Measuring the Quality of Data Collection in a Large Observational Cohort of HIV and AIDS

Abstract

The aim of this study was to examine the quality of data collection by studying the validity of collected data. Data were extracted from the clinic charts of two anonymous outpatients by 38 data collectors. A standard for the data to be collected was determined (168 items). The validity was measured by comparing the collected items with the standard; in this way, the percentages of the collected items that were ‘correct’ could be calculated. The percentage ‘correct’ was higher for clinic chart 1 (mean: 83% correct, SD 7%) than for clinic chart 2 (mean: 78% correct, SD 8%). All categories contained incorrectly collected data. These data were divided into missing data, incorrect start-stop dates, and surplus collected data. Almost all start-stop dates would change into ‘correct’ if ‘monthyear’ was considered correct (instead of the standard ‘daymonthyear’). Not all data collectors used specific protocols, and sources other than the written comments were not always checked. This study shows that a high proportion of data was correctly collected. However, the collection of start-stop dates was not optimal, and the collected data included surplus and missing data. Data collectors should be more knowledgeable about HIV disease and trained in the use of difficult protocols, so that they can better recognize what data to collect and how it should be collected. Among physicians, there should be more agreement about what information to record in the charts, to facilitate data extraction for data collectors.

AIDS Therapy Evaluation in the Netherlands (ATHENA) national observational HIV cohort: cohort profile

“…The unique strength of the cohort is its completeness, both in terms of coverage (ie, 98% of all HIV-positive people in care in the Netherlands), as well as the quality and extensiveness of the data collection. 34 Furthermore, follow-up and data collection continues uninterrupted for children who transfer from paediatric to adult HIV care and for people who transfer to another HIV treatment centre within the Netherlands.…”

Modelos de métrica para sistemas electrónicos de adquisición de datos y de procesamiento para investigación clínica con dispositivos cardiacos implantables

“…El proceso de introducción manual de los datos, como hemos podido cuantificar en los dos apartados anteriores, es caro y consume gran cantidad de tiempo, pero además, es propenso a la generación de errores. Distintos autores han cuantificado los errores de introducción de datos en estudios clínicos en valores incluidos en el rango del 0,66% al 27% (Büchele et al 2005, Goldberg et al 2008, Hillebregt et al 2010, Mitchel et al 2011. Es una práctica habitual en las empresas patrocinadoras o encargadas de la ejecución de un estudio clínico (CROs) el realizar una doble entrada de datos con el fin de minimizar el efecto de este error.…”