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	<title>Clinical Decision-Making &#8211; BIOENGINEER.ORG</title>
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	<title>Clinical Decision-Making &#8211; BIOENGINEER.ORG</title>
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		<title>Streamlined Lab Prognostics for Elderly Inpatients</title>
		<link>https://bioengineer.org/streamlined-lab-prognostics-for-elderly-inpatients/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 26 Jan 2026 15:56:44 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Clinical Decision-Making]]></category>
		<category><![CDATA[elderly inpatient care]]></category>
		<category><![CDATA[Elderly inpatient care** **Açıklama:** 1. **Geriatric prognostication:** Makalenin temel odağı yaşlı (geriatrik) hastalarda hastalık seyrini tahmin etmek (prognostikasyon). 2. **Laboratory]]></category>
		<category><![CDATA[geriatric prognostication]]></category>
		<category><![CDATA[Healthcare simplicity]]></category>
		<category><![CDATA[İçeriğe göre en uygun 5 etiket: **Geriatric prognostication]]></category>
		<category><![CDATA[laboratory-based assessment]]></category>
		<category><![CDATA[Laboratory-based prediction]]></category>
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					<description><![CDATA[In the realm of healthcare, the prognosis of older adults, particularly in inpatient settings, has prompted intense dialogue regarding the methodologies employed to achieve accurate assessments. A recent study spearheaded by researchers A. Kuriyama and N. Kuse sheds light on the efficacy of laboratory-based prognostication. It critically examines the interplay between simplicity and complexity in [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">321157</post-id>	</item>
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		<title>Doctors’ Views on AI Chatbots in Clinical Decisions</title>
		<link>https://bioengineer.org/doctors-views-on-ai-chatbots-in-clinical-decisions/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Thu, 22 Jan 2026 21:33:46 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Clinical Decision-Making]]></category>
		<category><![CDATA[clinical decision-making support]]></category>
		<category><![CDATA[Ethical AI in Healthcare]]></category>
		<category><![CDATA[Healthcare Technology` **Kısa açıklama:** * **AI Chatbots:** Makalenin temel konusunu doğrudan belirtiyor. * **Clinical Decision-Making:** AI'nın uygulandığı özel alanı vurguluyor. *]]></category>
		<category><![CDATA[İşte 5 uygun etiket: **AI in clinical settings]]></category>
		<category><![CDATA[İşte bu içerik için 5 uygun etiket: `AI Chatbots]]></category>
		<category><![CDATA[medical ethics]]></category>
		<category><![CDATA[Physician perspectives]]></category>
		<category><![CDATA[Physician perspectives on AI]]></category>
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					<description><![CDATA[The intersection of artificial intelligence and healthcare is a hotbed of innovation, exploration, and critical analysis. As the medical industry advances, a growing number of physicians are looking to AI-powered tools, particularly chatbots, to aid in clinical decision-making. A recent study titled “I Double Checked It with My Own Knowledge: Physician Perspectives on the Use [&#8230;]]]></description>
		
		
		
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		<title>How Organizational Support Influences Nurses’ Leadership in Tunisia</title>
		<link>https://bioengineer.org/how-organizational-support-influences-nurses-leadership-in-tunisia/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sun, 11 Jan 2026 18:55:48 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Clinical Decision-Making]]></category>
		<category><![CDATA[İçeriğe uygun 5 etiket: **Nursing leadership]]></category>
		<category><![CDATA[Organizational support]]></category>
		<category><![CDATA[Tunisia healthcare]]></category>
		<category><![CDATA[Workplace empowerment**]]></category>
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					<description><![CDATA[In an era when healthcare systems worldwide face unprecedented challenges, the role of nurses has become more crucial than ever. A recent groundbreaking study titled “Exploring how organizational support shapes nurses’ clinical leadership: evidence from Tunisia” provides profound insights into the dynamics of nursing leadership within healthcare organizations, specifically focusing on the Tunisian context. The [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">315916</post-id>	</item>
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		<title>ESMO Releases Groundbreaking Guidelines for the Safe Integration of Large Language Models in Oncology Practice</title>
		<link>https://bioengineer.org/esmo-releases-groundbreaking-guidelines-for-the-safe-integration-of-large-language-models-in-oncology-practice/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 20 Oct 2025 18:29:50 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI clinical practice integration]]></category>
		<category><![CDATA[AI in Oncology Practice]]></category>
		<category><![CDATA[Clinical Decision-Making]]></category>
		<category><![CDATA[ESMO 2025 Recommendations]]></category>
		<category><![CDATA[ESMO ELCAP guidelines]]></category>
		<category><![CDATA[Ethical AI governance in medicine]]></category>
		<category><![CDATA[Large language models in oncology]]></category>
		<category><![CDATA[Large Language Models Integration]]></category>
		<category><![CDATA[Patient Safety Guidelines]]></category>
		<category><![CDATA[Patient safety in AI healthcare]]></category>
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					<description><![CDATA[In a significant advancement for the medical field, particularly oncology, the European Society for Medical Oncology (ESMO) has introduced the ESMO Guidance on the Use of Large Language Models in Clinical Practice (ELCAP). This groundbreaking set of recommendations seeks to integrate artificial intelligence (AI) language models into oncology in a manner that prioritizes patient safety [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">283954</post-id>	</item>
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		<title>Factors Influencing Delirium Assessment by ICU Nurses</title>
		<link>https://bioengineer.org/factors-influencing-delirium-assessment-by-icu-nurses/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 01 Oct 2025 11:36:20 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Clinical Decision-Making]]></category>
		<category><![CDATA[Delirium Assessment]]></category>
		<category><![CDATA[Healthcare Education]]></category>
		<category><![CDATA[ICU Nursing Practice]]></category>
		<category><![CDATA[Interdisciplinary Collaboration]]></category>
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					<description><![CDATA[In recent years, the field of nursing, particularly within intensive care units, has garnered significant attention for its role in patient outcomes. A recent study published in the esteemed journal BMC Nursing sheds light on a critical yet often overlooked aspect: the evaluation of delirium by intensive care nurses. This cross-sectional descriptive study, led by [&#8230;]]]></description>
		
		
		
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