  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.

Information Retrieval;Human-Computer Interaction;Machine Learning