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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
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The problem
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Source: U.S. Census Bureau, 2000 a.
(Years)
(%)
Elderly by Age 2000 – 2030 in Europe
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(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)
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Demands
Living at own home …independently …at high quality of life
High quality of services Health care services Social care services
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Dilemma
Big pilot studies of possible “interventions by innovation” (too) expensive.
Small (selective) studies do not disclose the complex impacts on health & social care.
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The objective
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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.
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The method
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Dynamic modeling
(Milstein 2005)
Dynamic models address navigational questions
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Dynamic modelingIterative steps in system dynamics (SD) modeling
(Milstein 2005)
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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
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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+ –
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Dynamic feedback
(Milstein 2005)
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The intervention
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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
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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)
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The study
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Study idea
Analyze the comprehensive system System Thinking
Model the system System Dynamics (SD)
Run simulations Different assumptions (scenarios) Different periods of time
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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?
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The models
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The models
Economic model monetary units hard variables quantitative
Quality of Life (QoL) model units of quality soft variables qualitative
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Economics model
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Sector frames*
Demographics Expenditure on [system component] Financial sources of [system component]
*model components which can be simulated separately
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Sector frame “Demographics”
Three variants of population prognoses for Germany (based on data from the German Federal Statistical Office)
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Population prognoses for DE
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Coding “Demographics”
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SA system expenditures
1. Monthly membership fees; 2. cost of installation; 3. Extra costs for supplementary services
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SA system financial sources
…
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QoL model
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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
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(Khoshsima 2005)
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Simulation runs
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Elderly population (over 60)
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Economic model
First simulation scenario
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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%)
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First economic scenario
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First economic scenario
2005 2010
Public Funds
Private Sector
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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
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Suggestion first scenario
Investments should primarily be done by the public funds and other financing sources different from private out-of-pocket payments.
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QoL model
First QoL scenario
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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)
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Result first QoL scenario
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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)
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Result second QoL scenario
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Discussion
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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)
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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
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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
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Conclusions
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Conclusion
Further modeling and simulation studies are recommended.
Gradual switch from current to ICE system create realistic model of current system investigate migration