Engineering Resilience: Applying Systems Science to the ... · SIR (Apr 2nd - May 30th) 56.78 8% GR...

Engineering Resilience

Applying Systems Science to the COVID-19 Pandemic

2 NYC COVID-19 Patient Surge

7000%

3 Expanding Hospital Surge Capacity

Staffing

Supplies

Space

4 GNYHA Systems Engineering CoP

A forum for hospitals that invest in this unique and valuable discipline

A supportive, peer-based setting that provides professional development and

mentorship opportunities for health care systems engineers

A place for learning about systems science in the health care field

Email [email protected] to join the CoP

mailto:[email protected]

A Message from Dr. Khasawneh5

Dr. Mohammad Khasawneh

Professor and Chair of Systems

Science and Industrial Engineering

Director of the Healthcare Systems

Engineering Center

Binghamton University

A Dynamic SIR Model to Predict Daily COVID-19 Census for Capacity Planning

Asala Erekat, PhD Candidate1,2;

Greg Servis, MS2;1Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY

2Department of Strategic Operations and Implementation, Mount Sinai Health System, NY, NY

Agenda

1. Introduction

2. SIR Model

3. COVID-19 Demand Projection Models

4. COVID-19 Demand Projection Models – Results

5. Capacity Planning

6. Summary and Next Steps

7

Mount Sinai Health System (MSHS)

8

Process Improvement

Improve overall health system quality and efficiency by leading strategic process improvement and process engineering initiatives

Project Management

Execute and implement strategic initiatives focused on system integration, transformation, and the development of new programs and services

Labor Productivity Management

Optimize and realign organizational staffing to demand of services

Lean

Create a culture where process improvement and rapid problem solving help the organization achieve it’s goals and purpose

Encompassing the Icahn School of Medicine at Mount Sinai and eight hospital campuses in the New York metropolitan area, as well as a large, regional ambulatory footprint

Strategic Operations and Implementation

Executes strategic initiatives focused on planning, implementation, and improvement of key services and programs for Mount Sinai Health System

400+ ambulatory

locations

7,400 physicians

42,000+ employees

3,800 beds

3.4M OP and 574K

ED annual visits

150 operating

rooms

Project Overview

9

Background

• As the number of cases of COVID-19 has grown substantially in the greater New York area, Mount Sinai Health System has been considered one of the main responders to the pandemic

• Determining hospitalization rate for COVID-19 patients was challenging since it hasn’t been explored previously

Methodology

• Predictive epidemiology modeling✓ SIR (Susceptible, Infected, Recovered) Model

• Moving Average - Growth Rate• Resource allocation

Goal

• Forecast the future demand for COVID-19 patients• Understanding and adjusting:

✓ System’s capacity (Inpatient Beds & ICU Beds)✓ Staffing✓ Supplies (PPE)

Covid-19 Impact on MSHS

o Demand

– Inpatient COVID-19 census peaked on 4/6 with 2092 COVID-19 patients across the health system.

o Capacity

– Bed capacity was expanded 150% through conversion of all lobbies, cafeterias and other common spaces into patient care areas.

– Perioperative areas were converted to ICUs.

– Tents used outside of all EDs to accommodate overflow.

– Oxygen farms expanded at all sites to handle increased demand.

o PPE Usage at peak

– 16,360 N95 masks per day

– 50,000 surgical masks per day

– 4,000 Face shields per day

– 400,000 gloves per day

– 30,000 gowns per day

10

Capacity Planning Overview

11

COVID-19 Demand Model

Health System Supply Model

Inpatient Staffing

PPE

Environmental and Cleaning

Respiratory Equipment and Supplies

Nurses

Medical Assistants

• IP clinical workflows• ED workflows• Ambulatory workflows• PPE policy• 44 variables/assumptions

• Premier Labor Productivity • TSI Billing• Epic• COVID-19 Staffing Ratios• Staffing levels and activity

drivers Predict supply, capacity and staffing needs required to screen and care for COVID-19 patients over the next

several months

Inpatient Capacity

Beds

Isolation Rooms

Infrastructure

Respiratory Therapists

SIR Model

121. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model2. http://predictivehealthcare.pennmedicine.org/2020/03/14/accouncing-chime.html

o Population based epidemiology model that computes the theoretical number

of people infected with a contagious disease in a closed population over time

– Susceptible (S): number of susceptible individuals

– Infected (I): number of infected and infectious individuals

– Recovered (R): number of recovered individuals

o The model works based on the following recursive formulations:

