optimizing hospital capacity via a novel patient throughput model · 2019-02-14 · optimizing...

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Capacity Planning for Inpatient UnitsOptimizing Hospital Capacity via a Novel Patient Throughput Model

Session #150, Wednesday, February 13, 2019

Michael Schmidt, MD, ACMO Northwestern Medicine & Stephanie Gravenor, MBA, CEO Medecipher Solutions

Optimizing hospital operational decision making

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Michael Schmidt, MD FACEP

Has no real or apparent conflicts of interest to report.

Conflict of Interest

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Stephanie Gravenor, MBA

Ownership Interest:

Co-Founder/Owner – Medecipher Solutions

Conflict of Interest

4

Presenters

STEPHANIE GRAVENOR, MBAStephanie.Gravenor@medeciphersolutions.com

Hospital Operations Strategist

Chief Executive Officer, Medecipher

MICHAEL SCHMIDT, MD FACEPmischmid@nm.org

Associate Chief Medical Officer - Operations

Secretary Treasure - Medical Staff

Northwestern Memorial Hospital

Assistant Professor of Emergency Medicine

Northwestern University

Feinberg School of Medicine

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Does your hospital look like this?

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…or does it look more like this?

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Agenda

1) How does the complexity of a large academic

medical center affect patient throughput?

2) How can we determine optimal hospital capacity to

minimize wait times?

3) What solutions can be implemented to improve

patient flow and support future growth?

8

Describe factors unique to patient throughput that are

not captured by standard queueing assumptions

Recognize how occupancy rates impact patient wait

times

Apply the basics of operations research to determine

the targeted average occupancy of an inpatient

service line for the desired level of service

Identify the number of inpatient/observation beds

needed to achieve the desired utilization

Learning Objectives

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Agenda

1) How does the complexity of a large academic

medical center affect patient throughput?

2) How can we determine optimal hospital capacity to

minimize wait times?

3) What solutions can be implemented to improve

patient flow and support future growth?

10

NMH Campus Overview

1,319 employed Northwestern Medical Group physicians- 88% Central Region

- 17 Clinical Departments

- Designated Level 1 Trauma & Stroke Center - Comprehensive Emergency Department (~86K

visits FY15)

Medical/ Surgical

OB (Women’s Services)

ICU

(including Neonatal Level

III)

74 Licensed Operating Rooms

11,629 IP Surgeries, 22,480 OP Surgeries

Acute Mental Illness -

PsychiatryObservation Oncology

894-licensed bed academic medical center hospitalInpatient services spread across four downtown Chicago locations

Feinberg Pavilion1,084,203 gsf

Galter Pavilion939,058 gsf

Prentice Women’s Hospital

1,015,596 gsf

Primary clinical affiliate of Northwestern University’s Feinberg School of Medicine (FSM)

- Over 1,000 residents and fellows- 12 FSM departments ranked in NIH-funded top 20

- 69 Gen. Radiology / Fluoroscopy

- 10 Nuclear Med.- 26 Mammography- 52 Ultrasound

- 7 CT- 1 PET- 15 MRI- 13 Angiography

Equipment

Over 60,000 NMH Inpatient and Observation discharges in FY16Nearly 12,000 annual deliveries at Prentice

Lavin Family Pavilion

975,524 gsf

Diagnostic / Imaging Equipment

Arkes Family Pavilion

701,875 gsf

Olson Pavilion364,610 gsf

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84% Increase in Intra-System referrals to NMH

Cadence Joins NM2014

CentegraFall, 2018

KishHealth Joins NM2015

Marianjoy Joins NM2016

NM Strategic Plan Approved2009

Lake Forest Hospital Joins NM2010

NMFF/NMPG Form NMG and Join NM

2013

External Transfer RequestsNM Hospitals → NMH

+ 84 %

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NMH Within Larger Context

Essential Provider for the Local Community

Referral Center for NM and the Greater Chicago Area

National and International Referral Center

Local Regional National/ International

Patient Origin 71% 24% 5%

Case Mix Index (CMI) 2.18 2.20 2.96

NMH’s role has evolved as a result of community need, system growth and capability development

