Why should we invest in analytical capability?

‘The failure to use information properly in health and care means people can experience unnecessary levels of preventable ill health. Those using services can suffer harm when it could be avoided, could live in greater pain and distress than they need to, and are less likely than expected to experience a full recovery. Every day, interactions with health and care services can waste people’s precious time. In addition, taxpayers do not get full value: the productivity benefits that come from effective use of new technology – doing more for less – are not widely realised.’ National Information Board

The consequences of a shortage of analytical expertise are not always obvious or immediately visible. The effects can be hidden in a range of suboptimal decisions and choices based on limited or inappropriate evidence. Some of the areas of health care in which good data analysis is critical are:

  • clinical decision-making
  • innovation and improvement in care
  • board-level oversight of complex organisations and care systems
  • everyday management
  • responding to national initiatives and regulation
  • resource allocation
  • understanding patient flow
  • supporting new data and new digital tools
  • helping patients and the public to use information.

1. To support clinical decision-making

Support for clinical decision-making is one area where new technologies are changing quickly, as evidenced by the proliferation of tools and algorithms to help clinicians diagnose and manage disease. Development is rapid in both the public and private sectors,,, and in health care the digital future seems rich with possibility. As Robert Wachter noted, ‘Big-data techniques will guide the treatment of individual patients, as well as the best ways to organize our systems of care’.

There is growing interest in the possibilities of ‘big data’ and artificial intelligence (AI). Although much of this is, for the time being, aspirational, it is clear that such developments will require a skilled workforce that can ensure that tools are implemented in the right way. As noted in the Topol review, development of the workforce is critical for advancements to be fully realised, as staff will need to understand the issues of data validity and accuracy. Indeed, it is possible that the future clinical teams will include data scientists and bio-informaticians.

Some new technologies are already in widespread use: for example, analytical tools such as predictive-risk algorithms that use historical information to make predictions about a future event. These might use patients’ electronic records to predict the probability that an individual might suffer an adverse event (such as requiring an emergency admission to hospital). These tools can help clinicians identify where to focus preventive services to avoid acute health problems occurring. Such tools cannot replace clinical judgement, but they might enhance it.

At their best, these predictive tools can operate within existing information systems and seamlessly provide input to a clinical decision. In many cases, they can function as standalone tools. But for the best results they will often require analytical support, which means that using them becomes something more than simply switching on a software module.

So, when applying predictive risk algorithms, several questions need to be considered:

  • Are you able to extract the right data from operational systems?
  • Can you analyse aggregate patterns across patients?
  • Do you need to calibrate predictive models on local data?
  • Will the model perform as expected, given the differences in the way information is collected and coded at a local level?
  • What are the characteristics of high-risk patients and how can interventions be designed to improve the care they receive?

Other predictive tools are being tested as part of the Health Foundation’s Advancing Applied Analytics programme in areas such as general practice and mental health (Boxes 3 and 4).

Box 3: Exploring the use of a frailty measure in general practice

Example from the Health Foundation’s Advancing Applied Analytics programme

The Electronic Frailty Index (eFI) uses general-practice read codes to identify frailty in the practice population. It was developed by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care in Yorkshire and Humber.

The eFI tool is now available to all general practices in Midlothian. Midlothian Health and Social Care Partnership is running a project that draws on analysis and QI methods to explore how eFI can be used in primary care, and the implications for community health care as well as hospital services. The approach was to consider the whole system to identify all patients with frailty in Midlothian. Support was then provided to general practices to analyse their own data.

Box 4: Applying a risk-prediction tool in mental health settings

Example from the Health Foundation’s Advancing Applied Analytics programme

Risk-stratification tools are currently used across the NHS, for example to help identify which general practice patients are at the highest risk of being admitted to hospital. The potential benefits of this are that patients are prevented from experiencing an adverse event and emergency-care costs are avoided.

Birmingham and Solihull Mental Health NHS Foundation Trust is applying risk stratification to mental health by building models that predict the likelihood of an individual being admitted to psychiatric hospital. The aim is to develop and implement a risk-stratification model that will help clinicians prevent mental-health patients from requiring urgent hospital care. The model uses 4 years of historical clinical and sociodemographic data to provide an overall indication of the risk of a patient experiencing a mental health crisis. The data is drawn from a range of sources and is not limited to a set of patient characteristics such as age, diagnosis and previous hospital admissions.

