Applications of artificial intelligence in hospital quality management: a review of digital strategies in healthcare settings
DOI:
https://doi.org/10.51252/rcsi.v5i2.928Keywords:
clinical decision support, digital transformation, hospital efficiency, patient outcomes, process automation, risk predictionAbstract
This study analyzes the application of artificial intelligence (AI) in improving hospital quality management through a systematic review of 31 scientific articles indexed in Scopus. An exploratory methodology was used with selection criteria based on recency, thematic relevance, and methodological rigor. The research identifies the main applications of AI in the automation of clinical and administrative processes, clinical decision support, triage optimization, and detection of adverse events. The most widely used technologies include machine learning, deep neural networks, expert systems, and natural language processing. The results show measurable improvements in operational efficiency, diagnostic accuracy, patient safety, and strategic hospital planning. However, significant challenges remain regarding system interoperability, data quality, staff training, and ethical implications of automated decision-making. The study concludes that AI is a key tool for advancing toward more intelligent and quality-focused hospital models, although its adoption requires comprehensive strategies that address technical and regulatory barriers to ensure ethical, safe, and sustainable implementation.
References
Abukhadijah, H. J., & Nashwan, A. J. (2024). Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence. Global Journal on Quality and Safety in Healthcare, 7(3), 132–139. https://doi.org/10.36401/JQSH-24-4 DOI: https://doi.org/10.36401/JQSH-24-4
Afrash, M. R., Hosseini, A., Rabiei, R., Salari, S., Kianersi, S., & Sepehri, M. M. (2022). Design and Implementation of a Guideline-Based Workflow Software System for Improving the Chemotherapy Process. Shiraz E Medical Journal, 23(5). https://doi.org/10.5812/semj.119010 DOI: https://doi.org/10.5812/semj.119010
Agarwal, S., Glenton, C., Fønhus, M. S., Lewin, S., Tamrat, T., Mehl, G. L., Henschke, N., & Maayan, N. (2021). Decision-support tools via mobile devices to improve quality of care in primary healthcare settings. Cochrane Database of Systematic Reviews, 2021(7). https://doi.org/10.1002/14651858.CD012944.pub2 DOI: https://doi.org/10.1002/14651858.CD012944.pub2
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023). A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine, 13(6), 951. https://doi.org/10.3390/jpm13060951 DOI: https://doi.org/10.3390/jpm13060951
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 100333. https://doi.org/10.1016/j.jik.2023.100333 DOI: https://doi.org/10.1016/j.jik.2023.100333
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z DOI: https://doi.org/10.1186/s12909-023-04698-z
Amador-Fernández, N., Benrimoj, S. I., Gastelurrutia, M. Á., Graham, E. L., Martínez-Martínez, F., García-Cárdenas, V., Palomo-Llinares, R., Sánchez-Tormo, J., Baixauli Fernández, V. J., Pérez Hoyos, E., Fuertes González, R., & García Agudo, Ó. (2023). Identification of high-risk patients for referral through machine learning assisting the decision making to manage minor ailments in community pharmacies. Frontiers in Pharmacology, 14. https://doi.org/10.3389/fphar.2023.1105434 DOI: https://doi.org/10.3389/fphar.2023.1105434
Arora, A., Berven, S., Peterson, T. A., Lituiev, D., Butte, A. J., Jain, D., & Hadley, D. (2023). Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator. Spine, 48(1), E1–E13. https://doi.org/10.1097/BRS.0000000000004490 DOI: https://doi.org/10.1097/BRS.0000000000004490
Bai, E., Zhang, Z., Xu, Y., Luo, X., & Adelgais, K. (2025). Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems. BMC Medical Informatics and Decision Making, 25(1). https://doi.org/10.1186/s12911-024-02844-1 DOI: https://doi.org/10.1186/s12911-024-02844-1
