Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis
Background: The integration of artificial intelligence (AI) into healthcare systems has been postulated to carry significant economic implications. As hospitals globally pivot towards AIcentered solutions, understanding these economic outcomes becomes pivotal. Objective: This meta-analysis aimed to...
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2023
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1570 |
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| _version_ | 1857415064829558784 |
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| author | Hussein, Muayyad Mohammad |
| author_facet | Hussein, Muayyad Mohammad |
| author_role | author |
| dc.creator.none.fl_str_mv | Hussein, Muayyad Mohammad |
| dc.date.none.fl_str_mv | 2023 2024-03-28T09:59:17Z 2024-03-28T09:59:17Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1570 |
| dc.language.none.fl_str_mv | en |
| dc.title.none.fl_str_mv | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::thesis::master thesis |
| description | Background: The integration of artificial intelligence (AI) into healthcare systems has been postulated to carry significant economic implications. As hospitals globally pivot towards AIcentered solutions, understanding these economic outcomes becomes pivotal. Objective: This meta-analysis aimed to elucidate the economic impact of AI adoption in hospitals, gauging its influence on metrics such as return on investment (ROI) and cost-efficiency. Methods: Leveraging the PRISMA framework, relevant studies were sourced, screened, and selected. The Cochrane Collaboration's risk of bias tool assessed study quality. Pooled data results, heterogeneity, and sensitivity analyses were conducted, with the overarching findings presented through forest plots. Results: AI's integration in hospitals showcased a moderate positive economic impact (Hedges' g = 0.65, 95% CI [0.50, 0.80], p < 0.001). Subgroup analyses indicated nuances based on AI technologies, with neural networks in predictive analytics yielding the highest economic benefits. ROI-centric studies indicated substantial positive effects. Notable heterogeneity was observed, emphasizing the diverse nature of AI implementations and economic metrics. Conclusion: AI's introduction into hospital settings yields positive economic outcomes, solidifying its stance as a transformative force in healthcare economics. However, strategic, informed integration is imperative to maximize these benefits. |
| id | sorbonner_a58012914eb4c5d90ba43b71ea367b9e |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1570 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐AnalysisHussein, Muayyad MohammadBackground: The integration of artificial intelligence (AI) into healthcare systems has been postulated to carry significant economic implications. As hospitals globally pivot towards AIcentered solutions, understanding these economic outcomes becomes pivotal. Objective: This meta-analysis aimed to elucidate the economic impact of AI adoption in hospitals, gauging its influence on metrics such as return on investment (ROI) and cost-efficiency. Methods: Leveraging the PRISMA framework, relevant studies were sourced, screened, and selected. The Cochrane Collaboration's risk of bias tool assessed study quality. Pooled data results, heterogeneity, and sensitivity analyses were conducted, with the overarching findings presented through forest plots. Results: AI's integration in hospitals showcased a moderate positive economic impact (Hedges' g = 0.65, 95% CI [0.50, 0.80], p < 0.001). Subgroup analyses indicated nuances based on AI technologies, with neural networks in predictive analytics yielding the highest economic benefits. ROI-centric studies indicated substantial positive effects. Notable heterogeneity was observed, emphasizing the diverse nature of AI implementations and economic metrics. Conclusion: AI's introduction into hospital settings yields positive economic outcomes, solidifying its stance as a transformative force in healthcare economics. However, strategic, informed integration is imperative to maximize these benefits.2024-03-28T09:59:17Z2024-03-28T09:59:17Z2023Controlled Vocabulary for Resource Type Genres::text::thesis::master thesisapplication/pdfhttps://depot.sorbonne.ae/handle/20.500.12458/1570enoai:depot.sorbonne.ae:20.500.12458/15702024-03-28T18:00:36Z |
| spellingShingle | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis Hussein, Muayyad Mohammad |
| title | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| title_full | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| title_fullStr | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| title_full_unstemmed | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| title_short | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| title_sort | Assessing the Impact of Artificial Intelligence implementation in Hospitals on Healthcare Economics: A meta‐Analysis |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1570 |