automation in registry practice thames cancer registry jason hiscox, stephen richards, pam acworth...
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Automation in Registry Practice
Thames Cancer Registry
Jason Hiscox, Stephen Richards, Pam Acworth
Automated Registration Workshop4th December 2002
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
Total Processing Volume
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Anniversary Year
Ele
ctro
nic
Rec
ords
Pro
cess
ed
Processing Volume by Data Source
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Anniversary year
Num
bers
of
Reco
rds
ONS Events
Breast Screening
Clinical Audits
Trust Data
Extra Regional
Flagged Deaths
Cancer Deaths
Savings on Manual Collection
240 wte days
(25 records abstracted by tumour registrar per day)
18 wte days (1 day pre-processing,8 days validation correction,9 days matching and batch resolution)
Example: Tertiary referral centre with a caseload of approx. 6000 incident cases per year.
80-100 wte days
(60-75 registrations per operator per day)
Manual Collection
4 wte days
(1 day per quarter)
Abstraction:
Entry :
Electronic Processing
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.
Scalability - Pre-requisites
• The Availability of Data
• The Quality of the Data
• Confidence in Processing
technology
The Key Factors for Successful Scalability
Proportion of records processed without manual intervention of any
kind
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
TRUST1 TRUST2 TRUST3 CancerDeath
FlaggedDeath
NHSCRTrace
Man Resol
Manual Validation
Invalid
Valid
Automatic
Quality variation over time for a data source - approximate equilibrium
0%
20%
40%
60%
80%
100%
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
Batches Received over time
Automatic Valid Invalid Manual Validation Man Resol
Quality variation over time for a data source - quality degradation
0%
20%
40%
60%
80%
100%
25 28 31 34 37 40 43 46 49 52 55 58 61 65 68 71 74 77 80 83 86Batches Received Over Time
Automatic Valid Invalid Manual Validation Man Resol
Supplier specific confidence levels for patient and tumour matching
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
“You can’t have too much validation!”
Drill down functionality provides access to automated data to facilitate QA and build user confidence through transparency.
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.
• 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)
“You can’t have too much validation!”
Future development