• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
SHS ASQ 2010 Conference Presentation: Hospital System Patient Flow
 

SHS ASQ 2010 Conference Presentation: Hospital System Patient Flow

on

  • 1,829 views

SHS_ASQ 2010 Conference Presentation: Hospital System Patient Flow and Departments\’ Interdependency

SHS_ASQ 2010 Conference Presentation: Hospital System Patient Flow and Departments\’ Interdependency

Statistics

Views

Total Views
1,829
Views on SlideShare
1,825
Embed Views
4

Actions

Likes
1
Downloads
42
Comments
0

2 Embeds 4

http://www.linkedin.com 3
https://www.linkedin.com 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    SHS ASQ 2010 Conference Presentation: Hospital System Patient Flow SHS ASQ 2010 Conference Presentation: Hospital System Patient Flow Presentation Transcript

    • 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 Children’s Hospital and Health System Milwaukee, Wisconsin 1
    • 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
    • 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., O’Connor, 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Subsystem 1: Typical Emergency Department (ED) The high-level layout of the entire hospital system: ED structure and in-patient units 11
    • 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
    • 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
    • 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
    • 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 Re-calculated bounded distribution of LOS_ home, Hrs 3-Parameter (T ) orig f Gamma 500 480 500 460 f (T )original 480 460 440 f (T , LOS ) new  440 420 LOS  f (T ) 420 400 400 380 originaldT 380 360 360 340 340 320 0 Frequency 320 300 Frequency 300 280 280 260 260 240 240 220 Imposed LOS limit 6 hrs 220 200 200 180 180 160 160 140 140 120 LOS limit 120 100 100 80 80 60 f (T ) new  0, if T LOS 60 40 40 20 20 0 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12 LOS, Hrs LO Hrs LOS, T, Hrs T, Hrs 15
    • Simulation Summary and Model Validation SIMULATION SUMMARY & MODEL VALIDATION Scenario/option LOS for discharged LOS for Predicted ED Note home NOT more than admitted NOT diversion, % more than Current, 07 24 hrs 24 hrs 23.7% Actual ED (Baseline) diversion was 21.5% 1 5 hrs 6 hrs ~ 0.5 % Practically NO Currently 17% Currently diversion with LOS more 24% with than 5 hrs; LOS more than 6 hrs; 2 6 hrs 6 hrs ~ 2% Low single digits diversion 3 5 hrs 24 hrs ~4% Low single digits diversion Take-away: Take Away • ED diversion could be negligible (~0.5%) if patients discharged home stay NOT more than 5 • ED diversionand admittednegligible (~0.5%) if patients discharged home stay not more hrs could be patients stay NOT more than 6 hrs. • Relaxing of these LOS limits results in low digits % diversion that still could be acceptable than five hours and admitted patients stay not more than six hours. • Relaxing of these LOS limits results in a low digits percent diversion that still could be acceptable. 16
    • Simulation Summary – continued What other combinations of upper LOS limits are limits LOS arelow single digit percent ED What other combinations of upper possible to get a possible to get low diversion? single digits % ED diversion ? Perform full factorial DOE with two factors (ULOS_home and ULOS_adm) at six at 6 levels each Performed full factorial DOE with two factors ( ULOS_home and ULOS_adm) levels each using simulatedsimulated % diversion as a response function. using percent diversion as a response function. Simulated Div % as a function of upper LOS limits, hrs ULO S_home, hrs 24.0 5 22.5 6 21.0 8 19.5 10 Mean predicted Div % 18.0 12 16.5 Low single digits 24 15.0 % diversion 13.5 12.0 10.5 9.0 7.5 6.0 4.5 3.0 1.5 0.0 5 6 8 10 12 24 ULOS_adm, hrs 17
    • Conclusions for Subsystem 1: Emergency Department • ED diversion can be negligible (less than 1%) if hospital- admitted patients stay in ED not more than six hours. • Currently 24% of hospital-admitted patients in study hospital stay longer than this limit, up to 20 hours. • This long LOS for a large percentage of patients results in ED closure/diversion. 18
    • Subsystem 2: Typical Intensive Care Unit (ICU) Patients move between the units: • If no beds in CIC, move to SIC • If no beds in MIC, move to CIC, else SIC, else NIC • If no beds in SIC, move CIC • If no beds in NIC, move to CIC, else SIC 19
