1. Predictive models in health care
management in a German statutory
health insurance
Dr. Torsten Hecke MD, MPH
Predictive Risk 2012
London; June 13, 2012

2. TK at a glance
• Founded 1884 in Leipzig
• Corporation under Public Law
(statutory health insurance)
• 5.8 Mio. members
8.0 Mio. insured persons
• 11,816 employees
• 228 offices in Germany
• budget: 21.3 b € (2012)
• uniform contribution rate:
15,5% of accessible income
Predictive Risk 2012
Dr. Hecke
2
London; June 13, 2012

3. outline
1 morbidity-related data in health care in Germany
2 shaping health care provision: integrated care model for patients with
mental diseases
3 challenges / future projects
Predictive Risk 2012
Dr. Hecke
3
London; June 13, 2012

4. 1 what we have:
available data in TK
 data on hospital care
 patient identification
 general information about hospital (no. of beds, physicians, etc.)
 diagnosis ICD 10
quality
 DRG
 ICPM
 length of stay
 costs
 but: no quality related data
 prescriptions
 patient identification
 physician
 date of prescription
some hundred millions of
 PIP Code - Pharmacists Interface Product (German: PZN) observations a year
 ATC
 costs
 data on out-patient care
 patient identification
 physician
 diagnosis ICD 10
 costs
 date of healthcare provision
 medical devices (comparable to prescriptions)
 others
 patient satisfaction
 trend monitoring
Predictive Risk 2012
Dr. Hecke
4
London; June 13, 2012

5. 1 available data:
categories and time lag
-6 months -3 months today
insurees
longterm care category
out-patient diagnosis
disability diagnosis
hospital treatment diagnosis
time lag
data out-patient surgery
hospital treatment (EBM)
prescriptions
disabiliy days and costs
hospital days / costs
medical devices
integrated care models,diagnosis
Predictive Risk 2012
Dr. Hecke
5
London; June 13, 2012

6. 1 describing morbidity means performing intersectoral
analyses
Intra-sectoral perspective on intersectoral perspective on
health expenditures morbidity
hospital hospital
sick payments morbidity- sick payments
out-patient oriented out-patient
health care
drugs management drugs
(measures,
medical devices processes, medical devices
etc.)
home nursing home nursing
… …
focus Expenditures for each treatment / cases Total costs of populations / subpopulations
/ etc.
Total costs by diagnosis / “morbidity“
Index / benchmarks
…
core business of SHI at present
in the past future challenges
Predictive Risk 2012
Dr. Hecke
6
London; June 13, 2012

7. 1 GAMMA: inhouse classification tool to increase
transparency and applicability
accurate grouping of insurees
 based on ICDs and PIPs
 into homogeneous diagnosis groups ("HDG" = ICD
ICD
ICD
hierachical disease group) and drug groups PIP
("AMG") ICD PIP
 age splits PIP
ICD
PIP
ICD HDG: PIP
 15.000 ICDs lead into at least one of 248 HDGs PIP
PIP AMG:
 100.000 PIPs lead into at least one of 185 AMGs
﬈γ
perspective:
 validation of HDG by PIP and ATC HDGs AMGs
 implementation of correction factors
Predictive Risk 2012
Dr. Hecke
7
London; June 13, 2012

8. outline
1 morbidity-related data in health care in Germany
2 shaping health care provision: integrated care model for patients with
mental diseases
3 challenges / future projects
Predictive Risk 2012
Dr. Hecke
8
London; June 13, 2012

9. NWpG:
2
basic information
 TK-insurees with hospital treatment during the last 48
target months for psychiatric disorders or with defined
group priscriptions
 trialogue: covering family and dependants
 reduction of hospital treatment costs
(admission/readmission, duration)
typ: aims
 reduction of sick payments (days)
integrated
 improving quality of care
care
model  participation of patients for at least 3 years
contract  at-home-treatment, 24h-availability, psychiatric nurses, …
 regional differences upon available servicesproviders
assuming for risk of morbidity
 lump sum (per participant and per year)
remuneration  bonus / malus on defined goals and inclusion in accounts
of expenditures for hospital treatment into total budget
(merit-rating-system)
Predictive Risk 2012
Dr. Hecke
9
London; June 13, 2012

10. NWpG:
2
model of a prospective remuneration approach
TK insured persons with F-diagnoses
First step:
approx. 450,000 insurees
identification of insurees with
increased hospitalisation risk in Germany
Concentration
Maximum risk for population to
Minimal hospital cost risk those insurees
hospital expenditures
with a high
risk for
hospital
expenditures
Second step: TK insured persons with
increased hospital cost risk
approx. 50% of those insurees
Formation of groups of insurees
with different forecast hospital
costs for the following year
“Split variables”:
 Hospital expenditures
 F-diagnosis 1 2 3 4 5 6
 Out-patient medication
(anti-depressants/ anti-psychotropics)
10

11. development of the model under changing
2
conditions (examples)
necessarityy of model development:
 development of morbidity (prevalence, incidence)
 increased documented morbidity (in Germany)
 new treatment procedures (lenghth of hospital stay, prescriptions)
 knowledge in health care provision, modeling
contents :
 use of observations of hospital stays in last four years
 exclusion of insurees with short-term antidepressant treatment and supplementary exclusion
factors (age, longterm care, dementia, Alzheimer, foreign living address)
 inclusion of cost degression principle
 corrrection factors
consequences:
 level and number of remuneration groups
 changes of numbre of eligible insurees
 new situation for negotiation
Predictive Risk 2012
Dr. Hecke
11
London; June 13, 2012

12. 2 evaluation: morbidity orientation
description of contact contents
identification of groups
data management
propensity score
matching
statistical tests
calculation
results / interpretation
Predictive Risk 2012
Dr. Hecke
12
London; June 13, 2012

13. 2 results NWpG
difference of total expenditures between intervention and control
group
quaters
Predictive Risk 2012
Dr. Hecke
13
full model with defined variables for matching (Firth) London; June 13, 2012

14. outline
1 morbidity-related data in health care in Germany
2 shaping health care provision: integrated care model for patients with
mental diseases
3 challenges / future projects
Predictive Risk 2012
Dr. Hecke
14
London; June 13, 2012

15. 3 in SHI, predictive modelling is used for …
 identification of eligible insurees for health care models
 calculation of payment levels in models / contracts
 description of regional variances (same-time-model)
 detection of fraud / misuse
 description of likelihood of treatment errors / professional malpractice
Predictive Risk 2012
Dr. Hecke
15
London; June 13, 2012

16. predictive modelling
3
next steps
 regular validation of models
 process to manage use of a model in a changing framework:
 influence of other participating SHI
 develop models allowing migration of subpopulations from one group to
another
 manage negotiation procedures
 automation
 implementation of external data
 linkage to evaluation / reporting
 using model variables for evaluation
 savings
 longitudinal analysis
Predictive Risk 2012
Dr. Hecke
16
London; June 13, 2012

17. 3 challenges:
morbidity-based health care management
 availability of data
 significance of data: documented morbidity does not describe real situation
 limited validity of models:
 right time for a review
 interdependance from changing frameworks: health care acts, contracts,
strategies ...
 functionality of remuneration systems
 methodological requirements
 experiences with different models
 significance of high-utilizers
 interpretation of regional variances
 relation between variables of a model and those for evaluation
Predictive Risk 2012
Dr. Hecke
17
London; June 13, 2012

18. Thank you very much for your attention!
This presentation is based on the achievements of colleagues of the team
Morbidity-based Analyses and Strategies. Dr. M. Ramme has defined the model;
I expressly thank him and the team for their work.
dr.torsten.hecke@tk.de