| 1. |
- Bakidou, Anna, 1996, et al.
(författare)
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On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry
- 2023
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Ingår i: BMC Medical Informatics and Decision Making. - 1472-6947. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient’s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting. Methods: The Swedish Trauma Registry was used to train and validate five models – Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network – in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates. Results: There were 75,602 registrations between 2013–2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patientswere undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80–0.89 and AUCPR between 0.43–0.62. Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.
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| 2. |
- Jalo, Hoor, 1994, et al.
(författare)
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Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence : A scoping review protocol
- 2023
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Ingår i: BMJ Open. - : BMJ Publishing Group. - 2044-6055. ; 13:5
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Stroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there is an emerging need to improve the accuracy of the methods used to identify and characterise stroke in prehospital settings and emergency departments (EDs). This might be accomplished by developing computerised decision support systems (CDSSs) that are based on artificial intelligence (AI) and potential new data sources such as vital signs, biomarkers and image and video analysis. This scoping review aims to summarise literature on existing methods for early characterisation of stroke by using AI. Methods and analysis The review will be performed with respect to the Arksey and O'Malley's model. Peer-reviewed articles about AI-based CDSSs for the characterisation of stroke or new potential data sources for stroke CDSSs, published between January 1995 and April 2023 and written in English, will be included. Studies reporting methods that depend on mobile CT scanning or with no focus on prehospital or ED care will be excluded. Screening will be done in two steps: title and abstract screening followed by full-text screening. Two reviewers will perform the screening process independently, and a third reviewer will be involved in case of disagreement. Final decision will be made based on majority vote. Results will be reported using a descriptive summary and thematic analysis. Ethics and dissemination The methodology used in the protocol is based on information publicly available and does not need ethical approval. The results from the review will be submitted for publication in a peer-reviewed journal. The findings will be shared at relevant national and international conferences and meetings in the field of digital health and neurology. © 2023 BMJ Publishing Group. All rights reserved.
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| 3. |
- Jalo, Hoor, 1994, et al.
(författare)
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Stroke Prehospital Decision Support Systems Based on Artificial Intelligence: Grey Literature Scoping Review
- 2024
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Ingår i: Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2. - 2184-4305.
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Konferensbidrag (refereegranskat)abstract
- Stroke is a leading cause of mortality and disability worldwide. Therefore, there is a growing interest in prehospital point-of-care stroke clinical decision support systems (CDSSs), which with improved precision can identify stroke and decrease the time to optimal treatment, thereby improving clinical outcomes. Artificial intelligence (AI) may be a route to improve CDSSs for clinical benefit. Deploying AI in the area of prehospital stroke care is still in its infancy. There are several existing systematic and scoping reviews summarizing the progress of AI methods for stroke assessment. None of these reviews include grey literature, which could be a valuable source of information, especially when analysing future research and development directions. This paper aims to use grey literature to investigate stroke assessment CDSSs based on AI. The study adheres to PRISMA guidelines and presents seven records showcasing promising technologies. These records included three clinical trials, two smartphone applications, one master thesis and one PhD dissertation, which identify electroencephalogram (EEG), video analysis and voice and facial recognition as potential data sources for early stroke identification. The integration of these technologies may offer the prospect of faster and more accurate CDSSs in the future.
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| 4. |
- Seth, Mattias, 1993, et al.
(författare)
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Reviewing Challenges in Specifying Interoperability Requirement in Procurement of Health Information Systems
- 2024
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Ingår i: Studies in Health Technology and Informatics. - 1879-8365 .- 0926-9630. ; 310, s. 8-12
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Tidskriftsartikel (refereegranskat)abstract
- Procurement of health information systems (HIS) is a complex and critical task that requires early identification of interoperability requirements. However, specifying adequate requirements is often associated with several challenges. We examined relevant peer-reviewed literature and public documents (policy documents, annual reports, and newspapers) to summarize existing challenges in specifying interoperability requirement during procurement of HISs. In this study, 32 public documents and 2343 peer-reviewed articles were found using Google search engine, Springer, PubMed and ScienceDirect. Collected data were analyzed using a thematic coding schema. Our result shows that challenges related to describing the needs properly, conflicting needs and knowledge gaps are shared between most articles. Further research in the direction of developing a model that can bridge knowledge gaps, facilitate interdisciplinary collaboration, and help to avoid fuzzy requirements is needed.
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| 5. |
- Wallstén, David, et al.
(författare)
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Design for integrating explainable AI for dynamic risk prediction in prehospital IT systems
- 2023
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Ingår i: Artificial Intelligence, Social Computing and Wearable Technologies. - 9781958651896 ; 113, s. 268-278
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Konferensbidrag (refereegranskat)abstract
- Demographic changes in the West with an increasingly elderly population puts stress on current healthcare systems. New technologies are necessary to secure patient safety. AI development shows great promise in improving care, but the question of how necessary it is to be able to explain AI results and how to do it remains to be evaluated in future research. This study designed a prototype of eXplainable AI (XAI) in a prehospital IT system, based on an AI model for risk prediction of severe trauma to be used by Emergency Medical Services (EMS) clinicians. The design was then evaluated on seven EMS clinicians to gather information about usability and AI interaction.Through ethnography, expert interviews and literature review, knowledge was gathered for the design. Then several ideas developed through stages of prototyping were verified by experts in prehospital healthcare. Finally, a high-fidelity prototype was evaluated by the EMS clinicians. The primary design was based around a tablet, the most common hardware for ambulances. Two input pages were included, with the AI interface working as both an indicator at the top of the interface and a more detailed overlay. The overlay could be accessed at any time while interacting with the system. It included the current risk prediction, based on the colour codes of the South African Triage Scale (SATS), as well as a recommendation based on guidelines. That was followed by two rows of predictors, for or against a serious condition. These were ordered from left to right, depending on importance. Beneath this, the most important missing variables were accessible, allowing for quick input.The EMS clinicians thought that XAI was necessary for them to trust the prediction. They make the final decision, and if they can’t base it on specific parameters, they feel they can’t make a proper judgement. In addition, both rows of predictors and missing variables served as reminders of what they might have missed in patient assessment, as stated by the EMS clinicians to be a common issue. If given a prediction from the AI that was different from their own, it might cause them to think more about their decision, moving it away from the normally relatively automatic process and likely reducing the risk of bias.While focused on trauma, the overall design was created to be able to include other AI models as well. Current models for risk prediction in ambulances have so far not seen a big benefit of using artificial neural networks (ANN) compared to more transparent models. This study can help guide the future development of AI for prehospital healthcare and give insights into the potential benefits and implications of its implementation.
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