data quality toolbox for registrars mcss workshop december 9, 2003 elaine collins
TRANSCRIPT
Data Quality Toolbox for Registrars
MCSS Workshop
December 9, 2003
Elaine Collins
Quality Data Toolbox
• Artisan Registrar• Medium Computerized data• Raw Materials Medical information• Shaping tools Knowledge, skills• Directions Standards• Measuring tools Editing “tools”• Final Product Cancer record• Goodness Match to standards
Quality Data - Goodness
• Accurate
• Consistent
• Complete
• Timely
• Maintain shape across transformation and transmission
Measuring Tools
• Reabstracting studies
• Structured queries and visual review
• Text editing
• EDITS
• MCSS routine review
Exercises
• MCSS reabstracting study – 2003
• Sites: Breast, Corpus uteri, Lung, Melanoma, Testis, Soft tissue sarcoma
• 2000 diagnosis year
• 12 facilities
• Review of reported data – Structured query
• Review of reported data – Text editing
Reabstracting Studies
• Compares original medical record with reported cancer record
• Considered the “gold standard”
• Labor-intensive; all records used at initial abstracting may not be available; biased by reabstractor’s training and skills
Structured Queries
• Compares coding across series of records sorted by selected characteristics
• Useful for finding pattern discrepancies across many records
• Manual process; some comparisons may be converted to automated edits
Text Editing
• Compares text with coded values for individual records
• Useful for immediately identifying coding problems
• Manual process; most effective on completion of each individual case
EDITS
• Checks range validity for many fields, comparability of few fields for individual records
• Automated process, can be applied on completion of each record or on preparation of batch report; warnings and over-rides are alternatives to failures
• Expansion of interfield edits requires careful logic
Edits Analysis
• Edits to be included in MCSS Set• Edits in Hospital/Staging Edit Sets – C edits are
included in confidential data set• No Text Edits displayed• Criteria
– Valid codes/dates– Alpha/numeric– Timing– Interfield comparisons– Absolute conditions
MCSS Review
• Requests values for missing or unknown data; resolves conflicts between data items from multiple facilities and between data items updated by single facility
• Allows incorporation of information from multiple facilities
• Review for limited number of conditions
Same Discrepancies Found on Different Reviews
0
50
100
150
200
250
300
Reabstracting 216 155 275 149
Visual 99 110 159 66
Text 79 74 77 42
EDITS 0 16 1 5
MCSS 22 4 4 0
CANCER EXTENT STAGE SURGERY
Cancer Registrar – Resource for Quality Data
Registrar
Facility System
Medical Record
Physician
OtherRegistries
Patient
ICD-O
COC
AJCC
SEER
NAACCR
Facility Staff
CommitteesProtocols NCDB
CentralRegistry Quality
Monitors
CDC Cancer Research
CancerControl NAACCR Public
Data Inputs
• Patient data from facility systems
• Medical record reports and notes
• Pathology reports
• Staging forms
• Communication with physician offices
• Communication with other registries
• Communication with patients
Process Inputs
• Registrar training, knowledge, skills
• Coding standards – ICD-O-3, COC, AJCC, SEER, NAACCR
• Interpretations of standards – I&R, SEER Inquiry, Ask NAACCR
• Medical literature – printed and online
• Registry software data implementations
Sources of Error
• Patient data from facility systems
• Medical record reports and notes
• Pathology reports
• Staging forms
• Communication with physician offices
• Communication with other registries
• Communication with patients
Sources of Error
• Registrar training, knowledge, skills
• Coding standards – ICD-O-3, COC, AJCC, SEER, NAACCR
• Interpretations of standards – I&R, SEER Inquiry, Ask NAACCR
• Medical literature – printed and online
• Registry software data implementations
Types of Errors
• Missing/conflicting data
• Shared data errors
• Timing/coding errors
• Standards and interpretations – ambiguities, omissions, confusions, contradictions
• Discrepancies among local/central registry practice and national standards
Software Implementations
• Discrepancies between implementations and national standards
• Lack of registrar knowledge/training on correspondence between registry and exported data
• Logic errors in matching registry data to reporting formats
• Conversion errors
AJCC Staging Dilemma
• Are pathologic nodes required for pathologic stage grouping?
• How do Minnesota registrars answer this question?
Clinical/Pathologic Staging in Study BREAST CORPUS LUNG MELAN TESTIS SARCO
STAGE GROUPING
Single Group
cTcNcM, cST 54 1cTcNpM, cST 18pTcNcM, cST 9 2 2 3 21 1pTpNcM, cSTpTpNpM, cST 2
cTcNcM, pST 1pTcNcM, pST 5 37 4 31 27 10pTpNcM, pST 74 40 20 30 1 3pTpNpM, pST 6 1
Two Groups
c99, p99 3 6 9 2cST, p99 4 1 1c99, pST 4 1 6 1cST, pST 13 5 7 3 6 3
No Staging 1 4 7 5 3
Collaborative Staging
• Provides specific rules for coding known vs unknown staging elements
• Accommodates “best” stage for AJCC stage assignment
AHIMA 75th Annual ConferenceOctober, 2003 Minneapolis:
Coming Events
• Data mining
• ICD-10-CM
• SNOMED
• Natural language processing
AHIMA 75th Annual ConferenceOctober, 2003 Minneapolis:
Challenges
• What is our professional purpose?
• How do we envision ourselves as professionals?
Foundation for Quality Data
• Registrar’s commitment to registry purpose
• Registrar’s knowledge, understanding of cancer data
• Registrar’s management of communication technologies
• Registrar’s advocacy for data use
SUMMARY
• Consistent recording and reporting of quality cancer data requires commitment.
• Routine and regular review of data patterns facilitates data knowledge and quality.
• Passing EDITS assists but does not ensure data quality.
• Data standards change, use the manuals.• Welcome Collaborative Stage.