1 of Schedule 2 (Approved Use Cases and Products)), or any subsequent broad purpose agreed to be a use case through the Data Governance Group.
• Commissioned Health Service Organisations : Means organisations who provide health services in England pursuant to arrangements made with an NHS Body exercising functions in connection with the provision of such services.
• Common Law Duty of Confidentiality : The common law duty which arises when one person discloses information to another (e.g. patient to clinician) in circumstances where it is reasonable to expect that the information will be held in confidence.
• Confidential patient data : Information about a patient which has been provided in circumstances where it is reasonable to expect that the information will be held in confidence, including Confidential Patient Information.
• Confidential patient information : Has the meaning given in section 251(11) of the National Health Service Act 2006. See Appendix 6 of the National Data Opt Out Operational Policy Guidance for more information .
• Controller : Has the meaning given in UK GDPR being the natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the Processing of Personal Data (subject to Section 6 of the Data Protection Act 2018)
• Data Governance Group : Means a national group established by NHS England to provide oversight to the approach to data Processing and sharing across all Instances of the FDP and NHS-PET which will include membership from across FDP User Organisations
• Data platform : The NHS Federated Data Platform
• Data Security and Protection Toolkit (DSPT) : The Data Security and Protection Toolkit is an online self-assessment tool that FDP User Organisations are required to complete annually to demonstrate they are meeting required data protection and security standards
• Direct care : A clinical, social, or public health activity concerned with the prevention, investigation and treatment of illness and the alleviation of suffering of individuals. It includes supporting individuals’ ability to function and improve their participation in life and society. It includes the assurance of safe and high-quality care and treatment through local audit, the management of untoward or adverse incidents, person satisfaction including measurement of outcomes undertaken by one or more registered and regulated health or social care professionals and their team with whom the individual has a legitimate relationship for their care.
• Directly identifiable personal data : Personal Data that can directly identify an individual.
• DPIA: Data Protection Impact Assessments in a form that meets the requirements of UK GDPR
• FDP : Federated Data Platform
• FDP IG framework : The FDP IG Framework attached at section 27 (as the same may be updated from time to time) has been created to enable the management of the information governance (IG) workflow for FDP.
• FDP Programme : The NHS England Programme responsible for the procurement and implementation of the FDP across the NHS
• External IG advisory group : The advisory group established by NHS England to provide specialist IG advice to the FDP Programme which includes membership from external organisations including the Office of the National Data Guardian and the Information Commissioner’s Office
• FDP user organisations : NHS England, ICBs, NHS trusts and other NHS bodies (including a commissioned health service organisation) who wish to have an instance of the FDP.
• General FDP privacy notice : A privacy notice providing information on the Personal Data Processed in the FDP and by NHS-PET generally, including the Approved Use Cases for which Products will Process Personal Data.
• ICB : Integrated care board
• ICS : Integrated care system
• Instance : A separate instance or instances of the FDP deployed into the technology infrastructure of an individual FDP user organisation
• Joint controller : Has the meaning given in UK GDPR, being where two or more Controllers jointly determine the purposes and means of Processing Personal Data
• Joint controller arrangement : Has the meaning given in UK GDPR being an arrangement between two or more Joint Controllers who shall in a transparent manner determine their respective responsibilities for compliance with the obligations under UK GDPR, in particular as regards the exercising of the rights of the data subject and their respective duties to provide the information referred to in Articles 13 and 14 of UK GDPR and reflecting the roles and relationships of the Joint Controllers vis-à-vis the data subjects. The essence of the arrangement shall be made available to the data subject.
• Local FDP user organisation : Any organisation which holds an instance of FDP other than NHS England.
• MoU : The Memorandum of Understanding signed between NHS England and an NHS Trust, ICB or other NHS Body as may be amended from time to time in accordance with its terms.
• National Data Opt Out : The Department of Health and Social Care’s policy on the National Data Opt Out which applies to the use and disclosure of Confidential Patient Information for purposes beyond individual care across the health and adult social care system in England. See the National Data Opt Out Overview and Operational Policy Guidance for more information.
• NHS body : Has the meaning given in the National Health Service Act 2006, which includes ICBs, NHS trusts, NHS foundation trusts and other bodies concerned with NHS service delivery
• NHS FDP System IG Group : The user group established by NHS England for local IG leads to discuss and agree IG documentation for the initial and the subsequent deployment of other local Products NHS-PET The privacy enhancing technology (PET) solution which records data flows into the FDP and, where required, treats data flows to pseudonymise or anonymise them
• NHS-PET contractor : IQVIA Limited
• Ontology : Is a layer that sits on top of the digital assets (datasets and models). The ontology creates a complete picture by mapping datasets and models used in products to object types, properties, link types, and action types. The ontology creates a real-life representation of data, linking activity to places and to people.
• Operational data : Items of data that are not about individuals.
• Parties : NHS England, the platform contractor, the NHS-PET contractor and FDP userorganisations
• Personal data : Has the meaning given in UK GDPR being any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person . For the purposes of the DPIA this also includes information relating to deceased patients or service users. Personal data can be directly identifiable personal data or pseudonymised data.
• Personal Data Breach : Has the meaning given in UK GDPR being a breach of security leading to the accidental or unlawful destruction, loss, alteration, unauthorised disclosure of, or access to, personal data transmitted, stored or otherwise processed.
• Platform contract : The agreement between NHS England and the platform contractor in relation to the FDP dated 21 November 2023 as may be amended from time to time in accordance with its terms.
• Platform contractor : Palantir Technologies, UK Ltd
• Product A : product providing specific functionality enabling a solution to a business problem of an FDP user organisation operating on the FDP. A list of approved products is maintained in the IG Framework (set out in the Appendix at section 27).
• Product privacy notice : A privacy notice providing information on the personal data processed in the FDP and by NHS-PET in relation to each product, including the purposes for which the product processes personal data
• Process or processing : Has the meaning given in UK GDPR being any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.
• Pseudonymisation : Has the meaning given in UK GDPR being the processing of personal data in such a manner that the personal data can no longer be attributed to a specific individual without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the Personal Data are not attributed to an identified or identifiable natural person.
• Pseudonymised data : Personal data that has undergone pseudonymisation.
• Purpose based access Ccontrols or PBAC : Means user access to data is based on the purpose for which an individual needs to use data rather than their role alone as described more fully in the IG Framework.
• Role based access controls : Means user access controls is restricted to systems or data based on their role within an organisation. The individual’s role will determine what they can access as well as permission and privileges they will be granted as described more fully in the IG Framework.
• Security breach : Is a breach of security, and includes in the case of the Platform Contractor, a Breach of Security as defined in Schedule 2.4 (Security Management) of the Platform Contract.
• Special category personal data : Refers to the special categories of Personal Data defined in Article 9(1) of UK GDPR being Personal Data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and the processing of genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health or data concerning a natural person’s sex life or sexual orientation.
• UK GDPR : is as defined and referred to in the Data Protection Act 2018

