An integrated algorithm for performance optimization of neurosurgical ICUs

The aim of this study is performance optimization of neurosurgical ICUs.This study presents an integrated approach based on computer simulation and DEA.The required data is collected from a large hospital in Tehran, Iran.This approach can be generalized to any hospital organization.Previous studies did not incorporate all stated indicators considered in this study. This study presents an integrated simulation and data envelopment analysis (DEA) approach to increase the quality of service in a neurosurgical intensive care unit (ICU). The aim of this study is to capture the main factors which have negatively affects the patients' satisfactions and figure out their optimized levels. In order to avoid any interruption in ICU's routine functions and being able to convince the hospital's principals about the project's outcomes, a simulation model is developed and run for different scenarios. Then DEA is used to compare the outputs of different scenarios. These scenarios are generated by observing the effects of various parameters such as lengthening or shortening treatment times, decreasing or increasing patient volumes and removing or adding staff members. As the best of our knowledge, this is the first study that presents an integrated approach based on computer simulation and DEA to concurrently incorporate the stated factors and parameters for optimization of complex ICUs in developing countries. Therefore, the results of this study are more precise and reliable than previous studies because of concurrent consideration of the stated factors.

[1]  Franklin Dexter,et al.  Data Envelopment Analysis to Determine by How Much Hospitals Can Increase Elective Inpatient Surgical Workload for Each Specialty , 2004, Anesthesia and analgesia.

[2]  Ali Azadeh,et al.  Design of the integrated information system, business, and production process by simulation , 2008 .

[3]  V. Pettilä,et al.  The effect of emergency department delay on outcome in critically ill medical patients: evaluation using hospital mortality and quality of life at 6 months , 2006, Journal of internal medicine.

[4]  Charles L Sprung,et al.  Effect of infections on 30-day mortality among critically ill patients hospitalized in and out of the intensive care unit , 2008, Critical care medicine.

[5]  Franklin Dexter,et al.  Tactical Increases in Operating Room Block Time Based on Financial Data and Market Growth Estimates from Data Envelopment Analysis , 2007, Anesthesia and analgesia.

[6]  Charles L Sprung,et al.  Survival of critically ill patients hospitalized in and out of intensive care* , 2007, Critical care medicine.

[7]  A. Greasley,et al.  Using DEA and simulation in guiding operating units to improved performance , 2005, J. Oper. Res. Soc..

[8]  J. V. Oostrum,et al.  Applying Mathematical Models to Surgical Patient Planning , 2009 .

[9]  Jyh-Horng Chou,et al.  Improved differential evolution approach for optimization of surface grinding process , 2011, Expert Syst. Appl..

[10]  A. Alan B. Pritsker,et al.  Simulation with Visual SLAM and AweSim , 1997 .

[11]  Thomas R. Sexton,et al.  An Efficiency-Based Multicriteria Strategic Planning Model for Ambulatory Surgery Centers , 2010, Journal of Medical Systems.

[12]  D. Teres,et al.  The value and limits of severity adjusted mortality for ICU patients. , 2004, Journal of critical care.

[13]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[14]  Hsihui Chang,et al.  Taiwan quality indicator project and hospital productivity growth , 2010, Omega.

[15]  Alexander Kolker,et al.  Process Modeling of ICU Patient Flow: Effect of Daily Load Leveling of Elective Surgeries on ICU Diversion , 2009, Journal of Medical Systems.

[16]  Mustafa Y. Sir,et al.  Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services , 2013 .

[17]  Kaoru Tone,et al.  A slacks-based measure of super-efficiency in data envelopment analysis , 2001, Eur. J. Oper. Res..

[18]  K P McVeigh,et al.  Advantages of not using the intensive care unit after operations for oropharyngeal cancer: an audit at Worcester Royal Hospital. , 2007, The British journal of oral & maxillofacial surgery.

[19]  David A Harrison,et al.  The impact of the organization of high-dependency care on acute hospital mortality and patient flow for critically ill patients. , 2015, American journal of respiratory and critical care medicine.

[20]  Stéphane Hugonnet,et al.  The effect of workload on infection risk in critically ill patients* , 2007, Critical care medicine.

