Four key areas that can support progress on learning health systems

 

2.1. Introduction

As highlighted in Chapter 1, there is growing interest in learning health systems (LHSs) as a vehicle for improving care quality and service delivery. But given that LHSs are multifaceted and can be highly complex, it can be difficult to know where to begin. So, what should policymakers, and organisational and system leaders, be focusing on to support the development and spread of LHS approaches?

Our research and engagement with expert stakeholders highlighted four key areas where targeted action could help to advance the use of LHS approaches:

  • learning from data
  • harnessing technology
  • nurturing learning communities
  • implementing improvements to services.

These four areas, which link to the assets and capabilities underpinning LHSs as described in Chapter 1, form key lines of enquiry in this chapter. Specifically, we discuss each of the four areas in turn, setting out the current context, opportunities and challenges. In each case, we also report the results of our stakeholder survey to identify which of the challenges we describe currently require the most attention.

2.2. Learning from data

2.2.1. Key issues and opportunities

The potential of data for learning health systems

Better use of data has huge potential to improve the quality, safety and cost-effectiveness of care and address unwarranted variation, across the whole health service. The data can be quantitative or qualitative and be drawn from many sources. As discussed in Chapter 1, one of the characteristic features of an LHS is that it uses data generated from routine care delivery – whether clinical, operational or patient-reported. However, LHSs are of course not restricted to using these data and can draw on data from a wide range of sources, such as patient surveys, research and clinical trials, and increasingly data from technology such as smartphones and wearable devices (wearables), like smart watches and medical technologies, that are worn by individuals to track, analyse and transmit data.

Within quality improvement work, it is not uncommon to hear people distinguish between ‘data for care’ and ‘data for improvement’. But a more useful approach is to distinguish between the primary purposes of health care data (delivering direct clinical care) and the secondary purposes of such data (such as research, population health management and improvement). Both uses of data support the work of LHSs, in particular in the design of services and helping to build understanding about how they are performing and how they can improve.

As the volume, breadth and quality of data increase, so too do the opportunities for learning and improvement. Wider sources of data, such as data generated by smartphones and wearables, and data on the social determinants of health, are increasingly playing a valuable role by allowing us to build a more holistic picture of our populations. SAIL Databank (case study 7), a Trusted Research Environment that contains data about the whole population of Wales, shows how this wide range of data can be brought together.

Combining and analysing data from different sources can generate new clinical evidence. The Informatics Consult project (case study 8) shows how this can be done. It is particularly important for supporting treatment decisions for patients with complex health needs, where traditional forms of evidence are lacking.

Understanding the health and needs of people and their communities

The move towards better collaboration between health and care providers and commissioners – through, for example, health boards in Scotland and integrated care systems in England – provides an opportunity to improve the health of their populations. By integrating infrastructure, developing standards for interoperability (the ability of two or more systems to exchange information and use that information) and working collaboratively, these organisations can identify where within their populations the greatest needs lie, and work together with stakeholders, including members of the community, to design data-driven interventions to address those needs.

There are particular opportunities for better use of patient-reported data. But while the NHS collects a large amount of patient-reported data, they could be used much more effectively. This is where an LHS approach can help because it centres on the ambition of putting knowledge into practice.

Data collected from communities offer the opportunity to help health care services better understand what matters most to them. This includes national collections such as patient and service user experience surveys and patient-reported outcome measures, as well as local routes, from focus groups to service user surveys. Initiatives such as the Networked Data Lab, which the Health Foundation leads, are linking these datasets together to build a more complete picture of the relationships between the wider determinants of health, health needs, service use, patient pathways and health outcomes.

Data about particular health conditions, including on diagnosis, treatment and outcomes, also offer potential for LHSs – including the possibility of creating LHSs around particular conditions. Clinical registries and clinical audits, for example, allow comparisons between multiple sites, reveal variation in processes and outcomes, and identify where improvements can be made.

Making data easy to understand and actionable

While the growth in the volume of data means there is ever more knowledge that can be generated, the amount of data already exceeds what people working in health care can assimilate. There are other challenges too. Often data are not presented in a useful way, and long time lags (between data collection and use) mean that they can be too old to have relevance to health care decisions today. For example, as patient registries become able to collect data on patients’ priorities in real time, incorporating patient-reported data with clinical data, they could become a critical part of the infrastructure of LHSs where patients, clinicians and scientists work on service improvements together.

So, there is considerable scope to improve both the curation of data (the organisation and management of data from various sources) and the visualisation of the data – along with how and when the data can be accessed, so that health care professionals can see relevant insights in a timely and digestible way to support their decision making. For example, the Clinical Effectiveness Group at Queen Mary University of London (case study 5) has created interactive dashboards showing performance across GP practices in north-east London, allowing for the identification of areas requiring improvement.

Elsewhere, academics at the University of Edinburgh have developed a dashboard to support GPs to improve care for people with asthma across the UK (case study 9). The dashboard gives GPs weekly data on asthma indicators at their practice and shows how the data compare with those for other practices.

Building data analytics capability

Data analytics methods, tools and skills – including novel statistical models and data linkage – are a critical part of LHSs because they generate new knowledge and insights to support the learning and improvement process.

Our research and funding programmes at the Health Foundation show that the data analytics capability that exists in the health and care system is both underdeveloped and underused.

The impact of data analysts could be increased considerably through training and professional development, and through better access to the software tools required to generate insights from data. Specialist analytical expertise can also help to develop data literacy in the wider health care workforce. For example, the Flow Coaching Academy (case study 1), a model centred on a multidisciplinary micro-team approach to improving service delivery, shows how this potential can be exploited. By bringing data analysts into multidisciplinary team meetings known as ‘Big Rooms’, and coaching clinical staff in how to interpret data, the model enables teams to gain a better understanding of how services are performing and, critically, where improvements could be made.

Evaluating improvements

Testing and evaluation – which rely on collecting the right data – are a critical part of an LHS to show whether changes made have led to improvements. This mostly happens as an integral part of the iterative learning process, with the data collected in subsequent learning cycles being used to assess the service improvements implemented in previous cycles.

There are promising ways in which testing and evaluation can be done that marry robustness with timeliness. For example, the Children & Young People’s Health Partnership (case study 6) shows how running different evaluation models simultaneously can effectively support the learning and improvement process. By running randomised control trials at the same time as service evaluations, the partnership can give evidence quickly to clinicians to inform care improvements, and to commissioners and managers who can make rapid decisions about embedding new services into ‘business as usual’, while building a robust evidence base for children’s health care more widely.

2.2.2. Challenges

While some health care services and systems have made great progress in collecting and using data, our research suggests there are a range of challenges that stand in the way of using data to drive continuous learning and improvement in the manner that is required for LHSs. Several of these challenges have been highlighted in the government’s recent strategy Data Saves Lives, which also sets out steps to make progress on many of them.

LHSs require the availability of high quality, actionable data on a range of issues, including performance, outcomes, experiences and processes. This has been a particular challenge for patient data as progress towards the digitisation of patient records has been slow. Where data are recorded electronically, they do not always meet quality standards, nor are they always useful. Data can be incomplete or captured incorrectly.

