Provider time allotment tracking tool to effectively manage assignment commitments

Due to the rising demand with limited health service capacity, managing available resources effectively becomes an important task to reduce patient care delays and avoid unnecessary and costly capacity expansions. At the same time, staff satisfaction and/or burnout is a complementary consideration when designing optimal schedules. Deviation from the scheduled plan can cause delays in patient access and may lead to unsatisfaction among providers. Balancing demand management, staff satisfaction and generating optimized schedules quickly reveals the need for a tool that tracks provider time allotment over time, especially for the academic healthcare organization where providers are committed to multiple assignments, clinical and non-clinical. This tracking tool should allow management to proactively adjust allotment to unplanned changes in the schedule and increase participation. In this study, a tool is developed to track monthly provider assignments for the Department of Cardiovascular Medicine at Mayo Clinic, Rochester. The proposed tool produces two key outputs for each provider and assignment: 1) the recommended target workdays and 2) workday upper and lower bounds to accommodate for variability. This tracking tool is successfully implemented with implementation criteria, and the feedback is positive. The tool pulls the data systematically from the Mayo data platform and performs the necessary analysis on the data. It also automatically updates the values for the recommended target as well as upper and lower bounds for the remaining months in a year based on changes in the schedule so that provider commitment can be met at the end of year.

[1]  Janaina Figueira Marchesi,et al.  A mixed integer programming approach to the patient admission scheduling problem , 2019, Eur. J. Oper. Res..

[2]  Rainer Kolisch,et al.  Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service , 2013, Ann. Oper. Res..

[3]  Vincent Augusto,et al.  A stochastic optimization model for shift scheduling in emergency departments , 2015, Health care management science.

[4]  Andreas T. Ernst,et al.  Staff scheduling and rostering: A review of applications, methods and models , 2004, Eur. J. Oper. Res..

[5]  Hesham K. Alfares,et al.  Survey, Categorization, and Comparison of Recent Tour Scheduling Literature , 2004, Ann. Oper. Res..

[6]  Stephen E. Bechtold,et al.  A Comparative Evaluation of Labor Tour Scheduling Methods , 1991 .

[7]  Hamid Allaoui,et al.  A stochastic model to minimize patient waiting time in an emergency department , 2018, Operations Research for Health Care.

[8]  Melanie Erhard,et al.  State of the art in physician scheduling , 2018, Eur. J. Oper. Res..

[9]  R. Kolisch,et al.  Overutilization and underutilization of operating rooms - insights from behavioral health care operations management , 2017, Health care management science.

[10]  Peter J. H. Hulshof,et al.  A Framework for Healthcare Planning and Control , 2012 .

[11]  L. Dyrbye,et al.  Physician burnout: contributors, consequences and solutions , 2018, Journal of internal medicine.

[12]  Janaina Figueira Marchesi,et al.  A stochastic programming approach to the physician staffing and scheduling problem , 2020, Comput. Ind. Eng..

[13]  Erik Demeulemeester,et al.  Personnel scheduling: A literature review , 2013, Eur. J. Oper. Res..

[14]  Hoong Chuin Lau,et al.  On the complexity of manpower shift scheduling , 1996, Comput. Oper. Res..

[15]  Kenneth R. Baker,et al.  Workforce Allocation in Cyclical Scheduling Problems: A Survey , 1976 .

[16]  Andreja Pucihar,et al.  Trust, Innovation and Prosperity , 2013 .

[17]  Nysret Musliu,et al.  Modeling and solving staff scheduling with partial weighted maxSAT , 2017, Annals of Operations Research.

[18]  L. Green Capacity Planning and Management in Hospitals , 2005 .