A Bottom-up Simulation Method to Quantitatively Predict Integrated Care System Performance Contributed by: Zhengchun Liu, Dolores Rexachs, Francisco Epelde, Emilio Luque Presented by: Zhengchun Liu ([email protected] or http://zliu.info ) At: 16th International Conference on Integrated Care, Barcelona High Performance Computing for Efficient Applications and Simulation Research Group (HPC4EAS)

Computer Architecture & Operating Systems Department

Universitat Autònoma de Barcelona

@

@

&

MOTIVATION

1

Prediction, explanation & optimization are challenging for a complex system like Integrated Care system. For example, healthcare operations management, for which we want to: Predict system performance for a specific configuration, cost and benefit for a proposed change. Explain factors influencing performance, how the prediction is made and why it performs like this. Optimize changes to the system with constrain like budget. 1. 2.

2 A platform to study healthcare system related problems, like bacteria propagation. (e.g., MRSA infection). To study disordered system behavior based on integration of first-principles model and datadriven model (real operation data). Every decision we make is based on information, stop guess.

The way to achieve to goal: First-principles modeling to capture details of system behavior from the interaction of system components.

MOTIVATION

1

Prediction, explanation & optimization are challenging for a complex system like Integrated Care system. For example, healthcare operations management, for which we want to: Predict system performance for a specific configuration, cost and benefit for a proposed change. Explain factors influencing performance, how the prediction is made and why it performs like this. Optimize changes to the system with constrain like budget. 1. 2.

2 A platform to study healthcare system related problems, like bacteria propagation. (e.g., MRSA infection). To study disordered system behavior based on integration of first-principles model and datadriven model (real operation data). Every decision we make is based on information, stop guess.

The way to achieve to goal: First-principles modeling to capture details of system behavior from the interaction of system components.

Start with simulating the emergency departments.

My Agenda ☛ Introduction ☛ The Emergency Department Simulator ☛ Use of the Simulator ☛ Model Parameters Calibration Tool ☛ Demo applications ☛ Conclusion and Future work

HOW

IT WORKS

Abbreviations:

Arrival

P Patient R Registration Staff Tr Triage Nurse DA Doctors in area A DB Doctors in area B NA Nurse in area A NB Nurse in area B A Auxiliary Staff I Medical Image L Laboratory Test LW BS Leave Without aaaaaaa Being Seen

I

By their own

P

Area B

R

Nursing P

Triage By Ambulance

P

Clinical Assessment

Waiting Room

Nursing P

A

> Length of Stay

Medical test P

DB

> Quality of Service

LWBS Further Test

AL 1,2,3

Area A

Treatment NA

Tr AL 4,5

I

Treatment

Yes Ambu. ?

NA

Pending

N

> ... > Root-cause > Sensitivity

Transferred

ED Medical Finished

Macro-Level Feature Admitted? Dead emergence

Cross Scenario Analysis

> ...

Home

Yes

"Sensoring"

P

L

DA

> Throughput

Sn ... ... S3 S2 S1

P

Laboratory Test

Understandable Knowledge of the Complex System

Exit

Clinical Assessment Medical test

Emergence

No

No

OR

STATE)

Registration

Medical Image Test P

(RULES + STATE VARIABLES =>

Conceptual Model

Hospital

Micro-Level Behavior Simulation Boarding

Further Test

Scenario Simulation

Note: Every patient who comes through the door is an unknown, with a condition that unfolds over time in a functionally nonInteracti deterministic way. Theoretically Informat speaking, no two paths through this “system” are the same for any two patients. sv1 p1 sv2 p2 st3 p3

Behavior

I/O

emulate the system behavior

Agent(Patient):

via

acuity level age body condition location …

I/O

Simulation Scenarios Interacting

State variables changed

S1 S2 S3 ... ... Sn

mimic individual’s behavior

sv2 p2

Variables: I/O

Interaction

sv1 p1

System Input

State transition

sv3 p3 ...

...

Parameters & State Variables

... ...

sv1 p1

Interacti on

State transition when interact with other agents or with time elapsing

sv1 p1 sv2 p2 st3 p3

sv2 p2

... ...

st3 p3 ... ...

sv1 p1 sv2 p2 st3 p3 ... ...

sv1 p1

sv1 p1

sv2 p2

sv2 p2

st3 p3

st3 p3

... ...