Stakeholders we spoke to during our research told us that there are significant issues surrounding data access and sharing, including information governance and data security, which can limit the work of an LHS. In order for a team to be able to access data, a range of approvals are required, which vary in number and complexity due to a lack of standardisation. In addition, the regulation of data often lags the rate of innovation and the data that it produces. Financial costs associated with data access can also hamper the ability to use data to learn and improve. Health and care data must be in a readable, actionable format to have value, which often relies on data system vendors curating and cleaning the information they hold, and they can then charge sizeable fees.

It can be difficult to know what approvals are required to access data and from whom. For example, innovators involved in the RADAR project (case study 10) told us how introducing the MyWay digital health app into some parts of England required data-sharing agreements with each data controller. This included individual GP practices, clinical commissioning groups and Caldicott guardians (who are responsible for protecting the confidentiality of people’s health and care information and making sure it is used properly) compiling population-level data for the area, which took an enormous amount of time and effort to achieve. Conversely, use of the platform in north-west London was far simpler as data access is dealt with at the integrated care system level, where there is a structure for data controllers signing off electronically, and a population-level dataset already exists.

Our experience of funding data analytics projects at the Health Foundation suggests that linking and integrating datasets can also be particularly difficult, not only because of technical challenges, but also because of the way in which increased data linkages can challenge processes of anonymisation. The number and complexity of processes required to link data can be particularly challenging. For example, in the LAUNCHES QI study (linking audit and national datasets in congenital heart services for quality improvement), run by University College London, which sought to improve services for people with congenital heart disease by linking five national datasets, the process took 2.5 years to achieve, requiring nearly 50 documents for the data application processes, which needed to be submitted 162 times in total. Stakeholders told us that there are also challenges with integrating datasets, and insights derived from them, within electronic health records and that integrating analytical tools into electronic health records can be both difficult and very expensive.

System interoperability – the ability of two or more systems to exchange information and use that information – continues to be a challenge in health and social care. With an assortment of different data systems, and a lack of common standards, it can be difficult to gather and aggregate the data needed to drive an LHS. A 2020 National Audit Office report on digital transformation in the NHS in England highlights that while interoperability has been a priority for policymakers since 1998, progress has been slow, and a lack of clarity on standards to encourage new suppliers to enter the NHS could make interoperability harder to achieve.

For health care services to learn about and design the most effective interventions, they require data that go beyond routine clinical data – for example, data on the social determinants of health. Yet access to a wide-enough range of data can be challenging and many types of information are not captured in datasets. For example, routine NHS data do not include data on those who do not access health services, nor do they include information about a patient’s health in between interactions with the health service – both of which could improve understanding of the drivers of ill health.

The disproportionate impact of the COVID-19 pandemic on some groups has rightly highlighted the importance of tackling health inequalities, but to do this requires data that are representative of all patient populations and free from bias, which is often not the case. This can be due, for example, to a failure to capture data on characteristics (such as ethnicity) accurately, a lack of representation of different demographic groups in research or a lack of access to technologies that capture data., Such factors can render datasets biased, which can lead to decisions that either do not bring about improvements to service delivery or – worse – risk adverse outcomes and experiences for some service users. This is a particular concern for data-driven health technologies such as those using artificial intelligence and machine learning.

Understanding Patient Data research shows that most people support sharing patient data for individual care and a high proportion of people support sharing patient data for research where there is public benefit. But making sure that patients and the public, and also the health care workforce, perceive the collection, sharing and use of data to be trustworthy can be a challenge. Transparency and open dialogue with the public are important ways of achieving this but are not always addressed sufficiently.

Data need to be formatted and ‘cleaned’ to allow them to be analysed. This can be laborious and time-consuming work, which often fails to get the attention and resources it requires and is often done on an ad-hoc basis, leading to duplication of data. Both this data curation work and the subsequent data analysis require specialist skills and knowledge, and while there have been moves to build this capability across the health and care system – including through NHS England’s work to develop a National Competency Framework for Data Professionals in Health and Care – more is required. Another related issue is more general health data literacy across the workforce, so that staff, including those in management and leadership positions, can interpret data appropriately.

We asked our survey respondents which of the data challenges highlighted in bold above they considered to be the most significant for adopting LHS approaches; the results are shown in Box 6. (For each part of our survey, we also asked our respondents to tell us whether there were any challenges we might have missed, but the responses suggested that there were no significant omissions.)

Box 6: Data challenges – what our respondents said

As shown in Figure 6, the results indicate that all proposed factors were thought to be a challenge to some degree, averaging a score of 4.15 out of 5 (with 5 representing ‘a very significant challenge).

However, the highest-ranked challenge was ‘interoperability between data systems, both within and across organisations’ (4.6), followed by ‘linkage between datasets and integration of these datasets with electronic health records’ (4.5).

Case study 7: The Secure Anonymised Information Linkage (SAIL) Databank

Established in 2007, the Secure Anonymised Information Linkage (SAIL) Databank is a Trusted Research Environment holding anonymised individual-level data for the whole population of Wales. One of the world’s first Trusted Research Environments and hosted within Population Data Science at Swansea University, SAIL was set up to use data gathered in health and social care delivery to better inform research, improve services and inform population health strategy. It includes data that clinical interactions and interactions with social and community services generate, allowing for analysis of links between health outcomes and social factors.

SAIL operates according to a ‘privacy-by-design’ model, which uses physical, technical and procedural measures to safeguard the data it contains, prohibiting the sharing of data outside the databank without special dispensation. Also, to gain access to data held in the databank, prospective researchers must undertake a two-stage application process, which an independent Information Governance Review Panel assesses.

Population Data Science created the Secure eResearch Platform (SeRP), which powers the SAIL Databank, and allows researchers from across the world to access data linkage services and a wide range of data to answer important questions with the use of analytical tools. By 2020, SAIL had more than 1,200 registered users and has been used to deliver more than 300 research projects, including the development of National Institute for Health and Care Excellence (NICE) clinical guidelines. Strong relationships with many partners – such as Digital Health and Care Wales, the Welsh government and Public Health Wales – underpin the SAIL approach, which has enabled the use of data to inform decisions in policy and practice, notably those made in response to the COVID-19 pandemic through the One Wales collaboration.

From the beginning, the SAIL team recognised that public trust in the handling and sharing of personal data would be critical to the databank’s success. This trust relies on several interacting factors such as cultural values, personal preferences and mass media influences. To gain public trust, the SAIL team developed a programme of public involvement and engagement to assess public opinion and gain input into policies and practices, which a consumer panel – including members of the public – oversaw. The consumer panel also advises on routes and methods to engage with the population, recommends how information can be shared with the general public and assists in reviewing proposals from researchers applying to access the databank. Projects that have used SAIL are shared on its website, including a description and a list of outputs, to make sure there is communication and transparency with members of the public and stakeholders.

Case study 8: Informatics Consult

A Health Data Research UK (HDR UK) and Health Foundation Catalyst project

While clinical guidelines play an important role in health care delivery, they are not always backed by a robust evidence base in the form of clinical trials and research. This is made more challenging as the number of people with comorbidities increases and the health needs of the population become more complex, with there being many situations where a treatment indication and contraindication coexist for one patient, for example patients with both heart failure and kidney failure. Given that treatment for one condition can have an adverse impact on another, and there is limited evidence for some treatment options partly due to randomised control trials frequently excluding patients with several comorbidities, it can be difficult for clinicians to determine the best treatment for their patients.

