Is quality of registry treatment data related to registrar experience and workload? A study of Taiwan cancer registry data

J Formos Med Assoc. 2018 Dec;117(12):1093-1100. doi: 10.1016/j.jfma.2017.12.012. Epub 2018 Jan 10.

Abstract

Background: Although cancer treatment information has been collected through the Cancer Registry system in Taiwan for more than 10 years, the accuracy of such data has never been evaluated. This study examined the accuracy rate between registrar experience and on-site chart review for the first course of cancer treatment.

Methods: In this retrospective chart review study, 392 randomly selected medical records from 14 hospitals were re-abstracted by experienced abstractors. The kappa coefficients of accuracy for the abstracting data were calculated against the gold standard. Correlations between registrar background and workload were then identified through regression analysis.

Results: Regarding surgery type, low accuracy rates were noted for gastric cancer (84.0%), oral cavity cancer (84.6%), and bladder cancer (88.9%). For chemotherapy, low accuracy rates were observed for hematopoietic diseases (81.3%) and esophageal cancer (88.0%). For radiotherapy, low accuracy rates were noted for esophageal cancer (80.0%), cervical cancer (81.8%), and lymphoma (85.7%). When stratifying by surgery type after adjustment for hospital caseload, a high accuracy rate was found for cancer registrars who had progressed from basic to advanced licenses within 5 years of graduating.

Conclusion: The accuracy rate for the first course of cancer treatment was affected by the cancer type and the experience of cancer registrars, but it was not affected by the workload of cancer registrars. We recommend that cancer registrars with basic licenses upgrade to advanced licenses as soon as possible. Medical record collaboration should establish documentation for checklist of radiotherapy and surgical operation records.

Keywords: Data quality; Peer review.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Aged
  • Data Accuracy
  • Female
  • Hospitals
  • Humans
  • Logistic Models
  • Male
  • Medical Records / statistics & numerical data*
  • Middle Aged
  • Multivariate Analysis
  • Neoplasms / classification*
  • Neoplasms / therapy*
  • Registries / standards*
  • Retrospective Studies
  • Taiwan
  • Workload*