Integrating Artificial Intelligence and Contextual Factors in Emergency Care
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".
Deadline for manuscript submissions: 25 June 2026 | Viewed by 822
Special Issue Editors
Interests: prehospital; emergency medicine; risk scores; biomarkers; emergency medical services
Special Issues, Collections and Topics in MDPI journals
Interests: CPR; prehospital; emergency medicine; nursing; early warning scores
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Emergency medical systems, whether in urban or rural settings, face persistent challenges in ensuring timely and effective patient care. Health professionals often make critical decisions with limited information, under intense time pressure, and across highly variable environmental and resource conditions. Early Warning Tools (EWTs) have become fundamental in supporting early detection of clinical deterioration due to their simplicity and rapid applicability. Yet, conventional EWTs remain limited by static data inputs and the absence of continuous monitoring, particularly in resource-constrained or geographically diverse contexts.
The increasing availability of electronic health records (EHRs), along with real-time data from digital and environmental sources, offers new opportunities for more adaptive and inclusive early warning systems. By combining these data streams with artificial intelligence (AI) and big data analytics, it is possible to generate dynamic risk models that account for both clinical and contextual variables—such as urban density, infrastructure, or access to healthcare.
This broader, data-driven approach could enhance prognostic accuracy, tailor interventions to specific populations and settings, and support more equitable healthcare delivery. Ultimately, integrating AI with contextual awareness may redefine early risk assessment and decision-making in emergency medicine.
This Special Issue aims to collect innovative research that leverages AI to integrate these clinical and contextual data streams, ultimately improving the speed and accuracy of early warning systems in emergency care. We welcome submissions of original research and reviews on this topic.
You may choose our Joint Special Issue in JCM.
Prof. Dr. Ancor Sanz-García
Prof. Dr. José Luis Martín-Conty
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- emergency care
- artificial intelligence
- early warning systems
- data-driven healthcare
- predictive analytics
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