Making Predictive Analytics a Routine Part of Patient Care

Making Predictive Analytics a Routine Part of Patient Care

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.

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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?

Parkland Health and Hospital System in Dallas, Texas, has developed a validated EHR-based algorithm to predict readmission risk in patients with heart failure. Patients deemed at high risk for readmission receive evidence-based interventions, including education by a multidisciplinary team, follow-up telephone support within two days of discharge to ensure medication adherence, an outpatient follow-up appointment within seven days, and a non-urgent primary-care appointment. In a prospective study, the algorithm-based intervention reduced readmissions by 26%. Parkland’s success stems from focusing its algorithm on a specific population and tying it to discrete clinical interventions.

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Leverage the data from EHRs. Algorithms are only as reliable as the data they are based on. While algorithms for acute clinical issues (e.g., heart attack, septic shock) may not require large amounts of data to predict risk, algorithms that utilize greater amounts of clinical data have greater accuracy and potential clinical applications.

The Veteran’s Health Administration (VHA), the largest health system in the United States, has collected electronic data from its patients for over three decades. Beginning in 2006, the VHA built a corporate data warehouse as a repository for patient-level data across its national sites.

 



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