Over the last five years, electronic health records (EHRs) have been widely implemented in the United States, and health care systems now have access to vast amounts of data. While they are beginning to apply “big data” techniques to predict individual outcomes like post-operative complications and diabetes risk, big data remains largely a buzzword, not a reality, in the routine delivery of health care. Health systems are still learning how to broadly apply such analytics, outside of case examples, to improve patient outcomes while reducing spending. From a review of the literature on health systems that have successfully integrated predictive analytics in clinical practice, we have identified steps to make predictive algorithms an integrated part of routine patient care.
Determine the clinical decision. There is now a plethora of data available for nearly every potential clinical outcome. And where you have data, there is a potential predictive algorithm. But while it may be easy to develop clinical algorithms, it is equally necessary to be specific about which specific clinical decision(s) that algorithm will inform.
For example, there are many algorithms predicting a patient’s risk of hospital readmission (although the vast majority perform poorly). But simply knowing the percentage risk of readmission does not answer the questions that physicians and nurses typically ask before a patient is discharged: Should I discharge this patient now? Should I assign this patient to a readmission prevention intervention? Should this patient go to a short-term rehabilitation facility? Does she need a home care visit in the next two days?