The project looks at how the models can be used in practice, working with a number of the Trust’s community mental-health teams to pilot the risk-stratification model. Analysts will work with clinicians and managers to refine, test, implement and see how they can embed it into systems for routine clinical care. The goal is to understand its impact on clinical decision-making and make improvements as part of a continuous cycle of learning via a comprehensive evaluation process.

2. To support innovation and improvement in care

The NHS is awash with innovations designed to deliver ‘better’ care, triggered by a desire to improve quality of care, the need for financial solvency, or both. Examples include integrated models of care, digital-first approaches to primary care, new algorithms to detect diseases, and the establishment of rapid diagnostic services to detect cancers. Despite the hunger for innovation, however, there is often no way to know whether these changes will actually improve care (eg around reducing emergency admissions).,,

While traditional evaluation can help understand what works, the process can also be too slow or too restrictive, requiring the process of care to remain unchanged until the study has been completed (or allowing only for small changes). What is needed is an approach to monitoring the effects of innovation in close to real time, so that teams (local or national) can ‘course correct’ along the way. An example of such an evaluation model is given in Box 5.

These evaluations can reveal higher-quality care, such as a recent evaluation from the Improvement Analytics Unit. It found that residents of care homes who received enhanced support experienced 23% fewer emergency admissions than expected. Even when an evaluation reveals that the intervention has not delivered the gains that were anticipated, it still produces valuable learning. In a complex environment, not every change will produce the intended effect and it is important to identify where the results were not as expected.

Box 5: Improvement Analytics Unit and NHS vanguard local evaluation

The Improvement Analytics Unit is an innovative partnership between NHS England and the Health Foundation. It provides rapid feedback on whether progress is being made by local health care projects that focus on improving care and efficiency in England.

Robust statistical methods are used by the unit to evaluate local initiatives and interventions in health care, such as those being delivered as part of major national programmes (eg the integrated care systems). The unit aims to provide rapid feedback to local services and decision-makers to enable them to improve care.

The unit assesses whether the care outcomes for patients covered by the new initiatives are different in any significant way from the outcomes of patients who are not part of the initiative. The unit’s analysis will inform ongoing learning and improvement. This analysis can be combined with intelligence at a local level, guiding the development of improvement projects and change to services on the ground.

Implementing new approaches to the delivery of services can be challenging and take considerable time and effort from front-line teams. If these teams are going to have the best chance of improving patient care, they need better analytical support to help them understand the effects of their work to date and make improvements.

Questions that recur when supporting change are whether and how to provide help to clinical and managerial teams. It is often done using external consultants, but the downsides, apart from the initial expense, include the fact that the consultants’ skills and expertise are not transferred to the internal teams. The newly achieved solutions may not be sustainable. In 2016, the board of Taunton and Somerset NHS Foundation Trust took the bold step of making a big investment in developing permanent, internal improvement capability: creating the right culture, structure, tools and processes to enable and empower their workforce to improve from within. The decision was prompted by a concern that the use of external consultants had proved too costly and unsustainable. As a result, the Trust has successfully minimised spend on external consultants and now has a thriving team within the organisation to support improvement work. Box 6 outlines the model they have adopted.

Box 6: Developing in-house capability to support change

Taunton and Somerset NHS Foundation Trust

Historically, Taunton and Somerset NHS Foundation Trust’s approach was to use external ‘experts’ and management consultants to bolster the Trust’s ability to deliver improvement. This was costly and unsustainable. The results and benefits that promised were not always delivered, measured or sustained. The ‘experts’ the Trust worked with each had their own approach, tools and processes for delivering change. Projects often operated in organisational silos. There was no single, clear picture of improvement projects across the Trust and accountability between projects was inconsistent.

Staff at the Trust mapped the current structure of improvement projects, who was working on them and where they reported. This generated a proliferation of boards, steering groups and projects. They quickly realised that the Trust needed to radically rethink how it implemented improvement if it wanted to survive and thrive.