23. Joint Controller table

NHS England and Local FDP User Organisation Joint Controller Table – V1.0 13 March 2024

1. Introduction

The purpose of this table is to set out the Joint controller arrangement between NHS England and each Local FDP user organisation (each a party in this schedule) regarding the personal data processed in the Federated Data Platform and NHS-PET in order to clarify roles and responsibilities for the purposes of Article 26 of the UK GDPR. 

Article 26 of the UK GDPR governs the relationship between joint controllers. Article 26(1) of the UK GDPR provides that “Where two or more controllers jointly determine the purposes and means of processing, they shall be joint controllers. They shall in a transparent manner determine their respective responsibilities for compliance with the obligations under this Regulation, in particular as regards the exercising of the rights of the data subject and their respective duties to provide the information referred to in Articles 13 and 14, by means of an arrangement between them unless, and in so far as, the respective responsibilities of the controllers are determined by Union or Member State law to which the controllers are subject. The arrangement may designate a contact point for data subjects.”

Under Article 26(2) of the UK GDPR, “The arrangement referred to in paragraph 1 shall duly reflect the respective roles and relationships of the joint controllers vis-à-vis the data subjects. The essence of the arrangement shall be made available to the data subject.”

2. Transparent manner

The table below can be referred to in relevant Data Protection Impact Assessments ( DPIAs ). It will also stand as a stand-alone document which can be issued to anyone who requests it. It transparently sets out each Party’s respective obligations and responsibilities as joint Controllers in relation to the Data Platform and NHS-PET.

3. Respective responsibilities for compliance, in particular with regard to exercise of data subject rights and duties to provide information in Articles 13 and 14

The table below sets out each controller’s responsibilities for:

• compliance with the obligations under UK GDPR which apply to controllers,
• compliance with duties to provide the information referred to in Article 13 and Article 14, and
• compliance with the obligations under UK GDPR as regards the exercise of data subjects’ rights. 

This table constitutes the arrangement referred to in Article 26.

4. The arrangement may designate a contact point for data subjects

NHS England is designated as a contact point, for data subjects,

• in the table below for FDP Programme-wide queries and queries concerning the national instance; and
• in the General FDP privacy notice and in the transparency information, for queries concerning each product in the national instance.  

NHS England’s data protection officer is also named in the general FDP privacy notice and the transparency information for each product in the national instance of FDP as a contact point. 

Local FDP user organisations are designated as a contact point, for data subjects,

• in the table below for queries concerning their use of the local Instances; and
• in the General FDP privacy notice for each product in their local Instance;
• in the transparency information they provide for each Product in the local Instance which they deploy.

The Local FDP User Organisation’s Data Protection Officer should also be named in such transparency information of the Local FDP User Organisation.

5. The arrangement must reflect the respective roles and relationships of the joint controllers vis-à-vis the data subjects

In accordance with Article 26 of the of the UK GDPR this table sets out the roles and responsibilities of the following Parties:

• NHS England
• Local FDP user organisations

(together referred to as the FDP user organisations), in relation to the processing of personal data in the data platform and, when processing of personal data commences, in the NHS-PET.  

The FDP is a series of individual platforms referred to as Instances and each of NHS England and the Local FDP user organisations have their own instance and they each control the personal data held and processed within their own Instances.  

NHS England is the controller for the personal data which flows into, and is processed within, any approved Products it chooses to use within the national Instance.  

NHS England is a joint controller with each Local FDP user organisation in relation to the local Instances for design, governance, and service management of the data platform. This is because NHS England broadly determines the parameters for the use of the data platform.  

Local FDP user organisations are controllers for the personal data they flow into and process in their local Instances.  Each local FDP user organisation decides whether to use the Federated Data Platform, what data to commit to the Federated Data Platform and how to use it within those parameters.

Where the NHS–PET Contractor Processes Personal Data prior to it entering or leaving the national Instance then NHS England is the Controller of such Personal Data Processing.

Where the NHS-PET Contractor Processes Personal Data prior to it entering or leaving the local Instance then the Local FDP User Organisation is the Controller of such Personal Data Processing.

NHS England are a joint Controller with each Local FDP User Organisation in relation to the Local FDP User Organisation’s engagement of the NHS-PET Contractor to Process Personal Data prior to it entering or leaving a local Instance, specifically in relation to the design, governance, and service management of NHS-PET.

6. The essence of the arrangement shall be made available to the data subject

The essence of this arrangement is described in the General FDP Privacy Notice referred to above. This document is publicly available and can also be provided to data subjects on request to NHS England.

Key to roles and responsibilities in the table below.

To assist, where a party:

• has compliance responsibilities this has been identified with a ‘tick’
• does not have compliance responsibilities, this has been identified with a ‘cross’

Download a word version of this table .

24. Appendix – information governance framework

• Federated Data Platform: information governance framework

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Models and frameworks for assessing the implementation of clinical practice guidelines: a systematic review

• Nicole Freitas de Mello   ORCID: orcid.org/0000-0002-5228-6691 1 , 2 ,
• Sarah Nascimento Silva   ORCID: orcid.org/0000-0002-1087-9819 3 ,
• Dalila Fernandes Gomes   ORCID: orcid.org/0000-0002-2864-0806 1 , 2 ,
• Juliana da Motta Girardi   ORCID: orcid.org/0000-0002-7547-7722 4 &
• Jorge Otávio Maia Barreto   ORCID: orcid.org/0000-0002-7648-0472 2 , 4  

Implementation Science volume  19 , Article number:  59 ( 2024 ) Cite this article

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The implementation of clinical practice guidelines (CPGs) is a cyclical process in which the evaluation stage can facilitate continuous improvement. Implementation science has utilized theoretical approaches, such as models and frameworks, to understand and address this process. This article aims to provide a comprehensive overview of the models and frameworks used to assess the implementation of CPGs.

