4/24/2009 1 IFMA IFMA – HCC HCC May 11, 2009 May 11, 2009 San Francisco CA San Francisco CA San Francisco, CA San Francisco, CA “Bringing OR (Operations Research) to Healthcare Design” “Bringing OR (Operations Research) to Healthcare Design” Dave Eitel, MD, MBA Dave Eitel, MD, MBA Sean Sean O’Neill O’Neill - St. Onge St. Onge Greg Weigle, PE, Greg Weigle, PE, FACHE FACHE-KLMK KLMK Phases of A Project Phases of A Project Typical Phases of a Healthcare Facility Project • Planning (Strategic, Financial, Facility) • Programming • Schematic Design (SD) • Design Development (DD) • Construction Documents (CD) • Construction Administration (CA) • Occupancy & Use
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4/24/2009
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IFMA IFMA –– HCCHCCMay 11, 2009May 11, 2009
San Francisco CASan Francisco CASan Francisco, CASan Francisco, CA
“Bringing OR (Operations Research) to Healthcare Design”“Bringing OR (Operations Research) to Healthcare Design”
Dave Eitel, MD, MBADave Eitel, MD, MBASean Sean O’Neill O’Neill -- St. OngeSt. Onge
Ensure Program is AlignedDesign Document Comparison
to Program and Budget
1. Operational Process Design
2. Space Programming
3. Design -Beginning
with Schematic
DesignDon’t
• Form follows functions
• How should “it” function”
• Elminate waste
• Maximize value
Design
• Room by room space
allocation
• Based on established
scope
• Representation of process flow
and space allocation
• Uses master plan as initial
ph sical
be in a rush
to Draw!
• Maximize value
• “Value Stream” processes”
• Room elements support
process design
physical organizing
model
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1. Orientation & Interviews
1. Orientation & Interviews
2. Brainstorming Workshop &
Presentations
2. Brainstorming Workshop &
Presentations
4. Value Stream Mapping Step 2
Future State
4. Value Stream Mapping Step 2
Future State
3. Value Stream Mapping Step 1 Current State
3. Value Stream Mapping Step 1 Current State
5. Development of Space Program
5. Development of Space Program
6. Development of Conceptual
Schematic Design
6. Development of Conceptual
Schematic Design
VI. Operational Process Review
T i l L A hTypical Lean Approach
State of HealthcareState of Healthcare
A Quote from Dr. Pronovost:
• “The fundamental problem with the quality of American medicine is that we’ve p q yfailed to view delivery of health care as a science. The tasks of medical science fall into three buckets. One is understanding disease biology. One is finding effective therapies. And one is insuring those therapies are delivered effectively. That third bucket has been almost totally ignored by research funders, government, and academia. It’s viewed as the art of medicine. That’s a mistake, a huge mistake. And from a taxpayer’s perspective it’s outrageous.”
• There is a recognized need to improve the efficiency and the quality of care in hospitals, on the service delivery (non-clinical) side of health care decision making.
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Hospital by Definition...Hospital by Definition...
• Webster Dictionary – Hospital - hos·pi·tal - “an institution where sick or injured are given medical or surgical care”
• Historically, the hospital culture has been resistant to leverage the proven process engineering tools deployed in industry (TQM, Lean, 6 sigma, operations research, etc)., g , p , )
A Hospital is… A Collection of interA Hospital is… A Collection of inter--related Processes related Processes
• The hospital and its departments are systems – a collection of inter-related processes - that provide healthcare to patients.
• A simplified macro view - Patients flow to each department, are queued into location, cared for and discharged
• There are a number of proven tools and methodologies to improve the Healthcare process and infrastructure design.
• The engineered approach utilizes a data driven process which leverages lean thinking and operations researchg g p
• Operations Research by definition: Mathematical or scientific analysis of a process or operation, used in making decisions.
