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Approved research

Predicting unexpected care needs, cost spikes, and applicability of genetic testing in enhancing these models

Principal Investigator: Mr Gabe Richman
Approved Research ID: 55183
Approval date: November 25th 2019

Lay summary

Most of us don't expect to have a substantial decline in health next year. The small percentage of us that do experience an unexpected health change contribute disproportionately to the cost and burden on the health system. An ability to predict these in advance using modern statistical techniques and new sources of data such as genetics would benefit both the individual and the population. Over the next 12 months, we plan to analyze the UK Biobank data to predict when these health changes occur. These include changes from healthy to managing one or more conditions, and from one disease stage to the next. Through modern statistical techniques, we will evaluate the relative importance of some of newer data sources such as genetics in making these predictions. By evaluating these changes in health over a large group and long period of time, we can better understand the signals that indicate a near-term future change in health, who should receive proactive genetic and other testing/monitoring, and ultimately how we can continue to move toward medical interventions that manage future health risk as compared with controlling existing disease(s).