Matching physician resources with demand in a cancer hospital emergency center.

Abstract

276 Background: In early 2010 the Emergency Center (EC) at MD Anderson Cancer Center identified a need to adjust the physician staffing to better meet the demand of increasing patient work-load and improve physician satisfaction. There was also a strong interest in learning more about variations in work methods and how physician time was spent in the EC. Methods: The EC partnered with the Office of Performance Improvement (OPI) to perform a work sampling study and analyze how physicians’ time is spent by physician, time of day, and day of week. Also needed to properly match resources to demand was how the patient demand varied with time of day and day of week. Before the study was performed the tasks and categories of interest were established and training as well as a measurement study to ensure consistency from one data collector to another. A sampling plan and method were then developed to identify and record the frequency of tasks performed to be studied by shift, day, and physician. Patient data was collected through the internal computerized whiteboard to identify patient arrivals and occupancy. Results: The results showed the variation in tasks performed was most highly attributable to the individual physician rather than time of day and day of week. Percentages of the shifts were calculated, summarized, and presented to the EC leadership. This data was then combined with the patient volumes data in an interactive spreadsheet to simulate different scheduling patterns and provide recommendations to best match resources with workload. Conclusions: The project provided a valuable resource to contribute to identifying the best scheduling pattern for the EC including start times and shift length. The results have been further used to strive towards inter-physician consistency over the past two years and have led to additional studies and opportunities for improvement. By using a data based methods, even for qualitative studies, important process understanding and opportunities are available which can help break through barriers and improve data based decision making in health care.