... ...

HOW

IT WORKS

Abbreviations:

Arrival

P Patient R Registration Staff Tr Triage Nurse DA Doctors in area A DB Doctors in area B NA Nurse in area A NB Nurse in area B A Auxiliary Staff I Medical Image L Laboratory Test LW BS Leave Without aaaaaaa Being Seen

I

By their own

P

Area B

R

Nursing P

Triage By Ambulance

P

Medical test P

Nursing

A

DB

LWBS Further Test

AL 1,2,3

P

First-Principle Models

Clinical Assessment

Waiting Room

Area A

Treatment NA

Tr AL 4,5

I

Treatment

Yes Ambu. ?

NA

Pending No

Clinical Assessment Medical test P

Laboratory Test N

Transferred

ED Medical Finished

Macro-Level Feature Admitted? Dead emergence

> ...

Home

Yes

"Sensoring"

P

L

DA

They are not as quick >and to build, but they Length ofeasy Stay have many advantages.> Quality In terms of Serviceof simulation, firstEmergence principle models provide extrapolation in addition Understandable > Throughput Knowledge of the to the interpolation > ...provided by data-driven Complex System models. >They Root-causealso can be used for prediction, > Sensitivity explanation and optimization. Cross Scenario Analysis Sn ... ... S3 S2 S1

Exit

No

OR

STATE)

Registration

Medical Image Test P

(RULES + STATE VARIABLES =>

Conceptual Model

Hospital

Micro-Level Behavior Simulation Boarding

Further Test

Scenario Simulation

Note: Every patient who comes through the door is an unknown, with a condition that unfolds over time in a functionally nonInteracti deterministic way. Theoretically Informat speaking, no two paths through this “system” are the same for any two patients. sv1 p1 sv2 p2 st3 p3

Behavior

I/O

emulate the system behavior

Agent(Patient):

via

acuity level age body condition location …

I/O

Simulation Scenarios Interacting

State variables changed

S1 S2 S3 ... ... Sn

mimic individual’s behavior

sv2 p2

Variables: I/O

Interaction

sv1 p1

System Input

State transition

sv3 p3 ...

...

Parameters & State Variables

... ...

sv1 p1

Interacti on

State transition when interact with other agents or with time elapsing

sv1 p1 sv2 p2 st3 p3

sv2 p2

... ...

st3 p3 ... ...

sv1 p1 sv2 p2 st3 p3 ... ...

sv1 p1

sv1 p1

sv2 p2

sv2 p2

st3 p3

st3 p3

... ...

... ...

configuration

HOW

IT WORKS?

-

SIMULATION INPUT ORGANIZATION

✓admission staff ✓triage nurse ✓nurse ✓doctor ✓auxiliary ✓carebox ✓laboratory test ✓internal test ✓external test ✓hospital ward ✓ambulance. ✓…

Two areas: A and B for different patients

Hospital ward

Version 2.0

patient(input)

scenario Scenario = ED-Model-Configuration + Input (Patient)

CTAS:

1

5

Patient:

CTAS: Canadian Triage and Acuity Scale

configuration

HOW

IT WORKS?

-

SIMULATION INPUT ORGANIZATION

✓admission staff ✓triage nurse ✓nurse ✓doctor ✓auxiliary ✓carebox ✓laboratory test ✓internal test ✓external test ✓hospital ward ✓ambulance. ✓…

Resource

Capacity (#) day night 3 2 2 0 3 1 2 1 2 4 5 5 2 5 4 4 5 2 4 2 50 60 3

Avg. Attention Time (AT, minutes) first interaction follow-up 5 3 8 6 20 15 15 13 25 18 20 14 8 7 6 5 11 7 7 5 45 30 15

Two areas: A and B for different patients

junior admission sta↵ senior admission sta↵ junior triage nurse senior triage nurse junior doctor in area A senior doctor in area A junior nurse in area A senior nurse in area A junior doctor in area B senior doctor in area B junior nurse in area B senior nurse in area B medical imaging test room laboratory test place carebox in area A chair in area B auxiliary nursing sta↵

Should Execute Many Times for One Scenario

AT Distribution Gamma Gamma Gamma Gamma exponential exponential exponential exponential exponential exponential exponential exponential Beta Beta exponential

Statistical Hospital ward Model

Version 2.0

patient(input)

scenario Scenario = ED-Model-Configuration + Input (Patient)

+

CTAS: Canadian Triage and Acuity Scale

HOW

IT WORKS?

