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| Artificial Intelligence and Information Technology for Clinical Decision Support for Reducing Hospital-Acquired Conditions and Improving Patient Outcomes: A Systematic Literature Review
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Tyler Horn and Dr. Bruce Lazar, MBA, DM
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Abstract:
The United States healthcare payment model has shifted from a fee-for-service healthcare approach to a more quality-driven system. As more financial metrics for compensation are tied to quality, healthcare organizations are looking for ways to improve quality while still containing costs. Understanding and studying the potential of artificial intelligence might be critical to providing safety and quality patient care. Additionally, data-driven artificial intelligence may be cost-effective and could reduce some hospital-acquired pressure injuries. The systematic literature review aimed to determine whether using artificial intelligence and information technology tools for clinical decision support and guidance during hospital admissions helps reduce or minimize hospital-acquired conditions while improving patient outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a literature search transpired utilizing Public Medline and Cumulative Index to Nursing and Allied Health Literature. Data from the 23 germane articles were extensively analyzed using screening criteria focused on the research question. Four themes came to the forefront during the data examination process. These themes included (a) artificial intelligence, information technology, and machine learning tools, (b) clinical decision support systems, (c) hospital-acquired conditions and infections, and (d) improved patient outcomes. The findings indicated substantial potential for using artificial intelligence to help reduce hospital-acquired conditions and enhance patient outcomes. Since the technology is applied so infrequently, there is no definitive answer to using artificial intelligence to help reduce hospital-acquired conditions and infection; the implications of these findings provide health and informatics leaders, along with researchers, an opportunity to further develop and implement artificial intelligence within clinical decision support systems to help reduce hospital-acquired conditions and improve patient outcomes.
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