Towards a Dynamic Modeling of Integrated Social Health Services for Elderly at Home

D. Khoshsima1, A. Horsch2 1Universities of Heidelberg and Heilbronn2Munich University of Technology

Tromsø Telemedicine ConferenceJune 12-14, 2006, Tromsø, Norway

The problem

Source: U.S. Census Bureau, 2000 a.

(Years)

(%)

Elderly by Age 2000 – 2030 in Europe

(Nu

mb

er

of

Ho

us

eh

old

s)

# Household by Size 2001 – 2003 (D)

Source: Federal Statistical Office Germany, 2004

(Nu

mb

er

of

Ho

us

eh

old

s)

26400

26800

27200

27600

28000

2001 2002 2003

11200

11300

11400

11500

116001 person or 2 pepole household3 or more people household

op

(Years)

Demands

Living at own home …independently …at high quality of life

High quality of services Health care services Social care services

Dilemma

Big pilot studies of possible “interventions by innovation” (too) expensive.

Small (selective) studies do not disclose the complex impacts on health & social care.

The objective

The (ultimate) objective

To gain sufficiently deep insights into the dynamic behavior of (a sector of) the health and social care system in order to make good (political and organizational) decisions.

The method

Dynamic modeling

(Milstein 2005)

Dynamic models address navigational questions

Dynamic modelingIterative steps in system dynamics (SD) modeling

(Milstein 2005)

Dynamic modeling

Priority “Learning how actions in the present can

trigger plausible reactions both far away and over time” (Milstein 2005)

Why simulation? Complexity of real systems and mental

models exceeds our capacity to understand them without simulation

Viewpoints

(Hitchkins, http://sysdyn.clexchange.org)

Disjointed viewpoints

Cause

Effect

Cause

Effect

Cause

Effect

Linear control viewpoint

CauseEffect /Cause

Effect /Cause

Effect

Causal loop, nonlinear feedback viewpoint

Effect /Cause

Effect /Cause

Effect /Cause

Positive and negative causal loops

PopulationBirth Death+ –

Dynamic feedback

(Milstein 2005)

The intervention

Innovative Care for Elderly (ICE)

ICT-based system including Social alarm Automated home Virtual home for elderly Telecare (telemonitoring, etc.) Assistive technology Teleconsultation service Regional electronic patient record

Retrieve client

information

Requesting second opinion

Sending answers

Vital parameters

Monitoring Remote diagnostics

Cometo help

Triggeringalarm

Inform a friend

Inform ambulance

Callalarmservice

DeliverMedical care

Adjust household devices

Provide barrier-free home

Request social services

D

Cross-enterprise Access to Electronic Client Record based on client’s consent

Record access

Teleconsultation

Take the house key

Look up the client record

Give conciliar advise

Ask for client

consent

RIS / PACS

Monitor entrance

Control devicesDetect smoke

Social interaction Remote exercises

Sending andreceiving signals

HIS

Provide social services

A C

B

E

(Khoshsima, Horsch 2004)

The study

Study idea

Analyze the comprehensive system System Thinking

Model the system System Dynamics (SD)

Run simulations Different assumptions (scenarios) Different periods of time

Study questions

COSTS How should the costs be distributed among the different

stakeholders to gain a win-win situation for all? Is such a benefit-for-all situation achievable during the

lifetime of the system?

QUALITY OF LIFE (QoL) Which factors influence QoL? How may the system affect the clients’ QoL?

The models

The models

Economic model monetary units hard variables quantitative

Quality of Life (QoL) model units of quality soft variables qualitative

Economics model

Sector frames*

Demographics Expenditure on [system component] Financial sources of [system component]

*model components which can be simulated separately

Sector frame “Demographics”

Three variants of population prognoses for Germany (based on data from the German Federal Statistical Office)

Population prognoses for DE

Coding “Demographics”

SA system expenditures

1. Monthly membership fees; 2. cost of installation; 3. Extra costs for supplementary services

SA system financial sources

…

QoL model

QoL model

impact of ICE System on economical status

impact of ICE System on health status

impact of ICE System on social status

client‘s economicalstatus

client‘s socialstatus

client‘s healthstatus

client‘s qualityof life

impact of ICE System on economical status

impact of ICE System on health status

impact of ICE System on social status

client‘s economicalstatus

client‘s socialstatus

client‘s healthstatus

client‘s qualityof life

Causal loops diagram

(Khoshsima 2005)

Simulation runs

Elderly population (over 60)

Economic model

First simulation scenario

First economic scenario

Germany with the middle variant of population progression, leading to middle number of elderly

10% assigned to each service SA system 95% public / 5% private financing Other services with 100% private financing

NPOs, private social insurance, private insurance enterprises, private out-of-pocket (each 25%)

First economic scenario

First economic scenario

2005 2010

Public Funds

Private Sector

Results first economic scenario

Large investments needed in the first 2 years

Total costs reach constant level after approximately 4 years (“goal seeking”)

Private sector and public funds show almost same behavior

Suggestion first scenario

Investments should primarily be done by the public funds and other financing sources different from private out-of-pocket payments.

QoL model

First QoL scenario

First QoL scenario

Dominant impact of social aspects on QoL First run: based on first economic scenario impact

0 (max. negative)

50 (none)

100 (max. positive)

Result first QoL scenario

Second QoL scenario

Dominant impact of economical aspects on QoL First run: based on first economic scenario impact

0 (max. negative)

50 (none)

100 (max. positive)

Result second QoL scenario

Discussion

Economic model

Model shows plausible behavior, but its value is still limited.

Model needs enhancement to be realistic details on costs and expenditure processes more variables (e.g. market saturation,

client’s acceptance, quality of service)

QoF model

Model useful by making dependencies explicit offering a good basis for discussing

appropriate refinements or adjustments

Model runs are still of little value due to lack of data for parameterization results mainly depending on assumptions

Major problems

Complexity of real system and models Large number of variables Difficulty to get data for parameterization How to model “non-monetary” interests? The general problem with soft variables

Conclusions

Conclusion

Further modeling and simulation studies are recommended.

Gradual switch from current to ICE system create realistic model of current system investigate migration

Thanks for listening!

horsch@cs.uit.no