An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study

Abstract

Background: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology.

Objective: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance.

Methods: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals.

Results: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.

Conclusions: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

Keywords: IoT; data integration; early warning; infectious disease; infectious disease management; infectious disease surveillance; ontology; patient monitoring; public health; risk analysis; semantic interoperability.

Figure 1

An ontology model for Internet of Things–powered remote monitoring of a patient’s vital signs, health risk assessment, early warning, and proactive health risk management. CODO: The COviD-19 Ontology for cases and patient information; EFO: Experimental Factor Ontology; FOAF: The Friend Of A Friend ontology; IDOMAL: Malaria Ontology; NCIT: NCI Thesaurus OBO Edition; UO: Units of Measurement Ontology.

Figure 2

An ontology model for clinical management of infectious diseases in health care settings, supporting the seamless integration of data on vaccination status, laboratory test results, diagnosis, treatment, and underlying health conditions that may affect the course and outcome of an infectious disease. EFO: Experimental Factor Ontology; FOAF: The Friend Of A Friend ontology; GENEPIO: Genomic Epidemiology Ontology; ICDO: International Classification of Disease Ontology; IDO: Infectious Disease Ontology; IDOMAL: Malaria Ontology; LABO: clinical LABoratory Ontology; NCIT: NCI Thesaurus OBO Edition; OGMS: Ontology for General Medical Science; VO: Vaccine Ontology.

Figure 3

An ontology model for autonomously assessing infectious disease epidemic risk at the national level, with the goal of improving preparedness and promoting timely response. IDO: Infectious Disease Ontology; NCIT: NCI Thesaurus OBO Edition.

Figure 4

Implementation example using European Centre for Disease Prevention and Control rapid risk assessment methodology [63].

Figure 5

Implementation example of risk assessment based on the proposed framework by Lesmanawati et al [64].

Figure 6

An ontology model for integrating dynamic data on infectious disease case definition, testing strategy, epidemic threshold, and statistics such as morbidity and mortality to track the spread of the disease at the national level. IDO: Infectious Disease Ontology; IDOBRU: Brucellosis Ontology; NCIT: NCI Thesaurus OBO Edition; OBI: Ontology for Biomedical Investigations.

Figure 7

An ontology model for aggregating individual patient data into surveillance information to provide actionable insights for informed decision-making and timely public health interventions. CODO: The COviD-19 Ontology for cases and patient information; FOAF: The Friend Of A Friend ontology; GENEPIO: Genomic Epidemiology Ontology; ICDO: International Classification of Disease Ontology; IDO: Infectious Disease Ontology; IDOMAL: Malaria Ontology; NCIT: NCI Thesaurus OBO Edition; OBI: Ontology for Biomedical Investigations; VO: Vaccine Ontology.