Abstract 13: Evaluation of a FHIR-based Clinical Decision Support Tool for Calculating CHA2DS2-VASc Scores

Abstract

Background: Oral anticoagulants reduce stroke risk among atrial fibrillation (AF) patients, yet treatment rates remain low. A technology known as SMART on FHIR allows third party apps to integrate with electronic medical records (EMR) and provide decision support tools. University of Utah Health implemented an alpha version of an integrated MDCalc app - MDCalc on FHIR (MoF) - in the Epic EHR using a SMART on FHIR interface to enable multiple calculations, one of which was the CHA2DS2-VASc calculation. While MDCalc on FHIR is designed to automate inputs that are then reviewed by the practicing clinician with an opportunity to override with clinical judgment, we prospectively compared the automated app score - without physician oversight - with the clinician score, as an early measurement to evaluate accuracy and identify areas for improvement. Methods: We identified outpatient AF patients who were seen in the University of Utah’s cardiovascular clinics between 11/5/2018 and 12/7/2018. We used the MoF app to automatically calculate the CHA2DS2-VASc score within 24 hours of the identified clinic visit - without the MoF feature of allowing clinician interaction - and compared these values to the score documented by the clinician. We also categorized patients as either low risk of stroke or high risk of stroke and calculated the net reclassification index (NRI) using the app compared to documentation. Patients with an app score ≥2 were considered high risk, while patients with a documented score of ≥2 or were prescribed an oral anticoagulant were considered high risk. Results: We identified 200 AF patients, of whom 111 had a documented clinician score. The mean MoF app score was 3.79 (SD 1.86) compared to a mean clinician score of 3.25 (SD 1.63; p=0.02). The NRI was 13.5% (27 of 200) using the app compared to documentation. Ten percent (19 of 200) of patients were “up-classified” by the app, meaning they were high risk by app and low risk by the clinician. Four percent (8 of 200) of patients were “down-classified” by the app, meaning they were low risk by the app and high risk by the clinician. Upon review of these cases, and after accounting for patients who were or were not anticoagulated for a clinically relevant reason (history of bleeding, recent cardioversion, or patient preference), we found that three percent (5 of 200) of patients were “up-classified” by the app, and two percent (3 of 200) of patients were “down-classified” by the app, making the adjusted NRI 4% (8 of 200). Conclusion: SMART on FHIR enables third party vendors to create EMR-based apps, which could provide decision support and improve care. We found that our FHIR-app based approach tended to identify more comorbidities by using medication data to assign conditions, resulting in a higher CHA2DS2-VASc score compared to clinicians. These differences would have had a 4% effect on the actual decision to anticoagulate.