The Trust’s model

Based on internal data and mapping, as well as evidence of what had worked elsewhere, staff at the Trust proposed a new structure for improvement, comprising five elements:

  1. A proprietary ‘blended’ methodology that combines proven Institute for Health Improvement methodology with project management and benefits realisation. This blended approach is designed to ensure effective governance and monitoring of projects, and to drive out and capture project benefits.
  2. A dedicated improvement team with the technical skills and experience to partner with and coach clinical and operational teams to deliver improvement projects in their areas. The improvement team is centrally financed and structurally detached, and is focused on delivering results at the organisational level.
  3. A governance structure of clinician-led ‘improvement boards’ based on ‘constant’ themes within the hospital.
  4. A comprehensive and ambitious training plan to equip individuals and teams with the skills they need to improve their part of the organisation using Institute for Healthcare Improvement methodology.
  5. An evidence-based approach to improvement, ensuring that the Trust leverages the experience and best practice of others and proactively shares its own learning.

3. To facilitate board-level oversight of complex organisations and care systems

‘IT and information is the ownership of the board and the senior management, if they’re not using it to run their organisation they’re in the wrong job. There is nothing more complicated than running the NHS and if we don’t do it as smart as any organisation in the world then we’re really betraying the taxpayer and we’re betraying our patients.’Matthew Swindells

Good-quality information and intelligence is critical for a board to be effective. Box 7 gives an example from a study looking at the quality of care. A study of NHS providers suggested that one of the key elements in achieving successful provider transformation is insight from data analysis that enables a fact-based understanding of problems, informed decision-making and performance-tracking.

The task of organising complex information and presenting it in ways that are meaningful and relevant to board-level decision-makers should be one of the fundamental roles of the analysts. However, in many cases, reporting at board level falls short of what is required and relies on long, unprocessed lists of tables. Sometimes, the problem is not a shortage of information but rather an 'overabundance of irrelevant information’.

One study looking at how boards work on improving the quality of care ranked organisations in terms of the maturity of their approach to quality improvement (QI). They found that organisations with high levels of QI maturity received reports in which the data were clear and readable, and in which different sources of data were discussed together (eg data on staffing levels considered alongside data on staff wellbeing and patient experience). By contrast, reports to boards with low levels of QI maturity were characterised by a large volume of data, which was often not clearly presented, reviewed in silos and not linked to improvement actions.

An experienced NHS manager observed that ‘the NHS gathers a massive amount of data but largely fails to use it intelligently. Energy is misplaced … most is spent downloading and gathering data, followed by preparing reports, analysing data, and ultimately using the data to make decisions’.

A recurring issue is how board-level reports handle statistical uncertainty. Most measure-ment contains some degree of uncertainty arising from chance variation and basic statistical methods; this is a widely accepted way to distinguish a systematic trend from an ambiguous one. Yet one study found that of a total of 1,488 charts found in board reports, only 6% acknowledged the role of chance. This presents a risk that boards react to changes in metrics that are the result of chance and do not reflect any real change to the underlying care processes – wasting time and resources. One approach to addressing these problems is to move from Red Amber Green ratings to statistical process control charts, as suggested by NHS Improvement in the initiative Making Data Count.

Box 7: Analysing quality at the organisational level

To improve the quality of care and identify risk in the system, it is important that high-quality intelligence is available to teams across the organisation. One study of hospital boards found this could vary widely, despite organisations 'putting considerable time, effort and resources into data collection and monitoring systems'.

The study report describes how the better organisations typically used a variety of data sources: routinely collected data, data collection initiatives, and other sources like spot checks and audits. However, there were significant differences between organisations in how effectively that data was turned into 'actionable knowledge' and organisational response. Some organisations used information to detect issues (problem-sensing behaviour), while others used information less usefully to provide reassurance (comfort-seeking behaviour).

'Problem-sensing involved actively seeking out weaknesses in organisational systems, and it made use of multiple sources of data—not just mandated measures, but also softer intelligence […] Senior teams displaying problem-sensing behaviours tended to be cautious about being self-congratulatory; perhaps more importantly, when they did uncover problems, they often used strategies that went beyond merely sanctioning staff at the sharp end, making more holistic efforts to strengthen their organisations and teams.'

4. To improve everyday management

In terms of basic operational management, there are many opportunities for good analysis to make everyday tasks more efficient. New software tools allow better reporting and allow managers and clinicians to access information closer to where the decisions get made. Examples include Qlik, Tableau and Beautiful Information (Box 8).

Wrightington, Wigan and Leigh NHS Financial Trust has developed a suite of analytical apps that support the organisation and its provision of healthcare from ward to board. Their most renowned app supports their A&E department in monitoring demand (both current and predicted), wait times, decisions to admit and other aspects of patient flow. The app has become the ‘single version of the truth’ and supports both the department and the Trust executives in their decision-making. Since its introduction, the app has helped reduce the median length of stay by 30 minutes: improving discharge levels, reducing delays and minimising readmissions. Although the app may look simple, it contains complex algorithms that use things like weather data to determine how many people are likely to turn up at A&E in the hours and days ahead.

