https://doi.org/10.4081/itjm.2024.1721
Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes
Published: 15 April 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.
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Citations
Bhati D, Deogade MS, Kanyal D. Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review. Cureus 2023;15:e47731.
DOI: https://doi.org/10.7759/cureus.47731
Hopkin P. Fundamentals of risk management: understanding, evaluating and implementing effective risk management. London: Kogan Page Publishers, 2018.
NEJM Catalyst. What Is Risk Management in Healthcare? 2018. Available from: https://catalyst.nejm.org/doi/abs/10.1056/CAT.18.0197
Sugeris. Advanced protocols in Phoenix 5.0 model. n.d. Available from: https://sigeris.net
Lee DH, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Pub Health 2021;18:271.
DOI: https://doi.org/10.3390/ijerph18010271
Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res 2021;23:e25759.
DOI: https://doi.org/10.2196/25759
Cacciabue PC. Human error risk management for engineering systems: a methodology for design, safety assessment, accident investigation and training. Reliab Eng Syst Saf 2004;83:229-40.
DOI: https://doi.org/10.1016/j.ress.2003.09.013
Božić V. Fuzzy approach to risk management: enhancing decision-making under uncertainty. 2023. DOI:10.13140/RG.2.2.13517.82405
Carrasco Ramírez JG. AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems. J Artif Intell Gen Sci 2024;1:31-7.
DOI: https://doi.org/10.60087/jaigs.v1i1.p37
Dixit RR. Risk Assessment for Hospital Readmissions: Insights from Machine Learning Algorithms. Sage Sci Rev Appl Mach Learn 2021;4:1-15.
Trinh VQ, Zhang S, Kovoor J, et al. The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review. Int J Qual Health Care 2023;35:mzad077.
DOI: https://doi.org/10.1093/intqhc/mzad077
Locke S, Bashall A, Al-Adely S, et al. Natural language processing in medicine: a review. Trends in Anaesthesia and Critical Care 2021;38:4-9.
DOI: https://doi.org/10.1016/j.tacc.2021.02.007
Lin YK, Fang X. First, Do No Harm: Predictive Analytics to Reduce In-Hospital Adverse Events. J Manag Inform Syst 2021;38:1122-49.
DOI: https://doi.org/10.1080/07421222.2021.1990619
Srivastava D, Pandey H, Agarwal AK. Complex predictive analysis for health care: a comprehensive review. Bull Electr Eng Inform 2023;12:521-31.
DOI: https://doi.org/10.11591/eei.v12i1.4373
Buket B, Ferdiye T, Dilber UO. Harnessing of artificial intelligence for the diagnosis and prevention of hospital-acquired infections: a systematic review. Diagnostics 2024;14:484.
DOI: https://doi.org/10.3390/diagnostics14050484
Auerbach AD, Lee TM, Hubbard CC, et al. Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care. JAMA Intern Med 2024;184:164-73.
DOI: https://doi.org/10.1001/jamainternmed.2023.7485
Bedoya JCL, Castro JLA. Explainability analysis in predictive models based on machine learning techniques on the risk of hospital readmissions. Health Technol 2024;14:93-108.
DOI: https://doi.org/10.1007/s12553-023-00794-8
Zeinalnezhad M, Shishehchi S. An integrated data mining algorithms and meta-heuristic technique to predict the readmission risk of diabetic patients. Healthc Anal 2024;5:100292.
DOI: https://doi.org/10.1016/j.health.2023.100292
Aminabee S. The Future of Healthcare and Patient-Centric Care: Digital Innovations, Trends, and Predictions. In Emerging Technologies for Health Literacy and Medical Practice. Pennsylvania: IGI Global, 2024.
DOI: https://doi.org/10.4018/979-8-3693-1214-8.ch012
Rana MS, Shuford J. AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems. J Artif Intell Gen Sci 2024;1.
DOI: https://doi.org/10.60087/jaigs.v1i1.30
Bhuiyan MS, Chowdhury IK, Haider M, et al. Advancements in early detection of lung cancer in public health: a comprehensive study utilizing machine learning algorithms and predictive models. J Comput Sci Technol Stud 2024;6:113-21.
DOI: https://doi.org/10.32996/jcsts.2024.6.1.12
Ashtagi R, Musale V, Rajput VS, et al. Revolutionizing Early Liver Disease Detection: Exploring Machine Learning and Ensemble Models. Int J Intell Syst Appl Eng 2024;12:528-41.
Abbas GH. AI-based predictive modeling: applications in cardiology. Int J Surg Glob Health 2024;7:e0419.
DOI: https://doi.org/10.1097/GH9.0000000000000419
Abu Nasser MS, Abu-Naser SS. Predictive Modeling of Obesity and Cardiovascular Disease Risk: A Random Forest Approach. Int J Acad Inform Syst Res 2024;7:26-38.
Singhania K, Reddy A. Improving preventative care and health outcomes for patients with chronic diseases using big data-driven insights and predictive modeling. Int J Appl Health Care Anal 2024;9:1-14.
Chunduru A, Kishore AR, Sasapu BK, et al. Multi chronic disease prediction system using CNN and random forest. Sn Comput Sci 2024;5:157.
DOI: https://doi.org/10.1007/s42979-023-02521-6
Sarker M. Reinventing Wellness: How Machine Learning Transforms Healthcare. J Artif Intell Gen Sci 2024;3:116-31.
DOI: https://doi.org/10.60087/jaigs.v3i1.73
Kroenke K, Corrigan JD, Ralston RK, et al. Effectiveness of care models for chronic disease management: A scoping review of systematic reviews. PM R 2023;16:174-89.