The growing availability of large datasets and the tools to analyse them provides an opportunity for improving decision making, particularly for patient groups for whom robust evidence does not yet exist. The Informatics Consult platform allows clinicians to select a health condition and order an analysis of large datasets to aid decision making for the specific patient in front of them. The platform employs automated approaches for creating analysis-ready cohorts using the ‘DExtER’ tool. Within hours, the platform returns easily interpretable clinical information – including on the potential benefits of treatment, prognosis and mortality risk – which can support more personalised treatment plans.

Drawing on population data contained within electronic health records, the platform presents analysis in a way that is understandable for clinicians and can be discussed with their patients. This is not always straightforward, though. For example, many rare conditions do not have specific clinical codes, which leads to challenges incorporating them into the platform.

The project team plans to test the Informatics Consult platform in four NHS trusts to support decision making with patients who have both liver cirrhosis and atrial fibrillation, where the use of anticoagulants can treat the latter while making the former worse. A pilot conducted using Informatics Consult generated new clinical evidence for patients in this group, showing that the initiation of warfarin was common, and may be associated with lower all-cause mortality and may be effective in lowering stroke risk. Surveys of clinicians using the platform showed that 85% found information on prognosis useful and 79% thought they should have access to the platform as a service.

By providing information on the prevalence of conditions as well as information on the safety and efficacy of a particular medication, it is hoped that this will stimulate further initiatives to generate new analyses for a wider range of prognostic outcomes. Given the rising trends of multimorbidity, especially in younger people, the Informatics Consult may contribute to the creation of a knowledge base generated from real-world datasets to address the current gaps in randomised control trials (arising from the exclusion of patients with comorbid conditions).

Case study 9: Towards a national learning health system for asthma in Scotland

Asthma is a significant cause of ill health and hospitalisation in the UK, costing an estimated £1.1bn and leading to 1,400 deaths every year. Around one in every 14 people in Scotland are currently receiving treatment for it and 89 in every 100,000 people are hospitalised each year due to exacerbations (increases in severity). Timely patient data is key to understanding and preventing exacerbations.

To address this challenge, researchers at the University of Edinburgh are working towards developing a national LHS for asthma that will support clinicians to identify and address modifiable factors that can contribute to exacerbations.

By harnessing routinely collected, anonymised data from the Oxford Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID), the team created an online dashboard for asthma that gives GPs weekly updates on how their practice compares with their network across several indicators.

Information provided includes comparative data on asthma prevalence, vaccination uptake, smoking rates, hospitalisations and body mass index measurements. This enables GPs to see changes in their practice, compare themselves to other practices and rapidly respond to better support people with asthma, prevent exacerbations and potentially prevent avoidable deaths.

In repurposing routine data to generate knowledge that can then be incorporated into clinical practice in real time, the LHS for asthma is one of the first applications of LHS approaches at a national level outside the US. The project champions innovative approaches to near real-time data visualisation, allowing health care providers to compare care and service quality to evidence-based standards and drive improvement.

While the impact has so far been limited due to the effects of the COVID-19 pandemic and workforce pressures, the researchers are now seeking further funding to develop complementary behavioural, motivational and organisational interventions that can tackle the barriers to using the data to make improvements. They are also going to develop a learning-based prediction model in order to create a personalised risk assessment tool to further support clinicians to predict asthma attacks and reduce asthma morbidity and mortality.

2.3. Harnessing technology

2.3.1. Key issues and opportunities

While not a necessary part of an LHS, technology can clearly play a very important enabling role, for example by:

  • enhancing data collection and analysis
  • accessing high quality information through electronic health records
  • helping with clinical decision making
  • supporting the design and implementation of improvements.

Increasing the use of digital health technologies is a priority for the UK’s health and care systems to support both service delivery and improvement. For example, NHS England’s service transformation plans are currently seeking to capitalise on the potential of LHSs to generate and embed knowledge and drive improvements in health and care.

Enhancing data collection and analysis

Digital technologies can support LHSs by enhancing the way health data are collected, for example through devices such as mobile phones, wearables and sensors. These technologies can enable the real-time collection and analysis of health data that may have previously been prohibitively expensive, intrusive or time consuming to collect or understand. For example, the CFHealthHub (case study 3) shows how data gathered through the routine use of Bluetooth-enabled nebulisers helps clinicians to understand how well existing care pathways are supporting people with cystic fibrosis. By integrating technologies into the existing care pathway, this example shows how data can be used to support system learning and improve personalised support for people with long-term health conditions. The use of digital health technologies such as these may also mean that the data collected is of a higher quality when compared with data inputted into a system manually. But it is important to make sure that those using these technologies are comfortable with using them, capable of using them and motivated to do so.,

In addition to collecting data, digital health technologies also often incorporate additional functionality such as analytics and artificial intelligence (statistical tools and algorithms that enable computers to simulate elements of human behaviour such as learning, reasoning and classification), which seek to maximise the value of the data. Technologies like natural language processing (a branch of artificial intelligence) can make complex data available to LHSs in a timely way by transforming large amounts of qualitative data into an analysable format. For example, it can analyse free text patient feedback, which can then enable a better understanding of what matters most to service users.

Accessing high quality information through electronic health records

Electronic health record systems have the potential to make sure that the right information is available at the right time. As longitudinal records of people’s health information, they are an invaluable resource for front-line teams needing to access an individual’s up-to-date health history and make decisions about their care and treatment. Collectively, they also contain information about the health of populations, which can inform decisions about what services and support are needed, both now and in the future, as well as show whether existing models of care are delivering the desired outcomes. For example, Northern Ireland’s Encompass programme is showing how health and social care records can be integrated and made accessible through a single system so that patients, service users and health and care staff can access timely information.

The information in electronic health records can also be used to help design evidence-based improvements to care. For example, Cambridge University Hospitals’ eHospital programme (case study 11) used insights from its electronic patient records to develop systems and alerts for the early identification of sepsis, which the team estimate led to a 70% increase in patients receiving timely treatment.

Helping with clinical decision making

Electronic health records can be enhanced through the integration of digital tools like clinical decision support systems. These systems can strengthen medical decision making by matching an individual’s electronic health data to a clinical knowledge database to make personalised care recommendations for the clinician’s consideration. This allows clinicians to access and apply continually evolving clinical guidance when it is needed. Clinical decision support systems are an example of how a technology can be used for both delivering care and enabling improvement – for example by incorporating machine-readable guidance that can be automatically updated and improved over time.

Several hospital trusts – such as Great Ormond Street Hospital for Children NHS Foundation Trust, Nottingham University Hospitals NHS Trust, University Hospitals Birmingham NHS Foundation Trust and Liverpool University Hospitals NHS Foundation Trust – have integrated clinical decision support systems into their electronic patient record systems. For example, University Hospitals Birmingham NHS Foundation Trust has used its electronic health record system to improve decision making in prescribing by:

  • supporting clinician behaviour change
  • minimising unintended drug omissions
  • supporting decisions on dosing
  • reducing the unnecessary use of blood products.

Clinical decision support systems have major potential for improving services and have recently been identified as a priority in NHS England’s transformation plans. They have been shown to reduce mortality, harm and life-threatening events. But they depend largely on the availability of ‘computer-executable knowledge’, that is, having clinical knowledge available in a format that a computer can apply, which can present a number of professional and technical challenges to implement.

