1. Examples of Automation in Practice Trent Cancer Registry Alan Waterhouse Assistant Director (IM&T) Coventry - 4th December 2002 Trent Cancer Registry

2. Once upon a time... second generation system not coping very expensive 70000 backlog (almost 3 years) only PAS received electronically requirement to develop multiple sources poor analytical tools registry ‘re-inventing’ itself Trent Cancer Registry

3. Transaction Volumes (2001-2) Patient Administration46088 Pathology28528 Extra Regionals 2960 Cancer Deaths14391 Non-Cancer Deaths 6371 Trace34778 Other 246 (NHS Strategic Tracing Service30000) Trent Cancer Registry

4. Overall Process - All Data Sources >99% of transactions electronic 60% pass load/validation 11% auto create new patient/tumour 2% auto create existing patient/ new tumour 10% auto amend existing patient/tumour >99.9% of decisions at update automatic so around 14% of transactions ‘untouched by hand’ Trent Cancer Registry

5. Load/Validation by Data Source Trent Cancer Registry

6. Load/Validation -PAS Trent Cancer Registry

7. Load/Validation - Pathology Trent Cancer Registry

8. Load/Validation - Death Trent Cancer Registry

9. 7542 PAS Transactions Tumour Text to Code Algorithm 78.5 % Manual = Algorithm First Choice 16.4 % No Derivation -> Manual Coding 4.3 % Manual = Algorithm Second Choice 0.7 % Manual = Algorithm Third Choice 94.9 % NOT WRONG5.1 % WRONG Tumour Text to ICD Morphology Code Trent Cancer Registry

10. Patient Matching (PAS) 36492 PAS Transactions Patient Match Algorithm 38 % New Patient 11 % 15 % No Decision 47 % Existing Patient 3 % Manual Search 1 % No Decision Trent Cancer Registry

11. Tumour Matching (PAS) - Then 17177 PAS Transactions Tumour Match Algorithm 0 % New Tumour 19 % 100 % No Decision 0 % Existing Tumour 80 % Manual Search <1 % No Decision Trent Cancer Registry

12. Tumour Matching (PAS) - And Now 20422 PAS Transactions Tumour Match Algorithm 4 % New Tumour 12 % 40 % No Decision 56 % Existing Tumour 27 % Manual Search <1 % No Decision Trent Cancer Registry

13. Cancer Deaths / Pathology still all matched against database manually because site/morphology not trusted in coded form, it is coded manually from text recent investigations show that the first cancer ICD site mentioned (exc C80) on DC matches at 3 digit level with manual decision in 84% of cases look at disagreement - improve algorithm similar situation, but worse, for Pathology Trent Cancer Registry

14. Other Problem Areas Lack of standardisation (codes, defns, rules) Code set changes over time Mappings between code sets (ICD/Snomed) Initial blind faith in the system ‘rules’ as delivered Must be constantly vigilant to changes in data sources Trent Cancer Registry

15. Conclusion six years experience shows that.. a rules based system works and we can extend automation but... the rules are difficult to tease out from... our most valuable asset, the Tumour Registrars rules vary within E & W, and internationally registries need to share more Trent Cancer Registry

17. Process Steps Extract of data from source system Receipt and high level quality check Filter out previously received transactions Load and translate data to standard form Validation and correction Patient matching Tumour matching Update/Merge Trent Cancer Registry