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.

Journal: BMC psychiatry

Volume: 24

Issue: 1

Year of Publication: 2024

Affiliated Institutions:  Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, , , Barcelona, Spain. pmortier@researchmar.net. Hospital del Mar Research Institute, Barcelona Biomedical Research Park (PRBB), Carrer Doctor Aiguader, , , Barcelona, Spain. School of Public Health & National Suicide Research Foundation, University College Cork, Cork, Ireland. Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden. National Centre for Suicide Research and Prevention, Institute of Clinical Medicine, University of Oslo, Oslo, Norway. Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Research Institute, Barcelona, Spain. Neuropsychiatry and Drug Addiction Institute, Barcelona MAR Health Park Consortium PSMAR, Barcelona, Spain.

Abstract summary 

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.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.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.

Authors & Co-authors:  Mortier Amigo Bhargav Conde Ferrer Flygare Kizilaslan Latorre Moreno Leis Mayer Pérez-Sola Portillo-Van Diest Ramírez-Anguita Sanz Vilagut Alonso Mehlum Arensman Bjureberg Pastor Qin

Study Outcome 

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Statistics
Citations :  Global Burden of Disease Collaborative Network. Global burden of disease study 2019 (GBD 2019) results. Seattle U S. Published Online First: 2019.
Authors :  21
Identifiers
Doi : 220
SSN : 1471-244X
Study Population
Male,Female
Mesh Terms
Humans
Other Terms
Clinical decision support system;Hospital Emergency Service;Intentional self-harm;Knowledge bases user-Centred Design.;Machine learning;Risk Assessment;Routinely Collected Health data;Suicide
Study Design
Study Approach
Mixed-Methods
Country of Study
Publication Country
England