These data tools and clinical decision support systems can also be enhanced through machine learning to support decision making. One example is risk prediction tools, which can support both individual-level care decisions and population-level strategies. For example, Project Breathe (case study 12) applied artificial intelligence to data from self-monitoring devices to predict cystic fibrosis flare-ups 10 days earlier than would have previously been detected.

Artificial intelligence technologies are also being built into clinical decision support systems so that their outputs are based on more complex data analysis, to improve the evidence base for clinical decision making. Artificial intelligence is also being used in platforms such as mobile health apps and risk assessment tools, which can be a way for people to quickly access, view and understand complex information in order to make decisions about their health care. For example, the RADAR project (case study 10) explored whether a digital platform could support people with type 2 diabetes to make decisions about their health by displaying their risk of complications based on their health data. It also allowed clinicians to understand whether and when to intervene earlier to prevent complications and avoid hospitalisation.

Supporting the design and implementation of improvements

Technology can play a role in explicitly supporting the process of designing and implementing improvements. For example, digital communication technologies such as research platforms like Thiscovery (case study 13), collaboration spaces like the FutureNHS collaboration platform and software repositories like GitHub can accelerate the pace at which ideas, evidence, tools and solutions can be shared. And they can bring together stakeholders from across diverse backgrounds and geographies to collaborate, discuss ideas and design improvements.

Technologies can also support implementation by replicating everyday situations and allowing improvements to be tested in safe, while realistic and immersive, environments through simulation technologies such as virtual and augmented reality.

2.3.2. Challenges

Developing, integrating and scaling up technologies within health care is a complex process, and our research has identified several challenges for using technologies to support LHS approaches. These challenges encompass many elements of the health care system, from infrastructure, workforce and skills, to funding, evaluation and trust.

Having adequate, underlying IT infrastructure is essential, in terms of hardware, software and internet access. A recent House of Commons Health and Social Care Committee report stated that providers will be unable to capitalise on the potential of technology while continuing to ‘struggle with basic IT infrastructure’. But despite a focus from national policymakers, digital maturity remains a significant issue, with one in five NHS trusts still relying on paper records.

The most recent data from NHS England’s Digital Maturity Index also show that there remains a significant disparity between organisations, meaning that some will require greater levels of support than others before they can maximise the potential of existing technologies and effectively adopt new ones. And with integration being a vital element of current reforms across the UK, efforts to improve digital systems will need to go beyond acute care to incorporate primary care, community care and social care providers too. Primary care providers have made significant progress on the digitisation of patient records, but wider challenges remain. Community and social care providers continue to struggle with system fragmentation, legacy IT systems and insufficient access to national funding. While the COVID-19 pandemic has accelerated the uptake of technology in out-of-hospital settings, in particular video conferencing, and reduced the number of ‘digitally novice’ organisations, many community and social care providers still require support with fundamental aspects of IT, including cybersecurity and data protection.

Securing funding for the development and deployment of digital health technologies also remains a challenge for NHS organisations looking to innovate, adopt and spread technology. Recent Health Foundation analysis suggested that the funding landscape is complex and skewed towards early-stage innovation – implying there would be benefits to more support for testing, adoption and spread. Challenges also exist for companies looking to finance innovative ideas. The high-risk nature of innovation, coupled with the long timeframes required for development and the need to deliver a return on investment, mean that many innovations succumb to the ‘valley of death’ between research and commercialisation, with half of digital health start-ups failing within their first 2 years.

Critical to financing innovations, as well as for their wider adoption and scaling, is generating sufficient evidence to show their safety and efficacy in real-world settings. But as discussed earlier in this report, generating this evidence is costly and complex, particularly for digital health technologies that develop at speed, and can be high-risk. Conventional methodologies, such as ‘gold standard’ randomised control trials, are rarely affordable for the health care technology market, which is dominated by small and medium-sized enterprises. They can also be limited in their ability to account for the complexity of many digital health interventions, whose contexts can vary considerably and continually evolve. Lengthy trials may also be unsuited to the rapid pace of digital innovation, with technologies likely to have been updated or even superseded by the time the trial is concluded. Prolonged, single evaluation approaches rarely align with the ‘fail fast, fail often’ mantra of many technology innovators. Regulators such as the National Institute for Health and Clinical Excellence (NICE) also often prefer a spread of evidence from smaller economic, usability and implementation studies. However, the sheer number of technologies available, and the pace at which they develop, also mean that regulators are limited in their capacity to evaluate the ‘tsunami’ of new digital innovation.

Effective development, evaluation and implementation often requires health and care services, innovators and technology companies to work together effectively and there are challenges here too. For the health service, this includes the need to better articulate where technology can add the most value and to spot opportunities for innovation, while for industry this includes closer working with the health service to collaboratively identify and develop suitable solutions that meet NHS needs.

But developing productive relationships between service providers and innovators can be challenging. With the NHS being a national public service, relationships between health care and industry can be complex, and issues such as intellectual property, contracting and data access can slow or even completely stall progress. High levels of system fragmentation and a complex local and national innovation landscape also mean that innovators can be unclear as to who to contact and work with.

Making sure that technology and data tools are implemented effectively and integrating them successfully into workflows are also significant challenges. It is rarely enough to simply add new technologies into existing ways of working and expect transformation. Instead, effective integration requires sufficient capacity to collaboratively redesign roles, processes and care pathways to derive the benefits of the new functionalities that technology offers – in other words, focusing on the whole change and not just the technology.

Central to effective implementation is making sure there are the necessary levels of technological skill and literacy among both staff and service users. Recent analysis shows that a fifth of the UK population do not have even the most essential digital skills, creating challenges both for the effective uptake of new technologies and for making sure that the ‘digital divide’ does not widen existing inequalities. For example, low levels of digital skills, along with poor internet access and language barriers, can mean people remain excluded from services like online appointment booking and remote appointments.

Also fundamental is building public and staff trust in the use of new technologies. After the COVID-19 pandemic hit, new technologies were rolled out with impressive speed, but Health Foundation research has shown that not everyone is yet sold on technology-enabled care. A significant minority (36%) of UK adults surveyed during the first wave of the pandemic thought that while greater use of technology made sense during the pandemic, it was not something ‘for the long term’.

We asked our survey respondents which of the technology challenges highlighted in bold above they considered to be the most significant for adopting LHS approaches; the results are shown in Box 7.

Box 7: Technology challenges – what our respondents said

The results indicate that all proposed statements were thought to be a challenge to some degree (see Figure 7), averaging a score of 3.8 out of 5 (with 5 representing ‘a very significant challenge’).

However, the highest-ranked challenge was ‘ensuring effective implementation of technology and data tools and integrating them successfully into workflows’ (4.4), followed by ‘ensuring adequate underlying technological infrastructure, such as IT hardware and software, or internet/Wi-Fi connectivity’ (4.1).

 

Case study 10: Reducing the health burden of diabetes with artificial intelligence-powered clinical decision tools (RADAR)

A Health Data Research UK (HDR UK) and Health Foundation Catalyst project

Nearly 5 million people in the UK have diabetes and this is predicted to rise by another 500,000 in the next decade. For people living with diabetes, effective management is critical. Uncontrolled diabetes can have serious complications and lead to heart disease, stroke and kidney failure. Diabetes costs the NHS £10bn a year, primarily due to complications like amputation and blindness. So acting early to prevent complications both improves care and quality of life and avoids complication-associated expenditure.