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Emergency Department: 39%

Direct Admissions: 8%

External Transfers: 4%

Scheduled Surgery: 16%

Scheduled Procedures: 1 %

28%Planned

72% Unplanned

but Predictable

Medicine8,904 Admissions

ICU13,274 Admissions

Surgery12,277 Admissions

Women’s Services

11,089 Admissions

Oncology3,659 Admissions

Cardiology3,281 Admissions

14,773

Internal Transfers

Observation (FMOU)4,045 Admissions

Behavioral Health

778 Admissions

NMH

Labor and Delivery: 21%

Labor and Delivery: 11%

NMH: Admission Source & Destination

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Occupancy Increase Causes

Adult ALOS Admissions (thousands)

Patient Days(thousands)

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↓ Lower acuity care shift to

outpatient↓ Operational efficiencies

↓ Lack of bed availability ↓ Complex discharge mgmt.

↓ Expanded services at other

sites↓ 24/7 hospital model

↑ Population growth ↑ New treatments and therapies

↑ Changing demographics ↑ Readmission avoidance

↑Patient choice ↑ Case Mix Index

(2.10 to 2.32 FY15-17)

↑ System growth ↑ Comorbidities

Occupancy Increase Causes

Adult ALOS Admissions (thousands)

Patient Days(thousands)

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↓ Low acuity care shift to

outpatient↓ Operational efficiencies

↓ Lack of bed availability ↓ Complex discharge mgmt.

↓ Building capabilities at other

sites↓ 24/7 hospital model

↑ Population growth ↑ New treatments and therapies

↑ Changing demographics ↑ Readmission avoidance

↑Patient choice ↑ Case Mix Index

(2.10 to 2.32 FY15-17)

↑ New NM partnerships ↑ Comorbidities

Occupancy Increase Causes

Adult ALOS Admissions (thousands)

Patient Days(thousands)

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↓ Lower acuity care shift to

outpatient↓ Operational efficiencies

↓ Lack of bed availability ↓ Complex discharge mgmt.

↓ Expanded services at other

sites↓ 24/7 hospital model

↑ Population growth ↑ New treatments and therapies

↑ Changing demographics ↑ Readmission avoidance

↑Patient choice ↑ Case Mix Index

(2.10 to 2.32 FY15-17)

↑ System growth ↑ Comorbidities

Occupancy Increase Causes

Adult ALOS Admissions (thousands)

Patient Days(thousands)

+5.9%

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NMH’s Capacity Problem

Today ‘15 – ‘18

Mon 82% +5%

Tue 89% +2%

Wed 91% +2%

Thu 92% +2%

Fri 91% +1%

Sat 88% +2%

Sun 82% +4%

Average 9am Occupancy

2015

Today

Diversion, LWBS, Canceled External

Transfers

ED & PACU Boarding

Unmet Demand Wait Times

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Strategic Levers for Impacting Throughput

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Optimal Occupancy

85%Occupancy ?

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Agenda

1) How does the complexity of a large academic

medical center affect patient throughput?

2) How can we determine optimal hospital capacity

to minimize wait times?

3) What solutions can be implemented to improve

patient flow and support future growth?

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Determine the minimal number of beds needed in an inpatient unit to achieve service-level goals with high confidence.

Goal

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The ‘Goldilocks Principle’

JUST RIGHT

‘GOLDILOCKS PRINCIPLE’

CAPACITY

SE

RV

ICE

/

PE

RF

OR

MA

NC

E

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𝑴𝒂𝒙𝒊𝒎𝒖𝒎 𝑻𝒉𝒓𝒐𝒖𝒈𝒉𝒑𝒖𝒕 =𝒇𝒊𝒙𝒆𝒅 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒃𝒆𝒅𝒔 (𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚)

𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝑳𝑶𝑺

General Queueing Theory Insights

90 Bed Unit 4.5 Day LOS20 Arrivals/Day

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General Queueing Theory Insights

