We introduced a more effective approach to analysing data which ultimately improved service redesign with optimal cost effectiveness and outcomes.

Background

Population Health Management (PHM) is an approach that uses data to identify and anticipate the needs of population groups and individuals so that services act as early as possible to keep people well and target support where it will have the greatest impact. ​

Our business intelligence specialists supported Dudley Clinical Commissioning Group (CCG) with PHM, using integrated data analytics to help determine how best to commission preventative and interventional care.​

Action

We worked with the CCG and Public Health colleagues to produce the intelligence and insight needed for their decision-making. We analysed integrated datasets (taking primary, secondary, community and mental health care data along with population, epidemiology and prescribing data) to create a visualisation report. This segmented the blended data to group similar people together.​ Using machine learning tools, we searched the blended data (for example by extracting patterns of need, demand, deterioration, complexity and expense) for opportunities to systematically optimise population level commissioning.​We held a system level workshop to analyse opportunity, assess impact and determine priorities.​

Impact

Blending the CCG’s data with other sources produced a holistic picture and enabled data quality management. The insight led to better understanding of populations and unwarranted variation. This in turn meant interventions or service redesign could be targeted and tailored for maximum impact, optimising cost effective care and outcomes.​ Our triangulation of data sources at population level gave the commissioners new insight, for example regarding deprived Asian men’s utilisation of planned and unplanned care, older white affluent people’s use of mental health and A&E services, and GP socioeconomic profile against their prescribing costs. ​

Feedback

“Key to understanding the complex drivers behind some of the key health and wellbeing challenges for Population Health Management are linked data sets and the availability of data for the wider determinants of health and wellbeing.  Working with NHS ML on accessing and visualising these data sets has advanced our modelling and forecasting capabilities.  In Dudley one of the main challenges in population health is childhood obesity and the wider socio-economic data sets will allow much further interrogation of interesting correlations (e.g. between childhood obesity and lone parenting).  These new data will not only enhance forecasting and impact analysis but will also yield better targeted solutions, incorporating improved social marketing.”

Anthony Nicholls| Head of Intelligence and Analytics, Dudley Clinical Commissioning Group

Further information

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