JMIR Publications

ABSTRACT

Background:

Quality indicators 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 quality indicators for intensive care medicine. However, variability in how these are technically implemented across healthcare facilities currently limits their comparability.

Objective:

To develop unambiguous computer-interpretable representations of the DIVI quality indicators for intensive care medicine.

Methods:

We first decomposed the DIVI intensive care medicine quality indicators 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 quality indicators were then implemented in Fast Healthcare Interoperability Resources (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 quality indicators. The decomposition and semantic mapping were iteratively adjusted based on the experts’ feedback until the results accurately reflected the original intent of the quality indicators.

Results:

The ten DIVI quality indicators were structured 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. After iterative adjustments, the final representations were approved as accurate representations of the DIVI quality indicators.

Conclusions:

The processes and FHIR resources applied here allow for the development of reliable computer-interpretable representations of quality indicators. These representations can be used in automated quality management systems to standardize quality assessments across healthcare facilities. Clinical Trial: Not applicable.