The Trust has developed other apps that support the organisation's referral-to-treatment times, theatre efficiency, budget management, outpatient and inpatient care, and monitoring of variations in care.

Mark Singleton, Associate Director of Information Management and Technology for the Trust, said ‘We are so lucky to have such as a fabulous Business Intelligence team that have developed a recipe for success when it comes to working with Clinical Services and producing ground-breaking apps that support the organisation in so many different ways but ultimately to ensure the organisation provides the best care for its patients.’

Box 8: Example of an information tool for managers

Operational Control Centre

  • A web-based app available on any smartphone, tablet or desktop platform.
  • Provides aggregated, real-time data.
  • A proactive management tool that highlights bed capacity and delays in the system.
  • Available anytime and anywhere, in the hospital or off-site.
  • Control over access to unlimited users.
  • Developed by Beautiful Information, an NHS/private partnership.

Data analytics have also been beneficial to commissioners (Box 9).

Box 9: Using data analytics to support better commissioning decisions.

Bradford Districts Clinical Commissioning Group used the three-stage RightCare methodology (where to look, what to change, how to change) to focus on clinical programmes and identify value opportunities. They employed evidence-based methods with a clear emphasis on outcomes to inform the commissioning and delivery of programmes to improve heart health.

In its first year of operation, Bradford’s Healthy Hearts helped 14,000 patients in the Bradford area and has already potentially prevented 50 heart attacks and 50 strokes. More than 960 people in the Bradford area are now on vital stroke-preventing medicine, which has reduced the risk of stroke by up to 75% in these patients, and avoided nearly 88 devastating strokes a year. This is an anticoagulation rate of nearly 82%, the highest in the region. Over 4,500 patients at moderate to high risk of heart attack and stroke have been prescribed statins to reduce their risk. By switching to different statins, over 6,000 patients have reduced their cholesterol level. The risk of stroke for people with atrial fibrillation has been reduced by more than two-thirds by anticoagulant medication prescribed by a doctor.

5. To better respond to national initiatives and regulation

The local health and care agenda is often shaped by external demands from national government and arm’s-length bodies (Box 10). These will typically have a framework for accountability and performance assessment that is applied to local providers and commissioners of care. The tools used as the basis for these performance assessments frequently rely on complex analytical methods when defining performance targets or metrics, such as the Summary Hospital Mortality Index. Very often, local analytical teams are needed to interpret these national information measures and put them into local context.

For example, as part of the annual planning round, providers are obliged to generate demand forecasts for their key points of delivery (eg emergency admissions, outpatient referrals). For many organisations, these forecasts have been relatively naive in construction, ignoring core concepts like trends and seasonality. To bridge the gap and generate good-quality returns, NHS Improvement decided to develop univariate-time-series forecasting tools to help providers increase the level of sophistication and statistical rigour of their forecasts. The tools and outputs developed were delivered at scale using web-based interfaces and dedicated output files. The code driving the tools was developed on an open-source platform. It could then be shared with local analysts, who could re-use and amend it as necessary. This univariate-forecasting approach is now a standardised methodology for both NHS Improvement and NHS England.

It can be difficult for an individual organisation to ignore some of the approaches developed by national bodies, such as NHS England or the Care Quality Commission, when these are used in performance management or regulatory discussions. An organisation without sufficient analytical capability will be at a distinct disadvantage in such discussions.

Box 10: How government requirements and expectations can shape how data analysis is used within a service

Performance measurement and targets

For some time, a system of nationally mandated indicators and targets has been a national tool for driving policy changes. Interpreting changes in these indicators is often a more complex process than a superficial analysis suggests.

Population health management

As part of The NHS Long Term Plan, the NHS ‘will deploy population health management solutions to support [integrated care systems] to understand the areas of greatest health need and match NHS services to meet them’. Analytical methods such as population segmentation and impactibility modelling are key, and integrated care systems will need to be able to use them effectively.

Case-mix analysis

The increasingly complex language of healthcare resource groups has been used for almost 20 years in funding acute care. A fairly basic scheme has seen a variety of refinements and adaptions to incentivise changes to care.