DOI: https://doi.org/10.1002/pmrj.13027
Lee E, Choi MK. Factors associated with medication adherence among older adults with multimorbidity: a culture perspective. Geriatr Nurs 2024;55:297-303.
DOI: https://doi.org/10.1016/j.gerinurse.2023.11.018
Wang Z, Mortani Barbosa EJ. Socioeconomic factors and clinical context can predict adherence to incidental pulmonary nodule follow-up via machine learning models. J Am Coll Radiol 2024. Available from: https://www.jacr.org/article/S1546-1440(24)00274-6/abstract
DOI: https://doi.org/10.1016/j.jacr.2024.02.031
Adenyi AO, Okolo CA, OlorunsogoT, Babawarun O. Leveraging big data and analytics for enhanced public health decision-making: a global review. GSC Adv Res Rev 2024;18:450-6.
DOI: https://doi.org/10.30574/gscarr.2024.18.2.0078
Rath KC, Khang A, Rath SK, et al. Artificial intelligence (AI)-enabled technology in medicine-advancing holistic healthcare monitoring and control systems. In: Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem Edition 1. Boca Raton: CRC Press, 2024.
DOI: https://doi.org/10.1201/9781003429609-6
Classen DC, Rhee C, Dantes RB, Benin AL. Healthcare-associated infections and conditions in the era of digital measurement. Infect Control Hosp Epidemiol 2024;45:3-8.
DOI: https://doi.org/10.1017/ice.2023.139
Zhao AP, Li S, Cao Z, et al. AI for Science: Predicting Infectious Diseases. J Saf Sci Resil 2024.
DOI: https://doi.org/10.1016/j.jnlssr.2024.02.002
Ennis-O’Connor M, O’Connor WT. Charting the future of patient care: A strategic leadership guide to harnessing the potential of artificial intelligence. Healthc Manag Forum 2024;0.
DOI: https://doi.org/10.1177/08404704241235893
Sarker M. Revolutionizing healthcare: the role of machine learning in the health sector. J Artif Intell Gen Sci 2024;2:35-48.
DOI: https://doi.org/10.60087/jaigs.v2i1.p47
Kim HW, Shin DH, Kim J, et al. Assessing the performance of ChatGPT’s responses to questions related to epilepsy: A cross-sectional study on natural language processing and medical information retrieval. Eur J Epilep 2024;114:1-8.
DOI: https://doi.org/10.1016/j.seizure.2023.11.013
Navarro DF. Natural Language Processing Models in Clinical Medicine: development, validation and implementation of automated text analysis in electronic medical records. Macquarie University. Thesis. 2023. Available from: https://figshare.mq.edu.au/articles/thesis/Natural_Language_Processing_Models_in_Clinical_Medicine_development_validation_and_implementation_of_automated_text_analysis_in_electronic_medical_records/24330943/1
Sarella PNK, Mangam VT. AI-driven natural language processing in healthcare: transforming patient-provider communication. Indian J Pharma Pract 2024;17:21-6.
DOI: https://doi.org/10.5530/ijopp.17.1.4
Tavabi N, Singh M, Pruneski J, Kiapour AM. Systematic evaluation of common natural language processing techniques to codify clinical notes. PLoS ONE 2024;19:e0298892.
DOI: https://doi.org/10.1371/journal.pone.0298892
Suman JV, Mahammad FS, Kumar MS, et al. Leveraging natural language processing in conversational AI agents to improve healthcare security. Wiley 2024;38.
DOI: https://doi.org/10.1002/9781394200801.ch38
Syrowatka A, Bates DW. Artificial intelligence in adverse drug events. Artif Intell Clin Pract Acad Press 2024:383-7.
DOI: https://doi.org/10.1016/B978-0-443-15688-5.00014-0
Apostol ES, Truica CO. Advancements in eHealth data analytics through natural language processing and deep learning. arXiv 2024. Available from: https://arxiv.org/abs/2401.10850
Nicolini D, Waring J, Mengis J. Policy and practice in the use of root cause analysis to investigate clinical adverse events: Mind the gap. Soc Sci Med 2011;73:217-25.
DOI: https://doi.org/10.1016/j.socscimed.2011.05.010
Feng TT, Zhang X, Tan LL, et al. Near miss research in the healthcare system: a scoping review. J Nurs Adm 2022;52:160-6.
DOI: https://doi.org/10.1097/NNA.0000000000001124
Macrae C. Analyzing near-miss events: risk management in incident reporting and investigation systems. London: Centre for Analysis of Risk and Regulation, 2007. Avaiable from: https://www.lse.ac.uk/accounting/assets/CARR/documents/D-P/Disspaper47.pdf
Omar M et al. Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: a systematic review. MedRxiv, BMJ Yale, 2024.
DOI: https://doi.org/10.1101/2024.01.14.24301289
Singh K, Bhanushali A, Senapati B. Utilizing advanced artificial intelligence for early detection of epidemic outbreaks through global data analysis. Int J Intell Syst Appl Eng 2023;12:568-75.
Delon F, Bédubourg G, Bouscarrat L, et al. Infectious risk events and their novelty in event-based surveillance: new definitions and annotated corpus. Springer Link 2024. Available from: https://doi.org/10.1007/s10579-024-09728-w
DOI: https://doi.org/10.1007/s10579-024-09728-w
Espinosa L, Salathé M. Use of large language models as a scalable approach to understanding public health discourse. medRxiv 2024. Available from: https://www.medrxiv.org/content/10.1101/2024.02.06.24302383v1
DOI: https://doi.org/10.1101/2024.02.06.24302383
Sharmila Nirojini P, Kanaga K, Devika S, Pradeep P. Exploring the impact of artificial intelligence on patient care: a comprehensive review of healthcare advancements. Sch Acad J Pharm 2024;13:67-81.
