Learning health systemsand feedback loops

Learning health systems and feedback loopsA learning health system is one in which internal data and experience are systematically integrated with external evidence, with changes put into place based on the resulting knowledge and insight.

This requires the implementation of a continual cycle of assessment, feedback and learning, with systems and processes in place to test, adapt and re-test social marketing initiatives, preventive interventions and models of care.

Many health organisations aim to consistently learn in this way to provide the best quality, safe and efficient care. In a learning health system, leaders are committed to a culture of continuous learning and improvement. The organisations involved systematically gather and apply evidence in real-time to improve care, use technology to share evidence with clinicians to improve decision-making, promote the inclusion of patients as members of the learning team, and capture and analyse data and care experiences. They continually assess outcomes, and refine processes and training to create a feedback cycle for learning and improvement.

Feedback loops input into the crucial ‘refining’ stage of the learning cycle: understanding the situation through a needs assessment, identifying opportunities through an opportunity analysis, targeting objectives through algorithm development, pausing to conduct impact assessments, and then refining through the continual feedback loop.

Feedback loops include updating predictive models, using data to refine impactibility models, A/B testing of patient-facing and clinician-facing interfaces, and participating in and supporting more formal research and evaluation. Data generated in any risk stratification programme can also form a continuous feedback loop to improve performance.

Points to consider How will you demonstrate leadership support for a culture of ongoing

learning? What systems do you already have in place that could be combined to

embed learning across the ACS? At what point in the process will you stop to consider feedback? How will

the information be fed back into the organisation/s to improve performance?

How will you engage with all your stakeholders? How will you manage changes that come about through learning?

Needs assessment

Needs assessmentA needs assessment is the first step in the learning cycle of analyses. It is a systematic method of identifying the unmet health and healthcare needs of a population and making changes to meet those needs. It involves an epidemiological and qualitative approach to determining priorities, which incorporates both clinical and cost effectiveness and patients' perspectives.

Each local authority within an ACS geographical area will already have produced a JSNA for its population. The JSNA for an ACS should be undertaken jointly by NHS staff and local authorities and their public health teams, using a combination of NHS and local authority data, where possible linked at the person level.

The resulting JSNA will determine the health and care needs of the population and important segments within it. These segments can be demographic or geographic, and can be based on condition categories, the distribution of long term conditions, or other disease and multi-morbidity groupings. This involves understanding the wider determinants of health, analysing health inequalities, benchmarking against peers, measuring 'wellness' and its distribution, undertaking geospatial analysis, and using a range of simulations, system dynamics and other techniques to predict a range of demographic changes in the population and test their impact. Visualisations can help bring the data to life, including atlases of variation, patient timelines (theographs) and patient pathways (bow tie diagrams).

A more detailed understanding of prevalence than that available from JSNA can be obtained by looking within smaller geographical areas (localities), or at individual GP practices or practice clusters. In many parts of the country, there is great variation over quite small areas, and by understanding these differences commissioners and clinicians can target resources more effectively. To achieve this, it is usually necessary to analyse the care records of individuals, to build up a more detailed population picture.

Points to consider Are the necessary links in place for stakeholders to work together to

produce a JSNA across the ACS? Is everyone happy and able to share data and is it up to date and relevant?

What will you do with the information gained from the needs assessment? How will you link it into your cycle of learning?

What are the timescales you need to work to in order to take maximum advantage of the information yielded by the needs assessment?

What, if any, are the information governance implications?

Segmentation

SegmentationSegmentation is a process of grouping the population in order to identify specific groups or cohorts of patients and explore them in more detail. Ideally segmentation will define groups of similar patients who may be suitable for a particular intervention or new care programme.

Most health and care professionals already group the population intuitively for the purposes of delivering effective care (e.g. people over 75; people with long term conditions). Segmentation is a more formalised approach to this process.

There are four different ways to segment the population: by utilisation risk (risk stratification); by age and condition; by social and demographic factors; by behaviour. The most commonly used are by utilisation risk and by age and condition.

Population segmentation can be used to provide better health care for all and it is increasingly being used by health care organisations to understand the differing needs of different groups of people within a population so that services can be better planned and delivered.

