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Data Quality Toolbox for Registrars MCSS Workshop December 9, 2003 Elaine Collins Quality Data Toolbox • • • • • • • • Artisan Medium Raw Materials Shaping tools Directions Measuring tools Final Product Goodness Registrar Computerized data Medical information Knowledge, skills Standards Editing “tools” Cancer record 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 300 250 200 150 100 50 0 CANCER EXTENT STAGE SURGERY 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 Registrar – Resource for Quality Data ICD-O Medical Record Facility System Facility Staff Committees CDC Physician Patient Cancer Research AJCC SEER Other Registries Protocols COC Registrar Central Registry Cancer Control NCDB NAACCR Quality Monitors 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 cTcNpM, cST pTcNcM, cST pTpNcM, cST pTpNpM, cST cTcNcM, pST pTcNcM, pST pTpNcM, pST pTpNpM, pST 9 2 54 18 2 1 3 21 1 31 30 27 1 1 10 3 1 2 1 1 6 2 5 74 37 40 4 20 6 c99, p99 cST, p99 c99, pST cST, pST 3 4 4 13 6 1 5 7 9 1 6 3 No Staging 1 4 7 5 Two Groups 3 3 Collaborative Staging • Provides specific rules for coding known vs unknown staging elements • Accommodates “best” stage for AJCC stage assignment AHIMA 75th Annual Conference October, 2003 Minneapolis: Coming Events • • • • Data mining ICD-10-CM SNOMED Natural language processing AHIMA 75th Annual Conference October, 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.