DOI: https://doi.org/10.36347/sajp.2024.v13i02.003
Mortelmans L, Goossens E, De Graef M, et al. Evaluation of methods measuring medication adherence in patients with polypharmacy: a longitudinal and patient perspective. Eur J Clin Pharmacol 2024. Available from: https://link.springer.com/article/10.1007/s00228-024-03661-1
DOI: https://doi.org/10.1007/s00228-024-03661-1
Lauffenburger JC, Yom-Tov E, Keller PA, et al. The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial. npj Digit Med 2024;7:39.
DOI: https://doi.org/10.1038/s41746-024-01028-5
Jin Q, et al. PubMed and beyond: biomedical literature search in the age of artificial intelligence. eBioMedicine Lancet 2024;100:104988.
DOI: https://doi.org/10.1016/j.ebiom.2024.104988
Koonce TY, Giuse DA, Williams AM, et al. Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition, JMIR Med Inform 2024;12:e53516.
DOI: https://doi.org/10.2196/53516
Nguyen RT, Jain V, Acquah I, et al. Association of cardiovascular risk profile with premature all-cause and cardiovascular mortality in US adults: findings from a national study. BMC Cardiovasc Disord 2024;24:91.
DOI: https://doi.org/10.1186/s12872-023-03672-3
Grzenda A, Widge AS. Electronic health records and stratified psychiatry: bridge to precision treatment? Neuropsychopharmacol 2024;49:285-90.
DOI: https://doi.org/10.1038/s41386-023-01724-y
Christopoulou S.C. Machine learning models and technologies for evidence-based telehealth and smart care: a review. BioMedInformatics 2024;4:754-79.
DOI: https://doi.org/10.3390/biomedinformatics4010042
Kasula BY. Optimizing healthcare delivery: machine learning applications and innovations for enhanced patient outcomes. Int J Creative Res Comput Technol Design 2024;6:1-7.
Anup P, Krishna KP. Intelligent decision support system in healthcare using machine learning models. In: Recent Patents on Engineering. Bentham Science Publishers 2024;18:35-48.
DOI: https://doi.org/10.2174/1872212118666230606145738
Kolasa K, Admassu B, ì Hołownia-Voloskova M, et al. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024;24:63-115.
DOI: https://doi.org/10.1080/14737167.2023.2279107
Lambay MA, Mohideen SP. Applying data science approach to predicting diseases and recommending drugs in healthcare using machine learning models – a cardio disease case study. Multimed Tools Appl 2024. Available from: https://link.springer.com/article/10.1007/s11042-023-18035-5
DOI: https://doi.org/10.1007/s11042-023-18035-5
Ávila-Jiménez JL, Cantón-Habas V, del Pilar Carrera-González M, et al. A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records. Comput Biol Med 2024;169:107814.
DOI: https://doi.org/10.1016/j.compbiomed.2023.107814
Liao Z, Wang J, Shi Z, et al. Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision Support Systems. Ann Biomed Eng 2024:52:125-9.
DOI: https://doi.org/10.1007/s10439-023-03288-w
Kramer AA, et al., Prospective evaluation of a machine learning-based clinical decision support system (ViSIG) in reducing adverse outcomes for adult critically ill patients. Inform Med Unlocked 2024;44.
DOI: https://doi.org/10.1016/j.imu.2023.101433
DeGroat W, Abdelhalim H, Patel K, et al. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci Rep 2024;14:1.
DOI: https://doi.org/10.1038/s41598-023-50600-8
Naik K, Goyal RK, Foschini L, et al. Current status and future directions: the application of artificial intelligence/machine learning for precision medicine. Clin Pharmacol Therap 2023;115:673-86.
DOI: https://doi.org/10.1002/cpt.3152
Miao R, Dong Q, Liu X, et al. A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population. Front Pub Health 2024;12.
DOI: https://doi.org/10.3389/fpubh.2024.1365479
Haghish EF, Nes RB, Obaidi M, et al. Unveiling adolescent suicidality: holistic analysis of protective and risk factors using multiple machine learning algorithms. J Youth Adolescence 2024;53:507-25.
DOI: https://doi.org/10.1007/s10964-023-01892-6
Wu Y, Xin B, Wan Q, et al., Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: a cross-sectional study. Heliyon 2024;10:e27941.
DOI: https://doi.org/10.1016/j.heliyon.2024.e27941
Chattopadhyay S. MLMI: a machine learning model for estimating risk of myocardial infarction. artificial intelligence evolution. 2024;5:11-23. Available from: https://ojs.wiserpub.com/index.php/AIE/article/view/3714 (cited on March 19th, 2024)
DOI: https://doi.org/10.37256/aie.5120243714
Labilloy G, Jasra B, Widrich J, et al. Machine learning determined risk factors associated with non-adherence to timely surgery for breast cancer patients. Ann Breast Surg 2023;8.
DOI: https://doi.org/10.21037/abs-22-31
Zisiopoulou M, Berkowitsch A, Redlich L, et al Personalised preinterventional risk stratification of mortality, length of stay and hospitalisation costs in transcatheter aortic valve implantation using a machine learning algorithm: a pilot trial. Open Heart 2024;11:e002540.
DOI: https://doi.org/10.1136/openhrt-2023-002540
Chen Y, Wang B, Zhao Y, et al. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer. Nat Commun 2024;15:1657.
DOI: https://doi.org/10.1038/s41467-024-46043-y
Jose R, Syed F, Thomas A, Toma M. Cardiovascular health management in diabetic patients with machine-learning-driven predictions and interventions. Appl Sci 2024;14:2132.
