1 / 37

Timo Schulte; Health Care Analyst , OptiMedis AG

Three Dimensions in Comparison – Quality, Efficiency and Integration R esults of a Controlled Cohort Study of the Integrated Care System “ Gesundes Kinzigtal”. Timo Schulte; Health Care Analyst , OptiMedis AG Alexander Pimperl ; Head of Controlling/Health Data Analytics and IT, OptiMedis AG

Download Presentation

Timo Schulte; Health Care Analyst , OptiMedis AG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Three Dimensions in Comparison – Quality, Efficiency and IntegrationResults of a Controlled Cohort Study of the Integrated Care System “Gesundes Kinzigtal” Timo Schulte; Health Care Analyst, OptiMedis AG Alexander Pimperl; Head of Controlling/Health Data Analytics and IT, OptiMedis AG 13th International Conference on Integrated Care, Berlin, Thursday 11 of April 2013

  2. Agenda

  3. Agenda

  4. ICS Gesundes Kinzigtal = part of the region „Ortenau“ in Baden-Württemberg *Withoutdentistryandriskpool Start: 2006 About 31.000 insured persons of AOK & LKK are contractually included. 9.400 of these are “members“ of Gesundes Kinzigtal (GK) Contractual networking – about 270 cooperation partners with about 500 people About 58% of the regional physicians are partners of GK (no restriction on the free choice of doctors) Total cost of the insured ~ 62 Mio €* Balanced remuneration – classical (for physicians by physician association) and additional remuneration by GK

  5. Structure of the management company Medical experience regarding medical supply problems on site, contact with other regional providers Health economic knowledge, prevention, controlling- and management competence, investment capability Company shares: 66,6% MQNK e.V. (Ärztenetz) 33,4% Optimedis AG Contractingpartners Psychotherapists Hospitals Pharmacies … Physicians Two committed partners:

  6. Who and what is OptiMedis ? OptiMedis is a management company with health-economic background. In cooperation with physicians, hospitals and medical insurance companies it develops customized solutions for the integrated care of whole regions and by this secures the supply on site. Our objective: We want to improve the structures and processes of health care and thereby enhance the quality and efficiency of health care in order to create an additional and measurable value of health. OptiMedis is a family owned stock corporation. Currently, OptiMedis employs 12 people with health economics, management and IT background. www.optimedis.de Supervisory Board of the OptiMedis AG • Prof. Dr. Dr. Alf Trojan • Prof. Dr. Gerd Glaeske • Dr. Hans Jürgen Ahrens • Dr. Hans-Nikolaus Schulze-Solce • Dr. Manfred Richter-Reichhelm, Vorsitz • Prof. Dr. Eberhard Wille

  7. The economic basis – the contribution margin The management company invests and benefits from its success Management company Health insurance Total-Costs Contributionmargin Tangible investment: Normally expected costs (allocations by means of the Morbi-RSA algorithm) Additional payments for managementandsubstitutingactions/ prevention Intelligence investment: Management company Physicians know-how to streamline processes Know-How of the management (and OptiMedis AG) Cost cutting agreements (rebates and/or success remuneration)

  8. The pillars of optimization and quality-Integrated health care system Gesundes Kinzigtal Gesundes Kinzigtal GmbH Primary prevention Health programs Cross-cutting issues health lectures heart failure incentive program metabolic syndromes quality indicators back pain club sports healthcare world psychic crises depression health management course offers (e.g. aqua fitness) physicians+geriatric care etc. etc. Committed network partners

  9. Triple Aim of Gesundes Kinzigtal * Berwick DM, Nolan TW, Whittington J. (2008), The triple aim: care, health, and cost. Health Affairs 2008 May/June;27(3): 759-69. Improving health of populations (quality and health outcomes expressed by the reduction of mortality) Reducing the per capita cost of health care (efficiency expressed in the development of the contribution margin) Improving the patient experience of care (acceptance/ integration expressed in change rates referring to statutory health insurance membership)

  10. Agenda

  11. Data basis of the analysis • Pseudonymised population of 32.276 insured individuals of statutory health insurance AOK Baden-Württemberg • Until 31.12.2009 enrolled insurants according to the documentation of Gesundes Kinzigtal = 6.165 • Data from 2005 to February 2012 (cost data only until September 2011) * Ihle et al. 2009 – AGENS Gute Praxis Sekundärdatenanalyse

  12. Evaluation methods for the ICS Gesundes Kinzigtal All AOK+LKK insurants with residence Kinzigtal Enrolledinsurants LP NLP External comparative group/comparable region P P P P P P P P P P P Not enrolledinsurants P P P See for further evaluations www.ekiv.org

  13. Agenda

  14. Choice of study design • Complex intervention  Randomized controlled trials are ethically, practically & economically very difficult to implement • Propensity Score Matching* is used as amethod for risk adjustment • Logistic regression: Calculation of the probability (0-1) to participate in the ICS Gesundes Kinzigtal in the following year (calculation for each year) • Dependent variable: Enrollment in the ICS Gesundes Kinzigtal until 31.12.2009 * Rosenbaum & Rubin 1983; D‘Agostino 1998,Rossi et al. 2003; Gesler et al. 2005

  15. Choice of study design • Propensity Score Matching* is used as method for risk adjustment • Independent variables: Age, gender, Charlson-score*, diseases (ICD-diagnosis), pharmaceuticals (ATC-class), number of cases (by sector), number of prescribed drugs • Matching methodology: Nearest neighbor with caliper ± 0,01 without replacement; additionally: same gender, same age ± 2 years, Charlson-Score ± 1 * Charlson et al. 1987; Sundarajan et al. 2004