A systematic review was conducted following the Cochrane methodology, with adaptations to the "selection process" due to the unique nature of this review. The findings were reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Electronic databases were searched from their inception until May 15, 2023. A predetermined strategy and manual searches were conducted to identify relevant documents from health institutions worldwide. Eligible studies presented models and frameworks for assessing the implementation of CPGs. Information on the characteristics of the documents, the context in which the models were used (specific objectives, level of use, type of health service, target group), and the characteristics of each model or framework (name, domain evaluated, and model limitations) were extracted. The domains of the models were analyzed according to the key constructs: strategies, context, outcomes, fidelity, adaptation, sustainability, process, and intervention. A subgroup analysis was performed grouping models and frameworks according to their levels of use (clinical, organizational, and policy) and type of health service (community, ambulatorial, hospital, institutional). The JBI’s critical appraisal tools were utilized by two independent researchers to assess the trustworthiness, relevance, and results of the included studies.

Database searches yielded 14,395 studies, of which 80 full texts were reviewed. Eight studies were included in the data analysis and four methodological guidelines were additionally included from the manual search. The risk of bias in the studies was considered non-critical for the results of this systematic review. A total of ten models/frameworks for assessing the implementation of CPGs were found. The level of use was mainly policy, the most common type of health service was institutional, and the major target group was professionals directly involved in clinical practice. The evaluated domains differed between the models and there were also differences in their conceptualization. All the models addressed the domain "Context", especially at the micro level (8/12), followed by the multilevel (7/12). The domains "Outcome" (9/12), "Intervention" (8/12), "Strategies" (7/12), and "Process" (5/12) were frequently addressed, while "Sustainability" was found only in one study, and "Fidelity/Adaptation" was not observed.

Conclusions

The use of models and frameworks for assessing the implementation of CPGs is still incipient. This systematic review may help stakeholders choose or adapt the most appropriate model or framework to assess CPGs implementation based on their specific health context.

Trial registration

PROSPERO (International Prospective Register of Systematic Reviews) registration number: CRD42022335884. Registered on June 7, 2022.

Peer Review reports

Contributions to the literature

Although the number of theoretical approaches has grown in recent years, there are still important gaps to be explored in the use of models and frameworks to assess the implementation of clinical practice guidelines (CPGs). This systematic review aims to contribute knowledge to overcome these gaps.

Despite the great advances in implementation science, evaluating the implementation of CPGs remains a challenge, and models and frameworks could support improvements in this field.

This study demonstrates that the available models and frameworks do not cover all characteristics and domains necessary for a complete evaluation of CPGs implementation.

The presented findings contribute to the field of implementation science, encouraging debate on choices and adaptations of models and frameworks for implementation research and evaluation.

Substantial investments have been made in clinical research and development in recent decades, increasing the medical knowledge base and the availability of health technologies [ 1 ]. The use of clinical practice guidelines (CPGs) has increased worldwide to guide best health practices and to maximize healthcare investments. A CPG can be defined as "any formal statements systematically developed to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances" [ 2 ] and has the potential to improve patient care by promoting interventions of proven benefit and discouraging ineffective interventions. Furthermore, they can promote efficiency in resource allocation and provide support for managers and health professionals in decision-making [ 3 , 4 ].

However, having a quality CPG does not guarantee that the expected health benefits will be obtained. In fact, putting these devices to use still presents a challenge for most health services across distinct levels of government. In addition to the development of guidelines with high methodological rigor, those recommendations need to be available to their users; these recommendations involve the diffusion and dissemination stages, and they need to be used in clinical practice (implemented), which usually requires behavioral changes and appropriate resources and infrastructure. All these stages involve an iterative and complex process called implementation, which is defined as the process of putting new practices within a setting into use [ 5 , 6 ].

Implementation is a cyclical process, and the evaluation is one of its key stages, which allows continuous improvement of CPGs development and implementation strategies. It consists of verifying whether clinical practice is being performed as recommended (process evaluation or formative evaluation) and whether the expected results and impact are being reached (summative evaluation) [ 7 , 8 , 9 ]. Although the importance of the implementation evaluation stage has been recognized, research on how these guidelines are implemented is scarce [ 10 ]. This paper focused on the process of assessing CPGs implementation.

To understand and improve this complex process, implementation science provides a systematic set of principles and methods to integrate research findings and other evidence-based practices into routine practice and improve the quality and effectiveness of health services and care [ 11 ]. The field of implementation science uses theoretical approaches that have varying degrees of specificity based on the current state of knowledge and are structured based on theories, models, and frameworks [ 5 , 12 , 13 ]. A "Model" is defined as "a simplified depiction of a more complex world with relatively precise assumptions about cause and effect", and a "framework" is defined as "a broad set of constructs that organize concepts and data descriptively without specifying causal relationships" [ 9 ]. Although these concepts are distinct, in this paper, their use will be interchangeable, as they are typically like checklists of factors relevant to various aspects of implementation.

There are a variety of theoretical approaches available in implementation science [ 5 , 14 ], which can make choosing the most appropriate challenging [ 5 ]. Some models and frameworks have been categorized as "evaluation models" by providing a structure for evaluating implementation endeavors [ 15 ], even though theoretical approaches from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 13 ]. Two frameworks that can specify implementation aspects that should be evaluated as part of intervention studies are RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [ 16 ] and PRECEDE-PROCEED (Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development) [ 17 ]. Although the number of theoretical approaches has grown in recent years, the use of models and frameworks to evaluate the implementation of guidelines still seems to be a challenge.

This article aims to provide a complete map of the models and frameworks applied to assess the implementation of CPGs. The aim is also to subside debate and choices on models and frameworks for the research and evaluation of the implementation processes of CPGs and thus to facilitate the continued development of the field of implementation as well as to contribute to healthcare policy and practice.

A systematic review was conducted following the Cochrane methodology [ 18 ], with adaptations to the "selection process" due to the unique nature of this review (details can be found in the respective section). The review protocol was registered in PROSPERO (registration number: CRD42022335884) on June 7, 2022. This report adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 19 ] and a completed checklist is provided in Additional File 1.

Eligibility criteria

The SDMO approach (Types of Studies, Types of Data, Types of Methods, Outcomes) [ 20 ] was utilized in this systematic review, outlined as follows:

Types of studies

All types of studies were considered for inclusion, as the assessment of CPG implementation can benefit from a diverse range of study designs, including randomized clinical trials/experimental studies, scale/tool development, systematic reviews, opinion pieces, qualitative studies, peer-reviewed articles, books, reports, and unpublished theses.