DOI: https://doi.org/10.3390/app14052132
Kumar D, Balraj K, Seth S, et al. An improved machine learning-based prediction framework for early detection of events in heart failure patients using mHealth. Health Technol 2024. Available from: https://link.springer.com/article/10.1007/s12553-024-00832-z
DOI: https://doi.org/10.1007/s12553-024-00832-z
Lee K, Deniz B. Revolutionizing Big Data: AI Algorithms and Predictive Analytics. EasyChair Preprint no. 11685, 2024.
Ferrara M, Bertozzi G, Di Fazio N, et al. Risk management and patient safety in the artificial intelligence era: a systematic review. Healthcare 2024;12:549.
DOI: https://doi.org/10.3390/healthcare12050549
Knudsen J, Ghaffar U, Ma R, et al. Clinical applications of artificial intelligence in robotic surgery. J Robotic Surg 2024;18:102.
DOI: https://doi.org/10.1007/s11701-024-01867-0
Chi Zhang M, Hallbeck S, Salehinejad H, Thiels C. The integration of artificial intelligence in robotic surgery: a narrative review. Surgery 2024. Available from: https://www.surgjournal.com/article/S0039-6060(24)00068-0/abstract
DOI: https://doi.org/10.1016/j.surg.2024.02.005
Li C, Ye G, Jiang Y, et al. Artificial Intelligence in battling infectious diseases: a transformative role. J Med Virol 2024;96:e29355.
DOI: https://doi.org/10.1002/jmv.29355
Jahangiri S, Abdollahi M, Patil R, et al. An inpatient fall risk assessment tool: application of machine learning models on intrinsic and extrinsic risk factors. Mach Learn Appl 2024;15.
DOI: https://doi.org/10.1016/j.mlwa.2023.100519
Gershov S, Raz A, Karpas E, Laufer S. Towards an autonomous clinical decision support system. Eng Appl Artif Intell 2024;127.
DOI: https://doi.org/10.1016/j.engappai.2023.107215
Khosravi M, Zare Z, Mojtabaeian SM, Izadi R. Artificial intelligence and decision-making in healthcare: a thematic analysis of a systematic review of reviews. Health Serv Res Manag Epidemiol 2024;11.
DOI: https://doi.org/10.1177/23333928241234863
Shomali M, Liu S, Kumbara A, et al. The development and potential applications of an automated method for detecting and classifying continuous glucose monitoring patterns. J Diabetes Sci Technol 2024;0.
DOI: https://doi.org/10.1177/19322968241232378
Sarkar U, Bates DW. Using artificial intelligence to improve primary care for patients and clinicians. JAMA Intern Med 2024;184:343-4.
DOI: https://doi.org/10.1001/jamainternmed.2023.7965
Ahmadi A. Digital health transformation: leveraging ai for monitoring and disease management. Int J BioLife Sci 2024;2:349-63
Youni HA, Elfadil Eisa TA, Nasser M, et al. A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges. Diagnostics 2024;14:109.
DOI: https://doi.org/10.3390/diagnostics14010109
Dujeepa SD, Lee SS, Jillian Yeo HT, et al. Artificial intelligence in health professional training: a companion or an adversary? Singapore: Asia Pacific Scholar2024;9:1-2.
DOI: https://doi.org/10.29060/TAPS.2024-9-1/EV9N1
Vaughn J, Ford SH, Scott M, et al. Enhancing Healthcare Education: Leveraging ChatGPT for Innovative Simulation Scenarios. Clin Simul Nurs 2024;87.
DOI: https://doi.org/10.1016/j.ecns.2023.101487
Arzilli G, De Vita E, Pasquale M, et al. Innovative techniques for infection control and surveillance in hospital settings and long-term care facilities: a scoping review. Antibiotics 2024;13:77.
DOI: https://doi.org/10.3390/antibiotics13010077
Cyrielle NNT, Andigema A, Danaëlle, Mafo Kamga LD, Ewane E. Harnessing the Power of Artificial Intelligence for Enhanced Point-of-Care Quality Control in Healthcare. Preprints 2024:2024030608.
Lagua E, Mun HS, Ampode KMB, et al. Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs. J Animal Behav Biometeorol 2024;12:2024008.
DOI: https://doi.org/10.31893/jabb.2024008
Thakur H, Rao R. Emphasis of infection prevention and control: a review. J Popul Therap Clin Pharmacol 2024;31:2238-49.
Gurbuz K, Das K, Demir M, et al. Impacts of intelligent monitoring technology installation and additional modalities on hand hygiene compliance in a burn center: A quasi-experimental longitudinal trial. Burns, 2024, in press.
DOI: https://doi.org/10.1016/j.burns.2024.02.020
Minotti C, Aghlmandi S, Bielicki JA. Electronic hand hygiene monitoring tools for implementation of optimal hand sanitising adherence in neonatal intensive care. J Hosp Infect 2024. Available from: https://www.journalofhospitalinfection.com/article/S0195-6701(24)00076-8/abstract
DOI: https://doi.org/10.1016/j.jhin.2024.02.018
Abavisani M, Khoshrou A, Foroushan SK, Sahebkar A. Chatting with artificial intelligence to combat antibiotic resistance: Opportunities and challenges. Curr Res Biotechnol 2024;7.
DOI: https://doi.org/10.1016/j.crbiot.2024.100197
Howard A, Aston S, Gerada A, et al. Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance. Lancet Digit Health, 2024;6:E79-E86.
DOI: https://doi.org/10.1016/S2589-7500(23)00221-2
Zhang H, Zhao J, Farzan R, Otaghvar HA. Risk predictions of surgical wound complications based on a machine learning algorithm: a systematic review. Int Wound J 2024;21:e14665.
