AI Article Synopsis

  • The use of quality registries for healthcare has become vital for ensuring reliable information for various stakeholders like providers and patients, necessitating high-quality data.
  • The Dutch Institute for Clinical Auditing (DICA) outlines methods for ensuring data quality in its registries, including design, collection, analysis, and verification, specifically analyzing the Dutch Lung Cancer Audit for Surgery (DLCA-S).
  • Results showed that 98.2% of patients' data was usable with a 90.7% completeness rate, highlighting the effectiveness of data verification, which confirmed high quality in most hospitals while suggesting areas for improvement like standardized reporting.

Article Abstract

Background: Data of quality registries are increasingly used by healthcare providers, patients, health insurance companies, and governments for monitoring quality of care, hospital benchmarking and outcome research. To provide all stakeholders with reliable information and outcomes, reliable data are of the utmost importance.

Methods: This article describes methods for quality assurance of data-used by the Dutch Institute for Clinical Auditing (DICA)-regarding: the design of a registry, data collection, data analysis, and external data verification. For the Dutch Lung Cancer Audit for Surgery (DLCA-S) results of data analysis and data verification were assessed with descriptive statistics.

Results: Of all registered patients in the DLCA-S in 2016 (n=2,391), 98.2% was analysable and completeness of data for calculations of transparent outcomes was 90.7%. Data verification for the year 2014 showed a case ascertainment of 99.4%. Of 15 selected hospitals, 14 were verified. All these hospitals received the conclusion 'sufficient quality' on case ascertainment, mortality (0% under-registration) and complicated course (3.3% wrongly registered complications). One hospital was not able to deliver patients lists, and therefore not verified.

Conclusions: Quality of data can be promoted in many different ways. A completeness indicator and data verification are useful tools to improve data quality. Both methods were used to demonstrate the reliability of registered data in the DLCA-S. Opportunities for further improvement are standardised reporting and adequate data extraction.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230825PMC
http://dx.doi.org/10.21037/jtd.2018.04.146DOI Listing

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