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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Characterizing mental health diagnosis within Canadian primary care settings: Application of validated electronic
Leanne Kosowan1, Alexander G Singer2, Elissa M Abrams3
1Manitoba Primary Care Research Network Manager in the Department of Family Medicine at the University of Manitoba in Winnipeg and Canadian Primary Care Research Network Research Manager.
Objective:
To validate a primary care electronic medical record (EMR) case definition for mood and anxiety disorders (including depression, anxiety, and bipolar disorder) and schizophrenia that can be used to estimate prevalence and co-occurrence.
Design:
Retrospective cross-sectional study.
Setting:
Canada.
Participants:
De-identified EMR data was used from 1574 primary care providers participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 1,692,987 patients who had 1 or more visits with a primary care provider. The reference set included 2488 patients, with 434 positive and 2054 negative for 1 or more mental health conditions of interest. A second reference set for schizophrenia represented 760 patients (30 positive and 730 negative).
Main Outcome Measures:
The agreement of 29 case definitions was assessed against a reference set by reporting sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Prevalence was estimated and co-occurrence was assessed in the CPCSSN dataset (N=1,692,987).
Results:
The strongest definition for mood disorders captured anxiety, depression, and bipolar disorder with a sensitivity of 80.7%, specificity of 88.7%, PPV of 59.9%, and NPV of 95.7%; and an estimated prevalence of 21.8% (95% CI 21.7 to 21.9). The inclusion of psychosis did not improve agreement (sensitivity 95.2%, specificity 80.7%, PPV 51.0%, NPV 98.8%), but schizophrenia alone had high agreement (sensitivity 93.3%, specificity 100%, PPV 100%, NPV 99.9%).
Conclusion:
High co-occurrence of anxiety, depression, and bipolar disorder was found. Algorithms validated to capture these conditions together produced stronger agreement compared with individual definitions. Schizophrenia was less likely to co-occur with other mental health conditions and produced higher agreement when validated separately. Application of validated algorithms to capture mental health conditions can inform disease surveillance and health system planning.
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