    • Typical ICU Challenges ICU Performance Issues • Elective surgeries are usually scheduled for Operating Room block times without taking into account the competing demand from emergency and add-on surgeries for ICU resources. • This practice results in:  Increased ICU diversion due to ‘no ICU beds’.  Increased rate of medical and quality issues due to staff overload and capacity constraints.  Decreased patient throughput and hospital revenue. High Level ICU Analysis Goal • Establish a relationship between daily elective surgeries schedule, emergency and add-on cases and ICU diversion. • Given the number of the daily scheduled elective surgeries and the number of unscheduled emergency and add-on admissions, predict ICU diversion due to lack of available beds. 20
    • Baseline – Existing Number of Elective Cases ICU Census: Elective surgeries current pattern - No daily cap Red zone: Critical census limit exceeded Closed due to No ICU beds: 10.5 % of time 51 50 49 48 47 46 45 44 cns 43 42 41 40 39 38 37 36 wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 35 0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024 Hrs/ weeks 21
    • Conclusions for Subsystem 2: Intensive Care Unit • There is a significant variation in the number of scheduled elective cases between the same days of the different weeks (Monday to Monday, Tuesday to Tuesday, and so on). • Smoothing the number of elective cases over time (daily load leveling) is a very significant factor which strongly affects ICU closure time due to ‘no ICU beds.’ • Using Simulation it was demonstrated that daily load leveling of elective cases to not more than 4 cases per day will result in a very significant reduction of closure time due to ‘no ICU beds’ (from ~10.5% down to ~1%). 22
    • Subsystem 3: Typical Operating Rooms (OR) Design Challenges • Is the number of general and specialized operating rooms and pre/post operative beds adequate to meet the projected patient flow and volume increases? • If it is not, how many operating rooms and pre/post operative beds would be needed? • Ensure that the renovation cost is under control and maintain a high level of quality and satisfaction standards for surgical services. • Utilize Management Engineering to determine that the number of operating rooms and pre/post operative beds is not excessive. 23
    • The following performance criteria were used for the simulation model 1. Patient delay to be admitted to a preoperative surgical bed should not exceed 15 minutes. 2. Delay to enter operating room from a preoperative surgical bed should not exceed: General OR – 2 hours Urgent OR – 3 hours Cardiovascular OR – 5 hours Neurosurgery OR – 3 hours Orthopedic OR – 2 hours Cardiac Cath Lab – 2 hours 3. Percent of patients waiting longer than the acceptable delay to enter operating room from a preoperative surgical bed should not exceed 5%. 4. Delay to enter PACU beds from an operating room should not exceed 5 minutes. 5. Average annual utilization of operating rooms should be in the range of 60% to 90%. 24
    • The following simulation models were developed and analyzed Model 1: Baseline operations - all surgical services function as currently specified between two floors. Construct two general operating rooms onto upper level floor to serve otolaryngology, gastroenterology and pulmonary patient volume from lower level floor. Model 2: Move gastroenterology and pulmonary patient volume from upper level to a separate service area. Model 3: Separate service area for gastroenterology and pulmonary patient volume that includes 2 to 3 special procedure rooms and 8 to11 pre/post beds and PACU beds. Total annual patient volume included in the simulation models is in the range from 15,000 to 17,000. Decision variables were: The number of pre-operative beds and PACU beds, number of Operating Rooms and special procedure rooms and their allocation for surgical services. 25
    • Simulation Model Layout (Scenarios 1 – 3) Operating Rooms: OpR-general; U_OR-urgent; CV_OR-cardiovascular; Cath_OR-catheterization; SPR-special procedure. 26
    • Conclusions for Subsystem 3: Operating Rooms (OR) • Model 3 is selected as the best. Twelve Operating Rooms and four Special Procedure Rooms will be adequate to handle patient volume up to the year 2013. • Cath Lab could become an issue by 2013 with more than 10% of patients waiting longer than acceptable limit 2 hours. • All other performance criteria will be met. 27
    • Subsystem 4: Medical/Surgical Nursing Units (NU) Total number of specialized nursing units: 24 Total number of licensed beds: 380 Patient Length of Stay (LOS) is in the range from 2 days to 10 days; The most likely LOS is 5 days. Census (i) (current period) = census (i-1) (previous period) + [# admissions (i) – # discharges (i) ]; i = 1, 2, 3, ……. This is a dynamic balance of supply (beds) and demand (admissions). 28
