Constructing Holistic Patient Flow Simulation Using System Approach

Patient flow often described as a systemic issue requiring a systemic approach because hospital is a collection of highly dynamic, interconnected, complex, ad hoc and multi-disciplinary sub-processes. However, studies on holistic patient flow simulation following system approach are limited and/or poorly understood. Several researchers have been investigating single departments such as ambulatory care unit, Intensive Care Unit (ICU), emergency department, surgery department or patients’ interaction with limited resources such as doctor, endoscopy or bed, independently. Hence, this article demonstrates how to achieve system approach in constructing holistic patient flow simulation, while maintaining the balance between the complexity and the simplicity of the model. To this end, system approach, network analysis and discrete event simulation (DES) were employed. The most important departments in the diagnosis and treatment process are identified by analyzing network of hospital departments. Holistic patient flow simulation is constructed using DES following system approach. Case studies are conducted and the results illustrate that healthcare systems must be modeled and investigated as a complex and interconnected system so that the real impact of changes on the entire system or parts of the system could be observed at strategic as well as operational levels.

[1]  Mohammed Abdou Janati Idrissi,et al.  A Classification of Healthcare Social Network Analysis Applications , 2017, HEALTHINF.

[2]  Martin L. Puterman,et al.  Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation , 2009, Health care management science.

[3]  Ross Sparks,et al.  Modelling hospital length of stay using convolutive mixtures distributions , 2015, Statistics in medicine.

[4]  Benjamin A. Christensen Improving ICU patient flow through discrete-event simulation , 2012 .

[5]  Stephen Jarvis,et al.  Mining association rules for admission control and service differentiation in e‐commerce applications , 2018, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

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

[7]  Hyojung Kang,et al.  UVA emergency department patient flow simulation and analysis , 2016, 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS).

[8]  Lora Giangregorio,et al.  Researching Complex Interventions in Health: The State of the Art , 2016, BMC Health Services Research.

[9]  Sergey V. Kovalchuk,et al.  Simulation of Patient Flow and Load of Departments in a Specialized Medical Center , 2016 .

[10]  Vimla L. Patel,et al.  Considering complexity in healthcare systems , 2011, J. Biomed. Informatics.

[11]  Martin Pitt,et al.  A modelling tool for capacity planning in acute and community stroke services , 2016, BMC Health Services Research.

[12]  Dionne M. Aleman,et al.  A simulation model for perioperative process improvement , 2014 .

[13]  Filip De Turck,et al.  Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores , 2015, Artif. Intell. Medicine.

[14]  J. Westbrook,et al.  Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks. , 2011, Social science & medicine.

[15]  Diogo R. Ferreira,et al.  Business process analysis in healthcare environments: A methodology based on process mining , 2012, Inf. Syst..

[16]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[17]  SepúlvedaMarcos,et al.  Process mining in healthcare , 2016 .

[18]  Jorge Munoz-Gama,et al.  Process mining in healthcare: A literature review , 2016, J. Biomed. Informatics.

[19]  N. Litvak,et al.  A Survey of Health Care Models that Encompass Multiple Departments , 2009 .

[20]  Sara A Kreindler,et al.  The three paradoxes of patient flow: an explanatory case study , 2017, BMC Health Services Research.

[21]  S. Vrieze Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). , 2012, Psychological methods.

[22]  Scott A. McKinley,et al.  A flexible simulation platform to quantify and manage emergency department crowding , 2014, BMC Medical Informatics and Decision Making.

[23]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[24]  M. Gunal A guide for building hospital simulation models , 2012 .

[25]  Herbert Moskowitz,et al.  Improving patient flow at an outpatient clinic: study of sources of variability and improvement factors , 2009, Health care management science.

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

[27]  Giulia Bruno,et al.  Simulation-Based Analysis of Patient Flow in Elective Surgery , 2014 .

[28]  Richard J. Simard,et al.  Computing the Two-Sided Kolmogorov-Smirnov Distribution , 2011 .

[29]  Andy H. Lee,et al.  Determinants of Maternity Length of Stay: A Gamma Mixture Risk-Adjusted Model , 2001, Health care management science.

[30]  Yen-Chi Chen,et al.  A tutorial on kernel density estimation and recent advances , 2017, 1704.03924.

[31]  Sergey V. Kovalchuk,et al.  Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification , 2017, J. Biomed. Informatics.

[32]  W. Paterson,et al.  A Patient Flow Analysis: Identification of Process Inefficiencies and Workflow Metrics at an Ambulatory Endoscopy Unit , 2016, Canadian journal of gastroenterology & hepatology.

[33]  Vahab Vahdat,et al.  BUILDING A FLEXIBLE SIMULATION MODEL FOR MODELING MULTIPLE OUTPATIENT ORTHOPEDIC CLINICS , 2018, 2018 Winter Simulation Conference (WSC).

[34]  John Scott,et al.  Social Network Analysis, Overview of , 2009, Encyclopedia of Complexity and Systems Science.

[35]  Anatoli Djanatliev,et al.  Hospital processes within an integrated system view: A hybrid simulation approach , 2016, 2016 Winter Simulation Conference (WSC).

[36]  Young Ji Lee,et al.  Visualizing collaborative electronic health record usage for hospitalized patients with heart failure , 2015, J. Am. Medical Informatics Assoc..

[37]  Roberto Setola,et al.  A new model for the length of stay of hospital patients , 2016, Health care management science.

[38]  Renata Konrad,et al.  Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study , 2013 .