Population segmentation supports the design of new models of care as well as a more preventive, proactive approach. The current health and care system is often organised around services or specific conditions rather than putting individuals at the centre of care and support. This does not promote consideration of people’s needs in their totality or the most effective use of available resources. The common approach is to use clinical pathways for each condition, which means patients can end up on different pathways for multiple conditions with no holistic view of their needs.

However, through segmentation it is possible to interpret population-focused priorities in an appropriate context to shape planning for resources, care arrangements, and service delivery, ensuring that each person's health needs can be met effectively and efficiently.

For example, if the population were grouped into those in good health, in maternal/infant situations, with an acute illness, with stable chronic conditions, with a serious but stable disability, with failing health near death, with advanced organ system failure, and with long-term frailty, each group would have its own priorities and definitions of optimal health.

Data to inform segmentation can be drawn from a range of sources such as Hospital Episode Statistics (HES), Joint Strategic Needs Assessment and QOF Registry data.

Points to consider What will be the most effective way to segment the population to achieve

your goals? Which data sources will you use to conduct your segmentation? Which population segments will be targeted and why? How will you manage the competing demand for resources across the

different segments?

Risk stratification

Risk stratificationRisk stratification is a systematic process in which a population is divided into segments according to risk for a significant, costly and undesirable health outcome such as an unplanned hospital admission. It can be a tool for supporting care planning and personalised care, which are key elements of the vision and outcomes in the NHS Five Year Forward View.

Many health care systems use risk stratification to address the challenges of an increasing older population and decreasing budgets by improving the targeting of preventive care.

In this process, population segmentation is first used to group the local population by the kind of care they need as well as how often they might need it. Risk stratification then aims to understand who, within each segment, has the greatest risk of needing intense care such as a hospital admission.

Patient de-identified data can be used for population health planning, predicting future service demand for different risk cohorts, including their geographic distribution, health needs and co-morbidities.

Patient-identifiable data can be used to ‘case find’ those individuals at risk of such an adverse event, who might have this risk mitigated by being offered a proactive intervention. In both cases the data and process must be protected by robust information governance.

Risk stratification can be used to ensure that the highest-risk patients receive appropriate care for their needs (such as the input of multi-disciplinary teams); medium-risk patients might be referred to a lighter touch intervention (such as social prescribing); and lower-risk patients could be managed through usual care and self-care.

No risk stratification tool is ever completely accurate and it is therefore important to consider the adverse impact of false positive and false negative results as well as the benefits of true positive and true negative results. The aim of an effective risk stratification programme is to ensure that the benefits to the population outweigh its costs.

Points to consider Information governance (IG) for risk stratification can be complex. What

systems will you put in place? How will you reassure patients and gain consent to use their data? How will data be shared?

What are you looking to avoid over which time period? What is the most appropriate way to divide your population for this? What will your data source/s be?

Which predictive model will you use? Factors that should be considered in selecting a suitable model include the adverse outcome to be predicted, the accuracy of the predictions, the cost of the model and its software, and the availability of the data on which it is run.

If you are using risk stratification for case-finding, what you are going to do in response to the high-risk results?

Gap analysis

Ref Topics Priority Current Proposed Gap Resolution

Gap analysisA gap analysis is a detailed and systematic assessment of performance and outcomes. It can be used in a health system to identify issues such as deviation from NICE guidelines (e.g. missed immunisations, tests or diagnoses, or under-treatment), to highlight duplications in care and to identify ‘triple fail’ events (health care events that are simultaneously low quality, high cost, and offer a poor patient experience – such as an unplanned hospital admission or developing a bed sore).

A gap analysis enables commissioners to identify and focus on areas with gaps in care provision. As gaps are more common in deprived areas, a gap analysis can be used to inform work designed to reduce health inequalities.

Information obtained from a needs assessment, service review and market analysis can be collated to identify gaps in current service provision and can be used as the basis for developing strategic objectives and/or a commissioning strategy. A gap analysis is often pulled together by a combination of individual or small group analysis by key commissioners, followed by presentation of that analysis for consultation with other stakeholders. This allows the commissioners to think through proposed service changes and links with earlier research, before opening up the analysis to other stakeholders.

