Melanoma Tumor Depth Quality Audit: A Nonmatch Analysis.
Breslow
End Results (SEER) Program
Epidemiology
Surveillance
algorithm
data quality
melanoma tumor depth
Journal
Journal of registry management
ISSN: 1945-6123
Titre abrégé: J Registry Manag
Pays: United States
ID NLM: 9804163
Informations de publication
Date de publication:
2021
2021
Historique:
medline:
1
1
2021
pubmed:
1
1
2021
entrez:
1
6
2023
Statut:
ppublish
Résumé
The National Cancer Institute's Surveillance Research Program (SRP) received reports from cancer registries in the Surveillance, Epidemiology, and End Results (SEER) Program concerning the coding of melanoma tumor depth. To address these concerns, SRP developed an algorithm to identify melanoma depth measurement values and conducted a nonmatch analysis. A nonmatch analysis was conducted on 1,117 cases diagnosed between 2010 and 2017. With the help of Information Management Services, a natural language processing algorithm was developed to identify melanoma tumor depth values along with a gold standard for comparison. A randomly sampled data set was created to compare the algorithm-generated and gold standard values to the originally reported values; these were analyzed using SAS software version 9.4. Analyses were conducted to determine the distribution of nonmatches by demographics and estimate the distribution of nonmatches by the derived T variable according to the 7th edition of the American Joint Committee on Cancer (AJCC)'s Of the 1,117 cases, 849 cases (76%) were a match between the originally reported values and the gold standard. The majority of cases were found to be in male patients (60%) and non-Hispanic White patients (93%). When comparing derived AJCC-7 T based on the originally reported value to the gold standard, 16% of the original derived AJCC-7 T values were incorrect, with most of the nonmatches resulting in incorrectly coding a case as TX instead of T1. In total, 24% of cases were found to have a discrepancy in the originally recorded values. Decimal errors made up 3% of all cases in this nonmatch analysis. This algorithm may prove to be an essential tool in optimizing registry resources by flagging inconsistencies via automated text review to be adjudicated by registrars, improving their quality of data as needed.
Sections du résumé
Background
UNASSIGNED
The National Cancer Institute's Surveillance Research Program (SRP) received reports from cancer registries in the Surveillance, Epidemiology, and End Results (SEER) Program concerning the coding of melanoma tumor depth. To address these concerns, SRP developed an algorithm to identify melanoma depth measurement values and conducted a nonmatch analysis.
Methods
UNASSIGNED
A nonmatch analysis was conducted on 1,117 cases diagnosed between 2010 and 2017. With the help of Information Management Services, a natural language processing algorithm was developed to identify melanoma tumor depth values along with a gold standard for comparison. A randomly sampled data set was created to compare the algorithm-generated and gold standard values to the originally reported values; these were analyzed using SAS software version 9.4. Analyses were conducted to determine the distribution of nonmatches by demographics and estimate the distribution of nonmatches by the derived T variable according to the 7th edition of the American Joint Committee on Cancer (AJCC)'s
Results
UNASSIGNED
Of the 1,117 cases, 849 cases (76%) were a match between the originally reported values and the gold standard. The majority of cases were found to be in male patients (60%) and non-Hispanic White patients (93%). When comparing derived AJCC-7 T based on the originally reported value to the gold standard, 16% of the original derived AJCC-7 T values were incorrect, with most of the nonmatches resulting in incorrectly coding a case as TX instead of T1.
Conclusion
UNASSIGNED
In total, 24% of cases were found to have a discrepancy in the originally recorded values. Decimal errors made up 3% of all cases in this nonmatch analysis. This algorithm may prove to be an essential tool in optimizing registry resources by flagging inconsistencies via automated text review to be adjudicated by registrars, improving their quality of data as needed.
Identifiants
pubmed: 37260866
pii: jrm.2021.48.4.161
pmc: PMC10198396
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
161-167Informations de copyright
© 2021 National Cancer Registrars Association.
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