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

Abstract

Background: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.