  16. Methodologyofpropensity score matching Matching of independent variables • Quasi-experimental cohort study: Comparison of the results of cases (enrolled insurants) and control group (not-enrolled insurants) 2009 2008 2007 21.02.2008 06.06.2008 04.01.2009 28.03.2009 22.11.2009 28.12.2009 22.10.2008

  17. Methodologyofpropensity score matching • Observation relative toenrollmentdate (baseline) Analysis of the two populations + 1 Year -4 -3 -2 -1 0 +1 +2 +3 +4 Quarters Day of enrollment in Gesundes Kinzigtal

  18. Development of cases and comparison of the structure of the populations

  19. Significantlyhigherutilizationofmedicalservices in casegroup (example 2006) * Austin 2007; Murray et al. 2003

  20. Risk adjustment of case- and control-group by matching (standardized differences < 10)* * Exception: Duration of rehabilitation (Reason: low number of insured persons)

  21. Higher morbidity in case-group  adverse risk selection (example 2006)

  22. Risk adjustment of morbidity by matching (relative differences < ± 1,0%)

  23. Agenda

  24. Significantly lower mortality rate after ten quarters (exclusion of the first two quarters)

  25. Significantly longer survival time (Kaplan-Meier) IV: 907,5 vs. NIV: 901,3; Log-Rank-Test Chi-Quadrat: 18,95 (0,000*)

  26. Efficiency: Slightincreaseofthe relative difference in contributionmarginsofabout 151€ • The methodologyofcalculationofthecontributionmarginisanalogoustothecalculationofstatutoryhealthinsurances in Germany (onlyslightdifferenceswerenecessarybecauseofmissingdata – forfurtherinformationseethewholestudy http://optimedis.de/images/docs/aktuelles/121026_drei_dimensionen.pdf Forevaluationonlythe relative differencebetweenthetwogroupscanbeused(Methodologyofcalculationofcontributionmarginsisthe same foreachgroup)

  27. Acceptance: Significantly lower amount of individuals leaving their insurance Change rate= End ofinsureddayswithoutdocumentationof a dateofdeath

  28. Qualitative survey: Patients satisfaction is very high, Differences are used for active qualitative development Would you choose this doctor again in the future?(questions inter alia from white list, University Freiburg 2013, provisional results – survey is still in execution)

  29. Agenda

  30. Summary of the results

  31. Hypothesis: The complex intervention has an effect on the observed variables (but after such a short time?)

  32. Askingforplausibility 2,5 hoursofmotion higherlifeexpectancyofØ3,4 moreyears 7,5 hoursofmotion higherlifeexpectancyof Ø 4,5moreyears Moore SC, Patel AV, Matthews CE, Berrington de Gonzalez A, Park Y, et al. (2012) Leisure Time PhysicalActivityof Moderate toVigorousIntensityandMortality: A Large PooledCohort Analysis. PLoSMed 9(11): e1001335. doi:10.1371/journal.pmed.1001335 • Supportingfactsfortheoutcomes • Enrolledinsurantsgethigherattentionbyparticipatingphysicians (in caseof a documantedhighriskinsurantsget an additional check-up, objectiveagreements e.g. individual mobilityordietplans) • Intensive cooperationofphysicians (andotherprofessionals) andpatients – oftenassociatedwithexercises • Promotion ofthemembershipofsportsassociations (15 € Voucherforenrolledinsurants) and intensive cooperation

  33. Adjustments of the study by previous scientific feedback • Exclusionofindividualsfromthemortalityratescomparison, whodeceasedwithinthefirst half-year after enrollment • Observation not onlyofcalendaryears but relative totheenrollmentdate • Inclusionof all prescribeddrugs (by ATC) in thelogisticregression • Exclusionofincapacitytoworkfromthelogisticregression

  34. Possible bias in the analysis • Self-selection of insured individuals (health awareness) • (Un-)conscious selection by physicians • Lack of explanatory variables in the matching process (Social data) • Independent variables are systematically distorted (coding of diseases)

  35. Outlook • Do the outcomes of external evaluation (www.ekiv.org) strengthen our hypothesis? • Analysis of the enrollment rates per physician practice • Analysis of a longer period of time (+ 3 years) • Analysis of the development of costs by sector

  36. Literature • Braun & Greiner (2010): Gesundheitsökonomische Evaluation der Integrierten Versorgung OPTI-MuM • Hermann et al. (2006): Das Modell „Gesundes Kinzigtal“. Management-gesellschaft organisiert Integrierte Versorgung einer definierten Population auf Basis eines Einsparcontractings • Hildebrandt et al. (2010): Gesundes Kinzigtal Integrated Care: Improving Population Health by a Shared Health Gain Approach and a Shared Savings Contract • Hildebrandt et al. (2012): Triple Aim in Germany: ImprovingPopu-lationHealth, Integrated Health Care andReducingCostsof Care • Köster et al. (2009): Evaluationsmodul der IV "Gesundes Kinzigtal": Identifizierung und Abbau von Über-, Unter- und Fehlversorgung

  37. Literature • Moore et al.(2012): Leisure Time PhysicalActivityof Moderate toVigorousIntensityandMortality: A Large PooledCohort Analysis • Pimperl et al. (2013): Der BalancedScorecard Ansatz für Netzwerke im Gesundheitswesen: Case Study Gesundes Kinzigtal • Schulte et al (2013): Drei Dimensionen im internen Vergleich [http://optimedis.de/images/docs/aktuelles/121026_drei_dimensionen.pdf] • Siegel & Stößel (2009): Umgekehrte Risikoselektion in der Integrierten Versorgung Gesundes Kinzigtal

More Related