Examples of Automation in Practice

Trent Cancer Registry

Alan WaterhouseAssistant Director (IM&T)

Coventry - 4th December 2002

Trent Cancer Registry

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

Transaction Volumes (2001-2)Patient Administration 46088

Pathology 28528

Extra Regionals 2960

Cancer Deaths 14391

Non-Cancer Deaths 6371

Trace 34778

Other 246

(NHS Strategic Tracing Service 30000)

Trent Cancer Registry

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

% of Transactions Containing Validation Errors by Data Source

18.9

80.8 84.1

81.1

19.2 15.9

0%

20%

40%

60%

80%

100%

PAS Pathology Death

Fail

Pass

Load/Validation by Data Source

Trent Cancer Registry

% of Total PAS Validation Errors by Type

66.5

14.6

4 2.9 2 1.6 1.60

20

40

60

80

% of Errors

Load/Validation -PAS

Trent Cancer Registry

% of Total Pathology Validation Errors by Type

39.9 37.6

8.3 8.3

0

20

40

60

Postcode Clinician Hospital Hospital

% of Errors

Load/Validation - Pathology

Trent Cancer Registry

% of Total Death Validation Errors by Type

35.7 35.7

17.3

8

0

20

40

Place ofDeath

Hospital Postcode Birthplace

% of Errors

Load/Validation - Death

Trent Cancer Registry

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 WRONG 5.1 % WRONG

Tumour Text to ICD Morphology Code

Trent Cancer Registry

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

Tumour Matching (PAS) - Then17177 PAS Transactions

Tumour Match Algorithm

0 %

New Tumour

19 %

100 %

No Decision

0 %

Existing Tumour

80 %

Manual Search

<1 % No Decision

Trent Cancer Registry

Tumour Matching (PAS) - And Now20422 PAS Transactions

Tumour Match Algorithm

4 %

New Tumour

12 %

40 %

No Decision

56 %

Existing Tumour

27 %

Manual Search

<1 % No Decision

Trent Cancer Registry

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

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

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

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