Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project

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• dc.contributor.author Mortier, Philippe
• dc.contributor.author Amigo, Franco
• dc.contributor.author Conde, Susana
• dc.contributor.author Ferrer Forés, Maria Montserrat
• dc.contributor.author Latorre Moreno, Laura
• dc.contributor.author Leis Machín, Angela 1974-
• dc.contributor.author Mayer, Miguel Ángel, 1960-
• dc.contributor.author Pérez Solá, Victor
• dc.contributor.author Portillo-Van Diest, Ana
• dc.contributor.author Ramírez Anguita, Juan Manuel
• dc.contributor.author Sanz, Ferran
• dc.contributor.author Vilagut Saiz, Gemma, 1975-
• dc.contributor.author Alonso Caballero, Jordi
• dc.contributor.author Pastor Maeso, Manuel
• dc.contributor.author Qin, Ping
• dc.date.accessioned 2024-04-18T05:45:38Z
• dc.date.available 2024-04-18T05:45:38Z
• dc.date.issued 2024
• dc.description.abstract Background: Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored. Methods: PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software. Discussion: Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.
• dc.format.mimetype application/pdf
• dc.identifier.citation Mortier P, Amigo F, Bhargav M, Conde S, Ferrer M, Flygare O, et al. Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project. BMC Psychiatry. 2024 Mar 20;24(1):220. DOI: 10.1186/s12888-024-05659-6
• dc.identifier.doi http://dx.doi.org/10.1186/s12888-024-05659-6
• dc.identifier.issn 1471-244X
• dc.identifier.uri http://hdl.handle.net/10230/59813
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof BMC Psychiatry. 2024 Mar 20;24(1):220
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Clinical decision support system
• dc.subject.keyword Hospital Emergency Service
• dc.subject.keyword Intentional self-harm
• dc.subject.keyword Knowledge bases user-Centred Design
• dc.subject.keyword Machine learning
• dc.subject.keyword Risk Assessment
• dc.subject.keyword Routinely Collected Health data
• dc.subject.keyword Suicide
• dc.title Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion