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SHS ASQ 2010 Conference Presentation: Hospital System Patient Flow

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SHS_ASQ 2010 Conference Presentation: Hospital System Patient Flow and Departments\’ Interdependency
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  • 1. System Engineering and Management Science for Healthcare Examples and Fundamental Principles SHS/ASQ 2010 Conference and Expo February 26, 2010 Alexander Kolker, PhD Outcomes Operations Project Manager Childrens Hospital and Health System Milwaukee, Wisconsin 1
  • 2. Outline Main concept and some definitions. Typical hospital system as a set of interdependent subsystems: Subsystem 1: Emergency Department (ED). Subsystem 2: Intensive Care Unit (ICU). Subsystem 3: Operating Rooms (OR)- Surgical Department. Subsystem 4: Medical/Surgical Nursing Units (Floor_NU). Interdependency of subsystems. Main take-away. Summary of fundamental management engineering principles. 2
  • 3. This presentation is adapted from the following System Engineering Publications Kolker, A, Queuing Theory and Discreet Events Simulation for Healthcare: from Basic Processes to Complex Systems with Interdependencies. Chapter 20. In: Handbook of Research on Discrete Event Simulation: Technologies and Applications, 2009, pp. 443 - 483. IGI Global Publishing, Hershey, PA. Kolker, A, Process Modeling of Emergency Department Patient Flow: Effect of Patient Length of Stay on ED Diversion. Journal of Medical Systems, 2008, v. 32, N 5, pp. 389 - 401. Kolker, A, Process Modeling of ICU Patient Flow: Effect of Daily Load Leveling of Elective Surgeries on ICU Diversion. Journal of Medical Systems, 2009, v. 33, N 1, pp. 27 - 40. Kolker, A, Norell, B., OConnor, M., Hoffman, G., Oldham, K., The Use of Predictive Simulation Modeling for Surgical Capacity Expansion Analysis Presented at the 2010 SHS/ASQ joint Conference, Atlanta, GA, February 26, 2010 (poster session). Kolker, A, Effective Managerial Decision Making in Healthcare Settings: Examples and Principles. Quality Management Journal, 2009 (submitted). 3
  • 4. Main Concept Modern medicine has achieved great progress in treating individual patients. This progress is based mainly on hard science: molecular genetics, biophysics, biochemistry, design and development of medical devices and imaging. However relatively little resources have been devoted to the proper functioning of overall healthcare delivery as an integrated system, in which access to efficient care should be delivered to many thousands of patients in an economically sustainable way. (Joint report of National Academy of Engineering and Institute of Medicine, 2005). A real impact on efficiency and sustainability of the healthcare system can be achieved only by using healthcare delivery engineering which is based on hard science such as: probability theory, forecasting, calculus, stochastic optimization, computer simulation, etc. 4
  • 5. Some Definitions What is Management? Management is controlling and leveraging available resources (material, financial and human) aimed at achieving the performance objectives. Traditional (Intuitive) Management is based on Past experience. Intuition or educated guess. Static pictures or simple linear projections. Linear projection assumes that the output is directly proportional to the input, i.e. the more resources (material and human) thrown in, the more output produced (and vice versa). System output Resource input 5
  • 6. What is Management Engineering? Management Engineering (ME) is the discipline of building and using validated mathematical models of real systems to study their behavior aimed at making justified business decisions. This field is also known as operations research. Thus, Management Engineering is the application of mathematical methods to system analysis and decision-making. 6
  • 7. Scientific Management is Based On A goal that is clearly stated and measurable, so the decision-maker (manager) always knows if the goal is closer or farther away. Identification of available resources that can be leveraged (allocated) in different ways. Development of mathematical models or numeric computer algorithms to quantitatively test different decisions for the use of resources and consequences of these decisions (especially unintended consequences) before finalizing the decisions. The Underlying Premise of ME is Decisions should be made that best lead to reaching the goal. Valid mathematical models lead to better justified decisions than an educated guess, past experience, and linear extrapolations (traditional decision-making). 7
  • 8. Main Steps for System Engineering Analysis Step 1 Large systems are deconstructed into smaller subsystems using natural breaks in the system. Subsystems are modeled, analyzed, and studied separately. Step 2 Subsystems are then reconnected in a way that recaptures the interdependency between them. The entire system is re-analyzed using the output of one subsystem as the input for another subsystem. 8
  • 9. High-Level Layout of a Typical Hospital System Key ED Emergency Room Floor NU Med/Surg Units ICU Intensive Care Unit OR Operating Rooms WR Waiting Room 9
  • 10. Step 1 Deconstruction of the entire hospital system into Main Subsystems. Simulation and Analysis of the Main Subsystems: Subsystem 1: Emergency Department (ED). Subsystem 2: Intensive Care Unit (ICU). Subsystem 3: Operating Rooms (OR). Subsystem 4: Floor Nursing Units (NU). 10
  • 11. Subsystem 1: Typical Emergency Department (ED) The high-level layout of the entire hospital system: ED structure and in-patient units 11
  • 12. Typical ED Challenges ED Performance Issues ED ambulance diversion is unacceptably high (about 23% of time sample ED is closed to new patients). Among many factors that affect ED diversion, patient Length of Stay in ED (LOS) is one of the most significant factors. High Level ED Analysis Goal Quantitatively predict the relationship between patient LOS and ED diversion. Identify the upper LOS limit (ULOS) that will result in significant reduction or elimination ED diversion. 12
  • 13. ED simulation model layout Typical ED Simulation Model Layout Simulation Digital clock ED pre-filled at the simulation start Arrival pattern wk, DOW, time Mode of transp Mode of Transportation Disposition 13
  • 14. Modeling Approach ED diversion (closure) is declared when ED patient census reaches ED bed capacity. ED stays in diversion until some beds become available after patients are moved out of ED (discharged home, expired, or admitted as in-patients). Upper LOS limits (simulation parameters) are imposed on the baseline original LOS distributions: A LOS higher than the limiting value is not allowed in the simulation run. Take Away Baseline LOS distributions should be recalculated as functions of the upper LOS limits. 14
  • 15. Modeling Approach continued MODELING APPROACH (cont.) Given original distribution density and the the random value of what is the conditional Given original distribution density and the limiting value of limiting variable T, th e random variable T, distribution of the restricted random variable T? what is the conditional distribution of the restricted random va riable T ? Original unbounded distribution Distribution of LOS_ home, Hrs New re-calculated distribution
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