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095 DOI: https://doi.org/10.7861/fhj.2021-0095
Baki Kocaballi, A., Ijaz, K., Laranjo, L., Quiroz, J. C., Rezazadegan, D., Tong, H. L., Berkovsky, S., Coiera, E., & Willcock, S. (2020). Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners. Journal of the American Medical Informatics Association, 27(11), 1695–1704. https://doi.org/10.1093/jamia/ocaa131 DOI: https://doi.org/10.1093/jamia/ocaa131
Berge, G. T., Munkvold, B. E., Tveit, T. O., Ruthjersen, A. L., Sharma, J., & Granmo, O. C. (2023). Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02101-x DOI: https://doi.org/10.1186/s12911-023-02101-x
Burns, J., Williams, D., Mlinaritsch, D., Koechlin, M., Canning, T., & Neitzel, A. (2022). Early detection and treatment of acute illness in medical patients with novel software: a prospective quality improvement initiative. BMJ Open Quality, 11(3). https://doi.org/10.1136/bmjoq-2022-001845 DOI: https://doi.org/10.1136/bmjoq-2022-001845
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895 DOI: https://doi.org/10.1016/j.mex.2022.101895
Chigbu, U. E., Atiku, S. O., & Du Plessis, C. C. (2023). The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising. Publications, 11(1), 2. https://doi.org/10.3390/publications11010002 DOI: https://doi.org/10.3390/publications11010002
Cho, I., Dykes, P. C., Kim, M., & Song, M. R. (2023). Evaluation of an approach to clinical decision support for preventing inpatient falls: a pragmatic trial. JAMIA Open, 6(2). https://doi.org/10.1093/jamiaopen/ooad019 DOI: https://doi.org/10.1093/jamiaopen/ooad019
Corny, J., Rajkumar, A., Bézie, Y., Martin, O., Dode, X., Lajonchère, J.-P., Billuart, O., & Buronfosse, A. (2020). A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. Journal of the American Medical Informatics Association, 27(11), 1688–1694. https://doi.org/10.1093/jamia/ocaa154 DOI: https://doi.org/10.1093/jamia/ocaa154
Dai, L., Pan, X., Kang, M., Zhou, M., Wu, Z., Zheng, D., Liu, H., Chen, G., & Tian, X. (2024). Design and implementation of an automatic nursing assessment system based on CDSS technology. International Journal of Medical Informatics, 183. https://doi.org/10.1016/j.ijmedinf.2023.105323 DOI: https://doi.org/10.1016/j.ijmedinf.2023.105323
Davazdahemami, B., Peng, P., & Delen, D. (2022). A deep learning approach for predicting early bounce-backs to the emergency departments. Healthcare Analytics, 2. https://doi.org/10.1016/j.health.2022.100018 DOI: https://doi.org/10.1016/j.health.2022.100018
de Vries, S., Thierens, D., ten Doesschate, T., Oosterheert, J. J., Totté, J. E. E., Heutz, J. W., Boel, E., & Loeffen, Y. G. T. (2022). A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections. Computers in Biology and Medicine, 146. https://doi.org/10.1016/j.compbiomed.2022.105621 DOI: https://doi.org/10.1016/j.compbiomed.2022.105621
García-Alonso, C. R., Salinas-Pérez, J. A., Almeda, N., Gutiérrez-Colosía, M. R., Iruin-Sanz, Á., & Salvador-Carulla, L. (2022). Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: Efficiency, stability and entropy assessment of the mental health ecosystem of Gipuzkoa (Basque Country, Spain). Plos One, 17(3 March). https://doi.org/10.1371/journal.pone.0265669 DOI: https://doi.org/10.1371/journal.pone.0265669
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 DOI: https://doi.org/10.4081/itjm.2024.1721
Hazra, S., & Bora, K. S. (2025). Capitalization of digital healthcare: The cornerstone of emerging medical practices. Intelligent Pharmacy. https://doi.org/10.1016/j.ipha.2024.12.002 DOI: https://doi.org/10.1016/j.ipha.2024.12.002
