Variation reduction strategies for IP patient flow through

operating rooms

Brian Meitzner, B.S.

Management Engineer, William Beaumont Hospital, MI

Jayant Trewn, Ph. D

Adjunct Faculty, William Beaumont Hospital, MI

Objective

ØConceptualize a systems approach to identifying variation sources that affect patient flow

ØDefine Artificial vs. Natural Variation

Ø Identify schedule variation causes

ØDiscuss some approaches to reduce schedule variation

ØDiscuss application in a large hospital in Michigan

Factors Affecting Item Flow

ØResource capacity

Ø Input/arrival patterns

ØDeparture patterns

Ø Input storage

ØOutput storage

ØProcessing variations/Cycle time

ØC/O time

Ø Job/work scheduling practices

Modeling Item Flow Through A Service Unit

Servicing Unit•Capacity•Cycle Time•Change Over time•Item service time

Input storage

Preceding unit outputpattern

Succeeding unit inputpattern

Outputstorage

Buffer Buffer

Sources Of Variation Affecting Flow

Waiting time/capacity of storageAbandon queue pattern

Nature of input of preceding unitDeparture patternOutput source

Storage capacity

Unit change/over time

Servicing time

Unit capacity

Storage capacity

Abandon queue pattern

Arrival pattern

Characteristic

Storage capacity availabilityOutput storage

Change over practices

Process/methods variances

Server to unit variances

Unit to unit variance

Resource/s capacity availabilityServicing unit

Storage capacity availabilityInput storage

Waiting time/capacity of storage

Nature of output of preceding unitInput source

Source of variationFactor

Drivers To Improving Hospital Service Unit Flows

Health Care Advisory Board (HCAB) 2002

Strategy for the future health system

Ø Maximizing Hospital Capacity and Throughput

Ø Managing Hospitals at full capacity

Modeling A Hospital Service Unit

Unit performing Service

Preceding Units

Succeding Units

Patient Flow

Provider Practices Patient

Characteristics System Variances

Input Variations

Output Variations

Patient Backward Acuity Move

Identifying sources of variation in a typical hospital service

unit

Input Arrival Patterns

Ø Schedule/appointment variances

Ø Add-on/Emergent care/Urgent care variances

Ø Transportation availability

Ø Patient arrival/preceding unit output variances

Arrival Pattern Variances

Ø Artificial Variability

Ø Non-random

Ø Non-predictable – driven by unknown priorities

Ø Flow variability

Ø Elective/Scheduled flow

Ø Random arrivals

Ø Add-on/Emergent care/Urgent care cases

Service Unit Variances

Ø Service provider practice variation

Ø Methods

Ø Techniques

Ø Experience

Ø Etc.

Service Unit Variances Cont.

Ø Patient characteristic variation

Ø Patient demographics

Ø Patient diagnosis/symptoms

Ø Patients’ reception to treatment

Ø Patients’ transient state/instability

Ø Patient recovery

Ø Etc.

Service Unit Variances Cont.

Ø System/Servicing Unit variances

Ø Process/service delivery variations

Ø Equipment variances

Ø Availability, capacity, throughput, changeover times, etc

Ø Resource availability

Ø Procedure rooms, beds, physicians, nurses, etc.

Ø Etc.

Output Unit Variations

Ø Transit to admitting/discharging unit

Ø Availability/capacity

Ø Resources

Ø Admitting/discharging Unit

Ø Unit capacity/bed availability

Ø Discharging procedures

Ø Physician availability

Ø Discharge orders availability

Ø Transportation availability – Internal/external

Patient Flow Dampers

Ø Backward acuity moves

Ø Digressing patient condition

Ø Correction of errors in service unit

Sources Of Flow VariationØ Surgical scheduling

Ø Within day variability

Ø Between day variability

Ø Demand

Ø Numbers of patients presenting

Ø Clinical condition of patients

Ø Capacity

Ø Staff

Ø Space

Ø Time

Ø Equipment

Possible Solutions to Smoothen Flow

Ø Reduce scheduling variation

Ø Training

Ø Dedicated set of rooms

Ø Functionally separate facilities

Ø Separate scheduled and unscheduled patients

Solution investigated

Ø OR flow (throughput) smoothening utilizing schedule management

Case Study Reducing OR Patient Output

Variability

Ø Objectives

Ø Reduce between day variation in number of OR IP’s creating a demand for hospital beds