𝑭𝒊𝒙𝒆𝒅 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑩𝒆𝒅𝒔 = 𝑴𝒂𝒙𝒊𝒎𝒖𝒎 𝑻𝒉𝒓𝒐𝒖𝒈𝒉𝒑𝒖𝒕 𝒙 𝑨𝒗𝒈 𝑳𝑶𝑺

22 Arrivals/Day 4.5 Day LOS

𝑴𝒂𝒙𝒊𝒎𝒖𝒎 𝑻𝒉𝒓𝒐𝒖𝒈𝒉𝒑𝒖𝒕 =𝒇𝒊𝒙𝒆𝒅 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒃𝒆𝒅𝒔 (𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚)

𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝑳𝑶𝑺

99 Bed Unit

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Admission Delays by Occupancy Level

Fixed # of Available Beds

124 110 107

Avg.

Occupancy

Rate

Avg. # of

Delayed

Patients

Avg. Delay

per Patient

(Hours)

What is the relationship

between the timeliness

of the service and the

occupancy level?

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Queueing Theory Insight

Average Patient Delay (Hours)

Average # of Patients Delayed

Average Occupancy

# of Beds

# o

f D

ela

yed

Pat

ien

ts

Average Occupancy

# of Beds

Avg

. Del

ay (

Ho

urs

)

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Average Occupancy

# of Beds

# o

f D

ela

yed

Pat

ien

ts

Average Occupancy

# of Beds

Avg

. Del

ay (

Ho

urs

)

Queueing Theory Insight

With 124 available beds, average occupancy = 84%

124

84%

124

84%

Average Patient Delay (Hours)

Average # of Patients Delayed

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Average Occupancy

# of Beds

# o

f D

ela

yed

Pat

ien

ts

Average Occupancy

# of Beds

Avg

. Del

ay (

Ho

urs

)

Queueing Theory Insight

Average Patient Delay (Hours)

Average # of Patients Delayed

110

95%

110

95%

5.5 hours 7 patients

With 110 available beds, average occupancy = 95%

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Average Occupancy

# of Beds

# o

f D

ela

yed

Pat

ien

ts

Average Occupancy

# of Beds

Avg

. Del

ay (

Ho

urs

)

Queueing Theory Insight

107

98%

107

98%

Average Patient Delay (Hours)

Average # of Patients Delayed

39 hours 37 patients

With 107 available beds, average occupancy = 98%

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Admission Delays by Occupancy Level

Fixed # of Available Beds

124 110 107

Avg.

Occupancy

Rate84% 95% 98%

Avg. # of

Delayed

PatientsMinimal 7 37

Avg. Delay

per Patient

(Hours)Minimal 6 39

Delays increase exponentially as occupancy increases

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Patient Throughput Model: Improvement over prior Methods

Prior Methods Patient Throughput Model

Variability Day-of-weekDay-of-weekHour-of-dayRandom variability, via simulation

Service Times Mostly Averages Key characteristics of hospital patient flow

Occupancy

TargetAssumed 85% Relates service metrics with occupancy

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Patient Throughput Model: Key Features

Model Feature

1 Arrival Rate Arrival rate is random and time

of day & seasonally dependent

2 Service Rate Random patient recovery times

3 Discharge

Decision

Fixed times for physician

rounding & discharge decision

4 Discharge Delay Administrative delays between

discharge decision and

departure

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Model ValidationPredicted hourly discharge delay matches empirical data

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Model ValidationDiscrepancies with model approaching 100% occupancy

Hour of Day

Avg

. Occ

up

ancy

(%

)

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INPUT PARAMETERS

HourRequest Rate

(number/hour)Discharge Density (percentage/hour)