DOI: https://doi.org/10.1111/iwj.14665
Han T, Xiong F, Sun B, et al. Development and validation of an artificial intelligence mobile application for predicting 30-day mortality in critically ill patients with orthopaedic trauma. Int J Med Inform 2024;184.
DOI: https://doi.org/10.1016/j.ijmedinf.2024.105383
Dalcól C, Tanner J, de Brito Poveda V. Digital tools for post-discharge surveillance of surgical site infection. JAN 2024;80:96-109.
DOI: https://doi.org/10.1111/jan.15830
Wiemken TL, Carrico RM. Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance. Am J Infect Control 2024. Available from: https://www.ajicjournal.org/article/S0196-6553(24)00077-4/abstract#articleInformation
DOI: https://doi.org/10.1016/j.ajic.2024.02.007
Thakur H, Rao R. Emphasis of infection prevention and control: a review. J Popul Therap Clin Pharmacol 2024;31:2238-49.
Lee SH, Yang IS. Empowering hospital-associated infection prevention and control: a quasi-experimental study on the effect of scenario-based simulation training. Nurs Educ Pract 2024;76.
DOI: https://doi.org/10.1016/j.nepr.2024.103936
Langford BJ, Branch-Elliman W, Nori P, et al. Confronting the disruption of the infectious diseases workforce by artificial intelligence: what this means for us and what we can do about it. Open Forum Infect Dis 2024;11.
DOI: https://doi.org/10.1093/ofid/ofae053
S. Geetha, Tvisha Trivedi. An AI-Driven Multi-Management System for Medical Resource Allocation Providing Personalized Healthcare Services. In: Handbook of Research on Artificial Intelligence and Soft Computing Techniques in Personalized Healthcare Services. 1st Edition, Palm Bay: Apple Academic Press, 2024.
DOI: https://doi.org/10.1201/9781003371250-8
Angula TN, Dongo A. Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: a systematic review. GSC Biol Pharma Sci 2024;26:140-50.
DOI: https://doi.org/10.30574/gscbps.2024.26.2.0071
Smoke S. Artificial intelligence in pharmacy: a guide for clinicians. Am J Health Syst Pharma 2024. Available from: https://academic.oup.com/ajhp/advance-article-abstract/doi/10.1093/ajhp/zxae051/7613333?redirectedFrom=fulltext
DOI: https://doi.org/10.1093/ajhp/zxae051
Umar TP, Tanasov A, Stevanny B, et al. A digital health perspective on medication use and polypharmacy management for improving healthcare outcomes in geriatric patients. Geriatr Dentist Age Digit Technol 2024:1-39.
DOI: https://doi.org/10.4018/979-8-3693-0260-6.ch001
Liu S, McCoy AB, Peterson JF, et al. Leveraging explainable artificial intelligence to optimize clinical decision support. J Am Med Inform Assoc 2024;31:986-74.
DOI: https://doi.org/10.1093/jamia/ocae019
Sonaji P, Subramanian L, Rajesh M. Artificial intelligence-driven drug interaction prediction. World J Biol Pharma Health Sci 2024;17:297-305.
DOI: https://doi.org/10.30574/wjbphs.2024.17.2.0070
Zhu J, Che C, Jiang H, et al. SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction. BMC Bioinform 2024;25:39.
DOI: https://doi.org/10.1186/s12859-024-05654-4
Asfand-e-yar M, Hashir Q, Shah AA, et al. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep 2024;14:4076.
DOI: https://doi.org/10.1038/s41598-024-54409-x
Vijaya R, Carolin Lincy BJ, Raman GG, et al. Applications of Artificial Intelligence in Drug Delivery Systems. In: Artificial intelligence in Pharmaceutical Sciences, Bhupathyraaj M, Vijaya Rani KR, Essa MM (eds.). 1st Edition. Boca Raton: CRC Press 2023:42-53.
DOI: https://doi.org/10.1201/9781003343981-4
Harmanjit Singh, Nim DK, Randhawa AS, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Rev Clin Pharmacol 2024:1-11.
DOI: https://doi.org/10.1080/17512433.2024.2317963
Areej B, Nasserat R, Ilan Y. Overcoming low adherence to chronic medications by improving their effectiveness using a personalized second-generation digital system. Curr Pharma Biotechnol 2024. Available from: https://www.eurekaselect.com/article/138090
Chen CY, Chen YL, Scholl J et al. Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Computer Methods Programs Biomed 2024;243.
DOI: https://doi.org/10.1016/j.cmpb.2023.107869
Ling Ong JC, Jin L, Elangovan K, et al. Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties. arXiv 2024. Available from: https://arxiv.org/abs/2402.01741v2
DOI: https://doi.org/10.21203/rs.3.rs-4023142/v1
Rakesh M. The Intelligent prescription: unveiling the ai-driven future of healthcare. 2024:1-12. Available from: https://www.researchgate.net/publication/377384089_The_Intelligent_Prescription_Unveiling_The_AI-Driven_Future_of_Healthcare
Ng JY, Cramer H, Lee MS, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: novel opportunities in healthcare. Integr Med Res 2024;13.
DOI: https://doi.org/10.1016/j.imr.2024.101024
Chu X, Wu S, Sun B, et al. Data-driven quantification and intelligent decision-making in traditional Chinese medicine: a review. Int J Mach Learn Cyber 2024. Available from: https://link.springer.com/article/10.1007/s13042-024-02103-9
DOI: https://doi.org/10.1007/s13042-024-02103-9
Gobinath T, et al. Improved supply chain management in e-pharmacy supply chain using machine learning intelligence. Int J Intell Syst Appl Eng 2023;12:104-13.
Ala A, Goli A, Mirjalili S, Simic V. A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design. Appl Soft Comput 2024;150.