A gap analysis document includes the rationale for any proposed service developments along with the evidence gathered during analysis, and the commissioning implications of proposed developments.

Once the gaps have been identified, the next step is to analyse the data for opportunities to improve clinical and social care outcomes (an opportunity analysis).

Points to consider If you plan to establish a project group to carry out a gap analysis, who will

be on the group? It will need appropriate capabilities, knowledge of the trust and NICE expertise.

Use of a standard template, reporting and monitoring system is important. Consider the input needed from and engagement needed with key clinical leads/senior consultants and management.

When considering a gap analysis, consider the priorities for your trust and for other stakeholders. Are there competing priorities for limited resources?

How will you find or make time to manage the work? Have you agreed suitable resources?

How will you pilot your reports?

Opportunity analysis

Opportunity analysisOpportunity analysis involves looking at population data to identify incidences of low-quality, high-cost, poor-experience events and gauging how responsive each such event might have been to a cost-effective preventive intervention.

The approach includes detecting and mapping unwarranted variation and clinical and social care outcomes (such as unplanned hospital admissions, overly invasive treatment for a preference-sensitive condition, and over-medicalised care at the end of life) that can be influenced.

This work can be done within an ACS and between the ACS and its peers in relation to allocative, technical, and personal value, identifying gaps in care, wasteful duplications in care, and 'triple fail' events. Crucially, it also involves reviewing the literature for evidence-based interventions that have been shown to reduce the risk of such events cost-effectively.

An opportunity analysis can help determine whether an ACS is investing in the right mix of services to address the needs of its population. For example, is the balance of investment between cancer services versus respiratory services right? Or within respiratory services, is the balance of investment between asthma and obstructive sleep apnoea right?

It can also help answer questions such as whether the services offered by the ACS are being delivered as efficiently as possible and whether individual patients are receiving interventions that address their personal preferences.

An opportunity analysis process can include using predictive models to identify people at high risk of, for example, unplanned hospitalisation. Logic models can also be used to help identify areas of potential maximum value – where is best to invest money and effort for maximum impact?

Points to consider What are you trying to achieve from the opportunity analysis? Have you carried out a needs assessment, risk stratification and gap

analysis to inform your opportunity analysis? What is the most appropriate model to use for the situation? What will you do with the results of the analysis? Are there any competing priorities across the ACS in relation to the

balance of investment?

Impactibility

ImpactibilityOnce an opportunity analysis has been carried out to identify potential areas for improvement, impactibility models can identify which high-risk patients are most likely to be amenable to intervention (i.e. those who are most likely to be ‘impactible’) or which interventions would be most effective for which individual.

As such, predictive impactibility models can be used to predict individuals who are likely to acquire a disease or suffer an adverse event related to a disease, or whose health status may change, and which of these outcomes are impactible through an intervention such as taking or stopping a medication, carrying out a test, making a lifestyle change or changing the person’s environment.

While predictive risk models can be used for example to identify people at high risk of unplanned hospitalisation, some of the patients they identify may not be amenable to preventive care. Impactibility models therefore aim to identify the subset of those high-risk patients who are most likely to engage with and respond to various preventive interventions. This additional filter is intended to improve the cost-effectiveness of preventive care.

Ultimately, the aim of impactibility modelling is to identify the form of preventive care best matched to a high-risk patient’s individual characteristics. Models might include both varying the means of contact (e.g. nurse or doctor; time of intervention), the content and frequency of the intervention (for example, based on cultural preferences), and the use of incentives.

For example, to avoid unnecessary hospital admissions, it may be possible to target ‘upstream’ care not simply at those people who are at risk of future hospitalisation but more specifically to those at-risk people whose risks can be mitigated.

However, not all adverse events will be preventable – only around 20 per cent of hospital admissions in high risk individuals are amenable to prevention by intervention.

Points to consider Some of the strategies used to improve the impact of risk stratification

programmes could potentially worsen health care inequalities. There should be appropriate ethical reviews of both the design and implementation of impactibility models. How will you do this?

Which impactibility model/s will you use? Have you carried out the risk stratifications that impactibility modelling can

support? How will you use the resulting data? What section of the population will you look at? Who are you doing the

impactibility modelling on?