Ø Reduce within day variation in number of OR IP’s creating a demand for hospital beds

Service Unit – OR

OR Pre-OP Post-OP/Bed/Home

Patient Flow

Provider Practices Patient

Characteristics System Variances

Input Variations

· Scheduling

Output Variations

Scope Of Case Study

ØPilot smoothening project

ØOB/GYN surgery department

Mean+3Sigma Demand (Peaks) Average Demand

Gyn Surgery Bed Demand- 1 Gyn Surgery Bed Demand- 2

Day Total Day Total

1Sun 6 1Sun 0

2Mon 30 2Mon 9

3Tue 26 3Tue 7

4Wed 29 4Wed 8

5Thur 24 5Thur 6

6Fri 28 6Fri 7

7Sat 7 7Sat 1

Grand Total 151 Max weekly demand Grand Total 39 Average weekly demand

Demand based on Mean + 3 Sigma Demand based on Mean

Max Demand fluctuation 20% 6 Beds Max Demand fluctuation 33% 3 Beds

WBH - Royal Oak

Gyn Surgery IP Bed Demand

1/1/2004 to 8/30/2004

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Ideal Demand

Within Day VariabilityBetween Day Variability

Gyn OR In Patient Bed Demand

Problem

Smoothening Opportunity

ØPast

ØGynecology Division of the Department of Obstetrics and Gynecology once had a block boarding system.

ØDismantled due to generalized perceptions of inequity

ØCurrently

ØOpen surgical boarding, with few exceptions

ØGYN physicians favor block boarding

Smoothening Benefits Of Block Boarding

ØConsistency in scheduling

ØGuaranteed block time

ØPredictable schedules

Other BenefitsØ Minimizing operating room delays and inefficiencies

Ø Resolve scheduling conflicts

Ø Maximizing surgical scheduling efficiency

Ø Flexibility within a given room and block time

Ø Improving patient care

Ø Blocks physicians can rely on

Ø Reducing conflict of responsibilities for practitioners

Challenges To Block Scheduling

Ø How to schedule 97 surgeons who have medium to small case lengths (time) into 10 daily half blocks?

Ø How to change the culture from flexible scheduling (schedule when-ever-room is available) TO constrained scheduling (Block Scheduling)

Model Development Process (Cont)

ØThe model building dilemma

Ø65 physicians want surgery blocks

Ø32 physicians do not want surgery blocks

ØBlock demand (historical hours) varies from 0.22 hrs/week to 5.03 hrs/week

ØThe dilemma: How to assign 65 surgeons into reasonable block sizes

Involve Physicians In Design

Ø Create a committee of supporters and dissenters of the block boarding scheduling model

Ø Make model flexible to physician preferences

Ø Block

Ø No-block

Ø Design model with equity for all in mind

Model Development Process

Block Boarding Survey

Survey surgeons to determine block preferences

Ø Initial mailing – June 2003

Ø Follow-up mailing – August 2003

Ø Phone calls – February 2004

Ø Survey response rate 66/80 = 83 %

Ø Follow-up (phone call) – 17 additional responses

Survey ResultsØ Item 1: Are you interested in having Block Boarding privileges?

Yes 65 67%

No 19 20%

No Response 13 13%

Ø Item 2: Block type – Group or individual?

Group 17 38%

Individual 28 62%

Ø Item 3: Block preference – Rank order block time preference

Ø Item 4: Anticipated time per week if block is not needed

Ø Item 5: Comments

Model Development Process (Cont)

Ø Source: SIS schedule

Ø OR Rooms 34, 35, 36 and 37

Ø Overall utilization – 67%

OR Time Utilization Report (Jan to Dec 2003)

SurveyResponse n Survey %

Average

Surgery

Hrs/week

Estimated

Blocks per

week 1

%

Demand

NoResp 13 13% 10.06 2.01 5%

Yes (Blocks) 65 67% 158.14 31.63 84%

No (Blocks) 19 20% 19.35 3.87 10%

Total 188.40 37.68

Notes1

Estimated blocks per week is based on 5 hour blocks2

All cases inclusive of CTC and cases done in other OR's

67% physicians wanted block boarding and they performed 84% of the case vol.

