Araştırma Makalesi
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Sağlık Hizmetlerinde Hasta Güvenliği ve Yapay Zekâ: Bibliyometrik Bir Analiz

Yıl 2025, Cilt: 24 Sayı: 2, 764 - 782
https://doi.org/10.17755/esosder.1563082

Öz

Bu çalışmanın amacı, sağlık hizmetlerinde hasta güvenliği ile ilgili yapay zeka içerikli yayınların bibliyometrik analizini gerçekleştirmektir. Web of Science’da, 24/09/2024 tarihinde 1991-2024 yılları arasındaki hasta güvenliğinde yapay zeka konularını kapsayan İngilizce olarak yayınlanmış sağlık hizmetlerine yönelik araştırma ve derleme makaleler taranmıştır. Analizler açık kaynak kodlu R tabanlı bibliometrix aracı ve Biblioshiny web kullanıcı ara yüzü kullanılarak gerçekleştirilmiştir. Taramalar sonucu, hasta güvenliği ve yapay zeka konularına ilişkin 1273 yayına ulaşılmıştır. Araştırmaya dahil edilen makalelere 61 ülkeden 2267 kurumun dahil olduğu, bu yayınların 692 dergide yayınlandığı ve 7908 yazar tarafından yazıldığı saptanmıştır. Yayınların 2019 yılından sonra hızlı bir ivme kazandığı ve tüm yayınların %85,46'sının son beş yıldaki çalışmalardan oluştuğu belirlenmiştir. Bu konuda en fazla yayına sahip olan ülke Amerika Birleşik Devletleri ve en çok yayın yapılan dergi Cureus Journal of Medical Science’dır. Konuya ilişkin en fazla yayını olan yazar Bates D. W.’dir. Anahtar kelimeler ile eş-oluşum ağ analizi yapılmış ve 6 küme ortaya çıkmıştır. Hasta güvenliği ve yapay zeka konusunda yönergeler, etkililik, bilgisayar-destekli tespit konuları trend konular olarak ortaya çıkmıştır. Araştırma kapsamında gelecekteki hasta güvenliğinde yapay zeka araştırmalarını ilerletmek için özellikle uluslararası iş birliğine odaklanılması ve hasta güvenliğinde yapay zekanın kullanımına ilişkin sınıflandırmaların yapılması önerilmektedir.

