CRC screening in the Era of informatics: can data-based knowledge enhance coverage?

Transcription

1 CRC screening in the Era of informatics: can data-based knowledge enhance coverage? Institute of Biostatistics and Analyses Masaryk University National Reference Center Health Care Payers

2 Data -> Information -> Impact -> Effect? Disease diagnostics Screening performance European Council (/878/EC) + European Guidelines (2010) Screening efficacy & safety Screening effectiveness Compliance follow-up QA & QC + Communication! Target population Cancer burden Epidemiology Population risk assessment

3 Key problem: standardization of EHR? Cl. staging: I. II. III. IV. T: T1 T2 T3 T4 Date: / / Follow-up cotrol Yes/date No/why EUnetHTA collaboration via Europe EUnetHTA FINAL TECHNICAL REPORT - YEARS -

4 Electronic health care records where are you? SCREENING PROGRAMME SEPARATED KEY INFORMATION SOURCES NON-STANDARDIZED INFORMATION SYSTEMS? Diagnostics Clinical monitoring Health care payers

5 Solution? Multi-level information system

6 Five principal outcomes from the system Population reporting 1) Cancer burden (description & prediction) 2) Regional monitoring, equity of care -> Population risk analyses 3) Performance indicators Data-based networking of the diagnostic centers 4) Quality indicators -> Management decision support Clinical monitoring 5) Patient flow, follow-up support -> Real world evidence

7 Data -> information -> impact Population-based monitoring

8 Introducing the Czech National Cancer Registry CNCR Standardized and validated data, collected since 1977 > cases -> Morphology -> TNM, staging -> Therapeutic strategy -> Overall survival -> Death reasoning Regions Diagnostic centers

9 Population based monitoring: examples No. per,000 inhabitants Time-related trends: incidence and mortality incidence mortality 2009 % % % % 20% 0% TNM 2. ed Incidence stratified according to clinical stage TNM 3. ed TNM 4. ed. Clinical stage: Unstaged for objective reasons TNM 5. ed. TNM 6. ed Stage not recorded

10 Population based monitoring: examples INCIDENCE Colorectal carcinoma Predi cted values for 2012 (C18- C 20 ) Incidence 1 (% CI ) Stage I 1696 (1520; 18) Stage II 2014 (1835; 2192) Stage III 20 (19; 2239) Stage IV 1971 (1826; 2114) 2 Sta ge un known for objective reasons 5 (474; 736) 2 Sta ge un known without stated reason 197 (153; 2) TOTAL 8573 (7748; 9391) PREVALENCE Colorectal carcinoma Predi cted values for 2012 (C18- C 20 ) Prevalence (% CI ) Stage I (15 0; ) Stage II (15 338; ) Stage III (10 464; ) Stage IV (6 637; 7 011) Sta ge un known for objective reasons (31 66; 3 428) TOTAL (51 5; )

11 PHA CR: Czech Republic : imum in regions : imum in regions PHA: Capital City of Prague STC: Středočeský region JHC: Jihočeský region PLK: Plzeňský region KVK: Karlovarský region ULK: Ústecký region LBK: Liberecký region HKK: Královéhradecký region PAK: Pardubický region VYS: Vysočina region JHM: Jihomoravský region OLK: Olomoucký region ZLK: Zlínský region MSK: Moravskoslezský region Number pf cases per 000 persons STC JHC PLK KVK ULK LBK HKK PAK VYS JHM OLK ZLK MSK Population based monitoring: examples

12 Communication based on open reporting in analytical portals About project News Epidemiological analyses Publications, reports Software SVOD Analytic tools tutorial Incidence and mortality Time trends Regional overview Age analyses Clinical stages Epidemiological analyses International data Comparative standards Comprehensive overview

13 Communication based on open reporting in analytical portals

14 Data -> information -> impact Diagnostic monitoring

15 Finally resulting three key data sources FOBTs Equity in health Regional disparities 1 colonoscopy centers - local data collection - Cancer burden KVK Colonoscopy monitoring

16 . and finally reachable results 13.1% 22.7% 32.6%

17 Data -> information -> impact Clinical monitoring and follow-up support???

18 Can data-based knowledge enhance coverage? Czech colorectal cancer screening - coverage?