DOI: https://doi.org/10.1016/j.asoc.2023.111012
Shobhana N. AI-powered supply chains towards greater efficiency. Complex AI Dynamics and Interactions in Management, Nunes Figueiredo (eds.). Pennsylvania: IGI Global 2024:229-49.
DOI: https://doi.org/10.4018/979-8-3693-0712-0.ch011
Abbaoui W, Retal S, El Bhiri B, et al. Towards revolutionizing precision healthcare: a systematic literature review of artificial intelligence methods in precision medicine. Inform Medic Unlocked 2024;46.
DOI: https://doi.org/10.1016/j.imu.2024.101475
Ali M, Wani SUD, Dey T, et al. Artificial intelligence in the treatment of cancer: changing patterns, constraints, and prospects. Health Technol 2024. Available from: https://link.springer.com/article/10.1007/s12553-024-00825-y
DOI: https://doi.org/10.1007/s12553-024-00825-y
Torrente A, Maccora S, Prinzi F, et al. The clinical relevance of artificial intelligence in migraine. Brain Sci 2024;14:85.
DOI: https://doi.org/10.3390/brainsci14010085
Litvinova O, Kan Yeung AW, Hammerle FP, et al. Digital technology applications in the management of adverse drug reactions: bibliometric analysis. Pharmaceuticals 2024;17:395.
DOI: https://doi.org/10.3390/ph17030395
Xamdambek Yusupovich Abobakirov. Innovations in enhancing mechanisms for the detection of adverse drug reactions. World Sci News Sci Int J 2024;2.
Ratwani RM, Bates DW, Classen DC. Patient Safety and Artificial Intelligence in Clinical Care. JAMA Health Forum 2024;5:e235514
DOI: https://doi.org/10.1001/jamahealthforum.2023.5514
Ramirez JP, Jenkins K. Chapter 31 - Artificial intelligence for quality improvement. In: Intelligence-Based Medicine: Subspecialty Series. Intelligence-Based Cardiology and Cardiac Surgery, Chang AC, Limon A (eds.). Academic Press 2024:321-5.
DOI: https://doi.org/10.1016/B978-0-323-90534-3.00028-7
William J, Leon M. Bridging realms: Artificial intelligence and the convergence of big data analytics with IoT. EasyChair Preprint no. 11918, 2024. Available from: https://easychair.org/publications/preprint/lVMq
Khan Rony MK, Parvis MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nurs Open 2024;11.
DOI: https://doi.org/10.1002/nop2.2070
Rudroff T. Revealing the complexity of fatigue: a review of the persistent challenges and promises of artificial intelligence. Brain Sci 2024;14:186.
DOI: https://doi.org/10.3390/brainsci14020186
Akpuokwe CU, Adeniyi AO, Bakare SS. Legal challenges of artificial intelligence and robotics: a comprehensive review. Comput Sci IT Res J 2024;5:544-61.
DOI: https://doi.org/10.51594/csitrj.v5i3.860
Duffourc MN, Gerke S. Health care AI and patient privacy—Dinerstein v Google. JAMA 2024;331:909-10.
DOI: https://doi.org/10.1001/jama.2024.1110
Chen Y, Esmaeilzadeh P. Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges. J Med Internet Res 2024;26:e53008.
DOI: https://doi.org/10.2196/53008
Henry J, Abeer B. Healthcare data security: protecting patient information in sustainable data stores. EasyChair Preprint no. 12212, 2024. Available from: https://easychair.org/publications/preprint/pzZX
Polyportis A, Pahos N. Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation. Humanit Soc Sci Commun 2024;11:107.
DOI: https://doi.org/10.1057/s41599-023-02464-6
Sontan AD, Samule SV. The intersection of artificial intelligence and cybersecurity: challenges and opportunities. World J Adv Res Rev 2024;21:1720-36.
DOI: https://doi.org/10.30574/wjarr.2024.21.2.0607
Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci 2024;14:675.
DOI: https://doi.org/10.3390/app14020675
Schulte J, Engebretson P, Spanier M. Artificial intelligence usage and data privacy discoveries within mHealth. In: EPiC Series in Computing, Proceedings of 39th International Conference on Computers and Their Applications 2024;98:61-8.
Bender SML, Nicholson Price II W. Chapter 46 - Legal aspects of artificial intelligence in medical practice. In: Artificial Intelligence in Clinical Practice, Krittanawong C. (eds.). Cambridge (MA): Academic Press, 2024:401-11.
DOI: https://doi.org/10.1016/B978-0-443-15688-5.00046-2
Ofosu-Ampong K. Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions. Telemat Inform Reports 2024;14.
DOI: https://doi.org/10.1016/j.teler.2024.100127
Nishant R, Schneckenberg D, Ravishankar M. The formal rationality of artificial intelligence-based algorithms and the problem of bias. J Inform Technol 2024;39:19-40.
DOI: https://doi.org/10.1177/02683962231176842
Williams NH. Artificial Intelligence and Algorithmic Bias. In: Artificial Intelligence and Healthcare. International Library of Bioethics, Springer Link 2023;107:1-18.
DOI: https://doi.org/10.1007/978-3-031-48262-5_1
Flores L, Kim S, Young SD. Addressing bias in artificial intelligence for public health surveillance. J Med Ethics 2024;50:190-4.
DOI: https://doi.org/10.1136/jme-2022-108875
Ferrara E. The butterfly effect in artificial intelligence systems: Implications for AI bias and fairness. Mach Learn Applic 2024;15:100525.
DOI: https://doi.org/10.1016/j.mlwa.2024.100525
Hassija V, Chamola V, Mahapatra A, et al. Interpreting black-box models: a review on explainable artificial intelligence. Cogn Comput 2024;16:45-74.
