Health Inequalities

Embedding data-driven decision making into a Health Inequalities program, using machine learning tools

Our client, a Mental Health & Community Trust, sought our support to help improve its “finger in the air” service planning, and make it more data-driven. Strategy, partnership & transformation leads wanted to understand how local population service need was expected to change and be guided on how to address health inequalities to focus scare resource.

Using the Population Health & Inequalities Planning Tool, which combines Trust data with detailed population & health datasets, our client was able to highlight where population health inequalities were getting better or worse. Data science models built into the tool compare current service usage by population cohorts with what usage is expected to be (based on the population profile of each area) identifying which areas have the largest access gap.

Using the suite of planning views & tools, the Trust is now exploring & planning at every level, taking either a whole trust, locality or individual ward view. Predicted population changes are included to help forecast demand, and leads that are seeking to address particular issues, like lowering crisis or admission rates, are provided with the top drivers of the metric of concern, all driven by machine learning models built into the tool.

Work continues to roll out to more stakeholders across the organisation, who describe the analytics and insight provided as “brilliant” & “really useful”.

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