20.500.12556/DKUM-96788 Optimizacija zdravstvenih obravnav s pomočjo umetne inteligence: pregled literature Optimization of healthcare services with artificial intelligence: literature review Dostopnost zdravstvenih obravnav je ključna za pravičnost in učinkovitost zdravstvenega sistema. Umetna inteligenca se vse pogosteje uporablja kot orodje za izboljšanje razporejanja terminov, upravljanja virov in napovedovanja kliničnih potreb. Namen zaključne naloge je bil oceniti, kako UI vpliva na optimizacijo zdravstvenih obravnav ter ali prispeva k večji enakosti dostopa. Izvedli smo pregled, analizo in sintezo znanstvene literature. Literaturo smo iskali v bazah PubMed, CINAHL Ultimate, ScienceDirect in Cochrane Library. Uporabili smo pristop PRISMA ter kritično oceno z orodji JBI. Vključene raziskave smo predstavili s pomočjo evalvacijske tabele in tematske sinteze. Ugotovitve kažejo, da UI izboljšuje natančnost napovedovanja hospitalizacij, čakalnih časov in kliničnega poslabšanja. Sistemi za avtomatizirano triažo, napoved obremenjenosti urgentnih oddelkov ter razporejanje postelj lahko skrajšajo čakalne dobe in povečajo pretočnost bolnikov. UI prispeva k objektivnejšemu odločanju in učinkovitejši rabi virov. Čeprav UI izboljšuje organizacijske in klinične procese ter lahko krepi enakost dostopa, obstajajo tveganja glede pristranskosti in pomanjkljivosti podatkov. Variabilnost metodologij, omejitve v kakovosti podatkov ter etična vprašanja zahtevajo previdno implementacijo in nadzor. Accessibility of healthcare is crucial for equity and efficiency of the healthcare system. Artificial intelligence is increasingly being used as a tool to improve scheduling, resource management and predict clinical needs. The purpose of this thesis was to assess how AI affects the optimization of healthcare and whether it contributes to greater equality of access. We conducted a review, analysis and synthesis of scientific literature. We searched the literature in the databases PubMed, CINAHL Ultimate, ScienceDirect and Cochrane Library. We used the PRISMA approach and a critical appraisal with JBI tools. We presented the included studies using an evaluation table and thematic synthesis. The findings show that AI improves the accuracy of predicting hospitalizations, waiting times and clinical deterioration. Automated triage, emergency department workload prediction and bed allocation systems can reduce waiting times and increase patient throughput. AI contributes to more objective decision-making and more efficient use of resources. Although AI improves organizational and clinical processes and can enhance equity of access, there are risks of bias and data deficiencies. Variability in methodologies, limitations in data quality, and ethical issues require careful implementation and oversight. zdravstvena nega digitalna transformacija napovedni modeli razporejanje bolnikov healthcare digital transformation predictive models patient scheduling true false false [K. Kocbek] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2026-01-29 02:19:09 2026-03-12 09:14:23 2026-03-13 03:13:37 0000-00-00 00:00:00 2026 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616-083:004.8(043.2) 269637635 MAG_Kocbek_Katarina_2026.pdf MAG_Kocbek_Katarina_2026.pdf 1 5FAED749B2314CC35A708EDB05F1094D f51138a8fd0eed46562b2607a101acff5db54674156af1169963087c8af551b3 dbe31545-0cdf-11f1-8b1a-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203472 Fakulteta za zdravstvene vede 0 0 0