Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients

Abstract

Objectives: To update the sets of patient-centric outcomes measures ("standard-sets") developed by the not-for-profit organization ICHOM to become more readily applicable in patients with multimorbidity and to facilitate their implementation in health information systems. To that end we set out to (i) harmonize measures previously defined separately for different conditions, (ii) create clinical information models from the measures, and (iii) restructure the annotation to make the sets machine-readable. Materials and Methods: First, we harmonized the semantic meaning of individual measures across all the 28 standard-sets published to date, in a harmonized measure repository. Second, measures corresponding to four conditions (Breast cancer, Cataracts, Inflammatory bowel disease and Heart failure) were expressed as logical models and mapped to reference terminologies in a pilot study. Results: The harmonization of semantic meaning resulted in a consolidation of measures used across the standard-sets by 15%, from 3,178 to 2,712. These were all converted into a machine-readable format. 61% of the measures in the 4 pilot sets were bound to existing concepts in either SNOMED CT or LOINC. Discussion: The harmonization of ICHOM measures across conditions is expected to increase the applicability of ICHOM standard-sets to multi-morbid patients, as well as facilitate their implementation in health information systems. Conclusion: Harmonizing the ICHOM measures and making them machine-readable is expected to expedite the global adoption of systematic and interoperable outcomes measurement. In turn, we hope that the improved transparency on health outcomes that follows will let health systems across the globe learn from each other to the ultimate benefit of patients.

Keywords: health data interoperability; machine learning; outcomes measurement; patient centered; standardization.

Figure 1

Flow-diagram depicting process for harmonizing the ICHOM measures.

Figure 2

The final taxonomy was organized in two levels, with the first being based on the PICO-model for evidence-based decisions (38). The taxonomy was implemented as a tool for support of further developments including standard-sets for new diagnoses.