Hinson, J. S., Levin, S. R., Steinhart, B. D., Chmura, C., Sangal, R. B., Venkatesh, A. K., & Taylor, R. A. (2025). Enhancing Emergency Department Triage Equity With Artificial Intelligence: Outcomes From a Multisite Implementation. Annals of Emergency Medicine, 85(3), 288–290. https://doi.org/10.1016/j.annemergmed.2024.10.014 DOI: https://doi.org/10.1016/j.annemergmed.2024.10.014
Joshi, M., Mecklai, K., Rozenblum, R., & Samal, L. (2022). Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: A qualitative study. JAMIA Open, 5(2). https://doi.org/10.1093/jamiaopen/ooac022 DOI: https://doi.org/10.1093/jamiaopen/ooac022
Karalis, V. D. (2024). The Integration of Artificial Intelligence into Clinical Practice. Applied Biosciences, 3(1), 14–44. https://doi.org/10.3390/applbiosci3010002 DOI: https://doi.org/10.3390/applbiosci3010002
Kehayias, C. E., Yan, Y., Quirk, S., Bitterman, D. S., Bredfeldt, J. S., Aerts, H. J. W. L., Mak, R. H., Guthier, C. V., & Bontempi, D. (2023). Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1305511 DOI: https://doi.org/10.3389/fonc.2023.1305511
Kumar, R., Singh, A., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025). Adoption challenges to artificial intelligence literacy in public healthcare: an evidence based study in Saudi Arabia. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1558772 DOI: https://doi.org/10.3389/fpubh.2025.1558772
Lee, S.-Y., Ozaydin, B., Howard, S., Allgood, A., Hall, A. G., Banaszak-Holl, J. C., Hayes, L. W., Pritchett, A. O., Garretson, A. M., Bradley, H. M., Furr, A. L., & Wyatt, M. C. (2024). Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study. Jmir Research Protocols, 13. https://doi.org/10.2196/56049 DOI: https://doi.org/10.2196/56049
Li, X., Zheng, X., Shang, N., Yue, X., & Zhang, L. (2025). Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study. Frontiers in Pharmacology, 16. https://doi.org/10.3389/fphar.2025.1534552 DOI: https://doi.org/10.3389/fphar.2025.1534552
Maleki Varnosfaderani, S., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4), 337. https://doi.org/10.3390/bioengineering11040337 DOI: https://doi.org/10.3390/bioengineering11040337
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297 DOI: https://doi.org/10.1016/j.heliyon.2024.e26297
Mohammed, S., & Malhotra, N. (2025). Ethical and Regulatory Challenges in Machine Learning-Based Healthcare Systems: A Review of Implementation Barriers and Future Directions. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 100215. https://doi.org/10.1016/j.tbench.2025.100215 DOI: https://doi.org/10.1016/j.tbench.2025.100215
Murphree, D. H., Wilson, P. M., Asai, S. W., Demuth, G., Storlie, C. B., Quest, D. J., Lin, Y., Mukherjee, P., Chhugani, N., Mead, D., Herasevich, V., & Pickering, B. W. (2021). Improving the delivery of palliative care through predictive modeling and healthcare informatics. Journal of the American Medical Informatics Association JAMIA, 28(6), 1065–1073. https://doi.org/10.1093/jamia/ocaa211 DOI: https://doi.org/10.1093/jamia/ocaa211
Navarro-Cabrera, J. R., Valles-Coral, M. A., Farro-Roque, M. E., Reátegui-Lozano, N., & Arévalo-Fasanando, L. (2025). Machine vision model using nail images for non-invasive detection of iron deficiency anemia in university students. Frontiers in Big Data, 8. https://doi.org/10.3389/fdata.2025.1557600 DOI: https://doi.org/10.3389/fdata.2025.1557600
Navarro-Vega, J. C., Ulloa-Gallardo, N. J., Paz-Bustamante, D. R., Zegarra-Conde, D. G., & Nina-Choquehuayta, W. (2022). Análisis de datos y pronóstico de casos de la Covid-19 en el departamento de Madre de Dios de Perú utilizando técnicas LSTM. Revista Amazonía Digital, 1(2), e195. https://doi.org/10.55873/rad.v1i2.195 DOI: https://doi.org/10.55873/rad.v1i2.195
Palaniappan, K., Lin, E. Y. T., Vogel, S., & Lim, J. C. W. (2024). Gaps in the Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector and Key Recommendations. Healthcare, 12(17), 1730. https://doi.org/10.3390/healthcare12171730
