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Automation in Registry Practice

Automation in Registry Practice. Thames Cancer Registry. Jason Hiscox, Stephen Richards, Pam Acworth Automated Registration Workshop 4th December 2002. Registry Background. Established 1958 as South Metropolitan CR Population based since 1960 Merged with North Thames 1985

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Automation in Registry Practice

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  1. Automation in Registry Practice Thames Cancer Registry Jason Hiscox, Stephen Richards, Pam Acworth Automated Registration Workshop 4th December 2002

  2. Registry Background • Established 1958 as South Metropolitan CR • Population based since 1960 • Merged with North Thames 1985 • Database of 2 million registered tumours • approximately 70,000 new incident cases per year

  3. Total Processing Volume

  4. Processing Volume by Data Source

  5. Savings on Manual Collection Example: Tertiary referral centre with a caseload of approx. 6000 incident cases per year. Manual Collection Electronic Processing Abstraction: 240 wte days (25 records abstracted by tumour registrar per day) 4 wte days (1 day per quarter) 80-100 wte days (60-75 registrations per operator per day) 18 wte days (1 day pre-processing, 8 days validation correction, 9 days matching and batch resolution) Entry :

  6. Achieving Full Automation • Historically progress has been limited by the limited availability to the Registry of good quality data from NHS Trusts. • Would require a minimum fourfold increase in batch processing volume. (Approximately 400,000-500,000 transactions per year as a conservative estimate - but could easily be double that.) • Relies heavily on the Registry system’s ability to effectively scale up to those volumes. • Requires robust quality assurance and monitoring of processes and data quality.

  7. Scalability - Pre-requisites The Key Factors for Successful Scalability • The Availability of Data • The Quality of the Data • Confidence in Processing technology

  8. Proportion of records processed without manual intervention of any kind

  9. Quality variation over time for a data source - approximate equilibrium

  10. Quality variation over time for a data source - quality degradation

  11. Supplier specific confidence levels for patient and tumour matching

  12. Validation “You can’t have too much validation!” • 120+ Single field validations • 120+ Cross field validations • 40+ Post merge nightly QA validation runs • 100+ other ad hoc and periodic QA routines • Modular reusable validation code designed to provide • consistent support for both automated validation and • manual entry

  13. Drill down functionality provides access to automated data to facilitate QA and build user confidence through transparency.

  14. Lessons Learned • Automation can be a gradual and cautious process - building confidence in the process through a series of small steps. • Where the process needs to be scaled up for larger volumes a proactive approach to data quality needs to be adopted to ensure that problems are picked up as early in the process as possible. • The quality of the data received can significantly effect the efficiency and viability of automated registration and should be monitored carefully.

  15. Future development “You can’t have too much validation!” • More pre-processing record level validation • More post processing record level validation • Pre-commit record level validation • Standard data quality reports to suppliers • Full update roll-back (and re-apply)

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