Prioritization of patients in ICU: composite approach of multiple-criteria decision-making and discrete event simulation

Goal: This article aims to present a systematic approach to improve the resource allocation and human queues prioritization patterns. Design / Methodology / Approach: To achieve such a purpose, effective criteria using a fuzzy-Delphi method, and subject-related researchers’ views were obtained. Utilizing the Analytic Network Process method, the weights of each criterion was measured. Then, considering the established weights and using a fuzzy-TOPSIS method, a prioritization system via Discrete Event Simulation was developed. Results: Results indicate that the established approach properly improved the performance of the prioritization system in terms of resources and facilities allocation in neurology’s ICUs. Limitations of the investigation: A drawback of this research can be in states of emergency which limits the options at hand and the criteria proposed may set a drawback on the aim of the study. Practical implications: The results show that the proposed model can modify patient entry based on multiple criteria in terms of productivity and social justice in the patient queuing strategy. Originality / Value: The contribution of this research is threefold: the literature has been reviewed to conclude the criteria concerning decisions around ICUs, the concluded criteria filtered through an expert panel which can be relied based on the method, a real application of the steps proposed is presented which allows comparing the accuracy and efficiency of the decisions made in the hospitals.

[1]  Zhibin Jiang,et al.  Multi-objective capacity allocation of hospital wards combining revenue and equity , 2017, Omega.

[2]  Abdelaziz Dammak,et al.  Inpatient admission management using multiple criteria decision-making methods , 2019 .

[3]  Afshin Jamshidi,et al.  A new dynamic integrated framework for surgical patients' prioritization considering risks and uncertainties , 2016, Decis. Support Syst..

[4]  Abbas Al-Refaie,et al.  Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming , 2011, Int. J. Fuzzy Syst. Appl..

[5]  Norman T. J. Baii.  A STUDY OF QUEUES AND APPOINTMENT SYSTEMS IN HOSPITAL OUT-PATIENT DEPARTMENTS, WITH SPECIAL REFERENCE TO WAITING-TIMES , 1952 .

[6]  Lin Li,et al.  Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project , 2018, Appl. Soft Comput..

[7]  Gwo-Hshiung Tzeng,et al.  Fuzzy MCDM approach for planning and design tenders selection in public office buildings , 2004 .

[8]  Kevin O'Gorman,et al.  Priority queues : where social justice and equity collide , 2012 .

[9]  David R. Henderson,et al.  Reducing Wait Times for Health Care: What Canada Can Learn from Theory and International Experience , 2013 .

[10]  Amine Ali Zeggwagh,et al.  Determinants and outcomes associated with decisions to deny or to delay intensive care unit admission in Morocco , 2012, Intensive Care Medicine.

[11]  Matthew J. Martin,et al.  A simplified set of trauma triage criteria to safely reduce overtriage: a prospective study. , 2009, Archives of surgery.

[12]  Kannan Govindan,et al.  Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP , 2016 .

[13]  Patricio Donoso,et al.  Assessing an ambulance service with queuing theory , 2008, Comput. Oper. Res..

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

[15]  Mohammad Izadikhah,et al.  An algorithmic method to extend TOPSIS for decision-making problems with interval data , 2006, Appl. Math. Comput..

[16]  Amardeep Thind,et al.  The impact of delays to admission from the emergency department on inpatient outcomes , 2010, BMC emergency medicine.

[17]  J. Vincent,et al.  Ethics and end-of-life care for adults in the intensive care unit , 2010, The Lancet.

[18]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[19]  Eduardo Alves Portela Santos,et al.  A hybrid model to support decision making in emergency department management , 2020, Knowl. Based Syst..

[20]  Paula Tanabe,et al.  The Emergency Severity Index Version 4: changes to ESI level 1 and pediatric fever criteria. , 2005, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[21]  Yadati Narahari,et al.  Queueing network modelling and lead time compression of pharmaceutical drug development , 2001 .

[22]  G. Rubenfeld,et al.  Critical care and the global burden of critical illness in adults , 2010, The Lancet.

[23]  M Espallargues,et al.  Wide Social Participation in Prioritizing Patients on Waiting Lists for Joint Replacement: A Conjoint Analysis , 2008, Medical decision making : an international journal of the Society for Medical Decision Making.

[24]  Christopher A. Chung,et al.  Simulation Modeling Handbook: A Practical Approach , 2003 .

[25]  Criterios para priorizar a pacientes en lista de espera para procedimientos quirúrgicos en el Sistema Nacional de Salud , 2009 .

[26]  Po-Chien Chang,et al.  Fuzzy Delphi method for evaluating hydrogen production technologies , 2011 .

[27]  Marc Lambrecht,et al.  Modeling a Healthcare System as a Queueing Network: The Case of a Belgian Hospital , 2007 .

[28]  Victor B. Kreng,et al.  The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection , 2010, Expert Syst. Appl..

[29]  Özer Uygun,et al.  An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company , 2015, Comput. Ind. Eng..

[30]  Xiaohong Chen,et al.  A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients’ Prioritization , 2017, International Journal of Fuzzy Systems.

[31]  Moshe Haviv,et al.  Waiting times in queues with relative priorities , 2007, Oper. Res. Lett..

[32]  Shannon L. Harris,et al.  A Unified Framework for the Prioritization of Organ Transplant Patients: Analytic Hierarchy Process, Sensitivity and Multifactor Robustness Study , 2013 .

[33]  Refael Hassin,et al.  The use of relative priorities in optimizing the performance of a queueing system , 2009, Eur. J. Oper. Res..

[34]  Marc Lambrecht,et al.  The Optimal Allocation of Server Time Slots Over Different Classes of Patients , 2012, Eur. J. Oper. Res..

[35]  J. Kahn,et al.  Does space make waste? The influence of ICU bed capacity on admission decisions , 2013, Critical Care.

[36]  Kazem Sadegh-Zadeh,et al.  Handbook of Analytic Philosophy of Medicine , 2015 .

[37]  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.

[38]  A. MacCormick,et al.  Prioritizing patients for elective surgery: a systematic review , 2003, ANZ journal of surgery.

[39]  C. Sprung,et al.  Triage of intensive care patients: identifying agreement and controversy , 2013, Intensive Care Medicine.

[40]  Terry Young,et al.  Three critical challenges for modeling and simulation in healthcare , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[41]  J. Pines,et al.  National ED crowding and hospital quality: results from the 2013 Hospital Compare data. , 2014, The American journal of emergency medicine.

[42]  Azharul Karim,et al.  Emergency department resource optimisation for improved performance: a review , 2019, Journal of Industrial Engineering International.

[43]  J. Richardson,et al.  Effective triage can ameliorate the deleterious effects of delayed transfer of trauma patients from the emergency department to the ICU. , 2009, Journal of the American College of Surgeons.