Petch, J., Kempainnen, J., Saha, A., Sztur, P., Ranisau, J., Pettengell, C., Aviv, S., Butler, B., Levine, M., Pond, G., Bogach, J., & Allard-Coutu, A. (2023). Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center. JCO Clinical Cancer Informatics, 7. https://doi.org/10.1200/CCI.22.00182 DOI: https://doi.org/10.1200/CCI.22.00182
Rai, P., Knight, A., Hiillos, M., Kertész, C., Morales, E., Peltola, J., Terney, D., Larsen, S. A., Beniczky, S., & Østerkjerhuus, T. (2024). Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence. Frontiers in Neuroinformatics, 18. https://doi.org/10.3389/fninf.2024.1324981 DOI: https://doi.org/10.3389/fninf.2024.1324981
Roppelt, J. S., Kanbach, D. K., & Kraus, S. (2024). Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors. Technology in Society, 76, 102443. https://doi.org/10.1016/j.techsoc.2023.102443 DOI: https://doi.org/10.1016/j.techsoc.2023.102443
Shah, M., Dufendach, K. R., Shu, D., Prasath, V. B. S., Ni, Y., & Schapiro, A. H. (2021). Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants. Applied Clinical Informatics, 12(4), 856–863. https://doi.org/10.1055/s-0041-1735178 DOI: https://doi.org/10.1055/s-0041-1735178
Sheikh, M. S., Thongprayoon, C., Qureshi, F., Suppadungsuk, S., Kashani, K. B., Miao, J., Craici, I. M., & Cheungpasitporn, W. (2024). Personalized Medicine Transformed: ChatGPT’s Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units. Journal of Personalized Medicine, 14(3). https://doi.org/10.3390/jpm14030233 DOI: https://doi.org/10.3390/jpm14030233
Stoumpos, A. I., Kitsios, F., & Talias, M. A. (2023). Digital Transformation in Healthcare: Technology Acceptance and Its Applications. International Journal of Environmental Research and Public Health, 20(4), 3407. https://doi.org/10.3390/ijerph20043407 DOI: https://doi.org/10.3390/ijerph20043407
Valles-Coral, M. A., Navarro-Cabrera, J. R., Pinedo, L., Injante, R., Quintanilla-Morales, L. K., & Farro-Roque, M. E. (2024). Non-Invasive Detection of Iron Deficiency Anemia in Young Adults Through Finger-Tip Video Image Analysis. International Journal of Online and Biomedical Engineering (IJOE), 20(14), 53–70. https://doi.org/10.3991/ijoe.v20i14.50141 DOI: https://doi.org/10.3991/ijoe.v20i14.50141
Vodrahalli, K., Zou, J., Ko, J., Chiou, A. S., Novoa, R., Phung, M., Yekrang, K., Petrone, P., Daneshjou, R., & Abid, A. (2023). Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality. JAMA Dermatology, 159(5). https://doi.org/10.1001/jamadermatol.2023.0091 DOI: https://doi.org/10.1001/jamadermatol.2023.0091
Vueghs, C., Shakeri, H., Van der Cruyssen, F., & Renton, T. (2024). Development and Evaluation of a GPT4-Based Orofacial Pain Clinical Decision Support System. Diagnostics, 14(24). https://doi.org/10.3390/diagnostics14242835 DOI: https://doi.org/10.3390/diagnostics14242835
Woo, M., Bedoya, A., Alhanti, B., Lusk, S., Goldstein, B. A., Dunston, F., Blackwelder, S., & Lytle, K. S. (2020). Evaluation of ml-based clinical decision support tool to replace an existing tool in an academic health system: Lessons learned. Journal of Personalized Medicine, 10(3), 1–12. https://doi.org/10.3390/jpm10030104 DOI: https://doi.org/10.3390/jpm10030104
Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971 DOI: https://doi.org/10.1177/0739456X17723971
You, J. G., Landman, A., Mishuris, R. G., Hernandez-Boussard, T., & Pfeffer, M. A. (2025). Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01506-4 DOI: https://doi.org/10.1038/s41746-025-01506-4
Zarkowsky, D. S., Nejim, B., Hicks, C. W., Hubara, I., Goodney, P. P., & Malas, M. B. (2021). Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge. Vascular and Endovascular Surgery, 55(1), 18–25. https://doi.org/10.1177/1538574420954299 DOI: https://doi.org/10.1177/1538574420954299
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