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Christopher DeFlitch, MD, FACEP

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Chris DeFlitch, MD, FACEP Penn State Hershey Medical Center Connected (CIS) Physician Champion Founder, Penn State Partners for Healthcare Engineering Director & Vice-Chair, Dept of Emergency Medicine Healthcare Engineering with Physician Directed Queuing (PDQ) (PDQ) TM TM Success Stories-Triage and Process with EDIS
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Chris DeFlitch, MD, FACEPPenn State Hershey Medical Center

Connected (CIS) Physician ChampionFounder, Penn State Partners for Healthcare Engineering

Director & Vice-Chair, Dept of Emergency Medicine

Healthcare Engineering with Physician Directed

Queuing (PDQ)(PDQ)TMTM

Success Stories-Triage and Process with EDIS

HEALTHCARE TODAYHEALTHCARE TODAY

BOARDERS No Beds for ED Patients HALLWAY Patients Ambulance DIVERSION Dissatisfied Providers Dissatisfied Patients Regulatory Issues

Define Capacity as Bed

Emergency Department (50,000)Emergency Department (50,000)

Traditional Targets 37 patient stations

(1350 visits/station) 44K sq ft 0 stations short <1% LWOTS 0 Hallway beds 0 Boarders Satisfied Providers Satisfied Patients Patients Safe Right Care, Right

Location

Reality Capacity for 28,500 20 open stations

(2589 visits/station) 24K sq ft 17 stations short >7% LWOT Hallway care 10+ Boarders Dissatisfied Providers Dissatisfied Patients Safety, Regulatory? Some Care, Any Location

Use EDIS for Success Use EDIS for Success

Understand workflow, map processes Critical Resources Demand-Capacity Interdependencies

Apply Queuing and IE science to flow Define Value with EDIS DATA Redefine & Expand Capacity to Care

Limited Resources Limited Capital

Actually DO IT…..TRANSFORM

Add Operational DATA to WorkflowAdd Operational DATA to Workflow

Arrival distributions Critical Interval Processing Times Resources data, number and type Perspective Flows

Patient Provider Resources

Provider Perspective FlowsProvider Perspective Flows

“Typical” Arrival Patterns“Typical” Arrival Patterns

Define the ISSUE with Operational DataDefine the ISSUE with Operational Data

No Capacity

High Demand

Understand Queuing SystemsUnderstand Queuing Systems

Science of WAITING All queuing systems possess the same

basic elements: Customer (Demand) Resources (Capacity) Queues

When analyzed, it is clear that queuing systems are ubiquitous in healthcare.

“One mans WAIT is another mans WORK”

Value-Added Activity Value-Added Activity

Must be performed to meet customer needs

Adds form or feature to service

Enhances service quality

Customers willing to pay for this work

If you STOP the activity, would your customer complain? If yes, then it’s likely Value-Added.

George ML. Lean Six Sigma Pocket Toolbook. 2005

WASTE (non-value added)WASTE (non-value added) Handling beyond what

is minimally required to move work

Rework to fix errors Duplicative work Wait Idle time Delays Unnecessary motion Over processing (too

many steps to complete the job)

If you STOP activity, would any customer know the difference? If not, then it’s probably Non-Value-Added.

George ML. Lean Six Sigma Pocket Toolbook. 2005

Clinical Value AnalysisClinical Value Analysis

Clinical Value Streaming - TRIAGEClinical Value Streaming - TRIAGE

Clinical Value Streaming- CP TriageClinical Value Streaming- CP Triage

“Healthcare is the only industry I have ever heard of that actually has a name for a major category of waste. You have waiting rooms. Most organizations outside of healthcare would go bankrupt if they thought like this”.

“Healthcare is the only industry I have ever heard of that actually has a name for a major category of waste. You have waiting rooms. Most organizations outside of healthcare would go bankrupt if they thought like this”. Page 33 Lean-Six Sigma for Healthcare

Caldwell et al

“Infuse care into the queue (waiting) ….define capacity to CARE not a bed”“Infuse care into the queue (waiting) ….define capacity to CARE not a bed”

Penn State Healthcare Engineering TeamDeFlitch et al

Critical to Healthcare Engineer Critical to Healthcare Engineer

Burning Platform Defined CRITICAL Resource(s) &

interdependencies Boarders Ancillaries Information Providers Minimal Space

WITHOUT Adding Resources

Physician (or MLP) Determines Queue

passively Listens to RN traditional

“triage” Delegates Procedures Initiates work-up when no

beds

Triage Nurse(s) Arrives patient Manage Minor Emergency

Technician support Splint Transport

Physician Directed Queuing (PDQ)TMPhysician Directed Queuing (PDQ)TM

PDQ Year-to-Year Results Comparison PDQ Year-to-Year Results Comparison

Baseline 5.6% 8h 6m

71 min 93 min 5h 34m 5h 51m

Healthcare Engineered 2.7% 6h 16m

45 min 60 min 3h 9m 1h 23m

LWBSLength of

StayDoor-RmDoor-DrESI 4ESI 5

52% 23%

37% 35% 44% 76%

Current Front-End SpaceCurrent Front-End Space

AmbulanceWalk-In

TRIAGE

“need” 20K sq ft “need” 20K sq ft …..you get 7k …..you get 7k

Currently under ConstructionCurrently under Construction

Visitors to Hospital

PDQ Triage1

Triage2

Check in Checkout

Private Complex Dx Queues

Technician Staffing

Private Minor Dx Queues

Minor Emergency, Walk InMinor Emergency, Walk In

Visitors to Hospital

Triage

PDQFull Reg &

Checkout

Minor Emergency, AmbulanceMinor Emergency, Ambulance

Visitors to Hospital

Triage

PDQFull Reg &

Checkout

Room Required & Available, Walk In Room Required & Available, Walk In

Visitors to Hospital

Triage

Room Required & Available, Ambulance Room Required & Available, Ambulance

Visitors to Hospital

Triage1Mini Reg

Room Not Available, Walk In Room Not Available, Walk In

Visitors to Hospital

Private Complex Dx Queues

FullReg

Triage

PDQ

Room Not Available, Ambulance Room Not Available, Ambulance

Visitors to Hospital

Private Complex Dx Queues

FullReg

Triage

PDQ

Next Venues of Healthcare EngineeringNext Venues of Healthcare Engineering

Service Line Flow (Neurosurg) Peri-Op Processing (Operative Suites) Hospital Capacity Management Other Interdependent ED flows Informatics Project Management Quality Outcomes (MRSA, Diabetes) Process Simulation with OSGi

Future of HEALTHCARE ENGINEERINGFuture of HEALTHCARE ENGINEERING

Process simulation models Explicitly represent variability Predict interdependency Manage complex systems in a computer

Predict system performance under varying inputs (loads)

Compare alternative system designs Determine the effects of alternative

policies on system performance


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