Patient and staff surveys

Established as a national requirement some years ago and still one of the most commonly used comparative performance tools.

Understanding mortality differences

Over the past decade, the monitoring of hospital fatality rates has been the subject of intense national and local debate.,, However, a number of local organisations had begun to monitor hospital mortality rates; government interest following the Francis report added momentum to work on standardised measures, such as the Summary Hospital-level Mortality Indicator.

Variation in care and GIRFT

More recently, initiatives such as RightCare and Getting it Right First Time (GIRFT) have used a combination of centralised analysis to develop benchmarking data that local organisations cannot generate themselves and support to interpret and analyse the implications for the local context.

6. To better allocate resources

Moving resource allocation from simply reinforcing historical funding patterns to a system that represents need has been a recurrent theme in health policy since the 1960s. It is an area in which local interests battle hard for their share of the pot, and one where good analytical support is essential to understanding the evidence and weigh different arguments.

At a national level, the analysis can be complex and often involves expert teams advising on government strategies. As the National Audit Office noted, ‘Given the amount of money involved – equivalent to nearly £1,400 per person each year – the way in which the Department [of Health and Social Care] and NHS England allocate funding to local commissioners is a crucial part of the way the health system works. These decisions are complex, involving mathematical formulae and elements of judgement.’

There is some evidence that national policy of resource allocation had an impact on reducing inequalities between areas. Between 2001 and 2011, the proportion of resources allocated to deprived areas in England compared with that allocated to more affluent areas was associated with a reduction in absolute health inequalities from causes amenable to good health care.

Aside from the implementation of national allocation methods, there is also a need for better analysis to support ways to identify local priorities in allocating resources. As Geraldine Strathdee, Chair of the National Mental Health Intelligence Network, noted, without benchmarking data, NHS resources are allocated on the basis of historical patterns, guesswork or the loudest voice’.

7. To understand patient flow

The past few years have seen a recognition of the importance of understanding the way patients flow through the care system. Often, the best way to achieve that is through the use of sophisticated methods such as modelling, yet their uptake has been patchy.

Good examples do exist: for example, work to understand demand for long-term care in Kent (Box 11). Others have used tools such as simulation and queueing theory to look at scheduling community mental-health assessments. In Sheffield, local teams have applied simulation modelling to evaluate the reconfiguration of stroke services in Sheffield and South Yorkshire and this work has been integral to decision-making.

The challenge in adopting these tools routinely has been linked to a lack of capacity in health services; too few staff members are felt to have the training or ability to use the models. This is especially relevant considering current concerns with managing urgent and emergency care and flows.,

Box 11: Modelling demand for long-term care

The Kent public health team began supporting the Kent and Medway Primary Care Trust cluster with the local Long Term Conditions Year of Care Commissioning Programme in 2012. At the time, there was limited understanding of how to quantify and estimate the benefits of the Quality, Innovation, Productivity and Prevention (QIPP) 'long term conditions' model of care on the wider Kent health economy, other than high-level national evidence. Intelligence and analysis of service use within integrated care models focused on the effects on individual organisations, but did not reflect wider patient journeys across all care settings. The aim of the public-health, whole-population approach was to create a baseline profile of how individuals with complex care needs affect hospital services during periods of crisis, alongside their use of other services, compared with other individuals.

The information is being used to model demand for services and to assess the impact of service-change interventions across the whole health and care system. Analysis and dashboard metrics have been referenced in several key needs-assessment documents and other strategic plans. For example, a recent evaluation of a falls-prevention service by a community health provider used a linked, whole-population community health and hospital dataset to examine falls-related admissions before and after patients were referred to the falls service. This enabled a more sophisticated evaluation of whether the change in trends might actually be caused by patients using the new service.

The Health Foundation has been promoting local work to look at patient flow through the service. One recent review of a flow training programme in Wales noted the challenges of accessing the analytical time needed to support this work: ‘Another substantial constraint was that none of the local health boards had sufficient capacity and expertise in data collection and analysis to provide ongoing support to clinicians and make sure they had the right information in the right format for effective decision-making.’