Studies were categorized based on their methodological designs, which guided the synthesis, risk of bias assessment, and presentation of results.

Study protocols and conference abstracts were excluded due to insufficient information for this review.

Types of data

Studies that evaluated the implementation of CPGs either independently or as part of a multifaceted intervention.

Guidelines for evaluating CPG implementation.

Inclusion of CPGs related to any context, clinical area, intervention, and patient characteristics.

No restrictions were placed on publication date or language.

Exclusion criteria

General guidelines were excluded, as this review focused on 'models for evaluating clinical practice guidelines implementation' rather than the guidelines themselves.

Studies that focused solely on implementation determinants as barriers and enablers were excluded, as this review aimed to explore comprehensive models/frameworks.

Studies evaluating programs and policies were excluded.

Studies that only assessed implementation strategies (isolated actions) rather than the implementation process itself were excluded.

Studies that focused solely on the impact or results of implementation (summative evaluation) were excluded.

Types of methods

Not applicable.

All potential models or frameworks for assessing the implementation of CPG (evaluation models/frameworks), as well as their characteristics: name; specific objectives; levels of use (clinical, organizational, and policy); health system (public, private, or both); type of health service (community, ambulatorial, hospital, institutional, homecare); domains or outcomes evaluated; type of recommendation evaluated; context; limitations of the model.

Model was defined as "a deliberated simplification of a phenomenon on a specific aspect" [ 21 ].

Framework was defined as "structure, overview outline, system, or plan consisting of various descriptive categories" [ 21 ].

Models or frameworks used solely for the CPG development, dissemination, or implementation phase.

Models/frameworks used solely for assessment processes other than implementation, such as for the development or dissemination phase.

Data sources and literature search

The systematic search was conducted on July 31, 2022 (and updated on May 15, 2023) in the following electronic databases: MEDLINE/PubMed, Centre for Reviews and Dissemination (CRD), the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Epistemonikos, Global Health, Health Systems Evidence, PDQ-Evidence, PsycINFO, Rx for Change (Canadian Agency for Drugs and Technologies in Health, CADTH), Scopus, Web of Science and Virtual Health Library (VHL). The Google Scholar database was used for the manual selection of studies (first 10 pages).

Additionally, hand searches were performed on the lists of references included in the systematic reviews and citations of the included studies, as well as on the websites of institutions working on CPGs development and implementation: Guidelines International Networks (GIN), National Institute for Health and Care Excellence (NICE; United Kingdom), World Health Organization (WHO), Centers for Disease Control and Prevention (CDC; USA), Institute of Medicine (IOM; USA), Australian Department of Health and Aged Care (ADH), Healthcare Improvement Scotland (SIGN), National Health and Medical Research Council (NHMRC; Australia), Queensland Health, The Joanna Briggs Institute (JBI), Ministry of Health and Social Policy of Spain, Ministry of Health of Brazil and Capes Theses and Dissertations Catalog.

The search strategy combined terms related to "clinical practice guidelines" (practice guidelines, practice guidelines as topic, clinical protocols), "implementation", "assessment" (assessment, evaluation), and "models, framework". The free term "monitoring" was not used because it was regularly related to clinical monitoring and not to implementation monitoring. The search strategies adapted for the electronic databases are presented in an additional file (see Additional file 2).

Study selection process

The results of the literature search from scientific databases, excluding the CRD database, were imported into Mendeley Reference Management software to remove duplicates. They were then transferred to the Rayyan platform ( https://rayyan.qcri.org ) [ 22 ] for the screening process. Initially, studies related to the "assessment of implementation of the CPG" were selected. The titles were first screened independently by two pairs of reviewers (first selection: four reviewers, NM, JB, SS, and JG; update: a pair of reviewers, NM and DG). The title screening was broad, including all potentially relevant studies on CPG and the implementation process. Following that, the abstracts were independently screened by the same group of reviewers. The abstract screening was more focused, specifically selecting studies that addressed CPG and the evaluation of the implementation process. In the next step, full-text articles were reviewed independently by a pair of reviewers (NM, DG) to identify those that explicitly presented "models" or "frameworks" for assessing the implementation of the CPG. Disagreements regarding the eligibility of studies were resolved through discussion and consensus, and by a third reviewer (JB) when necessary. One reviewer (NM) conducted manual searches, and the inclusion of documents was discussed with the other reviewers.

Risk of bias assessment of studies

The selected studies were independently classified and evaluated according to their methodological designs by two investigators (NM and JG). This review employed JBI’s critical appraisal tools to assess the trustworthiness, relevance and results of the included studies [ 23 ] and these tools are presented in additional files (see Additional file 3 and Additional file 4). Disagreements were resolved by consensus or consultation with the other reviewers. Methodological guidelines and noncomparative and before–after studies were not evaluated because JBI does not have specific tools for assessing these types of documents. Although the studies were assessed for quality, they were not excluded on this basis.

Data extraction

The data was independently extracted by two reviewers (NM, DG) using a Microsoft Excel spreadsheet. Discrepancies were discussed and resolved by consensus. The following information was extracted:

Document characteristics : author; year of publication; title; study design; instrument of evaluation; country; guideline context;

Usage context of the models : specific objectives; level of use (clinical, organizational, and policy); type of health service (community, ambulatorial, hospital, institutional); target group (guideline developers, clinicians; health professionals; health-policy decision-makers; health-care organizations; service managers);

Model and framework characteristics : name, domain evaluated, and model limitations.

The set of information to be extracted, shown in the systematic review protocol, was adjusted to improve the organization of the analysis.

The "level of use" refers to the scope of the model used. "Clinical" was considered when the evaluation focused on individual practices, "organizational" when practices were within a health service institution, and "policy" when the evaluation was more systemic and covered different health services or institutions.

The "type of health service" indicated the category of health service where the model/framework was used (or can be used) to assess the implementation of the CPG, related to the complexity of healthcare. "Community" is related to primary health care; "ambulatorial" is related to secondary health care; "hospital" is related to tertiary health care; and "institutional" represented models/frameworks not specific to a particular type of health service.

The "target group" included stakeholders related to the use of the model/framework for evaluating the implementation of the CPG, such as clinicians, health professionals, guideline developers, health policy-makers, health organizations, and service managers.

