Electronic Health Record Use and Perceptions among Urologic Surgeons

Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists

OBJECTIVE

To assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited.

METHODS

A sequential explanatory mixed methods study from 2019 to 2020 was performed. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated.

RESULTS

Among a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use.

CONCLUSION

Urologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.

Electronic Health Record Usage Patterns Across Surgical Subspecialties

OBJECTIVES

This study aimed to utilize metrics from physician action logs to analyze surgeon clinical, volume, electronic health record (EHR) efficiency, EHR proficiency, and workload outside scheduled time as impacted by physician characteristics such as years of experience, gender, subspecialty, academic title, and administrative title.

METHODS

We selected 30 metrics from Epic Signal, an analytic tool in Epic that extracts metrics related to clinician documentation. Metrics measuring appointments, messages, and scheduled hours per day were used as a correlate for volume. EHR efficiency, and proficiency were measured by scores built into Epic Signal. Metrics measuring time spent in the EHR outside working hours were used as a correlate for documentation burden. We analyzed these metrics among surgeons at our institution across 4 months and correlated them with physician characteristics.

RESULTS

Analysis of 133 surgeons showed that, when stratified by gender, female surgeons had significantly higher EHR metrics for time per day, time per appointment, and documentation burden, and significantly lower EHR metrics for efficiency when compared to male surgeons. When stratified by experience, surgeons with 0 to 5 years of experience had significantly lower EHR metrics for volume, time per day, efficiency, and proficiency when compared to surgeons with 6 to 10 and more than 10 years of experience. On multivariate analysis, having over 10 years of experience was an independent predictor of more appointments per day, greater proficiency, and spending less time per completed message. Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours.

CONCLUSION

The burden associated with volume, proficiency, efficiency, and workload outside scheduled time related to EHR use varies by gender and years of experience in our cohort of surgeons. Evaluation of physician action logs could help identify those at higher risk of burnout due to burdensome medical documentation.

Perceived vs Actual Shared Decision-Making Behavior Among Urologists: A Convergent, Parallel, Mixed-Methods Study of Self-Reported Practice

OBJECTIVE

To evaluate the association between self-perceived use of shared decision-making among urologists with use of validated prediction tools and self-described surgical decision-making.

METHODS

This is a convergent mixed methods study of these parallel data from two modules (Shared Decision Making and Validated Prediction tools) within the 2019 American Urological Association (AUA) Annual Census. The shared decision-making (SDM) module queried aspects of SDM that urologists regularly used. The validated prediction tools module queried whether urologists regularly used, trusted, and found prediction tools helpful. Selected respondents to the 2019 AUA Annual Census underwent qualitative interviews on their surgical decision-making.

RESULTS

In the weight sampled of 12,312 practicing urologists, most (77%) reported routine use of SDM, whereas only 30% noted regular use of validated prediction tools. On multivariable analysis, users of prediction tools were not associated with regular SDM use (31% vs 28%, P = .006) though was associated with use of decision aids f (32% vs 26%, P < .001). Shared decision-making emerged thematically with respect to matching treatment options, prioritizing goals, and navigating challenging decisions. However, the six specific components of shared decision-making ranged in their mentions within qualitative interviews.

CONCLUSION

Most urologists report performing SDM as supported by its thematic presence in surgical decision-making. However, only a minority use validated prediction tools and urologists infrequently mention specific SDM components. This discrepancy provides an opportunity to explore how urologists perform SDM and can be used to support integrated strategies to implement SDM more effectively in clinical practice.