RADAR (Risk Algorithms for Decision Support and Adverse Outcomes Reduction) was a Better Care Catalyst project, led by North West London Health and Care Partnership, in collaboration with MyWay Digital Health, Imperial College Health Partners, AstraZeneca, the Institute of Global Health Innovation at Imperial College London and Imperial College Healthcare Trust. The aim of the project was to use data and artificial intelligence to improve clinical decision making and self-management for individuals with diabetes.

The project took models that predict the risk of complications in patients with type 2 diabetes, originally developed and validated in Scotland by MyWay Digital Health, and aimed to revalidate them using the longitudinally linked and ethnically diverse Discover dataset of more than 2.5 million people living in north-west London.

The team worked with clinicians and people with type 2 diabetes to understand whether a digital platform embedding predictive models could support self-management and enable clinicians to intervene earlier to prevent complications and hospitalisation. Specifically, the platform used algorithms to predict an individual’s risk for complications such as loss of sight, amputation and heart disease.

It included a digital interface where clinicians and people with diabetes could see their risk levels and how they compared to other members of the population. It also allowed users to see how losing weight, increasing activity or stopping smoking could reduce their risk, and work with their clinician to make changes. Users reported feeling empowered by being able to see their data, and by understanding how their actions could influence their long-term health.

Through workshops with users, the team was able to further develop the platform to make sure that it was easy to navigate and understand. In this way, the project enabled innovators to implement and further validate the risk-prediction algorithms as well as understand how artificial intelligence tools could potentially be delivered across a range of other conditions.

The RADAR project shows how emerging technologies like artificial intelligence and routinely collected data can be embedded into care pathways to support collaborative and personalised decision making. In particular, it highlights the value of having access to safe and secure population-level health data and the importance of user consultation for technological adoption and spread.

Case study 11: Cambridge University Hospitals’ eHospital programme

The eHospital programme provides a single integrated electronic patient record system for most inpatient and outpatient service information in Cambridge University Hospitals NHS Foundation Trust. Launched in 2014, the platform is accessible to trust staff, primary care colleagues and other hospitals in the region. It highlights some of the benefits that health care providers in the UK can achieve through using electronic record systems. The system is linked to the national GP Connect service, enabling clinicians to share and view primary care information.

With more than 95,000 people registered with the platform through the MyChart app, service users also benefit from greater access to health information, such as diagnoses, test results and medication history. Service users can view correspondence with clinicians, report their own data and self book appointments.

The team report that the eHospital programme has increased service user activation and empowerment and reduced the administrative and logistical burden on staff. The system also enables a range of digital services such as outpatient kiosk self-check-in, automated coding of outpatient procedures and automated letter generation, reducing the burden on administrative resources. Other significant impacts include improvements in medication safety and organisation-wide cost savings. The team has estimated that 124 nursing whole-time-equivalents are saved each year through direct recording of observations and medication administration at the bedside.

The system also supports service learning and improvement through the generation of usable real-time insights. For example, Cambridge University Hospitals NHS Foundation Trust has used insights from record data to develop alerts to support the early identification and treatment of possible sepsis.

The eHospital programme shows the importance of staff engagement and change management for successful implementation. The programme team worked with more than 1,000 hospital staff to make sure the system was configured to their needs. This included pre-launch sessions to illustrate new system workflows and gather feedback, as well as role-playing and simulations to understand how the system might interact with other processes. Patients played a critical role in configuration and deployment decisions for the MyChart platform, and the team has continuously gathered feedback, allowing for iterative improvement.

Learning from the programme highlights the need for staff education and training. For example, staff digital literacy was identified as a potential barrier to implementation, so intensive training was delivered to more than 12,000 staff before the launch. The eHospital team has also recognised the importance of building data analytics capability within the organisation, through recruiting data analysts and upskilling clinical teams in analytical methods. The team has also invested time in raising awareness about data quality and supporting staff with best practice approaches to recording information, which has led to a reduction in the need for data ‘cleaning’.

Case study 12: Project Breathe – artificial intelligence-driven clinical decision-making tools to manage cystic fibrosis

A Health Data Research UK (HDR UK) and Health Foundation Catalyst project

Cystic fibrosis, an inherited disorder affecting one in every 2,500 babies born in the UK, can cause severe damage to the lungs, digestive system and other organs. As a degenerative condition, it will get worse over a person’s lifetime and requires lifelong management. People with cystic fibrosis can also experience pulmonary exacerbations, which must be diagnosed and managed early and effectively to minimise any negative effect on their quality of life and survival.

In 2019, Project Breathe, a Better Care Catalyst project, tested whether self-reported data and data generated by home-monitoring devices could track health and wellbeing, reduce unnecessary clinic visits, better inform clinicians and improve quality of life for people living with cystic fibrosis. Collaborators from the Cambridge Centre for AI in Medicine at the University of Cambridge, Microsoft Research and social enterprise Magic Bullet (funded by the Cystic Fibrosis Trust, the National Institute for Health and Care Research and LifeArc) used these patient-owned data to develop adaptive artificial intelligence that could analyse and learn from the data in order to identify the early signs of exacerbations.

120 adults were given devices to self-monitor their lung function, activity, heart rate, oxygen saturation and weight. The pseudonymised data were stored in a secure, online system and provided to participants in real time via the Breathe app. Their clinicians also used the data to monitor early changes to health, detect signs of exacerbation and make informed decisions about when to see patients face to face and where changes to treatment might be needed.

The project has shown some early signs of impact. As a result of integrating the technology into their self-care routines, participants reported that they had developed a better understanding of their health and were able to adjust their plans or lifestyle to make sure they stayed well for longer. Almost all patients using the app have also been able to avoid unnecessary clinic attendance by remotely reviewing the data with their clinician.

The artificial intelligence analysis of the data has also predicted symptom flare-ups up to 10 days earlier than when antibiotics would typically have been given, meaning treatment can be given earlier and preventable hospitalisations and negative outcomes potentially avoided. The project has now recruited hundreds more participants from both the UK and Canada to better refine the algorithm and deepen learning.

Project Breathe is showing that, for long-term conditions such as cystic fibrosis, being able to collect and analyse up-to-date information and integrate this into decisions about care can be an effective way to empower people and support decision making. It also shows that doing so does not necessarily increase the burden on health care professionals and can simultaneously make it easier for them to monitor and manage their patients and intervene where necessary.

2.4. Nurturing learning communities

2.4.1. Key issues and opportunities

The kind of learning and improvement that LHSs undertake typically needs input from many people. This requires a community of people with different roles and backgrounds who are committed to taking part in the process (a ‘learning community’), along with an ability to bring them together, reconcile differing views and take decisions. Such learning communities can include ‘communities of practice’, improvement collaboratives and professional and patient networks. They are groups of motivated people – such as clinicians, patients, carers, researchers, analysts and improvement teams – who come together around a common goal to identify problems and collaborate on designing and implementing solutions and improvements.