Systemic View of the Operations– Systemic View of the Operations– Statistical Profiling– Predictive Analytics– Queuing Theory– Theory of Constraints– Optimization Engines– Discrete Event Simulation Models– Resource Planning / Scheduling– Animations
Traditional Versus EngineeredTraditional Versus Engineered
Traditional Approach – Operations • Defined team• Discovery
Engineered Approach – Operations • Defined team• DiscoveryDiscovery
– Interview process (Users Group)– Heuristics & Benchmarks– Operations Model Definition– Systems
Discovery– Interview process (Users Group)– Heuristics & Benchmarks– Operations Model Definition– Systems– Value Stream Mapping– Statistical Analysis/Predictive Analytics– Queuing Theory– Queuing Theory– Theory of Constraints– As is validation & challenge process– Discrete Event Simulation– Recommendation Validation
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Current StateCase Study Example
Case Study Case Study –– AsAs--Is State, OverviewIs State, Overview
Prototypical Emergency Department - Base Case:
• # of ED Beds:13 beds• ED Footprint: 5,800 DGSF• Like many ED’s today use ESI Triage to flow patients [quick sort & stream] at
the front door: supplies 5-level case mix data for design decision making• Current ED Flow is Serial Process, driven by two common “myths:
– 1. “Cannot see a less sick patient before a more sick patient”– 2. “Everybody needs a bed”
• 100% of the patients get an ED bed100% of the patients get an ED bed• Bed is assigned and utilized 100% by the patient during their entire ED stay• Patient movements from ED to ancillary departments are tracked manually
through verbal communications
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Case Study Case Study –– AsAs--Is State, ED ProfileIs State, ED ProfilePrototypical Emergency Department, Rural Community Hospital
Metric UOM TotalPatients Annual 26,250Patients per Bed Pts / Bed 2,019A it L l 1 % f P ti t 0 6%Acuity Level 1 % of Patients 0.6%Acuity Level 2 % of Patients 17.1%Acuity Level 3 % of Patients 37.0%Acuity Level 4 % of Patients 41.4%Acuity Level 5 % of Patients 3.9%
Metric UOM Min Avg MaxLWOTs % of Patients 1.2% 2.6% 4.6%Door 2 Bed MInutes 22 31 47Door 2 Doc Minutes 46 61 99Length of Stay Minutes 159 188 226Length of Stay Minutes 159 188 226Ambulance Diversions Hrs per Month 6 22 71
• This ED data represents a small rural community hospital with a seasonal peak during the summer months• The avg. patients per bed is higher than the typical 1,500-1,800 patients per bed per yr
Case Study Case Study –– AsAs--Is State, Facility LayoutIs State, Facility Layout
1Walk ‐ In Entrance
2
“Quick” RegistrationRegistration
10
3 FullTriage
4Ambulance Entrance
5 TraumaBeds
(Acuity 1,2)6
Mid Level
7LowerLevel Beds
(Acuity 4,5)
8 9
NurseStation
DoctorStation
Level Beds
(Acuity 3)
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Case Study Case Study –– AsAs--Is StateIs StatePrototypical Emergency Department – High Level Process Map
• Between each step in the process above, the patient may experience delays as a result of downstream bottlenecks or other constraints.
• The admission / discharge process shown may have a significant impact on upstream process steps because there is “pre-planning” of admissions / discharges.
Case Study Case Study –– AsAs--Is State, Physical Flow, Serial ProcessingIs State, Physical Flow, Serial Processing
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Case Study Case Study –– AsAs--Is State, Sample Patient ExperienceIs State, Sample Patient Experience
• This gantt chart represents a single patient experience through the “as-is” state with an acuity level of 4.
• Total Length of Stay was
EVENT START FINISHELAPSED TIME (Minutes)
Arrival 2/25/2009 8:49:00 AM 2/25/2009 8:49:00 AM 0.0Triage 2/25/2009 8:49:00 AM 2/25/2009 8:59:20 AM 10.3ED Bed 2/25/2009 8:49:00 AM 2/25/2009 9:00:00 AM 11.0DR Exam 2/25/2009 9:00:00 AM 2/25/2009 9:06:00 AM 6.0
g yapproximately three (3) hours.
Past Medical History 2/25/2009 8:49:00 AM 2/25/2009 9:07:44 AM 18.7RN Exam 2/25/2009 9:00:00 AM 2/25/2009 9:09:46 AM 9.8Full Registration 2/25/2009 8:49:00 AM 2/25/2009 9:15:31 AM 26.5Medication Order 2/25/2009 9:32:43 AM 2/25/2009 9:32:43 AM 0.0Radiology 2/25/2009 9:29:06 AM 2/25/2009 11:24:51 AM 115.7Discharge Pt 2/25/2009 11:47:32 AM 2/25/2009 11:57:51 AM 10.3
Length of Stay
PatientID 60012345 188 Minutes
Acuity Level 4 3.1 Hours
Future State
Traditional Planning + Lean +
Engineered Approach
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“One of the first concepts to get acrossis we are not seeking to improve that which should not be done in the first place.”
