Automatic summarization of risk factors preceding disease progression an insight-driven healthcare service case study on using medical records of diabetic patients
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In this study we consider the problem of how to derive insight from medical records to define and improve healthcare services. As noted in many guidelines, risk factors are important to determining the care plan of chronic disease patients, e.g., pre-diabetic or diabetic patients who have started on hemoglobin A1c (HbA1c) control medications. Whereas the traditional management of chronic disease relies on a predetermined set of risk factors, without regard to patient-specific status, literature and recently released guidelines have suggested a less-prescriptive approach that allows flexibility in disease management plans to account for patient-centric information shown in medical records. However, methods of systematically summarizing medical records into risk factors have not been evaluated to support such a patient-centric focus in healthcare services. In this study, we evaluated automatic methods that can identify risk factors important for classifying Diabetic patients at risk of worsen disease progression. In particular, we used the prescription of cardiovascular disease (CVD) medication as the indicator of CVD co-morbidity development in Diabetic patients. We evaluated the summaries obtained with different sources of health information on the risk stratification task and examined the quality of the generated summaries using various proposed intrinsic metrics. In addition, we evaluated to what extent we can reduce the whole medical records into a small set of risk factors. The evaluation illustrates the potential of risk factor summarization and hints on how it can be used to enable practitioners in care planning and to support complex follow-up services at both the point of care and the extended care settings.
KeywordsHealthcare service design Case study Disease management Data-driven analytics Medical record summarization
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