Especially because of this, when thinking about and developing LHSs, it is important to not only focus on the technical aspects such as data and technology but also consider the social and relational aspects of learning and improvement. Discussions of LHSs sometimes imply that iterative learning cycles have a kind of automaticity to them – turning practice into data, data into knowledge, knowledge into practice and so on – but as our description of the learning cycle in Figure 1 (Chapter 1) illustrates, sitting at the heart of each of these stages are social processes of deliberation, problem definition, priority setting and decision making. How well people collaborate at these critical stages of the learning cycle will significantly influence how well the LHS works and how successful the resulting changes will be. The stakeholders we interviewed and the literature we reviewed highlighted how building strong learning communities makes it more likely that improvement work will lead to positive, sustainable change. For example, an evaluation of the HipQIP project (case study 15), an improvement collaborative to improve hip fracture care, found that the learning environment created by bringing people together around a common purpose played an important role in the project’s success.

Bringing diverse perspectives to the improvement process

Healthy learning communities typically have a variety of characteristics. They incorporate diverse roles and perspectives. As well as offering opportunities for professionals from a range of backgrounds to collaborate on change, they can also offer a way for patients, service users and the wider public to engage in the learning and improvement process and shape services according to what works for them. This makes it more likely that changes will achieve the desired outcomes. For example, the National Children and Young People’s Diabetes Network worked with children and families from 173 paediatric diabetes units across England who were able to shape services by informing priorities for change, contributing to improvement initiatives and accessing peer support and information. The stakeholders we interviewed also highlighted how the inclusion of diverse voices from a variety of backgrounds can challenge assumptions and help address issues of inclusion, power and paternalism in care. Here, learning communities can benefit from the involvement of voluntary and community organisations because of their ability to harness knowledge and skills in local communities, as the Health Foundation’s Common Ambition programme shows.

Effective learning communities also have trusting relationships and a culture that supports an open and honest learning environment, with the psychological safety for people to raise concerns and try out new ideas and approaches.

Mechanisms for collaboration

Learning communities also need the ability and the mechanisms to bring people together to deliberate, to aggregate or reconcile differing views and to make decisions – whether in person or online. For example, the Health Foundation’s Q Lab UK (case study 14) works with a broad group of people with interest and expertise in a particular topic, including clinicians, people with lived experience as patients, carers and service users, researchers, commissioners and national system leaders. Through collaborative research activities, such as surveys, workshops and interviews, the Lab generates intelligence and shares the findings with teams testing service changes to support improvement efforts.

Meanwhile, the growth of online research platforms such as Thiscovery (case study 13), developed by The Healthcare Improvement Studies Institute, and VOICE, led by the University of Newcastle, allows a wide range of stakeholders to take part in improvement work over the phone, on their computer or face to face.

2.4.2. Challenges

Developing and sustaining inclusive learning communities can be a significant undertaking, and our research found several challenges that can arise in the process.

First, when setting up an LHS, there may be an initial need either to create a new network or learning community or to harness existing ones. The work involved in doing so should not be underestimated. There can be a belief that learning communities are naturally occurring, where interested parties will gravitate together to learn and collaborate. But in many cases, the formation of a learning community requires considerable bespoke design, nurturing and facilitation, not least to bring together a broad range of stakeholders.

Perhaps partly as a consequence of underestimating the work involved in forming and maintaining learning communities, there can be a lack of infrastructure and funding for convening people, to enable discussion and collaboration. For example, there can be difficulties in accessing suitable physical space for face-to-face collaboration, while variation in access to suitable software and hardware can make online collaboration difficult. The stakeholders we interviewed also highlighted how securing backfill to free up clinician time for improvement (getting cover for their role while they are engaged in such work) can be exceptionally difficult, but is nevertheless highly important. This can be especially challenging when there is a desire to collaborate in large groups or beyond single teams or organisations, where many participants may need such support.

Even when the right mechanisms and resources are available, clinicians, data analysts, researchers, quality improvers and other professionals still need to find the time and make the effort to be involved in improvement work. With the health service facing chronic workforce shortages and some of the most severe pressures in its history,, this can be difficult. For LHSs operating at a large scale or over long time periods, maintaining engagement and the dynamic nature of the learning and improvement cycle can be even more challenging.

The time and effort involved can be a particular barrier for patient and public participation; without adequate financial compensation or resources, contributions are often restricted to those who can afford to take part. Some service users also face other barriers, such as ill health or difficulties with communication, which make the burden of participation even greater. Without support to participate, there is a risk that those involved in learning communities are the most motivated and engaged users, rather than a broader, more representative group.

Another challenge is that cultural barriers can make it hard for people to come together around a common set of goals. While it is important to make sure that learning communities are diverse and representative, in some cases, diversity in professional and personal backgrounds, or in organisational approaches and culture, can mean that each stakeholder sees the issues differently, making consensus hard to achieve. In cases like this, the work involved in successfully facilitating the learning community will be greater. Language matters too: when bringing groups with varied expertise together, it is important to make sure that the language used is accessible and jargon free.

Sometimes initiatives can run into resistance to change. For example, overstretched staff already grappling with multiple proposals for reform may see the work of an LHS as ‘just another initiative’ – unless the rationale for change is clearly set out and the work is aligned with broader organisational strategies. On other occasions, staff or service users may be sceptical about proposed changes, or have legitimate quality or safety concerns, especially if they have not been involved early enough in discussions or in formulating the proposals.

Another barrier highlighted through our stakeholder discussions is a lack of clarity about, or competing perceptions of, what an LHS is and what it is trying to achieve. As we discussed in Chapter 1, there is no single definition of an LHS, and discussions can be highly theoretical, making the ideas difficult to apply in the real world. There is also a wide range of models of LHSs, making it difficult for individuals, organisations and systems to decide on the best approach.

Importantly, many of the issues discussed above highlight how successful learning communities – and successful LHSs – require effective leadership. In particular, effective leadership is a critical facilitator for:

  • aligning the activities of the LHS with wider operational and strategic priorities
  • securing resources and organisational support
  • gaining the buy-in of staff and teams across an organisation
  • creating a healthy improvement culture.

In this context, effective leadership includes behaviours such as being ‘problem framers’ rather than ‘problem solvers’, listening and observing, and stimulating reflection and critical thinking within teams. (While a detailed exploration of leadership is beyond the scope of this report, it is worth emphasising that this includes clinical leadership, which can have a significant impact on organisational performance.)

We asked our respondents which of the learning community challenges highlighted in bold above they considered to be the most significant for adopting LHS approaches; the results are shown in Box 8.

Box 8: Learning community challenges – what our respondents said

As shown in Figure 8, the results indicate that all proposed statements were thought to be a challenge to some degree, averaging a score of 3.9 out of 5 (with 5 representing ‘a very significant challenge’).

However, the highest-ranked challenge was ‘the time and effort it takes to participate in the work of a learning health system’ (4.3), followed by ‘absence of the leadership needed to put in place and use learning health system approaches’ (4.1).

Case study 13: Thiscovery

Many challenges facing health care could benefit from bringing together multiple forms of expertise to design solutions at scale. Thiscovery, an online research platform developed and hosted by The Healthcare Improvement Studies Institute (THIS Institute), helps to address this need. Founded on the principle of co-creation, it brings people who have questions about how to improve care together with people who can help build the evidence for answers – including, most importantly, patients and staff. The platform can enable a range of research, evaluation and consultation processes, including consensus-building, iterative development and the evaluation of solutions. Since April 2020, Thiscovery has been used to deliver more than 30 discrete research tasks and has more than 6,000 registered participants who can take part remotely and at different times.