    • Census (i) (current period) = census (i-1) (previous period) + [# admissions (i) – # discharges (i) ]; i = 1, 2, 3, ……. Simulated Census. Capacity 380 beds 390 Mon Tue Wed Thu Fri Sat Sun 380 capacity limit 370 census 360 350 340 330 320 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 148 152 156 160 164 168 days/ hours Take Away: Percent of time Nursing Units are full (% diversion) is about 16%. 29
    • Step 2 • Subsystems are 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. 30
    • Step 2 – continued • All subsystems are reconnected to each other. • The output of one subsystem is the input for another subsystem. 31
    • Hospital System Simulation Summary Too aggressive ED Downstream Less aggressive Downstream Current improvement: Units: Better ED improvement: Units: Better or Performance Metrics State patients admitted or worse than patients admitted words than current Baseline within 6 hours current state? within 10 hours state? 95% CI of the number of patients waiting to get to 25 – 27 8 – 10 Better 17 – 19 Better ED (ED in) 95% CI of the number of patients waiting hospital 57 – 62 64 – 69 Worse 57 – 62 Neutral admissions (ED out) Number of patients left not seen (LNS) after waiting more than 2 23 – 32 0 Better 0–3 Better hours 95% CI for % ED diversion 22% – 23% 0.4% – 0.5% Better 6.8% – 7.3% Better 95% CI for % ICU diversion 28% – 32% 30% – 34% Worse 28% – 32% Neutral 95% CI for % OR diversion 12% – 13% 13% – 15% Worse 12% – 13% Neutral 95% CI for % floor NU diversion 11% – 12% 11% – 12% Neutral 11% – 12% Neutral 32
    • Take-Away from Hospital System Simulation Summary Take Away • Too aggressive ED improvement results in worsening three out of seven hospital system performance metrics. • Less aggressive ED improvement is more aligned with the ability of downstream subsystems to handle increased patient volume. • This illustrates important Management System Engineering Principles: 33
    • Important System Engineering Principles • Improvement in the separate subsystems (local optimization or local improvement) should not be confused with the improvement of the entire system. • A system of local improvements is not the best system; it could be a very inefficient system. • Analysis of an entire complex system is usually incomplete and can be misleading without taking into account subsystems’ interdependency. 34
    • Main Take-Away Management Engineering helps to address the following typical pressing hospital issues: • How many beds are needed for each unit. • How many procedure rooms are needed for each service. • How many nurses/physicians should each unit schedule for the particular day and night. • How to reduce patient wait time and increase access to care. • How to develop an efficient outpatient clinic schedule. And so on, and so on… And the Ultimate Goal: How to manage hospital operations to increase profitability (reduce costs, increase revenue) while keeping high quality, safety and outcomes standards for patients. 35
    • Summary of Some Fundamental Management Engineering Principles • Systems behave differently than the sum of their independent components. • All other factors being equal, combined resources are more efficient than specialized (dedicated) resources with the same total capacity/workload. • Scheduling appointments (jobs) in the order of their increased duration variability (from lower to higher variability) results in a lower overall cycle time and waiting time. • Size matters. Large units with the same arrival rate (relative to its size) always have a significantly lower waiting time. Large units can also function at a much higher utilization % level than small units with about the same patient waiting time. • Work load leveling (smoothing) is an effective strategy to reduce waiting time and improve patient flow. 36
    • Summary of Some Fundamental Management Engineering Principles – continued • Because of the variability of patient arrivals and service time, a reserved capacity (sometimes up to 30%) is usually needed to avoid regular operational problems due to unavailable beds. • Generally, the higher utilization level of the resource (good for the organization) the longer is the waiting time to get this resource (bad for patient). Utilization level higher than 80% to 85% results in a significant increase in waiting time for random patient arrivals and random service time. • In a series of dependent activities only a bottleneck defines the throughput of the entire system. A bottleneck is a resource (or activity) whose capacity is less than or equal to demand placed on it. 37