DOI: https://doi.org/10.1007/s12559-023-10179-8
Cao J, Zhou T, Lam S, et al. Fuzzy inference system with interpretable fuzzy rules: Advancing explainable artificial intelligence for disease diagnosis—A comprehensive review. Inform Sci 2024;662.
DOI: https://doi.org/10.1016/j.ins.2024.120212
Mouloudj K, Lan Oanh LV, Bouarar A, et al. Adopting artificial intelligence in healthcare: a narrative review. In: The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics. Boca Raton: IGI Global 2024:1-20.
DOI: https://doi.org/10.4018/978-1-6684-9324-3.ch001
Jindal JA, Lungren MP, Shah NH. Ensuring useful adoption of generative artificial intelligence in healthcare. J Am Med Inform Assoc 2024:ocae043.
DOI: https://doi.org/10.1093/jamia/ocae043
Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Acad Med 2024;99:22-7.
DOI: https://doi.org/10.1097/ACM.0000000000005439
Ali O, Murray PA, Momin M, et al. The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technol Forecast Soc Change 2024;199.
DOI: https://doi.org/10.1016/j.techfore.2023.123076
Ivanashko O, Kozak A, Knysh T, Honchar K. The role of artificial intelligence in shaping the future of education: opportunities and challenges. Futur Educ 2024;4:126-46.
DOI: https://doi.org/10.57125/FED.2024.03.25.08
Ibrahim T, Rashad H. The evolving role of healthcare professionals in the age of ai: impacts on employment, skill requirements, and professional development. J Artif Intell Mach Learn Manag 2024;8:14-21.
Weidener L, Fischer M. Artificial intelligence in medicine: cross-sectional study among medical students on application, education, and ethical aspects. JMIR Med Educ 2024;10:e51247.
DOI: https://doi.org/10.2196/51247
Moldt JA, Festl-Wietek T, Fuhl W, et al., Assessing Artificial Intelligence Awareness and Identifying Essential Competencies: Insights from Key Stakeholders in Integrating Ai into Medical Education. Soc Sci Res Network (SSRN) 2024. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4713042
DOI: https://doi.org/10.2139/ssrn.4713042
Daouk-Öyry L, Alameddine M, Hassan N, et al. The catalytic role of Mystery Patient tools in shaping patient experience: A method to facilitate value co-creation using action research. PLoS One 2018;13:e0205262.
DOI: https://doi.org/10.1371/journal.pone.0205262
Farrokhi M, Khouzani SJ, Farrokhi M, et al. Artificial intelligence and deep learning for screening and risk assessment of cancers. Kindle, 2024;4:1-140. Available from: https://preferpub.org/index.php/kindle/article/view/book31
Wang Y, Wang L, Ma W, et al. Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings. BMC Infect Dis 2024;24:214.
DOI: https://doi.org/10.1186/s12879-024-09058-w
Conners KM, Avery CL, Syed FF. Advancing cardiovascular risk assessment with artificial intelligence: opportunities and implications in North Carolina. N C Med J 2024;85.
DOI: https://doi.org/10.18043/001c.91424
Messinis S, Temenos N, Protonotarios N, et al. Enhancing internet of medical things security with artificial intelligence: a comprehensive review. Comput Biol Med, 2024;170.
DOI: https://doi.org/10.1016/j.compbiomed.2024.108036
Romagnoli A, Ferrara F, Langella R, et al. Healthcare systems and artificial intelligence: focus on challenges and the international regulatory framework. Pharm Res 2024. Available from: https://link.springer.com/article/10.1007/s11095-024-03685-3
DOI: https://doi.org/10.1007/s11095-024-03685-3
Kahn SD, Terry SF. Who owns (or controls) health data? Sci Data 2024;11:156.
DOI: https://doi.org/10.1038/s41597-024-02982-1
Pesapane F, Summers P. Ethics and regulations for AI in radiology. In: Artificial Intelligence for Medicine. Cambridge (MA): Academic Press 2024:179-92.
DOI: https://doi.org/10.1016/B978-0-443-13671-9.00001-6
Tannier X, et al. Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse. Methods Inf Med 2024. Available from: https://www.researchgate.net/publication/378766215_Development_and_Validation_of_a_Natural_Language_Processing_Algorithm_to_Pseudonymize_Documents_in_the_Context_of_a_Clinical_Data_Warehouse
DOI: https://doi.org/10.1055/s-0044-1778693
Sarkar AR, Chuang YS, Mohammes N, Jiang X. De-identification is not always enough. arXiv preprint 2024. Available from: https://arxiv.org/abs/2402.00179
Kovačević A, et al. De-identification of clinical free text using natural language processing: A systematic review of current approaches. Artif Intell Med 2024:102845.
DOI: https://doi.org/10.1016/j.artmed.2024.102845
Muneshwara MS, Pushpa SK. A novel consensus algorithm for blockchain iot prototype in health care system. Int J Comput Digit Syst 2024;15:1-17.
Andersen ES, Birk-Korch JB, Röttger R, et al. Monitoring performance of clinical artificial intelligence: a scoping review protocol. JBI Evid Synth 2024;10:11124.
DOI: https://doi.org/10.11124/JBIES-23-00390
Sallam M, Barakat M, Sallam M. METRICS: establishing a preliminary checklist to standardize the design and reporting of generative artificial intelligence-based studies in healthcare education and practice. Interact J Med Res 2024:54704.
DOI: https://doi.org/10.2196/54704
Iguoba VA, Imoize AL. AIoMT training, testing, and validation. In: Handbook of Security and Privacy of AI-Enabled Healthcare Systems and Internet of Medical Things. Boca Raton: CRC Press 2024:394-410.
