Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients.

Abstract

Data quality issues can cause false decisions of clinical decision support systems (CDSSs). Analyzing local data quality has the potential to prevent data quality-related failure of CDSS adoption. To define a shareable set of applicable measurement methods (MMs) for a targeted data quality assessment determining the suitability of local data for our CDSS. We derived task-specific MMs using four approaches: (1) a GUI-based data quality analysis using the open source tool openCQA. (2) Analyzing cases of known false CDSS decisions. (3) Data-driven learning on MM-results. (4) A systematic check to find blind spots in our set of MMs based on the HIDQF data quality framework. We expressed the derived data quality-related knowledge about the CDSS using the 5-tuple-formalization for MMs. We identified some task-specific dataset characteristics that a targeted data quality assessment for our use case should inspect. Altogether, we defined 394 MMs organized in 13 data quality knowledge bases. We have created a set of shareable, applicable MMs that can support targeted data quality assessment for CDSS-based systemic inflammatory response syndrome (SIRS) detection in critically ill, pediatric patients. With the demonstrated approaches for deriving and expressing task-specific MMs, we intend to help promoting targeted data quality assessment as a commonly recognized usual part of research on data-consuming application systems in health care.