Data Mining and Electronic Health Records: Selecting Optimal Clinical Treatments in Practice

TL;DR

This paper explores how data mining of electronic health records can improve clinical decision support by predicting treatment outcomes, demonstrating a 70% success rate in a large mental healthcare setting.

Contribution

It evaluates the predictive capacity of EHR data for treatment outcomes, showcasing an adaptive approach in real-world mental health clinical decision support.

Findings

Achieved 70% success rate in predicting treatment outcomes

Utilized data mining techniques on large-scale EHR data

Demonstrated practical application in mental healthcare

Abstract

Electronic health records (EHR's) are only a first step in capturing and utilizing health-related data - the problem is turning that data into useful information. Models produced via data mining and predictive analysis profile inherited risks and environmental/behavioral factors associated with patient disorders, which can be utilized to generate predictions about treatment outcomes. This can form the backbone of clinical decision support systems driven by live data based on the actual population. The advantage of such an approach based on the actual population is that it is "adaptive". Here, we evaluate the predictive capacity of a clinical EHR of a large mental healthcare provider (~75,000 distinct clients a year) to provide decision support information in a real-world clinical setting. Initial research has achieved a 70% success rate in predicting treatment outcomes using these methods.