The category "health system" (public, private, or both) mentioned in the systematic review protocol was not found in the literature obtained and was removed as an extraction variable. Similarly, the variables "type of recommendation evaluated" and "context" were grouped because the same information was included in the "guideline context" section of the study.

Some selected documents presented models or frameworks recognized by the scientific field, including some that were validated. However, some studies adapted the model to this context. Therefore, the domain analysis covered all models or frameworks domains evaluated by (or suggested for evaluation by) the document analyzed.

Data analysis and synthesis

The results were tabulated using narrative synthesis with an aggregative approach, without meta-analysis, aiming to summarize the documents descriptively for the organization, description, interpretation and explanation of the study findings [ 24 , 25 ].

The model/framework domains evaluated in each document were studied according to Nilsen et al.’s constructs: "strategies", "context", "outcomes", "fidelity", "adaptation" and "sustainability". For this study, "strategies" were described as structured and planned initiatives used to enhance the implementation of clinical practice [ 26 ].

The definition of "context" varies in the literature. Despite that, this review considered it as the set of circumstances or factors surrounding a particular implementation effort, such as organizational support, financial resources, social relations and support, leadership, and organizational culture [ 26 , 27 ]. The domain "context" was subdivided according to the level of health care into "micro" (individual perspective), "meso" (organizational perspective), "macro" (systemic perspective), and "multiple" (when there is an issue involving more than one level of health care).

The "outcomes" domain was related to the results of the implementation process (unlike clinical outcomes) and was stratified according to the following constructs: acceptability, appropriateness, feasibility, adoption, cost, and penetration. All these concepts align with the definitions of Proctor et al. (2011), although we decided to separate "fidelity" and "sustainability" as independent domains similar to Nilsen [ 26 , 28 ].

"Fidelity" and "adaptation" were considered the same domain, as they are complementary pieces of the same issue. In this study, implementation fidelity refers to how closely guidelines are followed as intended by their developers or designers. On the other hand, adaptation involves making changes to the content or delivery of a guideline to better fit the needs of a specific context. The "sustainability" domain was defined as evaluations about the continuation or permanence over time of the CPG implementation.

Additionally, the domain "process" was utilized to address issues related to the implementation process itself, rather than focusing solely on the outcomes of the implementation process, as done by Wang et al. [ 14 ]. Furthermore, the "intervention" domain was introduced to distinguish aspects related to the CPG characteristics that can impact its implementation, such as the complexity of the recommendation.

A subgroup analysis was performed with models and frameworks categorized based on their levels of use (clinical, organizational, and policy) and the type of health service (community, ambulatorial, hospital, institutional) associated with the CPG. The goal is to assist stakeholders (politicians, clinicians, researchers, or others) in selecting the most suitable model for evaluating CPG implementation based on their specific health context.

Search results

Database searches yielded 26,011 studies, of which 107 full texts were reviewed. During the full-text review, 99 articles were excluded: 41 studies did not mention a model or framework for assessing the implementation of the CPG, 31 studies evaluated only implementation strategies (isolated actions) rather than the implementation process itself, and 27 articles were not related to the implementation assessment. Therefore, eight studies were included in the data analysis. The updated search did not reveal additional relevant studies. The main reason for study exclusion was that they did not use models or frameworks to assess CPG implementation. Additionally, four methodological guidelines were included from the manual search (Fig.  1 ).

PRISMA diagram. Acronyms: ADH—Australian Department of Health, CINAHL—Cumulative Index to Nursing and Allied Health Literature, CDC—Centers for Disease Control and Prevention, CRD—Centre for Reviews and Dissemination, GIN—Guidelines International Networks, HSE—Health Systems Evidence, IOM—Institute of Medicine, JBI—The Joanna Briggs Institute, MHB—Ministry of Health of Brazil, NICE—National Institute for Health and Care Excellence, NHMRC—National Health and Medical Research Council, MSPS – Ministerio de Sanidad Y Política Social (Spain), SIGN—Scottish Intercollegiate Guidelines Network, VHL – Virtual Health Library, WHO—World Health Organization. Legend: Reason A –The study evaluated only implementation strategies (isolated actions) rather than the implementation process itself. Reason B – The study did not mention a model or framework for assessing the implementation of the intervention. Reason C – The study was not related to the implementation assessment. Adapted from Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 . For more information, visit:

According to the JBI’s critical appraisal tools, the overall assessment of the studies indicates their acceptance for the systematic review.

The cross-sectional studies lacked clear information regarding "confounding factors" or "strategies to address confounding factors". This was understandable given the nature of the study, where such details are not typically included. However, the reviewers did not find this lack of information to be critical, allowing the studies to be included in the review. The results of this methodological quality assessment can be found in an additional file (see Additional file 5).

In the qualitative studies, there was some ambiguity regarding the questions: "Is there a statement locating the researcher culturally or theoretically?" and "Is the influence of the researcher on the research, and vice versa, addressed?". However, the reviewers decided to include the studies and deemed the methodological quality sufficient for the analysis in this article, based on the other information analyzed. The results of this methodological quality assessment can be found in an additional file (see Additional file 6).

Documents characteristics (Table  1 )

The documents were directed to several continents: Australia/Oceania (4/12) [ 31 , 33 , 36 , 37 ], North America (4/12 [ 30 , 32 , 38 , 39 ], Europe (2/12 [ 29 , 35 ] and Asia (2/12) [ 34 , 40 ]. The types of documents were classified as cross-sectional studies (4/12) [ 29 , 32 , 34 , 38 ], methodological guidelines (4/12) [ 33 , 35 , 36 , 37 ], mixed methods studies (3/12) [ 30 , 31 , 39 ] or noncomparative studies (1/12) [ 40 ]. In terms of the instrument of evaluation, most of the documents used a survey/questionnaire (6/12) [ 29 , 30 , 31 , 32 , 34 , 38 ], while three (3/12) used qualitative instruments (interviews, group discussions) [ 30 , 31 , 39 ], one used a checklist [ 37 ], one used an audit [ 33 ] and three (3/12) did not define a specific instrument to measure [ 35 , 36 , 40 ].

Considering the clinical areas covered, most studies evaluated the implementation of nonspecific (general) clinical areas [ 29 , 33 , 35 , 36 , 37 , 40 ]. However, some studies focused on specific clinical contexts, such as mental health [ 32 , 38 ], oncology [ 39 ], fall prevention [ 31 ], spinal cord injury [ 30 ], and sexually transmitted infections [ 34 ].