Queue Discipline Management in ActionQueue Discipline Management in Action
A Long Line for a Shorter Wait at the Supermarket A Long Line for a Shorter Wait at the Supermarket
New York Times June 23 2007New York Times June 23 2007
Whole Foods Grocery StoreWhole Foods Grocery Store
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Case Study Case Study –– Future State Future State -- TraditionalTraditional
Emergency Department, 10 year Growth Plan• Target goal: 40,000 patient visits per year
A d d i 28 b d ( 1 00 i i /b d)• Assumed need is 28 beds (approx 1500 visits/bed)• 21,000 DGSF, increase by 2x (750 DGSF/bed)• Operational Objectives;
– Length of Stay, <2.5 hours per Visit– Assumes ESI 2.8– Door 2 Doc Time: 30 minutes– LWOT, <3%– Serial Processing of Patients
Case Study Case Study –– Future State Future State -- EngineeredEngineered
• Discovery– User Group Interviews– Patient Volume (Arrival / Discharge / Admission)– Patient Volume (Arrival / Discharge / Admission)– Patient Statistics (D2D, Acuity, LWOTs, etc.)– 10 year growth plan
• Identify Appropriate Analytical Tools• Alternatives – Challenging Serial Approach to Flow
Si l ti S iti it T t Alt ti A h• Simulation – Sensitivity Test Alternative Approaches• Team Review and Confirm Consensus• Recommendations
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Case Study Case Study –– Future State, Process FlowFuture State, Process FlowPrototypical Emergency Department – High Level Process Map
• Future state design takes into consideration parallel processing of patients to minimize length of stay. Acuity levels 4 & 5 can be seen at the same time as higher acuity patients not in a main ED bedhigher acuity patients, not in a main ED bed.
• Lower acuity patients should utilize vertical placement instead horizontal “bed” placement in main ED.
• Overall goals: 1) Door to doc/extender time as close to ZERO as possible, 2) On the way to a continuous patient flow, “No Wait ED”
Case Study Case Study –– AsAs--Is State, Physical FlowIs State, Physical Flow
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Case Study Case Study –– Future State Future State –– Physical FlowPhysical Flow
1 Walk ‐ In Entrance
2 Waiting Room2 Waiting Room
3 Quick Registration
4 QuickSort
5Full Reg. / Financial
For some
6
Ambulance
Bed Side Regfor ESI 1 & 2s
Ambulance Entrance7Main ED
Exam Rooms
Case Study Case Study –– Future State Future State –– LayoutLayout
1
Walk ‐ In Entrance
Waiting Room
23
45
6QuickSort
Full Reg. / FinancialFor some
Ambulance
Waiting Room
Quick Registration
Bed Side Regf ESI 1 & 2
7Ambulance EntranceMain ED
Exam Rooms
for ESI 1 & 2s
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Case Study Case Study –– Future State Future State ––SimulationSimulation
• The images shown on slide depict an ED discrete event simulation with Flexsim Software.
• These images are not representative These images are not representative of the statistical simulation presented.
Case Study Case Study –– Future State Future State –– Engineered Engineered -- ResultsResults
• New Model• 19 Beds• New Holding Rooms• 40,000 Patient Visits per Year• LWOTs < 1%• Patients Triaged at Entry – Parallel Processing• A Main ED Bed is Not Assigned to All PatientsA Main ED Bed is Not Assigned to All Patients
– Patients that can remain vertical should remain vertical• DGSF – 8,000 DGSF
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ConclusionConclusion
• Operations Research tools can be used to improve the Hospital planning process
• With cost pressures, there is a need to be more precise during programming phase
• Simulation tools can help the team to challenge, test and optimize upon the current operating model
• Case study outlines a typical ED. Similar approach could be performed for entire hospital
• Analytical approach is applicable for improving existing operations y pp pp p g g pto reduce operational costs, improve patient and staff satisfaction
Presentations available at www.klmk.com & www.stonge.comNote two organizational links for your information: SHS, ASQ
How Can You Get ESI v.4 Triage?How Can You Get ESI v.4 Triage?