Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug 2;6(8):e27990.
doi: 10.2196/27990.

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study

Affiliations

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study

Esther Román-Villarán et al. JMIR Form Res. .

Erratum in

Abstract

Background: Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits.

Objective: The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care.

Methods: A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study.

Results: This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase.

Conclusions: An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.

Keywords: CDSS; adherence; anticoagulants; atrial fibrillation; clinical decision support system; complex chronic patients; functional validation; multimorbidity; ontology; polypharmacy.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) mappings.
Figure 2
Figure 2
Super-rule example. HAS: Hypertension, Abnormal Renal/Liver Function, Stroke; HAS-BLED: Hypertension, Abnormal Renal/Liver Function, Stroke, Bleeding History or Predisposition, Labile INR, Elderly, Drugs/Alcohol Concomitantly.
Figure 3
Figure 3
Tool interface.
Figure 4
Figure 4
Mini-rule.
Figure 5
Figure 5
Super-rule.

Similar articles

Cited by

References

    1. Gual N, Yuste Font A, Enfedaque Montes B, Blay Pueyo C, Martín Álvarez R, Inzitari M. [Profile and evolution of chronic complex patients in a subacute unit] Aten Primaria. 2017 Nov;49(9):510–517. doi: 10.1016/j.aprim.2016.11.010. https://linkinghub.elsevier.com/retrieve/pii/S0212-6567(16)30280-3 S0212-6567(16)30280-3 - DOI - PMC - PubMed
    1. World HO. Preventing Chronic Disease: a vital investment. Global Report. 2005. [2021-06-04]. http://www.who.int/chp/chronic_ .
    1. Boyd CM, Fortin M. Future of Multimorbidity Research: How Should Understanding of Multimorbidity Inform Health System Design? Public Health Rev. 2010 Dec 10;32(2):451–474. doi: 10.1007/bf03391611. - DOI
    1. Ollero BM. Consejería de Salud. Andalucia: Junta de Andalucia. Consejería de Salud; 2018. Proceso asistencial integrado - Atención a pacientes pluripatológicos.
    1. Harrison C, Britt H, Miller G, Henderson J. Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice. BMJ Open. 2014 Jul 11;4(7):e004694. doi: 10.1136/bmjopen-2013-004694. https://bmjopen.bmj.com/lookup/pmidlookup?view=long&pmid=25015470 bmjopen-2013-004694 - DOI - PMC - PubMed