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. 2019 Dec 20;17(1):73.
doi: 10.3390/ijerph17010073.

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

Ilia Semenov  1 , Roman Osenev  1 , Sergey Gerasimov  1 , Georgy Kopanitsa  2 , Dmitry Denisov  1 , Yuriy Andreychuk  1
Affiliations

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

Ilia Semenov et al. Int J Environ Res Public Health. .

Abstract

This paper is an extension of work originally presented to pHealth 2019-16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

Keywords: FHIR; clinical decision support; integration; semantic interoperability.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
General model of service of the platform. API: application programming interface.
Figure 2
Figure 2
Services of the Rule Engine. FHIR: fast healthcare interoperability resources.
Figure 3
Figure 3
Example of a graphical model of rules for assessing the risk of type 2 diabetes.
Figure 4
Figure 4
CDSS (clinical decision support system) rules editor.
Figure 5
Figure 5
Rules combination.
Figure 6
Figure 6
Free-text recommendations.
Figure 7
Figure 7
Bayes Engine component diagram
Figure 8
Figure 8
CDS Hook interaction workflow. HIS: hospital information system.
Figure 9
Figure 9
CDS manager structure model. EHR: electronic health record.
Figure 10
Figure 10
Example of the structure of a probabilistic model.
Figure 11
Figure 11
Example of probability tables for Gastroesophageal reflux disease.
Figure 12
Figure 12
Initial state of a Bayesian network.
Figure 13
Figure 13
Probability table for a chill symptom.
Figure 14
Figure 14
Service has received data on the presence of chill.
Figure 15
Figure 15
Service has received data that the patient has a chest pain on the right side during cough.
Figure 16
Figure 16
Service has received data that the patient has rusty sputum coming off.
See this image and copyright information in PMC

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