A Time Motion Study Evaluating the Impact of Geographic Cohorting of Hospitalists
BACKGROUND: Geographic cohorting (GCh) localizes hospitalists to a unit. Our objective was to compare the GCh and non-GCh workday.
METHODS: In an academic, Midwestern hospital we observed hospitalists in GCh and non-GCh teams. Time in patient rooms was considered direct care; other locations were considered ‘indirect’ care. Geotracking identified time spent in each location and was obtained for 17 hospitalists. It was supplemented by in-person observation of four GCh and four non-GCh hospitalists for a workday each. Multilevel modeling was used to analyze associations between direct and indirect care time and team and workday characteristics.
RESULTS: Geotracking yielded 10,522 direct care episodes. GCh was associated with longer durations of patient visits while increasing patient loads were associated with shorter visits. GCh, increasing patient loads, and increasing numbers of units visited were associated with increased indirect care time. In-person observations yielded 3,032 minutes of data. GCh hospitalists were observed spending 56% of the day in computer interactions vs non-GCh hospitalists (39%; P < .005). The percentage of time spent multitasking was 18% for GCh and 14% for non-GCh hospitalists (P > .05). Interruptions were pervasive, but the highest interruption rate of once every eight minutes in the afternoon was noted in the GCh group.
CONCLUSION: GCh may have the potential to increase patient–hospitalist interactions but these gains may be attenuated if patient loads and the structure of cohorting are suboptimal. The hospitalist workday is cognitively intense. The interruptions noted may increase the time taken for time-intensive tasks like electronic medical record interactions.
© 2019 Society of Hospital Medicine
Schedules detailing each team’s members and assigned units (if cohorted) were retrieved. For each observed day, the hospitalist was linked to his or her team type and unit. Team lists were retrieved to ascertain patient load at the start of the day. Data sources were merged to categorize observations.
Observation Categories for Locator Badge Data
The I-Badge® data provided details of how much time the hospitalist spent in each location (eg, nursing station, hallways, patient rooms). All observations in patient rooms were considered “direct care” while all other locations were categorized as “Indirect Care”. Observations were also categorized by the intensity of care provided on that unit, which included the Emergency Department (ED), Progressive Care Units (PCU), Medical-Surgical + PCU units (for units having a mixture of Medical-Surgical and PCU beds), and Medical-Surgical units.
In-person Observations
Four research assistants (RAs) were trained until interrater reliability using task times achieved an intraclass correlation coefficient of 0.98. Task categories included direct care (all time with patients), indirect care (computer interactions, communication), professional development, and travel and personal time. Interruptions were defined as “an unplanned and unscheduled task, causing a discontinuation, a noticeable break, or task switch behavior”.9 “Electronic interruptions” were caused by pagers or phones whereas in-person interruptions were “face-to-face” interruptions. When at least two tasks were performed simultaneously, it was considered multitasking. A data collection form created in REDCap was accessed on computer tablets or smartphones10 (Appendix Table 1). To limit each observation period to five hours, two RAs were scheduled each day. Observations were continued until the hospitalist reported that work activities were complete or until 5
Statistical Analysis
Due to the nested structure of the locator badge data, multilevel mode
Univariate three-level models predicting minutes spent in direct care were tested for each predictor. Predictors, described below, were selected due to their hypothesized relation to time spent in direct patient care, or to account statistically for differences among teams due to the observational nature of the study.12 Predictors were: Level 3, hospitalist characteristics (years since medical school, age, gender, international graduate, years at current hospital); Level 2, work day characteristics (number of units visited, number of patients visited, team type, weekday); and Level 1, individual observation characteristics (intensity of care on unit, number of visits to the same patient room per day
For total daily indirect care, a similar modeling process was used. A log normal distribution was used because the data was right-skewed and contained positive values. The restricted maximum likelihood method was used to calculate final estimates for models. Least square mean values for independent variables were subjected to backward transformation for interpretation. Post hoc pairwise comparisons between team types were conducted using Tukey–Kramer tests for direct and indirect care time. Analyses were conducted using SAS software version 9.4 (Cary, North Carolina).
The in-person observations were summarized using descriptive statistics. Exploratory analyses were performed using t-tests and Fisher’s exact tests to compare continuous and categorical variables respectively.
