P105: Observational study of distribution of time and activities over the course of an emergency physician's shift

Introduction: The growing scrutiny to improve Emergency Department (ED) wait times and patient flow have resulted in many efforts to increase efficiency and maximize patient throughput via systems improvements. This study investigates areas of efficiency improvement from the Emergency Physician (EP) perspective by examining EP workflow in a two phased observational time-motion study. In the initial phase, the distribution of time and activities of EPs were dissected to identify potential sources for streamlining to maximize physician productivity. The first phase was of the study was completed during the period immediately preceding the implementation of an Electronic Health Records (EHR). The second phase of the study will repeat the analysis one year post EHR implementation. This data will be dissected to again identify sources for streamlining in an EHR environment and to identify shifts in work flow from a paper-based system. Methods: An observational time motion study was conducted at St. Mary's Hospital ED, in Kitchener Ontario. An observer was paired with an EP for the duration of an 8 hour shift, to a total of 14 shifts in the first phase of the study. Nine task categories were measured concurrently with a stopwatch application on a tablet, along with the number of interruptions experienced by the EP. Means of each category were calculated and converted to percentages, representing the amount of time per 8 hour shift dedicated to each activity. The second phase will be repeated in Fall 2020, 1 year after EHR implementation. Results: A total of 14 shifts were observed, accounting for 112 hours of observation. EP's time was allocated amongst the following categories: direct patient interaction (40.8%), documentation (27.1%), reviewing patient results (18.4%), communicating with ED staff (7.63%), personal activities (5.7%), writing orders (5.1%), communicating with consultants (3.3%), teaching (1.7%) and medical information searches (1.3%). On average, EPs experienced 15.8 interruptions over the course of an 8 hour shift. Conclusion: In a paper charting system, the direct patient interaction accounts for the largest timeshare over the course of a given shift. However, the next two largest categories, documentation and reviewing patient data, both represent areas of potential streamlining via clerical improvements. Additionally, detailed measurements of EPs’ activities have proven feasible and provides the potential for future insight into the impact of EHR's on EP workflow.

Abstract 321: Association of Specialist Physician Productivity With Trainee and Staffing Levels in the Veterans Health Administration

Background: The effect of trainee, associate provider, and support staff levels on physician productivity by specialty is unknown. In 2013, the Veterans Health Administration (VHA) introduced a program to measure specialist physician productivity at the practice level, defined as the total work Relative Value Units (RVUs) generated by the physicians in a practice divided by the number of clinical full-time equivalents (FTEs) attributed to that practice. Data from this program can be utilized to understand the effect of specialty practice features on physician productivity. Methods: We extracted physician productivity levels and the numbers of trainees, associate providers, administrative support staff, and clinical support staff from fiscal year 2019 workforce reports produced by the VHA Office of Productivity, Efficiency, and Staffing for practices in four representative specialties: cardiology, gastroenterology, neurology, and surgery. We used linear regression to identify associations between physician productivity and trainee and staffing levels, adjusting for the complexity group of included practices as some practices do not perform procedures. Results: A total of 122 cardiology, 112 gastroenterology, 118 neurology, and 123 surgery practices with at least 0.5 clinical FTE were included. Physician (practice) productivity ranged from 2153 to 12,497 (mean 6899) RVUs/FTE for cardiology, 1189 to 13,435 (mean 7080) RVUs/FTE for gastroenterology, 1753 to 11,322 (mean 4154) RVUs/FTE for neurology, and 1761 to 8792 (mean 4251) RVUs/FTE for surgery. Physician productivity was positively associated with the number of trainees per clinical FTE for cardiology [coefficient 818 (95% CI 260, 1376) additional RVUs/FTE] and surgery [coefficient 253 (95% CI 56, 451) additional RVUs/FTE] but not for other specialties. Only neurologist productivity was positively associated with the number of associate providers per clinical FTE [coefficient 1095 (95% CI 128, 2061) additional RVUs/FTE]. There were no significant associations between physician productivity and the numbers of administrative and clinical support staff per clinical FTE. Conclusion: There is significant variation in VHA physician productivity across practices within each specialty. Physician productivity is positively associated with the number of trainees in a practice for some specialties, including cardiology, suggesting that trainees in those specialties may enhance physician productivity. The relationship between physician productivity and trainee and associate provider ratios varies by specialty. These specialty-specific associations can inform efforts to improve VHA physician productivity.