Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment

Demuth, Stanislas; Müller, Joris; Quenardelle, Véronique; Lauer, Valérie; Gheoca, Roxana; Trzeciak, Malwina; Pierre-Paul, Irène; De Sèze, Jérôme; Gourraud, Pierre-Antoine; Wolff, Valérie

doi:10.1007/s00415-023-11979-6

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…In suicide research, it has been shown that machine learning-based suicide risk predictions outperform all widely researched theories of suicide [ 47 ], opening the possibility to stratify patients with self-harm according to risk for adverse outcomes, such as death by suicide, and provide tailored intervention preventions. Despite their clear usefulness in other clinical domains [ 48 – 51 ], evidence of effective implementation of these techniques into clinically useful prediction tools for self-harm and related adverse outcomes is lacking. This may be due to the absence of a user-oriented personalised approach, i.e., the failure to actively involve both patients and clinicians in the development of this kind of software tools [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

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

Mortier,

Amigo,

Bhargav

et al. 2024

BMC Psychiatry

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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.

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