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1 Titel der Präsentation Untertitel der Präsentation am XX.XX.2050 Dr. med. Michael Wilke Dr. Wilke GmbH i.g.

2 Dr. Michael Wilke Managing partner inspiring.health since 2007 Systemic coach and moderator Management Consultant ( ) Introduction of German DRGs (based on AR-DRGs) from Head of DRG Conpetence at Munich Schabing Hospital Member of hospital management Member of Casemix advisory committe at Ministry of Health Involved in Casemix funding since 1997 Surgeon by training

3 Inspiring.health Privately owned research institute Focus on clinical economical analyses and optimization projects Assessment of innovative technologies and drugs Optimizing quality of care AND economical outcomes for hospitals in various fields Infectious diseases Antibiotic stewardship programs ICU OR Projects on cost data quality with medical associations (e.g. German Society for Gastroenterology)

4 Agenda ABF and DRG in Germany Some basic ideas on Quality of care Using routine data for analysis Quality adjusted reimbursement Conclusions

5 Germany Australia

6 German Healthcare at a glance 80,000,000 inhabitants (92% public health insurance) 16 states Public health insurance 141 agencies / companies Funding (app. 200 BN): 15.5% of the gross income 8.2 employee / pensioner 7.3 employer / pension fund Federal contribution of app. 7.5% (tax money) Stabilizing insurance rate!

7 German Healthcare at a glance High powered hospital care 2,045 hospitals, 501,000 beds 18.6 Million inpatients ALOS (2012) 7.6 days 83,415,795,000 spendings ( 64,210,000,000 acute care, 2013) In Germany every hospital is a legal entity, direct billing of EACH case Ownership: Public, state, municipality (36%) Private for profit hospital operating holdings (32%) Confessional, catholic, protestant (32%) Every hospital treats publicly and privately insured patients Need for balanced results/profits

8 German Healthcare at a glance Mild competition between public insurance agencies Need for transparency If contributions of insured people not sufficient insurer has to raise extra fee Insurers are purchasers hospitals are providers Contractual basis binding for ALL insurers Annual budget negotiation Main focus: Volume!, as prices are mostly nationally fixed Growth is possible but payment delayed 1 year

9 German Healthcare at a glance G-DRG in use for hospitals (except mental health) since 2003 Based on AR-DRG Version 4.1 Due to use as payment / billing system massive changes to the system Annual recalculation Biggest differences to Australia: Hospitals live or die depending on DRG payments and making profits (or at least balanced results) Every year hospitals go bancrupt closed, turned into aged care or sold to private hospital operating holdings

DRG Institutions in Germany Political framework by legislation, arbitration

from 2004: permanent Advice Doctor s association Nurse association From 2004:

calculation Handling proposals Medical scientific societies Head organizations of

10 DRG Institutions in Germany Political framework by legislation, arbitration Ministry of Health Self governing bodies Contract partners Insurance association from 2004: permanent Advice Doctor s association Nurse association From 2004: Comments Other Associations DRG-Institute (InEK) DRG classification Tariff calculation Handling proposals Medical scientific societies Head organizations of Pharmeceutical industry Med-Tech Industry Patient organizations DIMDI Maintenance of: ICD-10 GM OPS (procedures)

12 DRG Costing Cost bucket : Physician Salaries on Normal Ward Cost types: Labour, Drugs, Supplies, Infrastructure Cost centers: - Normal ward - ICU - Theatre -. 6/24/

13 Is everything covered by DRG? 6/24/

14 Blocks of hospital payment DRG ( 9) Co-payments ( 9) Payments acc. 6 KHEntgG, indiv.drgs (Abs.1), NUB (Abs.2), indiv. CO-P Adjustments based on infrastructure (remote location, ED Yes/No) Payment for outpatient care (ONLY specialized care & some surgery) 6/24/

15 Blocks of hospital payment DRG ( 9) National prices / weights Co-payments ( 9) Payments acc. 6 KHEntgG, indiv.drgs (Abs.1), NUB (Abs.2), indiv. CO-P Adjustments based on infrastructure (remote location, ED Yes/No) Individual negotiations Payment for outpatient care (ONLY specialized care & some surgery) 6/24/

16 Co-Payments (2014: n=159) 33 21% 25 16% 15 9% 16 10% Blood products Dialysis Drugs 70 44% Implants Special treamtents 16

17 DRG & Co-Payment Version 2014 DRGs With national relative weight To negotiate individually DRGs for day-care Same-day DRG Explicit Implicit Co-payments With national price Individual negotiation of price (+ 9) (+ 6) 43 (+ 3) 5 ( -- ) - 20 (- 3) 325 (- 4)