8. To support new data and new digital tools

Another reason to support analytical capability is that it could open the way to new data-driven technologies, for example machine-learning algorithms and AI, that could help with the diagnosis and management of health conditions. Data are essential to the development of these technologies, and the NHS has some of the best health care data in the world. The NHS Long Term Plan envisages a role for private-sector companies, with an aim to ‘encourage a world leading health IT industry in England with a supportive environment for software developers and innovators’. Successful delivery of this innovation agenda is likely to depend on joint working between NHS teams and industry. NHS analytical teams have a lot to contribute in this area. They understand how NHS data are being collected and why, and they can act as a valuable bridge between NHS clinicians and managers and data scientists in industry.

To take advantage of new methods, analysts must have access to the right software tools, particularly open-source programming tools that allow analysts to learn from each other (eg R and Python). In the early 2000s, open-source software began to gain acceptance, even among the sceptics. Today, open-source software is practically embedded in large, commercial organisations such as Facebook, Google, Twitter and banking and blue-chip corporations. They are taking full advantage and seeing the benefits of its power and scale. Some, such as Facebook, are actively developing and sharing their software tools within the wider open-source community, and some of those tools have been embraced in health care (eg Prophet).

The NHS has been much slower in accepting and seeing the value of open-source software, although the Department of Health and Social Care has recently announced that the newly created NHSX will ensure that all source code is open by default. One of the main barriers to wider deployment of open-source tools has been the reluctance of IT staff to install open-source software on secure health care systems. One analytics manager confided: ‘Getting an open-source application installed on my NHS laptop was a lengthy and arduous process. IT professionals were particularly risk averse to deploy software on their systems especially given the highly sensitive information they contain. We battled with the reluctance because we saw the value add it would give us. Now the software is installed, we are starting to realise the value add, why it’s so popular, and the awesome things it can do – you can see the reason why all the big organisations have embraced it. We are doing some things very differently now, it has allowed us to work more on our methodologies rather than churn.’

Open-source tools such as 'R', a statistical programme that is gaining recognition in commercial and public-sector applications, present an opportunity. Open-source offerings include generalised tools such as Python (used as the platform for GCSE computing) as well as niche analytical disciplines such as JaamSim (used for discrete-event simulation). Open-source tools enable analysts to explore and sample an array of new analytical tools and techniques they can ultimately deploy in their organisations. The challenge for the future is to take advantage of these exciting analytical tools by building analytical capability. This includes the networks needed to allow collaboration, such as the NHS-R Community (Box 12).

Box 12: How open-source software tools can support better analysis in health care

The NHS-R Community was established in 2017 with an Applied Analytics Award from the Health Foundation. It is an online and face-to-face community dedicated to promoting the learning, application and use of the open-source 'R' tool in the NHS in the UK.

One key aspect of this project was the way analysts can share resources (typically code) and expertise, and so improve analytical capability in the system. Moreover, the network had stimulated a wider conversation about what analysts can contribute to health care and the features of high-performing analytical teams. This is the kind of cross-organisational collaboration that the Health Foundation is seeking to encourage.

The NHS-R Community has so far achieved:

  • a dedicated website (https://nhsrcommunity.com)
  • delivery of problem-oriented workshops in Wales and Yorkshire
  • the 2018 NHS-R Community Conference to promote the use of R in the NHS, which was attended by 119 delegates from across the UK and Europe.

The UK government, in its Life Sciences Industrial Strategy, sees health-service data as being of value to those developing new digital tools. For example, the Open Prescribing project uses existing data streams on prescribing collected from GP practices by NHS Digital. The raw data files are huge, with more than 700 million rows, so a team at Oxford have put together some analytical tools that are freely available to GPs, managers and the public.

Over the coming years, NHS organisations may wish to provide private companies with access to NHS data, for example to help with the development of new algorithms or drugs. The benefit for the NHS might include seconded data scientists working alongside NHS teams. In that situation, we'd recommend an emphasis on skills transfer, so that the NHS builds its capability to conduct analysis in a sustainable way.

9. To help patients and the public use information

Investing in data analytics enables new information flows to and from patients and the general public. This information can help people make better-informed decisions about their care, as well as contribute more effectively to the development of their local services. There are also opportunities to engage the public in decisions about how data are used.

Table 2 lists some of the ways in which these information flows operate. Although many revolve around the use of new technological tools, it is important to recognise that there is still a significant analytical role in reporting, presenting and understanding the data.

For more information, go to www.qlik.com, www.tableau.com/learn/webinars/transforming-healthcare-data-insight or http://beautifulinformation.org/solutions/performance

M Singleton, personal communication, 2019.

§ P. Stroner, personal communication, 2019.

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