[21]  Armando Teixeira-Pinto,et al.  ResearchReducing mortality in severe sepsis with the implementation of a core 6-hour bundle : results from the Portuguese community-acquired sepsis study ( SACiUCI study ) , 2010 .

[22]  Mustafa Y. Sir,et al.  Estimating Admissions and Discharges for Planning Purposes – Case of an Academic Health System , 2011 .

[23]  Kathy Rowan,et al.  Delay to admission to critical care and mortality among deteriorating ward patients in UK hospitals: a multicentre, prospective, observational cohort study , 2015, The Lancet.

[24]  Philip Troy,et al.  Using simulation to determine the need for ICU beds for surgery patients. , 2009, Surgery.

[25]  F. Guerriero,et al.  Operational research in the management of the operating theatre: a survey , 2011, Health care management science.

[26]  A. Kramer,et al.  Effect of a rapid response system for patients in shock on time to treatment and mortality during 5 years* , 2007, Critical care medicine.

[27]  T. M. Kubiak,et al.  The Certified Six Sigma Black Belt Handbook , 2005 .

[28]  Brian T. Denton,et al.  Bi‐Criteria Scheduling of Surgical Services for an Outpatient Procedure Center , 2011 .

[29]  Shao-Jen Weng,et al.  Using simulation and Data Envelopment Analysis in optimal healthcare efficiency allocations , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[30]  Lawrence Rosenberg,et al.  A Java class library for simulating peri-operative processes , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[31]  T. Butler,et al.  Evaluation of operating room suite efficiency in the Veterans Health Administration system by using data-envelopment analysis. , 2006, American journal of surgery.

[32]  Kaoru Tone,et al.  A strange case of the cost and allocative efficiencies in DEA , 2001, J. Oper. Res. Soc..

[33]  Robert C. Rickards,et al.  Setting benchmarks and evaluating balanced scorecards with data envelopment analysis , 2003 .

[34]  T. M. Kubiak,et al.  The Certified Six Sigma Black Belt Handbook, Second Edition - Chapter 1 , 2009 .

[35]  Tiemi Matsuo,et al.  Impact of delayed admission to intensive care units on mortality of critically ill patients: a cohort study , 2011, Critical care.

[36]  Gokhan Metan,et al.  Effectiveness of neuraminidase inhibitors in reducing mortality in patients admitted to hospital with influenza A H1N1pdm09 virus infection: a meta-analysis of individual participant data. , 2014, The Lancet. Respiratory medicine.

[37]  Erwin W. Hans,et al.  Closing Emergency Operating Rooms Improves Efficiency , 2007, Journal of Medical Systems.

[38]  Elie Azoulay,et al.  Initiation of nutritional support is delayed in critically ill obese patients: a multicenter cohort study. , 2014, The American journal of clinical nutrition.

[39]  Brian Denton,et al.  Optimization of surgery sequencing and scheduling decisions under uncertainty , 2007, Health care management science.

[40]  David M Gaba,et al.  The future vision of simulation in health care , 2004, Quality and Safety in Health Care.

[41]  Donald B. Chalfin,et al.  Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit* , 2007, Critical care medicine.

[42]  Ali Azadeh,et al.  Design of practical optimum JIT systems by integration of computer simulation and analysis of variance , 2005, Comput. Ind. Eng..

[43]  Ira Horowitz,et al.  Analysis of capacity management of the intensive care unit in a hospital , 1999, Eur. J. Oper. Res..

[44]  Hung-Yi Chuang,et al.  Determining delayed admission to the intensive care unit for mechanically ventilated patients in the emergency department , 2014, Critical Care.

[45]  Ali Azadeh,et al.  Leanness assessment and optimization by fuzzy cognitive map and multivariate analysis , 2015, Expert Syst. Appl..

[46]  Y.U. Bing-Hua,et al.  Delayed admission to intensive care unit for critically surgical patients is associated with increased mortality. , 2014, American journal of surgery.

[47]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[48]  Krystal Hunter,et al.  The epidemiology of spontaneous fever and hypothermia on admission of brain injury patients to intensive care units: a multicenter cohort study. , 2014, Journal of neurosurgery.