Usage context of the models (Table  1 )

Specific objectives.

All the studies highlighted the purpose of guiding the process of evaluating the implementation of CPGs, even if they evaluated CPGs from generic or different clinical areas.

Levels of use

The most common level of use of the models/frameworks identified to assess the implementation of CPGs was policy (6/12) [ 33 , 35 , 36 , 37 , 39 , 40 ]. In this level, the model is used in a systematic way to evaluate all the processes involved in CPGs implementation and is primarily related to methodological guidelines. This was followed by the organizational level of use (5/12) [ 30 , 31 , 32 , 38 , 39 ], where the model is used to evaluate the implementation of CPGs in a specific institution, considering its specific environment. Finally, the clinical level of use (2/12) [ 29 , 34 ] focuses on individual practice and the factors that can influence the implementation of CPGs by professionals.

Type of health service

Institutional services were predominant (5/12) [ 33 , 35 , 36 , 37 , 40 ] and included methodological guidelines and a study of model development and validation. Hospitals were the second most common type of health service (4/12) [ 29 , 30 , 31 , 34 ], followed by ambulatorial (2/12) [ 32 , 34 ] and community health services (1/12) [ 32 ]. Two studies did not specify which type of health service the assessment addressed [ 38 , 39 ].

Target group

The focus of the target group was professionals directly involved in clinical practice (6/12) [ 29 , 31 , 32 , 34 , 38 , 40 ], namely, health professionals and clinicians. Other less related stakeholders included guideline developers (2/12) [ 39 , 40 ], health policy decision makers (1/12) [ 39 ], and healthcare organizations (1/12) [ 39 ]. The target group was not defined in the methodological guidelines, although all the mentioned stakeholders could be related to these documents.

Model and framework characteristics

Models and frameworks for assessing the implementation of cpgs.

The Consolidated Framework for Implementation Research (CFIR) [ 31 , 38 ] and the Promoting Action on Research Implementation in Health Systems (PARiHS) framework [ 29 , 30 ] were the most commonly employed frameworks within the selected documents. The other models mentioned were: Goal commitment and implementation of practice guidelines framework [ 32 ]; Guideline to identify key indicators [ 35 ]; Guideline implementation checklist [ 37 ]; Guideline implementation evaluation tool [ 40 ]; JBI Implementation Framework [ 33 ]; Reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework [ 34 ]; The Guideline Implementability Framework [ 39 ] and an unnamed model [ 36 ].

Domains evaluated

The number of domains evaluated (or suggested for evaluation) by the documents varied between three and five, with the majority focusing on three domains. All the models addressed the domain "context", with a particular emphasis on the micro level of the health care context (8/12) [ 29 , 31 , 34 , 35 , 36 , 37 , 38 , 39 ], followed by the multilevel (7/12) [ 29 , 31 , 32 , 33 , 38 , 39 , 40 ], meso level (4/12) [ 30 , 35 , 39 , 40 ] and macro level (2/12) [ 37 , 39 ]. The "Outcome" domain was evaluated in nine models. Within this domain, the most frequently evaluated subdomain was "adoption" (6/12) [ 29 , 32 , 34 , 35 , 36 , 37 ], followed by "acceptability" (4/12) [ 30 , 32 , 35 , 39 ], "appropriateness" (3/12) [ 32 , 34 , 36 ], "feasibility" (3/12) [ 29 , 32 , 36 ], "cost" (1/12) [ 35 ] and "penetration" (1/12) [ 34 ]. Regarding the other domains, "Intervention" (8/12) [ 29 , 31 , 34 , 35 , 36 , 38 , 39 , 40 ], "Strategies" (7/12) [ 29 , 30 , 33 , 35 , 36 , 37 , 40 ] and "Process" (5/12) [ 29 , 31 , 32 , 33 , 38 ] were frequently addressed in the models, while "Sustainability" (1/12) [ 34 ] was only found in one model, and "Fidelity/Adaptation" was not observed. The domains presented by the models and frameworks and evaluated in the documents are shown in Table  2 .

Limitations of the models

Only two documents mentioned limitations in the use of the model or frameworks. These two studies reported limitations in the use of CFIR: "is complex and cumbersome and requires tailoring of the key variables to the specific context", and "this framework should be supplemented with other important factors and local features to achieve a sound basis for the planning and realization of an ongoing project" [ 31 , 38 ]. Limitations in the use of other models or frameworks are not reported.

Subgroup analysis

Following the subgroup analysis (Table  3 ), five different models/frameworks were utilized at the policy level by institutional health services. These included the Guideline Implementation Evaluation Tool [ 40 ], the NHMRC tool (model name not defined) [ 36 ], the JBI Implementation Framework + GRiP [ 33 ], Guideline to identify key indicators [ 35 ], and the Guideline implementation checklist [ 37 ]. Additionally, the "Guideline Implementability Framework" [ 39 ] was implemented at the policy level without restrictions based on the type of health service. Regarding the organizational level, the models used varied depending on the type of service. The "Goal commitment and implementation of practice guidelines framework" [ 32 ] was applied in community and ambulatory health services, while "PARiHS" [ 29 , 30 ] and "CFIR" [ 31 , 38 ] were utilized in hospitals. In contexts where the type of health service was not defined, "CFIR" [ 31 , 38 ] and "The Guideline Implementability Framework" [ 39 ] were employed. Lastly, at the clinical level, "RE-AIM" [ 34 ] was utilized in ambulatory and hospital services, and PARiHS [ 29 , 30 ] was specifically used in hospital services.

Key findings

This systematic review identified 10 models/ frameworks used to assess the implementation of CPGs in various health system contexts. These documents shared similar objectives in utilizing models and frameworks for assessment. The primary level of use was policy, the most common type of health service was institutional, and the main target group of the documents was professionals directly involved in clinical practice. The models and frameworks presented varied analytical domains, with sometimes divergent concepts used in these domains. This study is innovative in its emphasis on the evaluation stage of CPG implementation and in summarizing aspects and domains aimed at the practical application of these models.

