A control engineering framework for managing whole hospital occupancy

Abstract As factors such as population growth, nation-wide closure of hospitals, and an aging population combine to strain the healthcare system of the United States (US), the demand for better resource and capacity planning increases. This paper proposes a five-step methodology to model and control whole hospital occupancy. The hospital system is viewed using a continuous-time, fluid tank analogy. The system is subsequently discretized and a framework using control theory and Model Predictive Control (MPC) is developed to assist in tactical decision making, while maintaining occupancy targets. The result is a customizable modeling approach that represents interactions between different hospital areas, and interactions between the hospital and the outside world, or the population seeking hospital services.

[1]  Eric Wolstenholme,et al.  A patient flow perspective of U.K. health services: exploring the case for new “intermediate care” initiatives , 1999 .

[2]  B. Malakooti Forecasting , 2013 .

[3]  M. Nowak,et al.  Dynamic multidrug therapies for HIV: a control theoretic approach. , 2015, Journal of theoretical biology.

[4]  Karl G. Kempf,et al.  A MODEL PREDICTIVE CONTROL FRAMEWORK FOR ROBUST MANAGEMENT OF MULTI-PRODUCT, MULTI-ECHELON DEMAND NETWORKS , 2002 .

[5]  Jeffery K. Cochran,et al.  A multi-class queuing network analysis methodology for improving hospital emergency department performance , 2009, Comput. Oper. Res..

[6]  Franklin Dexter,et al.  Changing Allocations of Operating Room Time From a System Based on Historical Utilization to One Where the Aim is to Schedule as Many Surgical Cases as Possible , 2002, Anesthesia and analgesia.

[7]  Jeffery K. Cochran,et al.  A queuing-based decision support methodology to estimate hospital inpatient bed demand , 2008, J. Oper. Res. Soc..

[8]  J. M. Szymanski,et al.  Simulating six sigma improvement ideas for a hospital emergency department , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[9]  Andrew J. Schaefer,et al.  The Optimal Timing of Living-Donor Liver Transplantation , 2004, Manag. Sci..

[10]  N. Halpern,et al.  Critical care medicine in the United States 1985–2000: An analysis of bed numbers, use, and costs* , 2004, Critical care medicine.

[11]  E. Enguidanos,et al.  Guidelines for ambulance diversion , 2000 .

[12]  S. Goodacre,et al.  Appropriate analysis and reporting of cluster randomised trials. , 2005, Emergency medicine journal : EMJ.

[13]  N. K. Kwak,et al.  Simulating the use of space in a hospital surgical suite , 1975 .

[14]  Daniel E. Rivera,et al.  Model Predictive Control for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management , 2008, IEEE Transactions on Control Systems Technology.

[15]  Karl G. Kempf,et al.  A Model Predictive Control framework for robust management of multi-product, multi-echelon demand networks , 2003, Annu. Rev. Control..

[16]  Panos Seferlis,et al.  A two-layered optimisation-based control strategy for multi-echelon supply chain networks , 2004, Comput. Chem. Eng..

[17]  Spyros G. Tzafestas,et al.  Model-based predictive control for generalized production planning problems , 1997 .

[18]  David C. Lane,et al.  Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department , 2000, J. Oper. Res. Soc..

[19]  Daniel E. Summers,et al.  A Dynamic Model to Support Surge Capacity Planning in a Rural Hospital , 2005 .

[20]  Ignacio E. Grossmann,et al.  Dynamic modeling and classical control theory for supply chain management , 2000 .

[21]  Karl G. Kempf,et al.  A hierarchical approach to production control of reentrant semiconductor manufacturing lines , 2003, IEEE Trans. Control. Syst. Technol..

[22]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[23]  G. Vassilacopoulos,et al.  A simulation model for bed allocation to hospital inpatient departments , 1985 .

[24]  C. Fernandes,et al.  How does fast track affect quality of care in the emergency department? , 2006, European journal of emergency medicine : official journal of the European Society for Emergency Medicine.

[25]  Jeffery K. Cochran,et al.  A multi-stage stochastic methodology for whole hospital bed planning under peak loading , 2006 .

[26]  Sally C. Brailsford,et al.  Emergency and on-demand health care: modelling a large complex system , 2004, J. Oper. Res. Soc..

[27]  Daniel E. Rivera,et al.  Simulation-based optimization of process control policies for inventory management in supply chains , 2006, Autom..

[28]  M. I. Henig,et al.  Reservation planning for elective surgery under uncertain demand for emergency surgery , 1996 .

[29]  Julie C. Lowery,et al.  Simulation of a hospital's surgical suite and critical care area , 1992, WSC '92.

[30]  C. Standridge A tutorial on simulation in health care: applications and issues , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[31]  L. McCaig,et al.  Analysis of ambulance transports and diversions among US emergency departments. , 2006, Annals of emergency medicine.

[32]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[33]  S. Goodacre,et al.  Who waits longest in the emergency department and who leaves without being seen? , 2005, Emergency Medicine Journal.

[34]  Rhonda J Rosychuk,et al.  Characteristics of patients who leave emergency departments without being seen. , 2006, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.