Workload balancing: staffing ratio analysis for primary care redesign

The objective of this paper is to investigate the staffing composition of chief care providers (e.g., physician (MD)) and support staff (e.g., medical assistant (MA)) under various task assignment settings to achieve the optimal operational efficiency. Specifically, we examine the effects of workload shifting and identify the proper ratio of MDs to MAs to attain an effective and efficient service level. Based on a Markov chain based framework that characterizes care providers’ activities during patients’ primary care clinic visits, analytical investigation and numerical experiments are conducted. The results articulate that the optimal staffing ratio is achieved when the workloads of MDs and MAs are balanced. To validate the findings under generic primary care clinic settings, discrete event simulation models are developed and extensive experiments are carried out. The sensitivity study elucidates that the balanced-workload optimality is not affected by system variations in patient volume, as well as arrival and service time distributions.

[1]  Jeffrey W. Herrmann,et al.  A Survey of Queuing Theory Applications in Healthcare , 2007 .

[2]  Timothy J. Lowe,et al.  Building intuition insights from basic operations management models and principles , 2008 .

[3]  Katie Coleman,et al.  The Group Health medical home at year two: cost savings, higher patient satisfaction, and less burnout for providers. , 2010, Health affairs.

[4]  Jingshan Li,et al.  Design and analysis of a health care clinic for homeless people using simulations. , 2010, International journal of health care quality assurance.

[5]  Thomas R Rohleder,et al.  Using simulation modeling to improve patient flow at an outpatient orthopedic clinic , 2011, Health care management science.

[6]  D. Baker,et al.  Estimating the staffing infrastructure for a patient-centered medical home. , 2013, The American journal of managed care.

[7]  Sally C. Brailsford,et al.  Advances and challenges in healthcare simulation modeling: tutorial , 2007, WSC.

[8]  Charles E Noon,et al.  Understanding the Impact of Variation in the Delivery of Healthcare Services , 2003, Journal of healthcare management / American College of Healthcare Executives.

[9]  Thomas R Rohleder,et al.  Modeling patient service centers with simulation and system dynamics , 2007, Health care management science.

[10]  Junwen Wang,et al.  Modeling and Analysis of Care Delivery Services Within Patient Rooms: A System-Theoretic Approach , 2014, IEEE Transactions on Automation Science and Engineering.

[11]  Jie Song,et al.  Design and analysis of gastroenterology (GI) clinic in Digestive Health Center of University of Wisconsin Health , 2016 .

[12]  Diwakar Gupta,et al.  Adaptive Appointment Systems with Patient Preferences , 2011, Manuf. Serv. Oper. Manag..

[13]  Wagner Coelho A. Pereira,et al.  Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow , 2007, Comput. Methods Programs Biomed..

[14]  T. Olsen,et al.  Review of modeling approaches for emergency department patient flow and crowding research. , 2011, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[15]  Jingshan Li,et al.  A System-Theoretic Approach to Modeling and Analysis of Mammography Testing Process , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  E. Muth The Reversibility Property of Production Lines , 1979 .

[17]  U. Reinhardt,et al.  A Product Function for Physician Services , 1972 .

[18]  Vincent Augusto,et al.  Multi-period capacity planning for maternity facilities in a perinatal network: A queuing and optimization approach , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[19]  Thomas Bodenheimer,et al.  In Search of Joy in Practice: A Report of 23 High-Functioning Primary Care Practices , 2013, The Annals of Family Medicine.

[20]  S.C. Brailsford,et al.  Tutorial: Advances and challenges in healthcare simulation modeling , 2007, 2007 Winter Simulation Conference.

[21]  Derekh D. F. Cornwell,et al.  Staffing Patterns of Primary Care Practices in the Comprehensive Primary Care Initiative , 2014, The Annals of Family Medicine.

[22]  S. Jacobson,et al.  Evaluating the Design of a Family Practice Healthcare Clinic Using Discrete-Event Simulation , 2002, Health care management science.

[23]  F. Hillier,et al.  On the Optimal Allocation of Work in Symmetrically Unbalanced Production Line Systems with Variable Operation Times , 1979 .

[24]  H. Wharrad,et al.  The global distribution of physicians and nurses. , 1999, Journal of advanced nursing.

[25]  Michael Pinedo,et al.  Appointment scheduling with no-shows and overbooking , 2014 .

[26]  Diwakar Gupta,et al.  Appointment scheduling in health care: Challenges and opportunities , 2008 .

[27]  Ji Lin,et al.  Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities , 2010, Ann. Oper. Res..

[28]  Jingshan Li,et al.  Modeling and analysis of work flow and staffing level in a computed tomography division of University of Wisconsin Medical Foundation , 2012, Health care management science.

[29]  Ronald E Giachetti,et al.  A queueing network model to analyze the impact of parallelization of care on patient cycle time , 2008, Health care management science.

[30]  John Toussaint,et al.  Innovation and Best Practices in Health Care Scheduling , 2015 .

[31]  Kerrie N. Paige,et al.  Learning SIMUL8: The Complete Guide , 2002 .

[32]  Ryan A Crowley,et al.  Principles Supporting Dynamic Clinical Care Teams: An American College of Physicians Position Paper , 2013, Annals of Internal Medicine.

[33]  Gary S. Kaplan,et al.  Transforming Health Care Scheduling and Access: Getting to Now. , 2015, Military medicine.

[34]  Christine A Sinsky,et al.  Practice profile. 'Core teams': nurse-physician partnerships provide patient-centered care at an Iowa practice. , 2010, Health affairs.