SHACL-Based Report Quality Evaluation for Health IT-Induced Medication Errors

Abstract

Patient safety event (PSE) reports are an important source of information for analyzing risks in healthcare processes. However, the reports’ quality is often low due to missing or imprecise information. We work towards an automatic analysis of reports and quality evaluation. To leverage a suitable data representation of health IT-induced medication error reports, we apply the Shapes Constraint Language (SHACL). We define an ontology representing these reports and construct a corresponding SHACL graph. Three authors manually annotate and transform 20 textual reports to the SHACL representation. Furthermore, we use this representation to compute a quality score for each report. The results indicate the suitability of SHACL as a representation of health IT-induced medication error reports, which paves a path of automatically extracting information from PSE reports using text mining and transform them to SHACL for quality evaluation.

Enhancing Semantic Web Technologies Using Lexical Auditing Techniques for Quality Assurance of Biomedical Ontologies

“…Very few initiatives exist in creating SHACL shapes for biomedical data graphs, and they mainly focus on data graphs representing Electronic Health Record (EHR) models [15] and patient information [16]. Other clinical use cases for SHACL shapes include validating medical guidelines to integrate Fast Healthcare Interoperability Resources (FHIR) into decisionmaking systems [17], validating medical reports to identify missing data [18], and validating clinical trial study data to detect missing values, wrong cardinalities, and incorrect values that do not adhere to a predefined set [16]. However, these biomedical data graphs are not as sensitive to class name semantics as biomedical ontology data graphs.…”