JMIR Publications

ABSTRACT

Background:

Vital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, healthcare information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) has already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning-based models.

Objective:

For IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications.

Methods:

We interviewed ten experts regarding healthcare system integration and defined an implementation guide. We then developed the FHIR Extract-Transform-Load (ETL) to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline.

Results:

The study data set include electronic health records (EHRs) of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least two to three times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using FHIR ETL. The measured vital signs include the systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the EHR information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful API.

Conclusions:

We successfully demonstrated a process that standardizes healthcare information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provide an implementation guide that includes data mapping, an integration process, and IHCA using vital signs. We also propose a clarifying system architecture and possible workflows.