-

SIMULATION OUTPUT CONFIGURATION

State information monitoring configuration

interaction information monitoring configuration

It is like: we could put a device (sensor) on each of the individuals to monitor their detailed activities. sensors are customizable and have process capability.

HOW

IT WORKS?

Extract

-

DIRECT SIMULATION DATA

Length of Stay, Occupancy, Length of Waiting, Efficiency, …

CALIBRATION -

AUTOMATIC TOOL

Purpose: Setting up a general model for the target system simulation; I.E., a general computational model TO specific ED simulator. Motivation: Enable the simulation users, e.g., ED manager, to calibrate parameters for their own ED system without the involvement of model developers. => promoting the application of simulation in ED studies. Challenge: Data Scarcity, Out the scope of Information System; Solution: Formed as an optimization problem; Process: selection of inputs, specifying the objective function, searching, and evaluating the calibration results

CALIBRATION -

such as length of stay), and part of the model parameters retrieved directly from real data. With respect to the unknown

SET UP YOUR OWN SIMULATOR (W HAT INFO . YOU NEED TO PROVIDE ) Although the empirical information is not accurate, it can dramatically reduce search space-size. Table 1 lists all the

parameters, empirical information such as boundary constraints, typical value can be obtained from experienced sta↵. parameters to be calibrated, as well as their boundary constraints. Thus the task is to search for an optimum set of

from your information system

from your experience

parameters which can lead to good (acceptable) fitness between the simulation results and actual data. Table 1: The parameters to be calibrated for the general agent-based model of emergency departments, in order to imitate the emergency department of Hospital of Sabadell . Note: LB and UB denotes Lower and Upper Boundary respectively, TV represents the Typical Value; all the units of time are in minutes. The Identity column corresponds to the circled numbers in Figure 1 denote the type of service.

Patient: arrival hour, day, acuity level, discharge time(date-time)

Identity Notation

System configuration: #doctor, #nurse, #labs (machine), #medical image, … (all about resource you have)

+

Description

LB

UB

TV

1

register T service

the parameter for registration service-time distribution model.

2

15

5

2

triage T service

the parameter for triage service-time distribution model.

5

20

10

3

nurseA T service

the average duration of service of nurses in area A.

8

30

16

4

doctorA T service

the average duration of service of doctors in area A.

8

30

18

5

nurseB T service

the average duration of service of nurses in area B.

5

20

12

6

doctorB T service

the average duration of service of doctors in area B.

5

20

15

7

imaging T service

the average duration for taking medical imaging.

20

40

25

8

lab T service

the average duration for taking laboratory test sample.

10

30

15

In summary, due to data scarcity, although the distribution of specific service duration cannot be fitted by such standard techniques as maximum likelihood estimation, we had some other time stamps which enable us to derive an

indirect approach to estimate the service-time distribution parameters. Our tool and general model

=

4. Model Calibration Calibration traditionally conceptualized as an step in model validation. It involves systematic adjustment of model parameters so that model outputs can accurately reflect the actual system behavior. To calibrate a model, three im-

value of parameters to set up your simulator (for your system) portant issues need to be addressed. The first issue is to select significant metrics to represent the emergent behavior

Example of uses, No. 1 The emergency department system is overcrowding, WHAT-IF we add 20 careboxes to the system?

Every decision we make is based on information, stop guess.

The influence of additional carebox on patients’ behavior (Area A).

benefits

the root-cause

Value

(a) length of stay

(b) length of waiting time (in treatment area)

Good?

(c) door-to-doctor time

Actual behavior

Macro (systemic level) Error

Our world is nonlinear

analytical prediction Singularity

Actual behavior

Extrapolation

Variables

Micro (component level)

Example of uses, No. 2

How can emergency departments respond to population aging: a simulation study.

1. Predict the effects of population aging on emergency department. 2. Make longterm plans and quantify their costs and benefits with the ED simulator. (explain) 3. Optimize changes to the ED system with constrain.

Information retrieved from real data (2014)

# tests

+

# Consultation

+ % H admission

=>

# LoS

Knowledge from actual data analysis: Elder patients need more care service and stay longer in ED.

Figure 5: Patient age distribution due to their severity.