Number of Beds

0 0.869 0.001 172

1 0.713 0.001

2 0.719 0.001 % Compound Annual Increase in Bed

Request Rate

3 0.703 0.001

4 0.603 0.000

5 0.663 0.001 0.00%

6 0.506 0.001

7 0.869 0.002

8 1.402 0.004

9 1.838 0.012

10 2.091 0.028

11 2.614 0.047

12 2.914 0.070

13 3.204 0.121

14 3.244 0.141

15 3.174 0.103

16 2.734 0.135

17 2.398 0.124

18 2.228 0.107

19 2.061 0.056

20 2.035 0.024

21 1.649 0.011

22 1.392 0.006

23 1.319 0.004

Sum 41.942 1.000

Throughput Model

1 Request Rate

Discharge Density

# of Beds

Growth

1 Service Line

Weekday/Weeken

d

Model Inputs

Model Variants

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0

1

1

2

2

3

3

4

Nu

mb

er

of

Req

uests

Hour of Day

Input: Hourly Bed Request Rate

INPUT PARAMETERS

HourRequest Rate

(number/hour)Discharge Density (percentage/hour)

Number of Beds

0 0.869 0.001 172

1 0.713 0.001

2 0.719 0.001 % Compound Annual Increase in Bed

Request Rate

3 0.703 0.001

4 0.603 0.000

5 0.663 0.001 0.00%

6 0.506 0.001

7 0.869 0.002

8 1.402 0.004

9 1.838 0.012

10 2.091 0.028

11 2.614 0.047

12 2.914 0.070

13 3.204 0.121

14 3.244 0.141

15 3.174 0.103

16 2.734 0.135

17 2.398 0.124

18 2.228 0.107

19 2.061 0.056

20 2.035 0.024

21 1.649 0.011

22 1.392 0.006

23 1.319 0.004

Sum 41.942 1.000

Throughput Model

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INPUT PARAMETERS

HourRequest Rate

(number/hour)Discharge Density (percentage/hour)

Number of Beds

0 0.869 0.001 172

1 0.713 0.001

2 0.719 0.001 % Compound Annual Increase in Bed

Request Rate

3 0.703 0.001

4 0.603 0.000

5 0.663 0.001 0.00%

6 0.506 0.001

7 0.869 0.002

8 1.402 0.004

9 1.838 0.012

10 2.091 0.028

11 2.614 0.047

12 2.914 0.070

13 3.204 0.121

14 3.244 0.141

15 3.174 0.103

16 2.734 0.135

17 2.398 0.124

18 2.228 0.107

19 2.061 0.056

20 2.035 0.024

21 1.649 0.011

22 1.392 0.006

23 1.319 0.004

Sum 41.942 1.000

Throughput Model

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Nu

mb

er

of

Dep

art

ure

s

Hour of Day

Input: Hourly Patient Departure Rate

39

0

1

1

2

2

3

3

4

Nu

mb

er

of

Req

uests

Hour of Day

Input: Hourly Bed Request Rate

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Nu

mb

er

of

Dep

art

ure

s

Hour of Day

Input: Hourly Patient Departure Rate

INPUT PARAMETERS

HourRequest Rate

(number/hour)Discharge Density (percentage/hour)

Number of Beds

0 0.869 0.001 172

1 0.713 0.001

2 0.719 0.001 % Compound Annual Increase in Bed

Request Rate

3 0.703 0.001

4 0.603 0.000

5 0.663 0.001 0.00%

6 0.506 0.001

7 0.869 0.002

8 1.402 0.004

9 1.838 0.012

10 2.091 0.028

11 2.614 0.047

12 2.914 0.070

13 3.204 0.121

14 3.244 0.141

15 3.174 0.103

16 2.734 0.135

17 2.398 0.124

18 2.228 0.107

19 2.061 0.056

20 2.035 0.024

21 1.649 0.011

22 1.392 0.006

23 1.319 0.004

Sum 41.942 1.000

Throughput Model

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Insights from Analysis

Current State

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Model Insights

Average Occupancy

# of Beds

Avg

. Del

ay (

Ho

urs

)Average Occupancy

# of Beds

% o

f p

atie

nts

Del

ayed

Avg. Patient Delay (Hours)

Avg. % Delayed

97%

172

97%

172

Avg Delay 3.9 Hours

Avg Peak-Hour Delay 5.1 Hours

Avg Daily Max Delay 8.4 Hours

> 0 Hours 53%

> 1 Hours 47%

> 2 Hours 41%

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Insights from our AnalysisAvg. Bed