– 𝑆𝑡+1 = −𝛽𝑆𝑡𝐼𝑡 + 𝑆𝑡– 𝐼𝑡+1 = 𝛽𝑆𝑡𝐼𝑡 − 𝛾𝐼𝑡 + 𝐼𝑡– 𝑅𝑡+1 = 𝛾𝐼𝑡 + 𝑅𝑡

o Parameters estimation– 𝛾: is the inverse of mean recovery time

in days

✓ 𝛾 = 1/recovery time

– 𝛽: is the effective contact rate

✓ 𝛽 = 𝜏 ∗ 𝑐

– 𝜏: transmissibility is the basic virulence of the pathogen

– c: average number of people exposed

✓ Estimate 𝛽 based on early phase growth and doubling time: 𝑔 =

21/𝑇𝑑 − 1, 𝛽 = 𝑔 + 𝛾

https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

http://predictivehealthcare.pennmedicine.org/2020/03/14/accouncing-chime.html

COVID-19 Demand Projection Models

Modified SIR Model

• Projection Horizon = 14 and 120 days

• S: susceptible population in NYC = 8.4 millions1

• I: infected population in NYC (as of today)2

• D: dead population in NYC (as of today)2

• P: percent population that is susceptible (reverse engineering)

• 𝑇𝑑: projected doubling time overtime2

• Recovery time: 14 days3

• Hospitalization rate: total hospitalization rate in NYC (reverse engineering)

• ICU rate: ICU hospitalization rate based on MSHS = 23%4

• Overall average LOS: 11 days4

• ICU average LOS: 22 days4

• Market share: MSHS market share = 13%5

• Current hospitalized COVID-19 at MSHS (as of today)4

• New COVID-19 admissions at MSHS (as of today)4

Moving Average Growth Rate Model

• MSHS actual census data was used to calculate the growth rate of daily census then:

• 3-days moving average model was applied to project future hospital census

• 5-days moving average model was applied to project future hospital census

13

1. NYC population estimates as off July 1st 2019, https://www.census.gov/quickfacts/newyorkcitynewyork2. John Hopkins University Dataset, https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data3. WHO reports from China, https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-

report.pdf#:~:text=Using%20available%20preliminary%20data%2C,severe%20or%20critical%20disease.4. Mount Sinai EPIC reports5. Based on DOH

https://www.census.gov/quickfacts/newyorkcitynewyork

https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data

https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf#:~:text=Using%20available%20preliminary%20data%2C,severe%20or%20critical%20disease.

o Calculate doubling time based on the following equation

– 𝑇𝑑 = 𝐿𝑜𝑔10(2)/log10(1 + 𝑔𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒)

o Fit multiple trend-lines and pick the model with the highest 𝑅2

– 2nd order Polynomial

o Project for the next 7-14 days

Doubling Time Projections

141. Downloaded the raw data for the infected cases in NYC from John Hopkins University GitHub. https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data

Doubling Time Projections

0.0

50.0

100.0

150.0

200.0

250.0

300.0

3/14/2020 4/14/2020 5/14/2020

Do

ub

lin

g T

ime

(D

ays)

Doubling Time Poly. (Doubling Time)

y = 0.0633x2 - 2.1297x + 16.878R² = 0.8668

https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data

COVID-19 Demand Projection Models - Output

o At the beginning the models used to get updated twice per week

– Not enough data points to project doubling time

– Many uncertainties and unknown parameters

o Currently, the error rate and the absolute mean error (MAE) are calculated first and based on the results (big deviation) the parameters get updated

