Applying FHIR in Remote Consultation in Dermatology: Interoperability of Information for Digital Dermatoscope

PENG, TZU-FANG; Chiu, Hung-Wen

doi:10.1145/3545729.3545739

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FHIR standard has emerged as a solution that promotes data exchange between HISs, facilitating interoperability [14], [15]. The adaptability of FHIR not only has simplified data access and sharing for healthcare providers, but has also catalyzed the development of new healthcare applications and services [16], [17]. Platforms such as KETOS utilize FHIR to allow researchers to conduct statistical analyzes and develop, train, and deploy machine learning models in a secure environment [18].…”

Section: Related Workmentioning

confidence: 99%

MoCab: A Framework for the Deployment of Machine Learning Models across Health Information Systems

Kuo,

Chen,

Tseng

2024

Preprint

View full text Add to dashboard Cite

Machine learning models are increasingly vital for clinical decision making. However, the integration of these models into health information systems (HISs) poses significant challenges, mainly due to the disparate formats of electronic health records (EHR) in various healthcare facilities. In response to this challenge, we proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs. MoCab simplifies the deployment process by importing and configuring saved prediction models, facilitating patient data retrieval from FHIR servers, and transmitting it to the prediction model for analysis. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts and uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop Apps for displaying laboratory results. Finally, MoCab offers the ability to continuously fine-tune and enhance the performance of deployed models over time. We demonstrate MoCab's efficacy through the successful integration of three model types, highlighting its potential in streamlining decision making amidst heterogeneous EHRs. Our proposed MoCab framework not only promotes the reusability of machine learning models across multiple EHRs but also contributes to improving clinical decision making, offering a promising solution to the challenges of integrating machine learning models into healthcare settings.

show abstract