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

Towards a Dynamic Modeling of Integrated Social Health Services for Elderly at Home. D. Khoshsima 1 , A. Horsch 2 1 Universities of Heidelberg and Heilbronn 2 Munich University of Technology Tromsø Telemedicine Conference June 12-14, 2006, Tromsø, Norway. The problem.

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

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  1. Towards a Dynamic Modeling of Integrated Social Health Services for Elderly at Home D. Khoshsima1, A. Horsch2 1Universities of Heidelberg and Heilbronn 2Munich University of Technology Tromsø Telemedicine ConferenceJune 12-14, 2006, Tromsø, Norway

  2. The problem

  3. Elderly by Age 2000 – 2030 in Europe (%) (Years) Source: U.S. Census Bureau, 2000 a.

  4. op # Household by Size 2001 – 2003 (D) (Number of Households) (Number of Households) (Years) Source: Federal Statistical Office Germany, 2004

  5. Demands • Living at own home • …independently • …at high quality of life • High quality of services • Health care services • Social care services

  6. Dilemma • Big pilot studies of possible “interventions by innovation” (too) expensive. • Small (selective) studies do not disclose the complex impacts on health & social care.

  7. The objective

  8. 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.

  9. The method

  10. (Milstein 2005) Dynamic modeling Dynamic models address navigational questions

  11. (Milstein 2005) Dynamic modeling Iterative steps in system dynamics (SD) modeling

  12. 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

  13. Cause Cause Cause Effect Effect Effect Effect /Cause Effect /Cause Effect Cause Linear control viewpoint Disjointed viewpoints Effect /Cause Effect /Cause + – Birth Death Population Effect /Cause Causal loop, nonlinear feedback viewpoint Positive and negative causal loops Viewpoints (Hitchkins, http://sysdyn.clexchange.org)

  14. Dynamic feedback (Milstein 2005)

  15. The intervention

  16. 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

  17. B A C E D Retrieve client information Inform ambulance Look up the client record Inform a friend Take the house key Call alarm service Monitor entrance Control devicesDetect smoke Social interaction Remote exercises Deliver Medical care Triggering alarm Come to help Adjust household devices Request social services Provide social services Provide barrier-free home Sending and receiving signals Monitoring Remote diagnostics Vital parameters Give conciliar advise Ask for client consent Requesting second opinion Teleconsultation Sending answers HIS RIS / PACS Record access Cross-enterprise Access to Electronic Client Record based on client’s consent (Khoshsima, Horsch 2004)

  18. The study

  19. Study idea • Analyze the comprehensive system • System Thinking • Model the system • System Dynamics (SD) • Run simulations • Different assumptions (scenarios) • Different periods of time

  20. 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?

  21. The models

  22. The models • Economic model • monetary units • hard variables • quantitative • Quality of Life (QoL) model • units of quality • soft variables • qualitative

  23. Economics model

  24. Sector frames* • Demographics • Expenditure on [system component] • Financial sources of [system component] *model components which can be simulated separately

  25. Sector frame “Demographics” Three variants of population prognoses for Germany (based on data from the German Federal Statistical Office)

  26. Population prognoses for DE

  27. Coding “Demographics”

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

  29. SA system financial sources …

  30. QoL model

  31. QoL model Causal loops diagram

  32. (Khoshsima 2005)

  33. Simulation runs

  34. Elderly population (over 60)

  35. Economic model First simulation scenario

  36. 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%)

  37. First economic scenario

  38. First economic scenario Private Sector Public Funds 2005 2010

  39. 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

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

  41. QoL model First QoL scenario

  42. 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)

  43. Result first QoL scenario

  44. 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)

  45. Result second QoL scenario

  46. Discussion

  47. 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)

  48. 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

  49. 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

  50. Conclusions

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