The small number of documents contrasts with studies that present an extensive range of models and frameworks available in implementation science. The findings suggest that the use of models and frameworks to evaluate the implementation of CPGs is still in its early stages. Among the selected documents, there was a predominance of cross-sectional studies and methodological guidelines, which strongly influenced how the implementation evaluation was conducted. This was primarily done through surveys/questionnaires, qualitative methods (interviews, group discussions), and non-specific measurement instruments. Regarding the subject areas evaluated, most studies focused on a general clinical area, while others explored different clinical areas. This suggests that the evaluation of CPG implementation has been carried out in various contexts.

The models were chosen independently of the categories proposed in the literature, with their usage categorized for purposes other than implementation evaluation, as is the case with CFIR and PARiHS. This practice was described by Nilsen et al. who suggested that models and frameworks from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 14 , 15 , 42 , 43 ].

The results highlight the increased use of models and frameworks in evaluation processes at the policy level and institutional environments, followed by the organizational level in hospital settings. This finding contradicts a review that reported the policy level as an area that was not as well studied [ 44 ]. The use of different models at the institutional level is also emphasized in the subgroup analysis. This may suggest that the greater the impact (social, financial/economic, and organizational) of implementing CPGs, the greater the interest and need to establish well-defined and robust processes. In this context, the evaluation stage stands out as crucial, and the investment of resources and efforts to structure this stage becomes even more advantageous [ 10 , 45 ]. Two studies (16,7%) evaluated the implementation of CPGs at the individual level (clinical level). These studies stand out for their potential to analyze variations in clinical practice in greater depth.

In contrast to the level of use and type of health service most strongly indicated in the documents, with systemic approaches, the target group most observed was professionals directly involved in clinical practice. This suggests an emphasis on evaluating individual behaviors. This same emphasis is observed in the analysis of the models, in which there is a predominance of evaluating the micro level of the health context and the "adoption" subdomain, in contrast with the sub-use of domains such as "cost" and "process". Cassetti et al. observed the same phenomenon in their review, in which studies evaluating the implementation of CPGs mainly adopted a behavioral change approach to tackle those issues, without considering the influence of wider social determinants of health [ 10 ]. However, the literature widely reiterates that multiple factors impact the implementation of CPGs, and different actions are required to make them effective [ 6 , 46 , 47 ]. As a result, there is enormous potential for the development and adaptation of models and frameworks aimed at more systemic evaluation processes that consider institutional and organizational aspects.

In analyzing the model domains, most models focused on evaluating only some aspects of implementation (three domains). All models evaluated the "context", highlighting its significant influence on implementation [ 9 , 26 ]. Context is an essential effect modifier for providing research evidence to guide decisions on implementation strategies [ 48 ]. Contextualizing a guideline involves integrating research or other evidence into a specific circumstance [ 49 ]. The analysis of this domain was adjusted to include all possible contextual aspects, even if they were initially allocated to other domains. Some contextual aspects presented by the models vary in comprehensiveness, such as the assessment of the "timing and nature of stakeholder engagement" [ 39 ], which includes individual engagement by healthcare professionals and organizational involvement in CPG implementation. While the importance of context is universally recognized, its conceptualization and interpretation differ across studies and models. This divergence is also evident in other domains, consistent with existing literature [ 14 ]. Efforts to address this conceptual divergence in implementation science are ongoing, but further research and development are needed in this field [ 26 ].

The main subdomain evaluated was "adoption" within the outcome domain. This may be attributed to the ease of accessing information on the adoption of the CPG, whether through computerized system records, patient records, or self-reports from healthcare professionals or patients themselves. The "acceptability" subdomain pertains to the perception among implementation stakeholders that a particular CPG is agreeable, palatable or satisfactory. On the other hand, "appropriateness" encompasses the perceived fit, relevance or compatibility of the CPG for a specific practice setting, provider, or consumer, or its perceived fit to address a particular issue or problem [ 26 ]. Both subdomains are subjective and rely on stakeholders' interpretations and perceptions of the issue being analyzed, making them susceptible to reporting biases. Moreover, obtaining this information requires direct consultation with stakeholders, which can be challenging for some evaluation processes, particularly in institutional contexts.

The evaluation of the subdomains "feasibility" (the extent to which a CPG can be successfully used or carried out within a given agency or setting), "cost" (the cost impact of an implementation effort), and "penetration" (the extent to which an intervention or treatment is integrated within a service setting and its subsystems) [ 26 ] was rarely observed in the documents. This may be related to the greater complexity of obtaining information on these aspects, as they involve cross-cutting and multifactorial issues. In other words, it would be difficult to gather this information during evaluations with health practitioners as the target group. This highlights the need for evaluation processes of CPGs implementation involving multiple stakeholders, even if the evaluation is adjusted for each of these groups.

Although the models do not establish the "intervention" domain, we thought it pertinent in this study to delimit the issues that are intrinsic to CPGs, such as methodological quality or clarity in establishing recommendations. These issues were quite common in the models evaluated but were considered in other domains (e.g., in "context"). Studies have reported the importance of evaluating these issues intrinsic to CPGs [ 47 , 50 ] and their influence on the implementation process [ 51 ].

The models explicitly present the "strategies" domain, and its evaluation was usually included in the assessments. This is likely due to the expansion of scientific and practical studies in implementation science that involve theoretical approaches to the development and application of interventions to improve the implementation of evidence-based practices. However, these interventions themselves are not guaranteed to be effective, as reported in a previous review that showed unclear results indicating that the strategies had affected successful implementation [ 52 ]. Furthermore, model domains end up not covering all the complexity surrounding the strategies and their development and implementation process. For example, the ‘Guideline implementation evaluation tool’ evaluates whether guideline developers have designed and provided auxiliary tools to promote the implementation of guidelines [ 40 ], but this does not mean that these tools would work as expected.

The "process" domain was identified in the CFIR [ 31 , 38 ], JBI/GRiP [ 33 ], and PARiHS [ 29 ] frameworks. While it may be included in other domains of analysis, its distinct separation is crucial for defining operational issues when assessing the implementation process, such as determining if and how the use of the mentioned CPG was evaluated [ 3 ]. Despite its presence in multiple models, there is still limited detail in the evaluation guidelines, which makes it difficult to operationalize the concept. Further research is needed to better define the "process" domain and its connections and boundaries with other domains.

