Modeling and analysis of care delivery services within patient rooms

In this paper, we introduce a Markov chain model to study the care delivery services within the patient rooms. Closed formulas to evaluate the patient length of stay and staff utilizations are developed. To illustrate the method, a numerical example is presented and monotonic properties are discussed. Such a model provides a quantitative tool for healthcare professionals to study and improve patient flow in care deliveries.

[1]  R. A. Green,et al.  Using queueing theory to increase the effectiveness of emergency department provider staffing. , 2006, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[2]  Gad Abraham,et al.  Short-Term Forecasting of Emergency Inpatient Flow , 2009, IEEE Transactions on Information Technology in Biomedicine.

[3]  Alexander Komashie,et al.  Modeling emergency departments using discrete event simulation techniques , 2005, Proceedings of the Winter Simulation Conference, 2005..

[4]  Jingshan Li,et al.  Modeling and analysis of hospital emergency department: An analytical framework and problem formulation , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[5]  P. H. Millard,et al.  A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department , 1998, Health care management science.

[6]  R. Hall,et al.  Patient flow : reducing delay in healthcare delivery , 2006 .

[7]  Jingshan Li,et al.  Modeling and analysis of the emergency department at University of Kentucky Chandler Hospital using simulations. , 2010, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[8]  Fatah Chetouane,et al.  Modeling and Improving Emergency Department Systems using Discrete Event Simulation , 2007, Simul..

[9]  Elia El-Darzi,et al.  Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions , 2005, Health care management science.

[10]  Gregory Dobson,et al.  Patient Flow in an ICU , 2008 .

[11]  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.

[12]  Les Mayhew,et al.  Using queuing theory to analyse completion times in accident and emergency departments in the light of the Government 4-hour target , 2006 .

[13]  Walter Ukovich,et al.  A Continuous Petri Net Model for the Management and Design of Emergency Cardiology Departments , 2009, ADHS.

[14]  R. Hall Patient flow : reducing delay in healthcare delivery , 2006 .

[15]  Alexander Kolker,et al.  Process Modeling of Emergency Department Patient Flow: Effect of Patient Length of Stay on ED Diversion , 2008, Journal of Medical Systems.

[16]  Sheldon Howard Jacobson,et al.  Application of discrete-event simulation in health care clinics: A survey , 1999, J. Oper. Res. Soc..

[17]  Peter G. Harrison,et al.  Approximate queueing network analysis of patient treatment times , 2007, ValueTools '07.

[18]  Dominik Aronsky,et al.  Forecasting emergency department crowding: a discrete event simulation. , 2008, Annals of emergency medicine.

[19]  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.

[20]  S. Samaha,et al.  The use of simulation to reduce the length of stay in an emergency department , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..