Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering

TL;DR

This paper introduces a hybrid dynamic and multi-collaborative filtering approach to enhance information retrieval from electronic health records, reducing physicians' information overload during patient care.

Contribution

It presents a novel method combining Markov models and collaborative filtering to improve relevance ranking of health record information for physicians.

Findings

Correctly prioritized relevant information in 46.7% of cases

Demonstrated effectiveness on real electronic health record data

Improved information retrieval accuracy for physicians

Abstract

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records for physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, to prioritize information based on various similarities among physicians, patients and information items. We tested this new method using real electronic health record data from the Indiana Network for Patient Care. Our experimental results demonstrated that for 46.7% of testing cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in.