A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study
- PMID: 35916719
- PMCID: PMC9382545
- DOI: 10.2196/27990
A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study
Erratum in
-
Correction: A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.JMIR Form Res. 2023 Feb 28;7:e46102. doi: 10.2196/46102. JMIR Form Res. 2023. PMID: 36854147 Free PMC article.
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.
©Esther Román-Villarán, Celia Alvarez-Romero, Alicia Martínez-García, German Antonio Escobar-Rodríguez, María José García-Lozano, Bosco Barón-Franco, Lourdes Moreno-Gaviño, Jesús Moreno-Conde, José Antonio Rivas-González, Carlos Luis Parra-Calderón. Originally published in JMIR Formative Research (https://formative.jmir.org), 02.08.2022.
Conflict of interest statement
Conflicts of Interest: None declared.
Figures





Similar articles
-
A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard.BMC Med Inform Decis Mak. 2019 May 10;19(1):97. doi: 10.1186/s12911-019-0806-z. BMC Med Inform Decis Mak. 2019. PMID: 31077222 Free PMC article.
-
Decision support systems in clinical practice: The case of venous thromboembolism prevention.Int J Risk Saf Med. 2015;27 Suppl 1:S104-5. doi: 10.3233/JRS-150709. Int J Risk Saf Med. 2015. PMID: 26639683
-
Sharing clinical decisions for multimorbidity case management using social network and open-source tools.J Biomed Inform. 2013 Dec;46(6):977-84. doi: 10.1016/j.jbi.2013.06.007. Epub 2013 Jun 25. J Biomed Inform. 2013. PMID: 23806275
-
Going digital: a narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice.Aust Health Rev. 2017 Dec;41(6):646-664. doi: 10.1071/AH16233. Aust Health Rev. 2017. PMID: 28063462 Review.
-
Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines.Artif Intell Med. 2020 Mar;103:101741. doi: 10.1016/j.artmed.2019.101741. Epub 2020 Jan 9. Artif Intell Med. 2020. PMID: 31928849 Review.
Cited by
-
An Ontology-Based Approach to Improving Medication Appropriateness in Older Patients: Algorithm Development and Validation Study.JMIR Med Inform. 2023 Jul 13;11:e45850. doi: 10.2196/45850. JMIR Med Inform. 2023. PMID: 37477131 Free PMC article.
-
Personalized Digital Care Pathways Enable Enhanced Patient Management as Perceived by Health Care Professionals: Mixed-Methods Study.JMIR Hum Factors. 2025 May 15;12:e68581. doi: 10.2196/68581. JMIR Hum Factors. 2025. PMID: 40373224 Free PMC article.
-
The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease.J Healthc Eng. 2023 May 29;2023:8550905. doi: 10.1155/2023/8550905. eCollection 2023. J Healthc Eng. 2023. PMID: 37284487 Free PMC article. Review.
References
-
- 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
-
- World HO. Preventing Chronic Disease: a vital investment. Global Report. 2005. [2021-06-04]. http://www.who.int/chp/chronic_ .
-
- 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
-
- Ollero BM. Consejería de Salud. Andalucia: Junta de Andalucia. Consejería de Salud; 2018. Proceso asistencial integrado - Atención a pacientes pluripatológicos.
-
- 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
LinkOut - more resources
Full Text Sources
Miscellaneous