Impact of Morbidity on Strategies in Healthcare Management
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1 Impact of Morbidity on Strategies in Healthcare Management Dr. Torsten L Hecke, MD MPH TK morbidity-based analysis and strategies Hamburg - may 10, 2010
2 key issues 1 Knowledge on morbidity allows to understand regional differences in healthcare provision. 2 TK uses morbidity-based data sets to predict developments of patients in defined settings and sets remuneration rules. 3 Morbidity-based analysis is crucial for fair p4p procedures. 4 Analysis on morbidity enables a statutory health insurance to focus on a range of different issues as contracting, claims management, settlements of healthcare managements systems, risk adjustment schemes. 2
3 outline 1 What is morbidity and what kind of data TK is dealing with? 2 Understanding morbidity: examples of regional differences in healthcare provision 3 Using knowledge on morbidity: examples for healthcare management approaches 4 wrap-up and future challenges 3
4 outline 1 What is morbidity and what kind of data TK is dealing with? 2 Understanding morbidity: examples of regional differences in healthcare provision 3 Using knowledge on morbidity: examples for healthcare management approaches 4 wrap-up and future challenges 4
5 morbidity what we know: available routine data set in TK data on hospital care patient identification general information about hospital (no. of beds, physicians, etc.) diagnosis ICD 10 DRG ICPM length of stay costs but: no outputs, no outcomes! data on out-patient care patient identification physician diagnosis ICD 10 costs date of healthcare provision prescriptions patient identification physician date of prescription PIP Code - Pharmacists Interface Product (German: PZN) ATC costs medical devices (comparable to prescriptions) others patient satisfaction trend monitoring intersectoral data mining and analysis 5
6 outline 1 What is morbidity and what kind of data TK is dealing with? 2 Understanding morbidity: examples of regional differences in healthcare provision 3 Using knowledge on morbidity: examples for healthcare management approaches 4 wrap-up and future challenges 6
7 understanding morbidity regression data model to explain regional differences effective morbidity documented morbidity not documented morbidity healthcare system ICD gender healthcare system contracts DDD age ATC contracts remuneration / price patients behaviour etc. systematic grouping AGG HMG HCC RxG remuneration / price patients behaviour etc. determine real expenditures calculated morb.-based expected expenditures residuum 7
8 regression data model: summary understanding and interpretations of results of morbidity-adjusted regional differences influence: regional contracting with providers: healthcare management cooperations (integrated care models, GP-centered contracts, disease management programs) p4p - approaches determination of innovative approaches in healthcare provision 8 TEAMorbi
9 understanding morbidity: predictive modelling predictive modelling enables: prediction of future illness and of necessary ressources for treatment estimation of future individualized expenditures TK uses predictive modelling for description of selection criterias for participants in healthcare models calculation of morbidity-adjusted remuneration groups (selective contracting) win-win-win-situation: higher acceptance of patients in healthcare models (by more individual care approaches) monetary benefit for provider monetary benefit for sickness fund 9 TEAMorbi
10 understanding morbidity: predictive modelling: next steps validation and stability of models change of model due to changing framework in a current contract increase of target population for selection work on models with monetary limits develop models allowing migration of subpopulations from one group to another evaluation report implement external data savings longitudinal analysis automation 10
11 outline 1 What is morbidity and what kind of data TK is dealing with? 2 Understanding morbidity: examples of regional differences in healthcare provision 3 Using knowledge on morbidity: examples for healthcare management approaches 4 wrap-up and future challenges 11
12 morbidity-orientation in healthcare management: morbidity in integrated care models lump-sum deduction avoiding unnecessary hospital treatments (inpatients) service active coordination prolonged opening rules for providers reduced waiting time patient information effectiveness 12 guaranteed defined treatments guideline-based treatment minium quantities in procedures qm and education / training p4p quality
13 outline 1 What is morbidity and what kind of data TK is dealing with? 2 Understanding morbidity: examples of regional differences in healthcare provision 3 Using knowledge on morbidity: examples for healthcare management approaches 4 wrap-up and future challenges 13
14 future challenges deeper understanding: of quality of data of intersectoral data analysis development of a detailed toolbox further connection of morbidity with healthcare management approaches (case- / disease- mt., innovations, ) 14
15 tool box mapping of contents of contracts quantity structure classification of target groups intersectoral selection of provision and costs morbidity-oriented development of costs healthcare provision model predictive modelling controlling and evaluation (efficacy, efficiency) cost model remuneration model 15
16 new healthcare management case- & disease management contracting claims management morbidity orientation evaluation patient orientation new healthcare management 16
17 Thank you very much for your attention!
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