One of its first projects arose in response to a problem that surfaced as the COVID-19 pandemic took hold: how to adapt clinical processes for COVID-19 scenarios. The obstetric emergency known as post-partum haemorrhage (heavy bleeding after birth) was one important case where new processes were needed to keep people safe and still deliver high quality care and experience. Having separate units devising processes in silos is time-consuming and wasteful, but it also wasn’t going to be possible to use the in-person meetings characteristic of pre-pandemic collaborative approaches.

A novel approach was required to enable rapid iterative learning at scale while minimising participant time and effort. Thiscovery enabled this with more than 100 experts in maternity care taking part through a novel consensus-building methodology to generate recommendations, with participants given rapid feedback via interactive charts. The project produced rapid consensus on 16 practical, actionable recommendations to adapt usual care for COVID-19. Major professional bodies and NHS services endorsed the results, including a video showing the adapted processes. The project continues to contribute to education, with more than 130,000 views of the online video, and the approach used has been codified into a formal methodology, which is already being used as the basis of other projects.

Recent projects show how Thiscovery can support learning communities and play a key role in LHSs by offering a mechanism to understand problems, co-create visions and evaluate solutions. The co-design approach is especially important in making sure that solutions can be developed and tested at scale in ways that allow diverse priorities, views and ideas to be respected, while the quality of engagement, ownership and endorsement increases the chance of impact.

Case study 14: Q Lab UK

Q is a community of thousands of people across the UK and Ireland, collaborating to improve the safety and quality of health and care. Members share their knowledge and support each other to tackle challenges, enabling faster progress in improving health and care.

Q supports a network of labs across the UK and Ireland. The network currently includes Q Lab UK and Q Lab Cymru. Q Lab UK uses creative and collaborative approaches to work on a single complex challenge and brings together organisations and individuals from across the UK and Ireland to pool knowledge about the topic. They then uncover insights and develop, test and scale ideas. The Lab also draws on approaches and tools from quality improvement and disciplines such as social innovation and design.

Since 2017, Q Lab UK has explored themes such as the importance of peer support and improving care for people living with both mental health problems and persistent back and neck pain. The Lab is currently working in partnership with NHS England on how to build staff and patient trust and confidence in technology-enabled remote monitoring, so that it can be scaled across the health and care system.

To explore an issue, the Lab works with a broad group of people with relevant interest and expertise from a range of backgrounds both within and outside of the health and care sectors. The Lab instigates collaborative research activities, such as surveys, workshops and interviews, to generate initial intelligence. The Lab then invites leaders within health care organisations to apply to become ‘test teams’. These teams are given tailored coaching and support to work together to identify potential solutions, and test and scale them over time.

The test teams, along with a range of contributors from the Q community, convene regularly through participatory workshops, where they learn and apply methods for design thinking and collaborative working. The learning and insights collected from the Lab community are shared widely and used to support improvement efforts in their own work and beyond.

A recent evaluation of Q Lab UK shows that teams appreciate the ways of thinking and working used in the Lab, particularly around participation and collaboration. People also value the opportunity and encouragement to be curious, to slow down and to ask bold questions. They also find the process a good opportunity to be exposed to methods that help better understand service users’ needs and to have the space to practise creative facilitation skills.

It is through Q Lab UK’s convening infrastructure and processes that participants develop new ideas, skills and capabilities. Importantly, they also work together as a learning community to build shared purpose, supportive relationships and collective energy, which helps to create the context and conditions for scaled impact in health and care.

2.5. Implementing improvements to services

2.5.1. Key issues and opportunities

While gathering data and drawing knowledge and insights from them are key components of any LHS, the goal is to put this into practice and make improvements to care quality and safety. This involves understanding how to identify, design and implement changes to service delivery, and then evaluating the impact of those changes to see whether they have had the desired effect.

Quality improvement skills and methods

Quality improvement – the systematic and coordinated use of specific methods and tools, with the aim of bringing about a measurable improvement in quality – can play an important role in supporting the design and implementation of service changes. Quality improvement tools include ‘driver diagrams’ to understand cause and effect, and methods for improvement such as ‘Lean’ principles or experience-based co-design. Quality improvement approaches sit well with LHSs as they are similarly based on an iterative approach to testing – for example through ‘plan–do–study–act’ cycles.

Training and coaching can help develop the use of quality improvement in practice and maximise its value. For instance, PINCER (case study 2), a pharmacist-led intervention to identify potentially harmful prescribing in general practice, teaches pharmacists how to use quality improvement tools and gives them the opportunity to apply these tools in general practices to help reduce prescribing errors. It includes action learning sets for pharmacists to discuss their experience with their peers and get advice on how to hone their quality improvement skills.

Beyond developing improvement skills, there are a range of other important ways in which the implementation of service changes requires support, which have been discussed in previous sections, such as providing clinical backfill or providing assistance with data analytics and evaluation.

Taking an organisation- or system-wide approach to improvement

Quality improvement tools and methods are not enough in themselves to drive improvement, however. A positive workplace culture that is conducive to improvement also matters, as does the presence of an integrated strategy for improvement at the organisation or system level. It can be difficult to implement and sustain improvements if they are not aligned to wider organisational or system priorities, or in the absence of visible, long-term support from senior leaders who are willing to give front-line teams the space and permission to do things differently.

The relationship between quality improvement and quality planning, control and assurance is also relevant here. Until recently, many organisations have treated these four elements of the ‘quality management system’ as separate entities, each with their own governance processes and strategic goals – a practice that has led to disconnected and sometimes conflicting aims and activities. This is now beginning to change. For example, organisations such as East London NHS Foundation Trust are choosing to take a single approach to quality that encompasses the full quality management cycle. The Trust’s approach is based on engaging staff and the local community at the planning stage to identify where improvement work should be targeted over a five-year period, using quality improvement methods to implement changes, and then making sure improvements are maintained.

Learning across boundaries and improving collaboratively

Learning from peers – particularly those who are facing the same challenges – can be an effective way to build solutions for improving health care services and address unwarranted variation, for example through models such as breakthrough collaboratives and action learning sets. For example, HipQIP (case study 15), an initiative to improve outcomes for people with hip fractures, shows how the collaborative model can drive improvement. Teams from across the UK collaborated to design changes to hip fracture care, test them in their respective organisations and share their results, supported by guidance in quality improvement. But while these kinds of models can be effective, their success depends on the context they are deployed in and whether they are sufficiently resourced.

2.5.2. Challenges

Our experience of funding innovation and improvement projects at the Health Foundation over the past two decades has shown that implementing change in health care services can be a challenging and complex endeavour, requiring specific capabilities and resources.

One key challenge can be a lack of skills and knowledge required to effectively implement improvements to care. Health Foundation research has found that these capabilities are essential for high-performing health care organisations, and they were recognised in the NHS Long Term Plan as an important area for development., A related challenge is how quality improvement is taught to staff.,, Sometimes the focus is primarily on the technical aspects of improvement, neglecting important relational aspects, such as how to build a team, motivate others and secure support. And some quality improvement programmes do not give staff enough opportunities to implement their skills.

Also a challenge is a lack of capacity – the time and space needed for people to effectively implement improvements to care. The capacity required to plan, set up, execute, evaluate and refine service changes is often underestimated and can be very difficult to find when services are understaffed and facing significant demand pressures. And both building capability and creating capacity require resourcing, so a lack of financial and other support to effectively implement improvements to care is another related challenge.

