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<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="emergency-medicine-and-critical-care" lang="en">
<front>
<journal-meta>
<journal-id journal-id-type="publisher">JOHS</journal-id>
<journal-id journal-id-type="nlm-ta">Journ of Health Scien</journal-id>
<journal-title-group>
<journal-title>Journal of HealthCare Sciences</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Journ of Health Scien</abbrev-journal-title>
</journal-title-group>
<issn pub-type="ppub">2231-2196</issn>
<issn pub-type="opub">0975-5241</issn>
<publisher>
<publisher-name>Radiance Research Academy</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">213</article-id>
<article-id pub-id-type="doi">http://dx.doi.org/10.52533/JOHS.2023.31109</article-id>
<article-id pub-id-type="doi-url"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Emergency Medicine and Critical Care</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>The Use of AI in Predicting Patient Outcomes and Deterioration in the Emergency Department
</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Akeel</surname>
<given-names>Amal</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Aljohani</surname>
<given-names>Ayed</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Alnasser</surname>
<given-names>Osama</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Alwabel</surname>
<given-names>Abdulmalik</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Alsaif</surname>
<given-names>Ibrahim</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Thabet</surname>
<given-names>Atyaf</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bakhsh</surname>
<given-names>Abdulmalik</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Albuhayri</surname>
<given-names>Yasir</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Alsalman</surname>
<given-names>Ahmed</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Aljohani</surname>
<given-names>Razan</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Suliman</surname>
<given-names>Talal Abu</given-names>
</name>
</contrib>
</contrib-group>
<pub-date pub-type="ppub">
<day>18</day>
<month>11</month>
<year>2023</year>
</pub-date>
<volume>3</volume>
<issue>11</issue>
<fpage>510</fpage>
<lpage>516</lpage>
<permissions>
<copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement>
<copyright-year>2009</copyright-year>
<license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/">
<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p>
</license>
</permissions>
<abstract>
<p>The emergency department is an ever-changing environment where patients arrive with a range of conditions and different levels of urgency. One of the struggles for ED healthcare professionals is to assess and manage the risk of clinical deterioration, as it can significantly impact patient outcomes, resource allocation, and decision-making processes. Unfortunately, existing approaches to identifying and responding to patients’ worsening conditions often rely on judgments, lack consistency, or experience delays. Hence, there is a need for objective, dependable, and timely tools to assist ED triage and patient care. Artificial intelligence (AI) is a modern field within computer science that aims to develop systems of performing tasks that typically require intelligence, like learning from data, reasoning logically, and making informed decisions. AI has gained usage in healthcare, encompassing aspects such as diagnosing illnesses, predicting outcomes, providing treatments, and monitoring patients. One area where AI has shown promise is in predicting outcomes and identifying signs of deterioration in the emergency department. This review provides an insight into the advancements in this field, discussing the advantages and challenges associated with utilizing AI to predict patient outcomes and detect deterioration in the ED. It discusses how AI can enhance ED triage and care__ampersandsign#39;s accuracy, efficiency, and interpretability by providing objective, timely predictions and explanations. It also tackles the concerns surrounding the quality and accessibility of data, potential biases in algorithms, ethical and legal considerations, human-computer interaction, and the integration of AI into clinical practices. The review concludes by highlighting AI__ampersandsign#39;s future directions and implications for patient safety, quality of care, and resource utilization in the ED.
</p>
</abstract>
<kwd-group>
<kwd>Artificial intelligence</kwd>
<kwd> Emergency department</kwd>
<kwd> Patient outcomes</kwd>
<kwd> Clinical deterioration</kwd>
<kwd> Prediction models</kwd>
</kwd-group>
</article-meta>
</front>
</article>