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Clinical review: The impact of data released through the Data Access Request Service

Results

Impacts of data released through DARS

The initial coding frame contained 101 Impacts. These were eventually grouped into 22 Impacts, through an iterative multidisciplinary process. The 22 Impacts were further grouped into five categories, each containing four to six impacts. Tables 2.1-2.5 show the impacts within each category. The Frequency column shows how many times each Impact was referred to within the sample of 82 applications. These Impacts are not mutually exclusive and often overlap.

Improving health and care Frequency
Direct benefits to clinical practice 41
Improving patient outcomes and experience of care 32
Protecting and improving public and population health 70
Supporting local quality improvement 42

Table 2.1 - Impact coding frame. n = 82. References can be counted more than once. Frequency of Impacts in the "Improving health and care" category

Supporting research activities Frequency
Further research and funding 15
Peer reviewed publications 32
Public engagement 30
Reporting, consultation, collaboration and training 61
Research findings and outputs 34

Table 2.2 - Impact coding frame. n = 82. References can be counted more than once. Frequency of Impacts in the "Supporting research activities" category

Supporting policy and planning Frequency
Reducing inequality, promoting accountability and public trust 43
Supporting health service planning and decision making 53
Supporting national policy and strategic planning 57
Supporting service reconfiguration or system redesign 21
Supporting standards in health service delivery 62

Table 2.3 - Impact coding frame. n = 82. References can be counted more than once. Frequency of Impacts in the "Supporting policy and planning" category

Supporting assessment and efficiencies Frequency
Benchmarking and performance monitoring 57
Efficiencies 47
Evaluation of resource impact 16
Health economic modelling 13

Table 2.4 - Impact coding frame. n = 82. References can be counted more than once. Frequency of Impacts in the "Supporting assessment and efficiencies" category

Improving data capabilities Frequency
Development and use of tools, toolkits, dashboards, platforms 26
Development of better data quality and collection processes 48
Enabling improved data access, analysis and outputs 36
Supporting adoption, scale-up, or roll-out 15

Table 2.5 - Impact coding frame. n = 82. References can be counted more than once. Frequency of Impacts in the "Improving data capabilities" category

The following sections define each of the impacts and provide examples, quoted directly from applicant’s yielded benefits statements.

1. Improved health and care

Download the data for this chart Figure 5: Improving health and care: proportion of references for each Impact

Protecting and improving public and population health

Population-level data provides a better understanding of local population health (health outcomes and their distribution) characteristics and their various causes. It includes risk factors and demographic change, which can improve health and social care, and public health provision, preventing disease and promoting health in general. Studies in epidemiology are included.

The yielded benefits statements sampled, reported significant impacts to population health at the local, regional and national level. It was the most cited impact within the Improved Health and Care Category. For example:

Improving patient outcomes and experience of care

Improvements in direct care to patients generally arise as a result of improved clinical decision making, based on insights from the data. This can include improved mortality indices, survival rates, patient safety measures, personalised treatment, improved access to treatment, service delivery, patient decision-making and information provision.

Supporting local quality improvement

The emphasis here is on "local", and the improvement in the quality of services including their delivery, through the work of local hospitals, local clinical commissioning groups (CCGs) and local authorities, where there are specific examples of improvements made.

Direct benefits to clinical practice 

The emphasis here is on clinicians' practice. This means any changes or improvements made to clinical practice, whether through the use of innovative medical technologies, identifying patients at risk, or:

  • targeting of patients for more prompt and accurate diagnosis and treatment
  • instigating new treatments or procedures
  • improvements in medical decision-making
  • health risk assessments
  • advice to patients

In all cases, the latest assessments of medical interventions such as diagnostic tests and clinical guidelines should inform best practice.

2. Supporting research activities

Download the data for this chart Figure 6: Supporting research activities: proportion of references for each Impact

Reporting, consultation, collaboration and training

This is the most commonly cited form of impact within the research category. It encompasses the activities associated with disseminating the findings of research that is based on DARS data, including in training programmes. It also includes collaborations between organisations, established to make use of DARS data. It excludes feeding back data to patients and the public, which is covered in the next section.

Public engagement

This work will make analyses, insights and summaries, based on DARS data, available to patients and the public.

Peer reviewed publications

These are cases where DARS data has contributed to a peer reviewed publication.

Further research and funding

These are cases in which the insights from studies, based on DARS data, have resulted in further investigation and successful funding bids taking place.

Research findings and outputs

These are cases in which research studies have depended on DARS data. They include accounts of research findings that are mainly descriptive and informative for a general readership, such as pharmaceutical companies, research groups, but not detailing the direct impact on clinical practice.
 

3. Supporting policy and planning

DARS data is widely used to improve the planning, delivery and evaluation of health and social care policy. This section outlines the various impacts that the data has on these processes.

 

Download the data for this chart Figure 7: Supporting policy and planning: proportion of references for each Impact

Reducing inequality, promoting accountability and public trust

In terms of social justice, these are cases in which DARS data is used to increase knowledge and confidence in how services may address health inequalities, and cases where accountability and public trust is promoted.