Assignment Delay (min)

Percent of Patients Delayed

(%)

Percent of Patients Delayed

> 1 hr (%)

Percent of Patients Delayed

> 2 hrs (%)

# of Beds

232 53 47 41 172

177 46 40 34 174

131 38 32 26 176

114 33 27 22 178

80 27 22 17 180

54 21 16 13 182

45 17 13 10 184

37 15 11 8 186

30 12 9 7 188

18 9 6 4 190

6 5 3 2 192

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Insights from Analysis

Current State

Target Range

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Insights from Analysis

Care Cohorts Current Beds

Current Avg. Occupancy

Current Avg.Bed

Assignment Delay

Target Avg. Occupancy (30 - 60 min Target Delay)

Target Number of Beds to Add

Medicine Inpatient

172 97% 232 (min) 89% – 92% 19 – 26

Cardiology (Inpatient)

32 97% 333 (min) 74% – 79% 9 – 11

Medicine Observation

20 85% 166 (min) 72% – 78% 3 – 5

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Key ApplicationsModel Finding Action Required

Larger, co-located cohorts offer

increased bed flexibility

• Pursue strategies that better align patient type with cohort type (e.g., all observation patients placed in designated unit)

• Consider increasing the size of cohorts• Assess opportunity to combine cohorts when clinically and

operationally feasible

Occupancy rates impact patient

wait times

• Continue to pursue operational efficiency opportunities, but also take immediate next steps to reduce patient wait times

NMH occupancy is above model

target occupancy

• Additional solutions may be needed to address occupancy concerns

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Agenda

1) How does the complexity of a large academic

medical center affect patient throughput?

2) How can we determine optimal hospital capacity to

minimize wait times?

3) What solutions can be implemented to improve

patient flow and support future growth?

47

Patient Flow Solutions

Expanded telemetry monitoring capabilities

Innovative Emergency Department split-flow operations Including a capacity expansion

Expanded physical bed capacityExpanded provider and nurse staffing on hospital service line

Expanded robust daily operations (Command Center, Peak Census/SWAT team, RTDC)

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Impact: Reduction in Diversion Hours% of the month on diversion

Telemetry Expansion: +8 telemetry licenses, 4/30/181

0%

10%

20%

30%

40%

50%

60%

4 5 6 7 8 9 10 11

1

Month:

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Impact: Reduction in Diversion Hours% of the month on diversion

ED Expansion: +10 monitored beds/pods, 8/20/182

0%

10%

20%

30%

40%

50%

60%

4 5 6 7 8 9 10 11

2

Month:

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Impact: Reduction in Diversion Hours% of the month on diversion

3 Hospital Bed & Staffing Expansion: +8 observation beds, 8/20/18.

0%

10%

20%

30%

40%

50%

60%

4 5 6 7 8 9 10 11

3

3 Hospital Bed & Staffing Expansion: +12 observation beds, 9/10/18

3

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Impact: Reduction in Diversion Hours% of the month on diversion

Expanded robust daily operations: New patient placement initiatives. Expanded hours & function for utilization management, 9/17/18

4

0%

10%

20%

30%

40%

50%

60%

4 5 6 7 8 9 10 11

4

Month:

4 Daily Throughput Command Center: High-level information and action hub to resolve flow issues in real time & mitigate delays, 10/17/18

4

Near zero diversion since command center initiation

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Additional Throughput Considerations

Upfront capacity management

“Virtual” capacity units

Rehab/Post-Acute Care●

Physical Command Center●

●

●

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Optimizing Hospital Capacity

Sustained Daily Operations(Continuous process improvement)

Capacity

Assessment(Right-sizing)

Leadership Commitment

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Thank You & Contact Info

STEPHANIE GRAVENOR, MBA

Stephanie.Gravenor

@MedecipherSolutions.com

MICHAEL SCHMIDT, MD FACEP

mischmid@nm.org

Additional Team Members: Ohad Perry, Jing Dong, Yue Hu, Jenny Siemen, Rachel Cyrus