SubsidieMeestersEnhancing emergency department safety, efficacy and cost-effectiveness by artificial intelligence
Develop a machine learning-based clinical decision support system for emergency medicine to enhance diagnosis accuracy, patient safety, and cost-effectiveness through validated algorithms and patient data integration.
Projectdetails
- Background: Emergency care costs are increasing in developed societies, both in rates of emergency department (ED) visits per person and in costs per visit, and are growing faster than other areas of healthcare spending.
With limited and unstructured data, ED staff make quick decisions about probabilities for multiple diagnoses and risks. Both underestimation and overestimation of these probabilities lead to increased costs and patient harm. Hence, there is a desperate need for clinical decision-support systems in the ED.
Aim
To develop a clinical decision support system for emergency medicine doctors, using sensor data, health records data, and patient-reported data, validated in a randomized clinical trial, in order to improve the safety, efficacy, and cost-effectiveness of emergency care.
Objectives
We will:
- Develop machine learning (ML)-powered diagnosis and risk prediction algorithms for common and dangerous conditions based on age, sex, presenting complaints, previous diagnoses, ECGs, and vital parameters.
- Develop and validate a patient-centered technical platform for collecting, storing, and sharing patient-reported data and three-dimensional symptom drawings.
- Develop ML-powered diagnosis and risk prediction algorithms for common and dangerous conditions based on patient-reported data and symptom drawings.
- Conduct a large-scale prospective ED data collection for internal and external validation of ML models using a common format for online applications and for further data collection.
- Develop a Bayesian network-powered ED-based clinical decision support system that generates probabilities for diagnoses and 30-day mortality risks and suggestions for the most valuable next step, from data in multiple formats, with visual representation of probabilities, risks, uncertainties, and Bayes factors for potential next steps.
- Conduct a randomized clinical trial investigating the usefulness, effectiveness, and safety of the new decision support system.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.497.200 |
| Totale projectbegroting | € 2.497.200 |
Tijdlijn
| Startdatum | 1-10-2022 |
| Einddatum | 30-9-2027 |
| Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- UPPSALA UNIVERSITETpenvoerder
Land(en)
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Primaire zorg: van routinematige naar datagedreven en zelflerende zorgHet project onderzoekt de haalbaarheid van een beslisondersteuningstool voor primaire zorg in India, gericht op het optimaliseren van zorg door het gebruik van Real World data. | MIT Haalbaarheid | € 20.000 | 2021 | Details |
MEDLINE V3.0: AI BASED TRIAGE PROCEDURESMedical Booking B.V. ontwikkelt een meertalig AI-gestuurd triagesysteem dat gebruikmaakt van spraaktechnologie en patiëntinformatie om de zorgdruk te verlagen en triage-uitkomsten te verbeteren. | MIT Haalbaarheid | € 20.000 | 2020 | Details |
Decision support system voor medische diagnosesNobleo ontwikkelt een AI-gestuurd decision support systeem voor medische specialisten om diagnoses te verbeteren, zorgkosten te verlagen en de haalbaarheid van de oplossing te onderzoeken. | MIT Haalbaarheid | € 20.000 | 2020 | Details |
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This project aims to enhance myocardial infarction risk prediction by integrating data from wearable devices, biomarkers, and AI to identify novel risk factors for improved clinical decision-making.
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