15

Mean Absolute Error (patients) Average Error Rate

SIR (Apr 2nd - May 30th) 56.78 8%

GR - 3 Days (Apr 16th - May 30th) 33.27 7%

GR - 5 Days (Apr 16th - May 30th) 33.00 6%

COVID-19 Demand Projection Models – Accuracy

161. Census projections from SIR and growth rate models 2. Actual Census: EPIC census data

COVID-19 Demand Projection vs. Actual DataApr 2nd - May 30th

0

500

1000

1500

2000

4/2

/20

20

4/9

/20

20

4/1

6/2

020

4/2

3/2

020

4/3

0/2

020

5/7

/20

20

5/1

4/2

020

5/2

1/2

020

5/2

8/2

020

MSH

S C

en

sus

Actual Census SIR Model Projections GR - Avg. Last 3 Days GR - Avg. Last 5 Days

Model Impact

17

The model was presented to leadership on 3/15. The outputs generated proactive action on:

o Development of additional supply chain channels for PPE procurement

o Policy changes on staffing ratios to convert to crisis mode staffing plans

o Sourcing of agency staff to fill staffing gaps

o Redeployment of residents and clinical staff from non-inpatient areas to support inpatient work

o Expansion of bed capacity and conversion of spaces to increase the number of ICU beds

o PPE usage policies to ensure all staff had the equipment required.

Summary

o Time series forecasting models weren’t beneficial in this situation, hence,

learning epidemiology modeling was essential

o Selecting the appropriate parameters and their estimation for the models used in

this work were the most challenging parts

– Reverse engineering

o The models used in this work give more accurate results for short term predictions rather than long term

– Run the model frequently based on the error rate

o Manage the expectations of these models

Next Steps

o Observe the number of unique admissions and census on daily basis in a control

chart to detect any process changes to monitor the second wave

o Test other scenarios of social distancing especially when NYC opens up again

o Include the percent of people tested positive for COVID-19 antibodies (herd

immunity) as a damping factor

Summary and Next Steps

18

Thank you!

Any Questions?

19

20

GYNHA Engineering Resilience: Applying Systems Science to the COVID-19 Pandemic WebinarJune 4, 2020

Adapting the ED for Pandemic Readiness with Simulation Modeling

WHO WE ARE

Alice Gittler, MA, MSDirector of Healthcare Design [email protected]

Debbie Phillips. AIASenior Medical Planner [email protected]

Our vision is to design places that elevate the human experience; our goal is to build a design culture that can transform the most common buildings and landscapes into meaningful experiences.

ArchitectsInterior DesignersEngineersPlannersResearchers

GNYHA Webinar | June 4, 2020 | EwingCole 21

Farouq Halawa, PhDSenior Research AssociateHealthcare Improvement and Design [email protected]




SIMULATION IN DESIGN & OPERATIONS


Using systems and industrial

engineering tools during

design

11%

Leveraging simulation models for

decision making on an ongoing basis

of health systems reported using

simulation modeling to improve

workflow during design in 2017

HFM/ASHE Survey 2017

Halawa, F., Madathil, S. C., Gittler, A., & Khasawneh, M. T. (2020). Advancing evidence-based healthcare facility design: a systematic literature review. Health Care Management Science, 1-28

GNYHA Webinar | June 4, 2020 | EwingCole

ED ADAPTATION - CASE STUDY

23

How to quickly adapt existing and temporary clinical space needs

within and adjacent to the emergency department to meet expected

peak COVID-19 patient surge and existing ED visit volume?

• Expected demand due to the pandemic

• Capacity needed for screening and treatment needs

• Capacity needed for inpatient bed need

• Minimize wait times

• Minimize the risk of additional COVID-19 transmission

?

?

?


5-STEP APPROACH

24

Determine patient demand using

epidemiological modeling

MODEL DEMAND

Assess existing bed use

using current state

simulation model to

identify opportunities

ASSESSCAPACITY

Assess facility

infrastructure to find

ways to

utilize/quickly adapt

existing capacity

ASSESS THE FACILITY

Confirm feasibility

of proposed

solutions using

simulation

modeling

TEST SOLUTIONS

Develop “bundled”

design and

operational

solutions

DESIGN SOLUTIONS

1. EPIDEMIC DEMAND MODELING


The SIR Model [1]

• Susceptible (S), Infected (I), Recovered (R)

• β Effective contact rate of the disease

• 𝛾 Mean recovery rate

• Population N = S + I + R

Sources: Kermack and McKendrik; USA Today; Tolia et al. 2020; CDC

Inputs Value

Available data 3 weeks

β 0.64

I initial 130

𝛾 0.50

N 1.357 million

% of ED visits 12% of infected [2]