Patients’ age distribution prediction model age year year NED (year) = Page · N · P · D re f age rate ED

Regarding that age PED

=

Replace Page ED in Equation 1 with Equation 2, we get:

age NED

Nre f ·

f Dre age

(1)

(2)

Zhengchun Liu et al. / for submitting for peer review xx (2016) 1–12 year Dage age age year NTable = reused · NinEDthis· Pmodel. rate ED (year) f 1: Notations Dage

8

(3)

With Equation 3, demography prediction and reference data in 2014, it is possible to roughly predict the number

Notations

Description

age NED

The number of patients due to age interval in 2014 (5 years in this study).

of patients will attend to the emergency department. The prediction will be given by patients age interval. With the demography information collected from INE, we made a ten-year patient arrival prediction from 2014 to 2023. The

Nreage Theoftotal number ofofpeople in the catchment area in ofFigure the hospital. f distribution arrival patients the prediction was illustrated 6.

*

Dyear distribution of5various age groups in the target area inincreased year (population pyramid). to see from Figure that, because of population aging,catchment the elder patient and younger patient age It is clearThe decreased. With emergency department simulator, and who the patient arrival prediction, it is possible to quantitively Page The probability of a person (due to age) will go to ED. ED evaluate year Prate

the response of the emergency department.

The ratio of population in year to the reference year (2014).

3. ED performance prediction Model assumption : The probability of a person

7

who will go to ED at least once per year depends on lots of factors, here we assume that the probability is depends on age and do not change over different year. That is to say, a fix probability will be used above the section studied of thepatient predictionattend of arrival aging in the future 15 years, and Figure 6 throughout the future toThe predict number topatients’ ED and, thecharacteristic age distribution of the patients.

*

illustrated the prediction. It is clear that the ED will have stress to deal with more old patients, this section will

Got from the Instituto Nacional de Estadística (INE) http://www.ine.es/

give the quantitive prediction of ED performance via simulation. Our previous work has developed a simulator

Patients’ age distribution prediction in the future

As input to the simulator to see QoS in the future

Knowledge from prediction: there will be more elder patients and less young patients attend ED

ED performance predicted by simulation Population aging

Patient arrival (input)

System configuration (resource)

scenario

Simulator and Analysis tool

System Performance (QoS)

Length-of-Stay

Leave-without-being-seen

Door-to-Doctor Time

Make plans in advance.

Population aging

proposal (changes)

Patient arrival (input)

System configuration (resource)

scenario

Propose longterm plans and quantify their cost and benefit with the ED simulator.

Simulator and Analysis tool

System Performance (QoS)

future performance without any changes

Effect of Changes?

future performance with: 3+ nurse in area A, 11+ careboxes

Door-to-Doctor Time

Length-of-Stay

future performance without any changes

future performance with: 3+ nurse in area A, carebox: 49 => 60

Leave without being seen

Optimize alternatives with constraint (e.g., budget, space) — work in process.

Conclusions & Future Work Conclusions: (1) A General Agent-Based Model for EDs (Spanish type); (2) Designed and Implemented an auto-calibration tool; (3) with this tool, we can have: Every decision we make is based on information, stop guess.

In summary, start from simulating the emergency departments, our efforts proved the feasibility and ideality of using agent-based model & simulation techniques to study healthcare system.

Future Work: (1) Population aging; How can emergency departments respond to population aging: a simulation study. (2) A step towards building a full model of integrated care system.

Thank you for Your Attention!

A Bottom-up Simulation Method to Quantitatively Predict Integrated Care System Performance Contributed by: Zhengchun Liu, Dolores Rexachs, Francisco Epelde, Emilio Luque Presented by: Zhengchun Liu ([email protected] or http://zliu.info ) At: 16th International Conference on Integrated Care, Barcelona High Performance Computing for Efficient Applications and Simulation Research Group (HPC4EAS)

Computer Architecture & Operating Systems Department

Universitat Autònoma de Barcelona

@

@

&

Zhengchun Liu, Dolores Rexachs, Francisco Epelde, Emilio Luque ...

Optimize changes to the system with constrain like budget. 1. A platform to study healthcare system related problems, like bacteria propagation. (e.g., MRSA infection). 2. To study disordered system behavior based on integration of first-principles model and data- driven model (real operation data). Every decision we make ...

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