Oral Presentations

O-9: Evaluating the Effectiveness of an EMR based tool for Metabolic Monitoring of Primary Care Patients on Atypical Antipsychotics

Daniel Brewster, M.D., Saurabh Kalra, M.D.,
Mohini Patel, M.D.,
Timothy Rudd, M.D.

McMaster University
Centre for Family Medicine


Keywords: Atypical Antipsychotics, Metabolic, Monitoring, EMR

Atypical antipsychotics (AAPs) are increasingly being prescribed by primary care providers as first-line treatment for psychiatric disorders as well as for off-label use. However, they have a known metabolic side effect profile including hyperglycemia, hyperlipidemia, and weight gain, which are often not monitored. Our objective was to determine if the implementation of an Electronic Medical Record (EMR) toolbar could increase the rate of metabolic monitoring for patients on AAPs.

Our study population included patients of eight Family Physicians from a Community Family Health Team. The EMR toolbar was implemented on November 15, 2017. Metabolic monitoring rates were compared five months before and after tool implementation. We calculated the percentage change in the monitoring of metabolic parameters to evaluate the effectiveness of our intervention.

The analysis included a total of 134 charts in the pre-intervention group and 148 charts in the post-intervention group, from which 112 charts were paired data. Overall, there was an increase in monitoring of weight, lipids, and glucose (28%, 61%, and 9%, respectively) post-intervention. In the paired data set, there was also a similar increase in monitoring of lipids and glucose (53% and 9%, respectively); however, a decrease in monitoring of weight (5%) was noted post-intervention.

The data shows that adherence to metabolic monitoring guidelines for AAPs remains low; however, increased monitoring of lipids and glucose was consistently observed in the combined and paired data set post-intervention. Future research can explore a more detailed methodological approach to differentiate whether a patient was due for screening at the time of data abstraction in order to determine effectiveness of EMR tools.