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Striking a match between FHIR-based patient data and FHIR-based eligibility criteria
Learning Health Systems ; 2023.
Article in English | Web of Science | ID: covidwho-2321554
ABSTRACT
Inputs and Outputs The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.

Purpose:

Ultimately, such a system will play a "matchmaker" role in facilitating point of-care recognition of patient-specific clinical decision support.Specifications The eligibility criteria are defined in HL7 FHIR (version R5) Evidence Variable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.Application The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https//fevir.net/110192 provides a method for sharing functions using the same information model.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Observational study / Prognostic study Language: English Journal: Learning Health Systems Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Observational study / Prognostic study Language: English Journal: Learning Health Systems Year: 2023 Document Type: Article