Walton NA, Nagarajan R, Wang C, et al. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc 2024;31:536-41.
DOI: https://doi.org/10.1093/jamia/ocad211
Ghebrehiwet I, Zaki NM, Damseh R, Mohamad MS. Revolutionizing personalized medicine with generative AI: a systematic review. Res Square 2024. Available from: https://www.researchgate.net/publication/377674841_Revolutionizing_Personalized_Medicine_with_Generative_AI_A_Systematic_Review
DOI: https://doi.org/10.21203/rs.3.rs-3888891/v1
Karalis VD. The integration of artificial intelligence into clinical practice. Appl Biosci 2024;3:14-44.
DOI: https://doi.org/10.3390/applbiosci3010002
Hastings J. Preventing harm from non-conscious bias in medical generative AI. Lancet Digit Health 2024;6:e2-e3.
DOI: https://doi.org/10.1016/S2589-7500(23)00246-7
Evans H, Snead D. Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them? Histopathology 2024;84:279-87.
DOI: https://doi.org/10.1111/his.15071
Sallam M, Al-Farajat A, Egger J. Envisioning the future of ChatGPT in healthcare: insights and recommendations from a systematic identification of influential research and a call for papers. Jordan Med J 2024;58.
DOI: https://doi.org/10.35516/jmj.v58i1.2285
Khang A, Abdullayev A, Hrybiuk O, Shukla AK. (eds.). Computer vision and AI-integrated IoT technologies in the medical ecosystem. Boca Raton: CRC Press, 2024.
DOI: https://doi.org/10.1201/9781003429609
Lee J, Kim H, Kron F. Virtual education strategies in the context of sustainable health care and medical education: a topic modelling analysis of four decades of research. Med Educ 2024;58:47-62.
DOI: https://doi.org/10.1111/medu.15202
Chang CY, Yang CL, Jen HJ, et al. Facilitating nursing and health education by incorporating ChatGPT into learning designs. Educ Technol Soc 2024;27:215-30.
Khalifa M, Albadawy M, Iqbal U. Advancing clinical decision support: the role of artificial intelligence across six domains. Comput Methods Programs Biomed Update 2024:100142.
DOI: https://doi.org/10.1016/j.cmpbup.2024.100142
Kizilcec RF, Shung DL, Sung JJY. Human-machine interaction: AI-assisted medicine, instead of AI-driven medicine. In: Artificial Intelligence in Medicine. Cambridge (MA): Academic Press 2024:131-40.
DOI: https://doi.org/10.1016/B978-0-323-95068-8.00010-8
Jhade S, Gangavarapu SP, Channabasamma, Rozhdestvenskiy OI. Smart medicine: exploring the landscape of ai-enhanced clinical decision support systems. In: MATEC Web of Conferences. EDP Sci 2024:01083.
DOI: https://doi.org/10.1051/matecconf/202439201083
Gordon M, Daniel M, Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Med Teach 2024:1-25.
DOI: https://doi.org/10.1080/0142159X.2024.2314198
Al Naqbi H, Bahroun Z, Ahmed V. Enhancing work productivity through generative artificial intelligence: a comprehensive literature review. Sustainability 2024;16:1166.
DOI: https://doi.org/10.3390/su16031166
Rash I, Shi K, Sussel R, et al. Virtual 3D simulation technology for interprofessional team training. Simulation Gaming 2024;55:281-301.
DOI: https://doi.org/10.1177/10468781231222969
Hofmann P, Lämmermann L, Urbach N. Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders. Int J Inf Manag 2024;75:102728.
DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102728
Das SK, Dasgupta RK, Roy SD, Shil D. AI in Indian healthcare: From roadmap to reality. Intell Pharma 2024. Available from: https://www.sciencedirect.com/science/article/pii/S2949866X24000285#:~:text=AI%20can%20streamline%20various%20administrative,focus%20more%20on%20patient%20care.
Kueper JK, Emu M, Banbury M, et al. Artificial intelligence for family medicine research in Canada: current state and future directions: report of the CFPC AI working group. Can Fam Physician 2024;70:161-8.
DOI: https://doi.org/10.46747/cfp.7003161
Nguyen HS, Voznak M. A bibliometric analysis of technology in digital health: Exploring health metaverse and visualizing emerging healthcare management trends. IEEE Access 2024;12.
DOI: https://doi.org/10.1109/ACCESS.2024.3363165
Luu VP, Fiorini M, Combes S, et al. Challenges of artificial intelligence in precision oncology: public-private partnerships including national health agencies as an asset to make it happen. Ann Oncol 2024;35:154-8.
DOI: https://doi.org/10.1016/j.annonc.2023.09.3106
Aliferis C; Simon G. Artificial Intelligence (AI) and Machine Learning (ML) for healthcare and health sciences: the need for best practices enabling trust in AI and ML. In: Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Cham: Springer Int Pub 2024:1-31.
DOI: https://doi.org/10.1007/978-3-031-39355-6_1
Patel AU, Gu Q, Esper R, et al. The crucial role of interdisciplinary conferences in advancing explainable AI in healthcare. Preprints 2024. Available from: https://www.preprints.org/manuscript/202401.0231/v1
DOI: https://doi.org/10.20944/preprints202401.0231.v1
Bortolini VS, Colombo C. Artificial Intelligence in Medicine: the need to see beyond. Brazil J Law Technol Innov 2024;2:71-89.
DOI: https://doi.org/10.59224/bjlti.v2i1.71-89
How to Cite
Guerra, R. (2024). Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes. Italian Journal of Medicine, 18(2). https://doi.org/10.4081/itjm.2024.1721
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