18 Agenda ABF and DRG in Germany Some basic ideas on Quality of care Using routine data for analysis Quality adjusted reimbursement Conclusions

19 The Quality dimensions (Donabedian) Structural quality Process quality Outcome quality

20 Quality dimensions Structural quality Infrastructure Availability of technology Skills of staff Process quality Occurence rate of complications / adverse events Adherence to guidelines / pathways Outcome quality LOS (ICU-days), Costs Mortality

QM - Cycle Quality management is the same like treating patients Therapy Implement change Re-assessment Measure results Testing and assessing Data driven analyses Complications Infections

21 QM - Cycle Quality management is the same like treating patients Therapy Implement change Re-assessment Measure results Testing and assessing Data driven analyses Complications Infections Benchmarking Planning of Therapy Define activities Structure Process Define KPI and targets Process Outcomes Diagnoses Interpretations of test results peer review Identify areas for improvement Structure Process Outcome (as results)

22 Agenda ABF and DRG in Germany Some basic ideas on Quality of care Using routine data for analysis Quality adjusted reimbursement Conclusions

23 Using routine data for quality analyses Pro s Data is readily available Low efforts in data acquisition Usually standardized datasets Easy comparison Realistic picture if good data quality Con s Routine data does not allow to make the diagnosis Not always all clinical information is at hand Data quality is limiting factor ( Garbage in Garbage out )

24 Routine data available in Germany DRG/ABF data Diagnoses (ICD-10) no POA Flag! Procedures (OPS) DRG LOS and other episode data (age, gender, postcode, ) Discharge information ( 07=death ), costs (some hospitals) Mandatory quality data collection Structural information for every hospital Over 200 Quality indicators routinely collected Mostly for high volume indications If a hospital does not deliver penalties

25 Applications using routine data Open to the public (Internet): Hospital comparisons using quality reports and ICD / OPS data Health Navigator by AOK (insurance organization) Quality report data by a non-profit Website (hospital driven)

26 Applications using routine data Programs that can be joined (associated with costs) QSR = Quality assurance with routine data Initiated as joint venture from a hospital group and the AOK Uses hospital data and follow-up information form outpatient records Active participants receive reports AOK uses results in budget negotiations QuMiK = Hospital driven Quality and benchmarking initiative Analyses of routine data Calculation of Indicators Benchmarking / Comparison Results are only accessible for participating hospitals

27 Applications using routine data Programs that can be joined (associated with costs) 3M Quality Reporting = Combination of DRG and Q-Indicators (similar to QSR) Economical Analysis possible Cross-Checks between DRG and Quality data nothing forgotten? Uses hospital data Benchmarking (app. 200 hospitals), only for participants IMR = Infection Management with Routine data Analyses of routine data, Data enrichment with infection grouping Clinical an economical analyses (Occurence, Mortality, LOS, costs) Benchmarking / Comparison (220 hospitals) Basis for peer reviews, Controlling of results via timeline analysis of KPI

28 Health Navigator Searching a hospital by indication and Postcode (Knee replacement; my postcode )

29 Hospital Dist. Cases /Year Recommend by patients Infrastructure Quality Indicators

30 Indicator details Knee replacement Quality Outcome - details Unplanned Follow-up Surgery Surgical Complic. Mortality Explanation of Symbols: (-) = hospitals performs badly (below average) (o) = hospital is within average (+) = hospital is better than average)

31 Hospital portal (

32 Detailed results on each indicator Luxation of the Example prostheses quality reports Hospital result National average National Quality target Surgical Site Infection Unplanned Re-Surgery

33 Do occurence rates reflect quality?? If you treat a small number of patients, each complication has a huge effect on the percentage! Pts./Yr. 50 SSI (absolute No.) National Target 3% SSI (%) 2,0% 4,0% 6,0% 8,0% Whether 1 or 2 SSI occur is absolutely within the statistical variation! Not EVERY infection is PREVENTABLE

34 QSR / HELIOS indicators

35 QuMiK Publicly available: - Group quality report - Containing mandatory data - PDF / non interactive

36 3M Quality Report

37 IMR Infection Management with Routine data 37

38 IMR Infection Management with Routine data A dedicated algorithm was developped Decoding infections: Deriving clinically meaningful infection denominators from coded data eases the dialogue with the clinicians E.g.: T81.4 in combination with an operation becomes Surgical site infection during hospital stay Detecting the infection source (community/hospital aquired infection CAI / HAI) Infection = PDX CAI Infection is SDX algorithm determines HAI or unknown In Germany no POA Flag! would make the algorithm easier

39 IMR Infection Management with Routine data Algorithm example Probability of HAI or CAI derived from clinical expert assessment: Some Infections paired with special PDX are either CAI or HAI Pneumonia (J15-J18) as SDX with heart failure (I50) is in most cases CAI