% of imaging use 48% [4]

% of admitted patients 28% [4]

LOS of admitted patients 10-14 days [3]

Rate of change of susceptible population over time

Rate of change of infected population over time

Rate of change of recovered population over time

1. EPIDEMIC DEMAND MODELING


0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1 6

11

16

21

26

31

36

41

46

51

56

61

66

71

76

81

86

91

96

10

1

10

6

Nu

mb

er o

f In

fect

ion

s

Days of Epidemic

Predict…

• 38,000 predicted number of infected people during the peak time

• At apex, 225 predicted ED visits per day

Infected Population Predicted ED Visits

Days of Epidemic

Avg prediction accuracy = 82%


Historical data (1 year)

2. CAPACITY ASSESSMENT

27

7

45

45 5

67

8

1112

16

1415

1312

131212

109 9

8 8

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Ho

url

y ED

vis

ito

rs

Hours of the day

Hourly Arrival Rate (Peak Month)

• 205-249 Average Daily ED Visits• Most patients are mid-acuity (58%)• Peak hours: 12 pm – 3 pm

249231 221 217 225

214 205

MON TUES WEDS THURS FRI SAT SUN

Nu

mb

er

of

Pat

ien

ts

Average Daily ED Volume by Day of Week


27 significant flows informed patient flow in the model2. CAPACITY ASSESSMENT

28

162 flow variants, 27 key flows

Location codes A: Ambulance triage B: Pod 5C: Pod 4D: Pod 3E: Imaging G: Pod 1I: Pod 2S: Enter K: Exit M: TraumaL: Triage

Flow 1: (SLGK) Patient enters → Triage → Pod 1 → Departs

Flow 2: (SLIK) Patient enters → Triage → Pod 2 → Departs


2. CAPACITY ASSESSMENT

29

• Slight excess capacity was identified• Through reshuffling patient assignments, a

