1
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
3
Stephanie Gravenor, MBA
Ownership Interest:
Co-Founder/Owner – Medecipher Solutions
Conflict of Interest
4
Presenters
STEPHANIE GRAVENOR, [email protected]
Hospital Operations Strategist
Chief Executive Officer, Medecipher
MICHAEL SCHMIDT, MD [email protected]
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?
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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?
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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
Additional Team Members: Ohad Perry, Jing Dong, Yue Hu, Jenny Siemen, Rachel Cyrus