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2b – Developing plans: population health management

Population health management is an alternative “lens” to traditional needs assessment. It involves looking at the same population using patient-level data arranged into needs-based segments or clusters. It explores resource utilisation based on commonality of risk to describe care needs, facilitating optimisation of care provision and resource use in line with prudent healthcare  principles.

Population health management as an approach in Wales has been piloted in Cwm Taf Morgannwg (CTM) University Health Board and comprises the closely-linked components of segmentation, risk stratification and case-mix adjusted variation analysis. Importantly, traditional needs assessment and population health management should be seen as complementary as they address distinct planning and healthcare delivery needs; they both share the challenge of converting data into actionable intelligence.

Segmentation has the following features:

  • Segmenting the population based on a range of factors can identify groups by their holistic need and ability to benefit from anticipatory care.
  • Segmentation involves linkage of primary, secondary and, where available, community and social care datasets to take account of socio-demographic variables, morbidities, care utilisation (e.g. elective inpatient admissions, non-elective inpatient admissions, outpatient first & follow-up attendances, Emergency Department visits, GP practice visits and prescriptions), cost and risk factor information.
  • Segments (groups of patients) are derived on the basis of shared needs profiles.
  • Segmentation results in CTM revealed that significant and complex healthcare need was a feature across age groups and was driven more by deprivation and behavioural risk factors than by age and functional limitation.
  • To support planning improvements in population health, evidenced-based actions need to be identified and implemented on a per-segment basis. The tailoring of interventions to specific segments is considered the best way of ensuring the most effective use of healthcare resources.

Risk stratification
Risk stratification has the following features:

  • To support clinical decision-making, risk stratification of individual patients is based on the Johns Hopkins ACG model (so there are both clinical/ individual-level and population-level benefits to the approach).
  • Risk scores are compiled from various variables (e.g. age, sex, medication, disease, etc.) and can be applied to various predictive models (e.g. probability of emergency hospital admission).
  • These scores can be used at a GP practice, cluster and health board level to identify individuals or groups of patients within the highest risk groups and to enable the management and reduction of risk through targeted and anticipatory care.

Case-mix adjusted variation
Case-mix adjusted variation analyses build on the segmentation and risk stratification data and have the following features:

  • Case-mix adjusted analyses yield both crude and adjusted healthcare utilisation indices for practices, clusters and health boards.
  • Case mix adjustment allows for a true comparison between providers or areas.
  • A reporting platform allows comparison of case-mix adjusted rates with other anonymised GPs in the cluster, the cluster average, the health board average, and potentially across clusters nationally.
  • Once again, evidenced-based interventions must be identified to inform management based around anticipatory and preventive care.

Local implementations
There is no all-Wales programme approach to population health management. A proposal sponsored by Directors of Public Health to develop the approach with a small number of clusters in all health boards was made via the National Primary Care Board; this secured support in principle from Welsh Government but is yet to be funded, partly due to the Covid pandemic. For an update on current activity and to determine whether these tools are available to support needs assessment, clusters should contact their primary care and/or local public health teams.