Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study

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, health care information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 Fast Healthcare Interoperability Resources (FHIR) have already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning–based models. Objective In this paper, 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 10 experts regarding health care system integration and defined an implementation guide. We then developed the FHIR Extract Transform Load to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline. Results The study data set includes electronic health records of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 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 the FHIR Extract Transform Load application. The measured vital signs include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the electronic health record information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful application programming interface. Conclusions We successfully demonstrated a process that standardizes health care information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provided an implementation guide that includes data mapping, an integration process, and IHCA assessment using vital signs. We also proposed a clarifying system architecture and possible workflows. Based on FHIR, we integrated the 3 different systems in 1 dashboard system, which can effectively solve the complexity of the system in the medical staff workflow.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

“…These AI models and algorithms may accomplish various tasks, such as data extraction, clinical decision assistance, and prognosis prediction. In addition, AI may forecast multiple health-related results, such as cancer, sepsis, heart failure, in-hospital cardiac arrest, and COVID-19-related resource utilization [ 30 , 32 , 40 , 41 , 42 , 43 ]. Several measures, including area under the curve (AUC), precision score, positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, calibration, and F-measure, were used to evaluate the performance of algorithms.…”

“…FHIR is the most current HL7 system standard [ 54 ]. It was first introduced in March 2014, and multiple technical design studies conducted between 2018 and 2022 favored FHIR as their preferred standard [ 40 , 41 , 42 , 43 ]. HL7 messaging systems were used by [ 31 , 32 , 37 , 38 ] to gather their input information, and some authors tried to improve their data collection quality using HL7 version 2.…”

“…HL7 messaging systems were used by [ 31 , 32 , 37 , 38 ] to gather their input information, and some authors tried to improve their data collection quality using HL7 version 2. Nevertheless, adopting the most recent standard, FHIR can increase the study’s reliability due to its modern design, integrated data exchange, standardized resources, and enhanced support for current healthcare use cases, such as patient portals [ 41 , 55 , 56 , 57 ]. However, other studies conducted during different time frames did not specify the standard used [ 29 , 30 , 33 , 34 , 35 , 36 , 39 ].…”

“…Using pre-existing web standards such as RESTful Application Programming Interfaces (API) and XML or JSON data exchange formats, which are lightweight and easy for individuals and machines to understand, has helped FHIR gain preference [ 5 , 58 ]. For HIEs, using Restful API offers many advantages, including scalability, speed, and adaptability, as demonstrated by Amrollahi et al [ 42 ], Tseng et al [ 41 ], and Henry et al [ 43 ]. Still, it is also necessary to handle their complexities and security risks.…”