40 Length-of-stay (LOS) analysis HAI (hospital acquired) Source unclear CAI (community acquired) NO Infection HAI (hospital acquired) Source unclear AI (community acquired) NO Infection Patients with hospital aquired infections exceed the LOS of their DRGs

41 A little case example Hospital in North-Rhine-Westfalia Detected high losses in outliers In-depth analyses was requested 50% of all outliers had infection problem

42 The DRG-paradigm DRG pay average cost (based on average resource consumption and/or ALOS). From a certain LOS no profit anymore 6.000, ,00 Erlös- und Kostenverlauf einer DRG LOS-LTP ALOS LOS-HTP 4.000, ,00 Profit zone Problem zone Losszone 2.000, , Erlös Kosten

43 Cost analysis - HAI Cost analysis before project started Red = outlier Payment Costs Profit/Loss (avg. and total Excess LOS is associated with massive Financial LOSS

44 Mortality analysis Respiratory infections Huge difference in Mortality depending on medical specialty: - Pneumology does good - Other internal medicine not

45 Peer Review = getting the diagnosis To determine whether results are fate or improvement is possible Analysis of medical records / charts Review of Onset date of infection Assessment: did we chose the right initial therapy? If not: Could we have chosen better antibiotics? If yes: Did the therapy affect the LOS?

46 Results peer review Overview IAT = Inital Antibiotic Therapy ABG = Antibiogram Low rates of correct initial antibiotic therapy (IAT)

47 Action items = planning the Therapy Optimize initial antibiotic Therapy Trainings Case examples Infection consulting Update internal guidelines (based on actual national guidelines) Introduce Antimicrobial StewardShip (ABS) programme

48 Results after 1 year of ABS Cost analysis after project Savings of > realized 24. Juni 2014

49 Agenda ABF and DRG in Germany Some basic ideas on Quality of care Using routine data for analysis Quality adjusted reimbursement Conclusions

50 Quality adjusted payment / reimbursement Reasons: Obvious: Why should we pay, when the hospital does something wrong? Other: In Germany hospital sector has fastest growing expenses Hospital sector

51 Quality adjusted payment / reimbursement Current situation in Germany: New government announced initiative to bind payment on outcome quality the good, the bad and the ugly Insurance companies want tool to pay less (regardless how) Hospitals claim having best quality (in the country, the region, the world ) Moreover: CaseMix Volume growing much faster than costs efficiency gains!?! Internationally known as P4P or P4Q or never pay / never again (USA)

52 Quality adjusted reimbursement Pro s Sounds good (important for politicians) Would really put strong incentive on best quality Con s Measuring quality is endlessly complex Today s tools are like lab testing Diagnosis (= Quality measuring) to be obtained by individual analyses Data quality is paramount Risk adjustment is only possible for a few clinical entities Small number, big effect problem! Massive buerocracy

53 Quality adjusted reimbursement Pro s Con s ctd. Very little international evidence that P4Q achieves intended results No-pay for re-admission already in place (in Germany app. 2.3%) Not every hospital acquired condition is avoidable blaming the wrong Most important see next slide

54 Quality adjusted payment / reimbursement Is already existing With ABF/DRG funding in place, patients with complications / infections (= sometimes bad quality) are already financially unattractive Bad quality is unprofitable (hence higher payments / relative weights) Last but not least: Enhanced transparency causes bad marketing effect with high complication rate

55 Quality adjusted payment / reimbursement Possibly higher impact: Strict control of indication is the service really needed Looking at evidence Stay away from nonsense (e.g. choosing wisely)

56 Agenda ABF and DRG in Germany Some basic ideas on Quality of care Using routine data for analysis Quality adjusted reimbursement Conclusions

57 Routine data are an useful resource Analysis of clinical conditions possible Connection between clinical outcomes and economic results readily available In-depth analysis is facilitated as you can focus on certain patient groups (processes) in the hospital However: Diagnosis must be found via detailed analysis Clear correlation between quality of care and economical results

58 Better quality-of-care is profitable Better quality of care leads to less complications Less complications shorten LOS Shorter LOS = less costs = better profitability in DRG

59 Titel der Präsentation Thank you! Untertitel der Präsentation am XX.XX.2050 ;-) Dr. Wilke GmbH Dr. med. Michael Wilke Dr. Wilke GmbH i.g.

International Innovations to Improve the Quality and Value of Health Care: The German case

International Innovations to Improve the Quality and Value of Health Care: The German case Reinhard Busse, Prof. Dr. med. MPH FFPH Dept. Health Care Management, Berlin University of Technology (WHO Collaborating