Supporting service reconfiguration or system redesign

DARS data can be used for service reconfiguration or system redesign at local, regional and national levels.

Supporting national policy and strategic planning

Policy analysis, advice, planning and commissioning decisions in particular to align actions with national strategic goals can rely on a combination of DARS, open NHS Digital data and data from other sources.

Supporting health service planning and decision making

Planning and prioritising services, programmes, interventions and patient flows/care pathways, for investment or development in order to meet demand often relies on DARS data.

Supporting standards and health service delivery

DARS data can be used to help plan, develop, and apply health service standards (such as guidelines) and performance indicators. These result from, and lead to, reviews, surveillance and audit work, and improved clinical governance.
 

4. Supporting assessments and efficiencies

Download the data for this chart Figure 8: Supporting assessment and efficiencies: proportion of references for each Impact

Benchmarking and performance monitoring

DARS data is used to develop benchmarks for national, regional and peer group comparisons. Benchmarks enable monitoring of a range of practice indicators to analyse and address variations, and to inform commissioning decisions. Monitoring can also trigger investigations into clinical practice for health and safety.

Health economic modelling

DARS data is used to power mathematical models to estimate the effects of an intervention on valued outcomes and costs.

Evaluation of resource impact

DARS data is used to evaluate the efficiency of practices, activities, initiatives, or technologies, which can include the costs or savings associated with them.

Efficiencies

DARS data can help patients, professionals and services save both time and money through:

  • better commissioning
  • reductions in hospital admissions
  • fewer readmissions
  • shorter length of hospital stays
  • using less resources
  • easing of workforce pressures

5. Improving data capabilities

Download the data for this chart Figure 9: Improving data capabilities: proportion of references for each Impact

Enabling improved data access, analysis and outputs

DARS data can enable improved data access, compared to some other sources, in terms of timeliness, speed of access, more up-to-date versions, enabling more responsive analyses. DARS data can support improved analysis in terms of the possibility of a broader range of analyses, or more specific analyses, or better comparative analyses, through data linkages.

Development of better data quality and collection processes

DARS data is of high quality. Through the use of linked data, aggregated data sets, large-scale or longitudinal data sets, or individual level data sets, DARS data is more accurate, reliable and complete according to a range of variables. DARS data has been collected from a variety of sources and enables identification of 'missing' data.

Development and use of tools, toolkits, dashboards, platforms

DARS data can be used by other systems that make it more accessible and usable for specific groups to improve care and commissioning.

Supporting adoption, scale-up, or roll out

DARS data can be used to support adoption of innovations or to monitor their use and scale-up.

Impacts by applicant type

Different types of applicants realised different Impacts through their use of DARS data. Table 3 shows the frequency with which each category of Impact was realised by each applicant type. Figures 10 to 15 visualise the relative importance of each type of Impact within each applicant type.

Improving data capabilities Improving health and care Supporting assessments and efficiencies Supporting policy and planning Supporting research activities
Academic and research organisations 29 57 10 62 62
Commissioners 12 24 36 32 2
Data analytics organisations 33 17 26 26 34
Local authorities 10 8 11 26 14
National policymaking bodies 11 8 11 26 14

Table 3: frequency with which each category of Impact was realised by each applicant type

 

 

Download the data for this chart Figure 10: Types of Impact realised by academic and research organisations

Figure 10 shows that as expected, academic and research organisations make a significant impact on research activities, but that they also support improvements to health and care and to policy and planning. By contrast, they make little impact on data capabilities or assessments and efficiencies. This might represent an opportunity for NHS Digital or funders to target these areas for further development.

 

Download the data for this chart Figure 11: Types of Impact realised by commissioners

Figure 11 suggests that commissioners support more work on assessments and efficiencies as well as policy and planning. Perhaps there is scope to help them have a more direct impact on health and care or to improve data capabilities within their areas.

 

Download the data for this chart Figure 12: Types of Impact realised by data analytics organisations

Figure 12 shows that data analytics organisations, including commercial organisations, have a broad range of impacts, apart from in improving health and care. There may be lessons to learn from these organisations that would be transferable to others.

 

Download the data for this chart Figure 13: Types of Impact realised by health trusts

Figure 13 shows that trusts have a broad range of impacts when they do use data, but that total applications from trusts are low. There are likely to be capacity issues here, but a drive to help trusts make more use of data might deliver significant impact. Despite their proximity to patient care, trusts appear to be relatively weak at using data to improve health and care. This may suggest an area for improvement, for example, by increasing the timeliness and accessibility of our data.

 

Download the data for this chart Figure 14: Types of impact realised by local authorities

Local authorities have a bipolar distribution of impacts, focusing mostly on improving health and care. Further work could focus on understanding how we could help them to increase impact in other areas.

 

Download the data for this chart Figure 15: Types of impact realised by local authorities

Unsurprisingly, national policy making bodies had strength in supporting policy and planning, with little direct impact in other areas. There might be an opportunity to work with these organisations to identify how they could make broader impact using our data.

Last edited: 15 January 2020 10:58 am