The effect of remote scribes on primary care physicians' wellness, EHR satisfaction, and EHR use.

BACKGROUND: Physician burnout is a major problem in the United States. Small studies suggest scribes can improve clinician satisfaction, but scribe programs have not been evaluated using separate control groups or structured measures of electronic health record (EHR) use. METHODS: We conducted a pre-post, non-randomized controlled evaluation of a remote scribe pilot program introduced in September 2019 in an academic primary care practice. Scribes were paired with physicians via an audio-only cellphone connection to hear and document in real-time. Physician wellness was measured with the 10-item Mini-Z and 16-item Professional Fulfillment Index. EHR use was measured using vendor-derived platforms that provide routine EHR-related data. RESULTS: 37 of 38 scribe users (97.4%) and 68 of 160 potential control physicians (42.5%) completed both pre and post intervention questionnaires. Compared with controls, scribe users had improvements in Mini-Z wellness metrics including Joyful Workplace (mean improvement 2.83, 95%CI 0.60, 5.06) and a single-item dichotomized burnout measure (OR 0.15, 95%CI 0.03, 0.71). There were significant reductions among scribe users compared to controls in total EHR time per 8 scheduled hours (-1.14 h, 95%CI -1.55, -0.72), and an increase in the percentage of orders with team contribution (10.4%, 95%CI 5.2, 15.6). These findings remained significant in adjusted analyses. CONCLUSIONS/IMPLICATIONS: A remote scribe program was associated with improvements in physician wellness and reduced EHR use. Healthcare organizations can consider scribe programs to help improve wellness among their physician workforce.

Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.

Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls. We engaged an interdisciplinary design team (ED providers, health services researchers, information technology/predictive analytics professionals, and outpatient Falls Clinic staff) to collaboratively develop a system that successfully met user requirements and integrated seamlessly into existing ED workflows. Our rapid-cycle development and evaluation process employed a novel combination of human-centered design, implementation science, and patient experience strategies, facilitating simultaneous design of the CDS tool and intervention implementation strategies. This included defining system requirements, systematically identifying and resolving usability problems, assessing barriers and facilitators to implementation (e.g., data accessibility, lack of time, high patient volumes, appointment availability) from multiple vantage points, and refining protocols for communicating with referred patients at discharge. ED physician, nurse, and patient stakeholders were also engaged through online surveys and user testing. Successful CDS design and implementation required integration of multiple new technologies and processes into existing workflows, necessitating interdisciplinary collaboration from the onset. By using this iterative approach, we were able to design and implement an intervention meeting all project goals. Processes used in this Clinical-IT-Research partnership can be applied to other use cases involving automated risk-stratification, CDS development, and EHR-facilitated care coordination.