The domain of "sustainability" was only observed in the RE-AIM framework, which is categorized as an evaluation framework [ 34 ]. In its acronym, the letter M stands for "maintenance" and corresponds to the assessment of whether the user maintains use, typically longer than 6 months. The presence of this domain highlights the need for continuous evaluation of CPGs implementation in the short, medium, and long term. Although the RE-AIM framework includes this domain, it was not used in the questionnaire developed in the study. One probable reason is that the evaluation of CPGs implementation is still conducted on a one-off basis and not as a continuous improvement process. Considering that changes in clinical practices are inherent over time, evaluating and monitoring changes throughout the duration of the CPG could be an important strategy for ensuring its implementation. This is an emerging field that requires additional investment and research.

The "Fidelity/Adaptation" domain was not observed in the models. These emerging concepts involve the extent to which a CPG is being conducted exactly as planned or whether it is undergoing adjustments and adaptations. Whether or not there is fidelity or adaptation in the implementation of CPGs does not presuppose greater or lesser effectiveness; after all, some adaptations may be necessary to implement general CPGs in specific contexts. The absence of this domain in all the models and frameworks may suggest that they are not relevant aspects for evaluating implementation or that there is a lack of knowledge of these complex concepts. This may suggest difficulty in expressing concepts in specific evaluative questions. However, further studies are warranted to determine the comprehensiveness of these concepts.

It is important to note the customization of the domains of analysis, with some domains presented in the models not being evaluated in the studies, while others were complementarily included. This can be seen in Jeong et al. [ 34 ], where the "intervention" domain in the evaluation with the RE-AIM framework reinforced the aim of theoretical approaches such as guiding the process and not determining norms. Despite this, few limitations were reported for the models, suggesting that the use of models in these studies reflects the application of these models to defined contexts without a deep critical analysis of their domains.

Limitations

This review has several limitations. First, only a few studies and methodological guidelines that explicitly present models and frameworks for assessing the implementation of CPGs have been found. This means that few alternative models could be analyzed and presented in this review. Second, this review adopted multiple analytical categories (e.g., level of use, health service, target group, and domains evaluated), whose terminology has varied enormously in the studies and documents selected, especially for the "domains evaluated" category. This difficulty in harmonizing the taxonomy used in the area has already been reported [ 26 ] and has significant potential to confuse. For this reason, studies and initiatives are needed to align understandings between concepts and, as far as possible, standardize them. Third, in some studies/documents, the information extracted was not clear about the analytical category. This required an in-depth interpretative process of the studies, which was conducted in pairs to avoid inappropriate interpretations.

Implications

This study contributes to the literature and clinical practice management by describing models and frameworks specifically used to assess the implementation of CPGs based on their level of use, type of health service, target group related to the CPG, and the evaluated domains. While there are existing reviews on the theories, frameworks, and models used in implementation science, this review addresses aspects not previously covered in the literature. This valuable information can assist stakeholders (such as politicians, clinicians, researchers, etc.) in selecting or adapting the most appropriate model to assess CPG implementation based on their health context. Furthermore, this study is expected to guide future research on developing or adapting models to assess the implementation of CPGs in various contexts.

The use of models and frameworks to evaluate the implementation remains a challenge. Studies should clearly state the level of model use, the type of health service evaluated, and the target group. The domains evaluated in these models may need adaptation to specific contexts. Nevertheless, utilizing models to assess CPGs implementation is crucial as they can guide a more thorough and systematic evaluation process, aiding in the continuous improvement of CPGs implementation. The findings of this systematic review offer valuable insights for stakeholders in selecting or adjusting models and frameworks for CPGs evaluation, supporting future theoretical advancements and research.

Availability of data and materials

Abbreviations.

Australian Department of Health and Aged Care

Canadian Agency for Drugs and Technologies in Health

Centers for Disease Control and

Consolidated Framework for Implementation Research

Cumulative Index to Nursing and Allied Health Literature

Clinical practice guideline

Centre for Reviews and Dissemination

Guidelines International Networks

Getting Research into Practice

Health Systems Evidence

Institute of Medicine

The Joanna Briggs Institute

Ministry of Health of Brazil

Ministerio de Sanidad y Política Social

National Health and Medical Research Council

National Institute for Health and Care Excellence

Promoting action on research implementation in health systems framework

Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

International Prospective Register of Systematic Reviews

Reach, effectiveness, adoption, implementation, and maintenance framework

Healthcare Improvement Scotland

United States of America

Virtual Health Library

World Health Organization

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This study is supported by the Fundação de Apoio à Pesquisa do Distrito Federal (FAPDF). FAPDF Award Term (TOA) nº 44/2024—FAPDF/SUCTI/COOBE (SEI/GDF – Process 00193–00000404/2024–22). The content in this article is solely the responsibility of the authors and does not necessarily represent the official views of the FAPDF.

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NFM and JOMB conceived the idea and the protocol for this study. NFM conducted the literature search. NFM, SNS, JMG and JOMB conducted the data collection with advice and consensus gathering from JOMB. The NFM and JMG assessed the quality of the studies. NFM and DFG conducted the data extraction. NFM performed the analysis and synthesis of the results with advice and consensus gathering from JOMB. NFM drafted the manuscript. JOMB critically revised the first version of the manuscript. All the authors revised and approved the submitted version.

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Supplementary Information

13012_2024_1389_moesm1_esm.docx.

Additional file 1: PRISMA checklist. Description of data: Completed PRISMA checklist used for reporting the results of this systematic review.

Additional file 2: Literature search. Description of data: The search strategies adapted for the electronic databases.

13012_2024_1389_moesm3_esm.doc.

Additional file 3: JBI’s critical appraisal tools for cross-sectional studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for cross-sectional studies.

13012_2024_1389_MOESM4_ESM.doc

Additional file 4: JBI’s critical appraisal tools for qualitative studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for qualitative studies.

13012_2024_1389_MOESM5_ESM.doc

Additional file 5: Methodological quality assessment results for cross-sectional studies. Description of data: Methodological quality assessment results for cross-sectional studies using JBI’s critical appraisal tools.

13012_2024_1389_MOESM6_ESM.doc

Additional file 6: Methodological quality assessment results for the qualitative studies. Description of data: Methodological quality assessment results for qualitative studies using JBI’s critical appraisal tools.

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Freitas de Mello, N., Nascimento Silva, S., Gomes, D.F. et al. Models and frameworks for assessing the implementation of clinical practice guidelines: a systematic review. Implementation Sci 19 , 59 (2024). https://doi.org/10.1186/s13012-024-01389-1

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Received : 06 February 2024

Accepted : 01 August 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s13012-024-01389-1

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