Earlier on we highlighted how the culture of an organisation – the shared ways of thinking, feeling and behaving – is a critical ingredient for an effective LHS. The experience of NHS Nightingale Hospital London (case study 4), which was set up temporarily to create sufficient intensive care capacity to treat patients with COVID-19, shows how an effective LHS relies on the right culture and behaviours among staff at every level – not only among senior leaders. As Nelson and others put it, an LHS is more than simply ‘technical infrastructure’; strong values and a commitment to collective learning need to govern it. Consequently, the absence of a culture of learning and improvement within health and care services can be a barrier to developing LHSs. Research suggests that the prevailing culture in some services is too often defensive, inward looking and hierarchical – characteristics that not only inhibit the improvement of quality but also can have an adverse impact on it.,,

Given the relatively underdeveloped nature of LHSs in the UK, it is fair to say that, while there are many successful examples of improvement going on, few of them take place within an LHS. For instance, they may be time-limited projects or projects not necessarily taking a systematic approach to analysing and drawing knowledge from data. And where actions to improve service delivery do occur within an LHS, there may not be explicit acknowledgement that an LHS approach is being taken. Indeed, many of the teams we interviewed for this report whose work exemplified LHSs were not aware that their projects were taking LHS approaches. For these reasons, while this report documents a series of case studies, there remains a lack of evidence or practical examples of successful LHSs, showing what impact they have had and why. Other issues contribute to this evidence gap too. It is often the case that improvements are incremental and accrue over time. The stakeholders we interviewed told us that there can also be a reluctance to learn from failure, so where attempts to build LHSs have failed, they are less likely to be reported. In addition, our interviewees also indicated that traditional evaluation models such as randomised control trials do not lend themselves to LHSs.

Another challenge for implementing improvements to services is the gap between health care research and practice. Research can provide a critical input into the work of an LHS, and an important objective will be to quickly translate research findings into improvements in practice. But research does not always have a clear practical application and the results do not always reach health care professionals. Even when they do, significant work may be required to interpret and implement the findings. Our case study of PReCePT2 (case study 16), which aims to increase the uptake of magnesium sulphate to reduce brain injury in preterm babies, shows that even though there was compelling evidence to support the use of magnesium sulphate, it took several years of systematic quality improvement work and national support to embed it into widespread practice. Thiscovery (case study 13), an online collaborative research platform developed by The Healthcare Improvement Studies Institute, shows one way in which this gap can be bridged – by bringing together researchers and practitioners and making sure that research projects are designed to have a practical application from the outset.

We asked our survey respondents which of the improvement challenges highlighted in bold above they considered to be the most significant for adopting LHS approaches; the results are shown in Box 9.

Box 9: Improvement challenges – what our respondents said

As shown in Figure 9, the results indicate that all proposed statements were thought to be a challenge to some degree, averaging a score of 4 out of 5 (with 5 representing ‘a very significant challenge’).

However, the highest-ranked challenge was a ‘lack of capacity (time, space and so on) for people to effectively implement improvements to care’ (4.6), followed by ‘lack of financial and other support’ (4.0) and ‘the gap between health care research and practice, which hinders the translation of research into practice’ (4.0).

Case study 15: HipQIP – hip fracture quality improvement programme

Hip fracture is the most common serious injury in older people and costs the NHS and social care more than £1bn a year. Nearly a third of people who fracture their hip will die within a year and a fifth of patients will not return to their own home. Northumbria Healthcare NHS Foundation Trust leads the hip fracture quality improvement programme (HipQIP), which aims to improve care and outcomes for people presenting to hospital with a fragility hip fracture (one that results from minimal trauma, such as a slip or fall). Initially this was developed for use within Northumbria but it has been scaled successfully to five acute hospital trusts – four in England and one in Scotland.

The programme focuses on prioritising additional nutrition for patients with hip fractures; implementing a surgical care bundle, pain block in A&E and surgery within 36 hours; and carrying out root cause analysis of any deaths; all while gathering outcome data and learning from different sites on their approaches to improve care.

Independent peer reviews of each department allowed for accurate baselines before interventions. Teams then attended learning events to review evidence and share ideas about improvement solutions. They tested and implemented iterative changes in their local settings and collected data to measure the impact of the work. This approach helped to build a strong ‘breakthrough collaborative’ – a learning community across the five different trusts that offered a safe space for both raising concerns and sharing learning between individuals and organisations.

In an 8-month period, the four English hospitals recorded 119 fewer deaths than expected if mortality had remained at the baseline and 77 fewer deaths than at the hospitals used as controls during the same period. 30-day mortality rates in hospitals within the collaborative reduced from 9.2% to 5.8%. Furthermore, 100 extra patients returned home instead of going to a nursing or residential home, and length of hospital stay was reduced by 2 days compared with the baseline.

In successfully scaling to five additional trusts, HipQIP shows the importance of working with peers to share learning and spread good practice, which can facilitate more robust improvement. Independent peer evaluations provided the necessary baseline to help trusts objectively understand where they started from and where to focus their improvement efforts. The programme also shows the importance of evidence-based quality improvement approaches, such as the Institute for Healthcare Improvement’s Collaborative Model, which enabled providers to confidently identify areas of good practice and areas requiring further iterative improvement.

Case study 16: Reducing brain injury through improving uptake of magnesium sulphate in preterm deliveries (PReCePT2)

Around 60,000 babies are born prematurely in the UK each year. These preterm babies are at greater risk of brain injury and conditions such as cerebral palsy, which affects 500 premature babies a year. Evidence shows that mothers at risk of premature delivery who take magnesium sulphate during preterm labour and before birth can reduce the risk of their babies developing cerebral palsy by a third. The cost of a dose is less than £1, but less than half of eligible mothers were receiving the treatment in 2016.

From 2014 to 2015, the PReCePT1 project was implemented in five West of England maternity units. During the project, maternity units and mothers co-developed a Quality Improvement Toolkit, which provided practical tools and training to increase knowledge and awareness of using magnesium sulphate to protect against preterm brain injury. As a result, the number of women receiving the preventative treatment in these units increased by an average of 25%.

Following this success, the project was supported to scale up nationally, called PReCePT2. From 2017 to 2020, 13 maternity units received an enhanced quality improvement intervention in addition to the standard model of implementation, including bespoke one-to-one and team coaching and access to learning events. ‘Snapshot’ tools, such as communication and implementation plans, were developed so that staff could understand how much progress they had made, alongside tools to plan how changes would be sustained.

The project shows how using data to evidence the need for improvement and sharing service users’ stories to highlight impact can be effective ways of gaining buy-in. PReCePT2 uses performance data to stimulate action, including data on the number of people not getting magnesium sulphate, cases that could be avoided and the cost comparison between the intervention and cases of cerebral palsy.

PReCePT2 also highlights the importance of bringing together all professionals involved in a care pathway to collaborate on improvement, and of planning for long-term sustainability at the outset. PReCePT has now been adopted by all 152 maternity units in England and magnesium sulphate uptake has increased to more than 85% for eligible mothers. As a result, it is estimated that 48 cases of cerebral palsy were prevented between 2018 and 2021, saving £38.4m in lifetime health and social care costs.

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