Impact of COVID-19 lockdown on patient-provider electronic communications

Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures

OBJECTIVE

To evaluate the ability of routinely collected electronic health record (EHR) use measures to predict clinical work units at increased risk of burnout and potentially most in need of targeted interventions.

METHODS

In this observational study of primary care physicians, we compiled clinical workload and EHR efficiency measures, then linked these measures to 2 years of well-being surveys (using the Stanford Professional Fulfillment Index) conducted from April 1, 2019, through October 16, 2020. Physicians were grouped into training and confirmation data sets to develop predictive models for burnout. We used gradient boosting classifier and other prediction modeling algorithms to quantify the predictive performance by the area under the receiver operating characteristics curve (AUC).

RESULTS

Of 278 invited physicians from across 60 clinics, 233 (84%) completed 396 surveys. Physicians were 67% women with a median age category of 45 to 49 years. Aggregate burnout score was in the high range (≥3.325/10) on 111 of 396 (28%) surveys. Gradient boosting classifier of EHR use measures to predict burnout achieved an AUC of 0.59 (95% CI, 0.48 to 0.77) and an area under the precision-recall curve of 0.29 (95% CI, 0.20 to 0.66). Other models' confirmation set AUCs ranged from 0.56 (random forest) to 0.66 (penalized linear regression followed by dichotomization). Among the most predictive features were physician age, team member contributions to notes, and orders placed with user-defined preferences. Clinic-level aggregate measures identified the top quartile of clinics with 56% sensitivity and 85% specificity.

CONCLUSION

In a sample of primary care physicians, routinely collected EHR use measures demonstrated limited ability to predict individual burnout and moderate ability to identify high-risk clinics.

Demographics and clinical features associated with rates of electronic message utilization in the primary care setting

INTRODUCTION

Electronic messages are growing as an important form of patient-provider communication, particularly in the primary care setting. However, adoption of healthcare technology has been under-utilized by underserved patient populations. The purpose of this study was to describe how adoption and utilization of electronic messaging occurred within a large primary care urban-based patient population.

METHODS

In this retrospective study, the frequency of electronic messages initiated by adult outpatient primary care patients was observed. Patients were classified as either non-portal adopters, non-message utilizers, low message utilizers, and high message utilizers. Logistic regression modeling was used to compare factors associated with message utilization rates to determine disparities in access.

RESULTS

Among a sample of 27,453 ethnically diverse adult patients from the Houston, Texas Metropolitan area, 33,497 unique messages were sent (1.22 messages/patient). Message burden was predominantly derived by a small number of high utilizers (individuals who sent 3 or more messages), who comprised 15.7 % of the study population (n = 4302) but accounted for 77 % of the message volume (n = 25,776). These high utilizers were typically older, White, English speaking, from middle to upper income zip codes, had higher number of comorbidities, and a higher number of clinical visits.

CONCLUSIONS

Most inbox messages were generated by a small number of patients. While it was reassuring to see older and sicker individuals utilizing electronic messaging, patients from minority and/or lower income background utilized electronic messaging much less. This may propagate systematic bias and decrease the level of care for traditionally underserved patients.