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Monitoring Physicians’ Prescription Patterns on Electronic Health Record: the Prescription Pattern Around Clinical Event (PACE) Algorithm

Yoon, Dukyong; Park, Inwhee; Schuemie, Martijn J.; Park, Man Young; Hong, Seung Kwon; Park, Rae Woong
Department of Medical Informatics
Background: Electronic health records (EHRs) have gained attention as a valuable data source for medical research, as its adoption rate continues to rise. However, no method for the monitoring of physicians' prescription patterns has been established.

Methods: Since EHR maintain all prescription data as well as clinical events that occur during the care of patients, we hypothesized that a physician's prescription pattern can be monitored from EHR. In this study, we developed a novel algorithm named PACE, Prescription pattern Around Clinical Event. This algorithm analyzes distribution of the prescription of specific drugs around the time of a clinical event. Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event.

Results: Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. In the proof of concept study, prescription changes with regard to hyperkalemia and CDAD were well represented by the algorithm, and the observed patterns well correlated with the physician's knowledge on hyperkalemia (Cohen's kappa, 0.714).

Conclusion: The algorithm successfully assessed the prescribing patterns from the EHR data. The prescription patterns were well correlated with physicians' knowledge. We expect that this algorithm will enable quantification of prescribers' adherence to clinical guidelines and be used to facilitate improved prescribing practices.
Guideline adherenceprescription monitoringprescription patternquality of health carequality indicator
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