COVID unit could be created within the ED

Area Studied Maximum pod utilization

Pod 1 75%

Pod 2 85%

Pod 3 67%

Pod 4 66%

Pod 5 45%

Finding available capacity within the ED

3. FACILITY ASSESSMENT


Assess opportunities to utilize/quickly adapt existing capacity

30

1. Locate all Access Points & Circulation Nodes/Pathways

2. Identify Spaces for Adaptive Re-Use –Interior or Exterior

3. Identify Negative Pressure Rooms & Negative Pressure Potential

4. Determine Access for Behavioral Health Patients w/ COVID-19

STAFF ENTRY

ASC ENTRY


GNYHA Webinar | June 4, 2020 | EwingCole 31STAFF ENTRY

VERTICAL CIRCULATION –ELEVATOR OR STAIR

ASC ENTRY


2. Identify Spaces for Adaptive Use – Interior or Exterior



STAFF ENTRY

ASC ENTRY

3. FACILITY ASSESSMENTAssess opportunities to utilize/quickly adapt existing capacity

32

ENDO

PARKINGAREA


• Locate all Access Points & Circulation Nodes/Pathways

• Identify Spaces for Adaptive Use – Interior or Exterior

• Identify Negative Pressure Rooms & Negative Pressure Potential

• Determine Access for Behavioral Health Patients w/ COVID-19

STAFF ENTRY

ASC ENTRY


33GNYHA Webinar | June 4, 2020 | EwingCole


2. Identify Spaces for Adaptive Use – Interior or Exterior



STAFF ENTRY

ASC ENTRY


34GNYHA Webinar | June 4, 2020 | EwingCole

• Locate all Access Points & Circulation Nodes/Pathways

• Identify Spaces for Adaptive Use – Interior or Exterior

• Identify Negative Pressure Rooms & Negative Pressure Potential

• Determine Access for Behavioral Health Patients w/ COVID-19

1. ACCESS: Revised Access to Pass Through Triage/ Screening Tent

2. ADAPTATION: Used Parking for Screening & Surgery Center/Pod 2 for Inpatients & COVID Staff Support

3. NEGATIVE PRESSURE: Modified Surgery Center & Pod 2 to Negative Pressure

4. BEHAVIORAL HEALTH: Behavioral Health Patients Enter via Screening Tent

STAFF ENTRY

ASC ENTRY


4. DESIGN SOLUTIONSProposed ED Solution

35

4. DESIGN SOLUTIONSExisting Flow

36

4. DESIGN SOLUTIONSCOVID-Adapted Flow

37

5. TESTING


Confirm Feasibility of Proposed Solutions Using Simulation Modeling

38

Questions of interest

• Expected demand

• Capacity needed for screening

and treatment needs

• Capacity needed for inpatient

bed need

• Minimize wait times

• Minimize the risk of additional

COVID-19 transmission

FINDINGS


Surge Tent Configuration

• 4 COVID triage rooms

• 1 sorting station

• 2 staff members

• COVID patients wait time < 10 min

The relief tent and revised circulation will minimize cross-traffic and keep wait times under 10 minutes

FINDINGS


The three combined adaptations can accommodate 98% of the COVID inpatient need at the peak epidemic bed demand

FINDINGS


Optimized, Adapted ED

Area Existing Condition Post-adaptation

Outpatient Surgery Center

8 ORs30 Prep/Recovery bays

16 Beds (2 per OR) 22 Bays (Decompressed)

Screening tent N/A1 Sorting Station4 Triage rooms 2 Portable X-Ray Machines

ED Pod 2*4 Isolation Rooms (ED)6 Bays (ED)

4 Isolation Rooms (COVID)6 Bays (COVID)

ED Pod 1 35 bays 25 bays

*(ED Patients Relocated to Other Pods)

FINDINGS


ED Pod Utilization is not significantly changedCOVID inpatient units will reach 100% during apex

TAKEAWAYS


• Focus on Agile Design to Allow Flexibility with Minimal Disruption

• Leverage Existing Models to Test Multiple Options

• Incorporate Regular Capacity Modeling into Preparedness Planning

• Supportive Technologies such as Predictive Modeling, Digital Twin, RTLS

THANK YOU


Questions?

Alice Gittler, MA, MSDirector of Healthcare Design [email protected]

Debbie Phillips. AIASenior Medical Planner [email protected]

Farouq Halawa, PhDSenior Research AssociateHealthcare Improvement and Design [email protected]




REFERENCES

Client Name | Job Name | EwingCole45

[1] WO Kermack, AG McKendrick. A contribution to the mathematical theory of epidemics Proc R Soc Lond, 115 (1927), pp. 700-721.

[2] COVID, CDC, and Response Team. "Severe outcomes among patients with coronavirus disease 2019 (COVID-19)—United States, February 12–March 16, 2020." MMWR Morb Mortal Wkly Rep 69.12 (2020): 343-346

[3] https://www.usatoday.com/in-depth/news/2020/04/10/coronavirus-ventilator-how-works-why-covid-19-patients-need/2942996001/ (Accessed 6/3/2020)

[4] Tolia, Vaishal M., Theodore C. Chan, and Edward M. Castillo. "Preliminary Results of Initial Testing for Coronavirus (COVID-19) in the Emergency

Department." Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health (2020)

[5] Halawa, F., Madathil, S. C., Gittler, A., & Khasawneh, M. T. (2020). Advancing evidence-based healthcare facility design: a systematic literature review. Health Care Management Science, 1-28

https://www.usatoday.com/in-depth/news/2020/04/10/coronavirus-ventilator-how-works-why-covid-19-patients-need/2942996001/

SUPPLEMENTAL SLIDE 1


Model Validation

Comparison Throughput (Walk-ins) Throughput (ambulance visits)

Average daily arrivals

SDAverage daily arrivals

SD

Simulation 51 8 170 27

Historical data 2018-2019

49 11 172 30

P value 0.06 >0.05 0.244 >0.05

SUPPLEMENTAL SLIDE 2


Patients by ESI

Type Questions in Q&A Box

Q & A48

49 GNYHA Contacts

Wing Lee

Director, Quality, Patient

Safety & Clinical Programming

[email protected]

Durward Rackleff

Senior Director, Quality &

Patient Safety

[email protected]

Contact us with ideas/requests for future programming



Engineering Resilience: Applying Systems Science to the ... · SIR (Apr 2nd - May 30th) 56.78 8% GR...

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