Computer-Interpretable Quality Indicators for Intensive Care Medicine: Development and Validation Study.

Background: Quality indicators (QIs) can help assess intensive care quality, identify potential for improvement, and ultimately enhance patient outcomes. Therefore, the German Interdisciplinary Association of Critical Care and Emergency Medicine (DIVI) has developed QIs for intensive care medicine. However, variability in how these are technically implemented across health care facilities currently limits their comparability.

Objective: The aim of the study is to develop unambiguous computer-interpretable representations of the DIVI QIs for intensive care medicine using Fast Healthcare Interoperability Resources (FHIR) and to establish a replicable process for translating narrative QIs into standardized digital formats.

Methods: We first decomposed the narrative DIVI intensive care medicine QIs into two sets of semantic concepts that characterize (1) the targeted patient population and (2) the care aspect specified by each indicator. We mapped the concepts to international vocabularies, defining a supplementary code system for concepts not appropriately represented in existing vocabularies. The decomposed and semantically mapped QIs were then implemented in FHIR using an implementation guide we previously developed to represent clinical practice guideline recommendations. As the translation process holds risks of inducing logical and semantic deviations, the final FHIR representations were back-translated into a narrative form and reviewed with clinical experts, including the authors of the original QIs. The decomposition and semantic mapping were iteratively adjusted based on the experts' feedback until the results accurately reflected the original intent of the QIs.

Results: The 10 DIVI QIs were decomposed into 31 separately measurable indicators, including 9 structural indicators, 17 process indicators, and 5 outcome indicators. All process and outcome indicators were successfully specified as computer-interpretable representations in FHIR. In total, 58 unique medical concepts were used, of which 52 (90%) could be mapped to concepts from international vocabularies. The remaining 6 concepts-mostly intensive care unit-specific scores or roles-were defined in a supplementary code system. Nested Boolean logic and temporal conditions were fully supported using standard FHIR mechanisms. After iterative adjustments, the final representations were approved as accurate representations of the DIVI QIs by the clinical expert panel.

Conclusions: Our work demonstrates that the structured process developed here enables the unambiguous, computer-interpretable representation of QIs for intensive care. These representations can be used in automated quality management systems to standardize quality assessments across health care facilities. Our newly defined structured process can serve as a blueprint for similar efforts in other specialties. The here-developed computer-interpretable QIs are openly available for reuse and ongoing maintenance. Future work will focus on piloting these indicators in real-world clinical systems and extending the framework to include structural indicators.