    • Summary of Some Fundamental Management Engineering Principles – continued • An appointment backlog can remain stable even if the average appointment demand is less than appointment capacity. • The time of peak congestion usually lags the time of the peak arrival rate because it takes time to serve patients from the previous time periods (service inertia). • Reduction of process variability is the key to patient flow improvement, increasing throughput and reducing delays. 38
    • APPENDIX 39
    • What is a Simulation Model? A Simulation Model is the computer model that mimics the behavior of a real complex system as it evolves over the time in order to visualize and quantitatively analyze its performance in terms of: • Cycle times. • Wait times. • Value added time. • Throughput capacity. • Resources utilization. • Activities utilization. • Any other custom collected process information. • The Simulation Model is a tool to perform ‘what-if’ analysis and play different scenarios of the model behavior as conditions and process parameters change. • This allows one to build various experiments on the computer model and test the effectiveness of various solutions (changes) before implementing the change.
    • How Does a Typical Simulation Model Work? A simulation model tracks the move of entities through the system at distinct points of time (thus, discrete events.) The detailed track is recorded of all processing times and waiting times. In the end, the system’s statistics for entities and activities is gathered. Example of Manual Simulation (step by step) Let’s consider a very simple system that consists of: • a single patient arrival line. • a single server. Suppose that patient inter-arrival time is uniformly (equally likely) distributed between 1 min and 3 min. Service time is exponentially distributed with the average 2.5 min. (Of course, any statistical distributions or non-random patterns can be used instead). A few random numbers sampled from these two distributions are, for example: Inter-arrival time, min Service time, min 2.6 1.4 2.2 8.8 1.4 9.1 2.4 1.8 …. …. and so on… and so on…. 41
    • We will be tracking any change (or event) that happened in the system. A summary of what is happening in the system looks like this: Event # Time Event that happened in the system 1 2.6 First customer arrives. Service starts that should end at time = 4. 2 4 Service ends. Server waits for patient. 3 4.8 Second patient arrives. Service starts that should end at time = 13.6. Server idle 0.8 minutes. 4 6.2 Third patient arrives. Joins the queue waiting for service. 5 8.6 Fourth patient arrives. Joins the queue waiting for service. 6 13.6 Second patient (from event 3) service ends. Third patient at the head of the queue (first in, first out) starts service that should end at time 22.7. 7 22.7 Patient #4 starts service…and so on. In this particular example, we were tracking events at discrete points in time t = 2.6, 4.0, 4.8, 6.2, 8.6, 13.6, 22.7 DES models are capable of tracking hundreds of individual entities, each with its own unique set of attributes, enabling one to simulate the most complex systems with interacting events and component interdependencies. 42
    • Basic Elements of a Simulation Model • Flow chart of the process: Diagram that depicts logical flow of a process from its inception to its completion. • Entities: Items to be processed (i.e. patients, documents, customers, etc.) • Activities: Tasks performed on entities (i.e. medical procedures, document approval, customer checkout, etc.) • Resources: Agents used to perform activities and move entities (i.e. service personnel, operators, equipment, nurses, physicians.) Connections: • Entity arrivals: They define process entry points, time and quantities of the entities that enter the system to begin processing. • Entity routings: They define directions and logical condition flows for entities (i.e. percent routing, conditional routing, routing on demand, etc.) 43
    • Typical Data Inputs Required to Feed the Model • Entities, their quantities and arrival times Periodic, random, scheduled, daily pattern, etc. • Time the entities spend in the activities This is usually not a fixed time but a statistical distribution. The wider the time distribution, the higher the variability of the system behavior. • The capacity of each activity The maximum number of entities that can be processed concurrently in the activity. • The size of input and output queues for the activities (if needed). • The routing type or the logical conditions for a specific routing. • Resource Assignments The number of resources, their availability, and/or resources shift schedule. 44