Matt Sperrin — Lecturer in Health Data Science at the University of Manchester
Peter Diggle — Professor of Statistics and Associate Dean at Lancaster University
Ben Brown — Clinical Academic Fellow in Primary Care at the University of Manchester
Ben Bridgewater — Hon. Professor and Cardiac Surgeon at University Hospital of South Manchester
For any patient presenting with a myocardial infarction, the local health system might reasonably ask “Was there an opportunity missed to prevent or delay that event?”
The opportunity might have been in smoking cessation services, early detection in primary care, medication in blood pressure and lipid control, revascularisation procedures etc. Clinical guidelines and care pathways synthesise the evidence for best care in most long term conditions. So it should be possible to use linked health e-records to compare ‘expected care’ from guidelines with ‘observed care’ from analysis of the integrated records. The difference between observed and expected care may then indicate missed opportunities for intervention, and aspects of real world healthcare where evidence from trials might not generalise, for example in patients with comorbidities. Most clinical audits, however, focus on settings rather than whole pathways of care across different settings. This creates islands of data and analysis, compounding the lack of generalisability of evidence by applying it too narrowly to detect “missed opportunities”.
MOD focuses on the gap between idealised care and what happens in reality. Using routinely collected data from electronic health records the tool will establish missed opportunities, determine their significance and provide intelligence on how to avoid them in future. It will initially focus on cardiovascular disease with plans to encompass a range of conditions. MOD represents a new approach to healthcare quality improvement. By providing information in a cost-effective, timely and automated way it will support better targeted decision-making by policymakers, providers and commissioners. Patients and clinicians will also have a foundation for highlighting and helping to avoid missed opportunities before they arise.
- To develop a health infomatics methodology for detecting missed opportunities in routinely collected healthcare data.
- To discover computable clinical intelligence methods that can improve health outcomes.
- To develop informatics interventions that can improve clinical governance with new intelligence methods.