National quality registries: how to improve the quality of data?

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.

Keywords: Clinical audit; data accuracy; data verification; lung cancer; quality assurance.

Figure 1

Data collection and feedback processes of DICA registries. 1a, in survey patients are registered one by one by the registrar. Data quality is facilitated directly by required items, conditions, validations and help texts. 1b, by batch processing, hospitals can extract data from EPR and deliver it in ‘bulk’. 2a, a real-time signalling list shows all registered patients for which data are missing or erroneous. 2b, in the validation report hospitals get feedback on their submitted batches, both on patient level as in summary. 3, DICA is responsible for data cleaning, analysis, and feedback of results to hospitals and the scientific committee. 4, in MyDICA, accessible by doctors and hospital boards, weekly updated results are shown in tables and funnel plots. EPR, electronic patient records; MRDM, medical research data management.

Figure 2

Methods for quality assurance during data entry, applied in the web-based Survey of the Dutch Lung Cancer Audit for Surgery (DLCA-S).

Figure 3

Funnel plot with the hospital results on the ‘completeness indicator’ of all hospitals registering in the Dutch Lung Cancer Audit for Surgery (DLCA-S) in 2016. *, completeness of registration: ‘the percentage of registered patients in which all items required for calculations of transparent quality indicators are registered’.