Model Development Process (Cont)

ØPhysicians will be assigned to blocks based on the model

ØBlock assignment based on historical room utilization reports

ØBlock assignment will be monitored

ØBlock Oversight Committee

Model Development Process (Cont)

Block Profile

ØBlock Scope: OR’s 34, 35, 36 and 37

ØTotal Blocks available per week: 36

ØOpen scheduling blocks for surgeons not participating in block scheduling

GYN Block Model

Mon AM Mon PM Tue AM Tue PM Wed AM Wed PM Thu AM Thu PM Fri AM Fri PM

Room 1Block Open Block Block Block Block Block Block Open

Room 2Block Open Block Block Block Block Block Block Open

Room 3Block No-Block Block Open Block Open Open Open No-Block

Room 4No-Block No-Block No-Block No-Block No-Block No-Block No-Block No-Block No-Block

LEGEND

Block Block Boarding Surgeons Block Staff in-service

No-Block Surgeons who are not block boarding Department education time

Open Open blocks for all (1st scheduled, 1st served)

Block Assignment Rules

ØBasis for assigning surgeons to blocksØ1st block preference in survey

ØAverage weekly surgery hours

ØOpen Blocks provided for block overflows

Model Implementation

Ø Transition phase – April 1st to May 31st 2005

Ø Block Boarding Transitional Phase –grandfather previously scheduled cases

Ø Model warm-up

Ø Steady State – June 1st to October 31st 2005

Ø No model adjustments made – revision of block assignments

Results Of Case Study

Performance Improvement Measurement

Ø Data Source

Ø Scope: All surgery Inpatients scheduled by OB/GYN physicians in Rooms 34 to 37 (OB/GYN patients only)

Ø Time frame: Jan to Oct 2005

Ø Data Source: Surgical Information System (SIS)

Data Analysis Elements

ØDependent Variable:

Ø Number of In-Patients leaving OR

Ø Independent Variables:

Ø Time of completion of surgery

Ø Type of Block (In-Block, Out-Block, No-Block)

Ø Phase (Pre-Blocks, Transition, Steady State)

Ø Weekday of surgery

ØCovariate

Ø Total surgeries per day

Ø Parse out variation due to surgical volume trend

Surgical Volume Trend

OB/GYN Inpatient Volume Trend

y = 2.5455x + 186.2

R2 = 0.1641

150

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct

Total Volume

Linear (Total Volume)

ØEffect of volume trends removed by placing trend as a covariate in ANOVA analysis

Data Analysis Model

ØAnova Analysis – Hierarchical Model

ØRemoval of effect of covariate – volume trend

ØModel

Ø Block Type * Phase * WeekdayØ Between Days Variation

Ø Block Type * Phase * Weekday * Time Ø Within Day Variation

Ø Block Type * Phase * TimeØ Within Time Bucket variation

Data Analysis Results

Tests of Between-Subjects Effects

Dependent Variable: Arrivals

1683.884a 351 4.797 17.299 .000

57.183 28 2.042 7.364 .000

148.987 245 .608 2.193 .000

435.682 70 6.224 22.443 .000

61.603 1 61.603 222.136 .000

1346.116 4854 .277

3030.000 5205

SourceModel

Beween DaysVariation

Within Days Variation

Within Time BucketVariation

Covariate - VolumeTrends

Error

Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .556 (Adjusted R Squared = .524)a.

Day to Day Schedule Variation

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Time of Week

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Pre-Model

1/1/05 to 3/31/05

Transition

4/1/05 to 5/31/05

Post

6/1/05 to 10/31/05

Block vs. Non-Block Schedule Variation

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No Block

Current

6/1/05 to 10/31/05

Block

Current

6/1/05 to 10/31/05

Reduced Variation

Observation: Reduction in within day variation

Discussion Of ResultsØ Reduced Within day variation p=0.00

Ø Operational Impact

Ø OR backup is managed – PACU bed availability is stable

Ø Hospital bed demand is stable – PACU output is stable

Ø Patient discharges can be planned

ØBetween day variation p=0.00

Ø Operational Impact

Ø Hospital bed demand is stable

Ø Bed utilization is even across week

Future Model Improvements

ØAdjust block boarding model to reflect utilization

Ø Adjust block time for block surgeons boarding surgeries out of their block

ØSmoothen between day variation

Ø Move high demand block volumes (Mon, Tue, Wed) to low demand days (Thu and Fri) to smoothen IP volumes by day of week