Kaynakça

  • Aasvang, E. K., ve Meyhoff, C. S. (2023). The future of postoperative vital sign monitoring in general wards: Improving patient safety through continuous artificial intelligence-enabled alert formation and reduction. Current Opinion in Anaesthesiology, 36(6), 683–690. https://doi.org/10.1097/ACO.0000000000001319
  • Abdullah, K. H. (2022). Bibliometric analysis of safety behaviour research. Asian Journal of Behavioural Sciences, 4(2), 19–33. https://doi.org/10.55057/ajbs.2022.4.2.2
  • Alonso, A., ve Siracuse, J. J. (2023). Protecting patient safety and privacy in the era of artificial intelligence. Seminars in Vascular Surgery, 36(3), 426–429. https://doi.org/10.1053/j.semvascsurg.2023.06.002
  • Aria, M., ve Cuccurullo, C. (2017). Bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Bates, D. W., Levine, D., Syrowatka, A., Kuznetsova, M., Craig, K. J. T., Rui, A., Jackson, G. P., ve Rhee, K. (2021). The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digital Medicine, 4(1), 54. https://doi.org/10.1038/s41746-021-00423-6
  • Begoli, E., Bhattacharya, T., ve Kusnezov, D. (2019). The need for uncertainty quantification in machine-assisted medical decision making. Nature Machine Intelligence, 1(1), 20–23. https://doi.org/10.1038/s42256-018-0004-1
  • Çayırtepe, Z., ve Şenel, A. C. (2022). Risk management ın ıntensive care units with artificial ıntelligence technologies: Systematic review of prediction models using electronic health records. Journal of Basic and Clinical Health Sciences, 6(3), 958–976. https://doi.org/10.30621/jbachs.993798
  • Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., ve Sun, J. (2015). Doctor AI: Predicting clinical events via recurrent neural networks. JMLR workshop and conference proceedings, 56, 301–318. http://www.ncbi.nlm.nih.gov/pubmed/28286600%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5341604
  • Choudhury, A., ve Asan, O. (2020). Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Medical Informatics, 8(7), 1–30. https://doi.org/10.2196/18599
  • Clancy, C. M. (2010). Common formats allow uniform collection and reporting of patient safety data by patient safety organizations. American Journal of Medical Quality, 25(1), 73–75. https://doi.org/10.1177/1062860609352438
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., ve Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Doyon, O., ve Raymond, L. (2024). Surveillance and patient safety in nursing research: A bibliometric analysis from 1993 to 2023. Journal of Advanced Nursing, 80(2), 777–788. https://doi.org/10.1111/jan.15793
  • DSÖ. (2019). Patient safety. https://www.who.int/news-room/facts-in-pictures/detail/patient-safety, Erişim Tarihi: 20.09.2024.
  • DSÖ. (2021). Global strategy on digital health 2020-2025. Içinde World Health Organization. https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf
  • Durfee, W., ve Iaizzo, P. (2018). Medical device innovation handbook (Edition 9). Institute for Engineering in Medicine and Earl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, USA. z.umn.edu/mdih
  • Ehrmann, S., Ibarra-Estrada, M., Perez, Y., Pavlov, I., McNicholas, B., Roca, O., Mirza, S., Vines, D., Garcia-Salcido, R., Aguirre-Avalos, G., Trump, M. W., Nay, M., ve Dellamonica, J. (2020). Awake prone positioning for COVID-19 acute hypoxaemic respiratory failure: a randomised, controlled, multinational, open-label meta-trial. Lacent Respiratory Medicine, 9(January), 1387–1395. https://doi.org/10.1016/ S2213-2600(21)00356-8
  • Ferrara, M., Bertozzi, G., Fazio, N. Di, Aquila, I., Fazio, A. Di, Maiese, A., Volonnino, G., Frati, P., ve Russa, R. La. (2024). Risk management and patient safety in the artificial intelligence era : A Systematic Review. Healthcare, 12, 1–15. https:// doi.org/10.3390/healthcare12050549
  • Fosso Wamba, S., ve Queiroz, M. M. (2023). Responsible artificial intelligence as a secret ingredient for digital health: Bibliometric analysis, insights, and research directions. Information Systems Frontiers, 25(6), 2123–2138. https://doi.org/10.1007/s10796-021-10142-8
  • Guo, Y., Hao, Z., Zhao, S., Gong, J., ve Yang, F. (2020). Artificial intelligence in health care: Bibliometric analysis. Journal of Medical Internet Research, 22(7), e18228. https://doi.org/10.2196/18228
  • Habli, I., Lawton, T., ve Porter, Z. (2020). Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/BLT.19.237487
  • He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., Jolla, L., Jolla, L., Jolla, L., Tongren, B., Hospital, B. T., Ophthalmology, B., ve Science, V. (2020). The practical implementation of artificial intelligence technologies in medicine. Nat Med, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0.
  • Howell, M. D. (2024). Generative artificial intelligence, patient safety and healthcare quality: A review. BMJ Quality ve Safety, 1, bmjqs-2023-016690. https://doi.org/10.1136/bmjqs-2023-016690
  • Huang, H. C., Wang, C. H., Chen, P. C., ve Lee, Y. Der. (2019). Bibliometric analysis of medication errors and adverse drug events studies. Journal of Patient Safety, 15(2), 128–134. https://doi.org/10.1097/PTS.0000000000000205
  • Jabali, A. K., Waris, A., Khan, D. I., Ahmed, S., ve Hourani, R. J. (2022). Electronic health records: Three decades of bibliometric research productivity analysis and some insights. Informatics in Medicine Unlocked, 29(February), 100872. https://doi.org/10.1016/j.imu.2022.100872
  • Jimma, B. L. (2023). Artificial intelligence in healthcare: A bibliometric analysis. Telematics and Informatics Reports, 9(June 2022). https://doi.org/10.1016/j.teler.2023.100041
  • Johnson, E. A., Dudding, K. M., ve Carrington, J. M. (2024). When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nursing Inquiry, 31(1), 1–8. https://doi.org/10.1111/nin.12583
  • Kaplan, B. (2001). Evaluating informatics applications - Clinical decision support systems literature review. International Journal of Medical Informatics, 64(1), 15–37. https://doi.org/10.1016/S1386-5056(01)00183-6
  • Kreps, G. L., ve Neuhauser, L. (2013). Artificial intelligence and immediacy: Designing health communication to personally engage consumers and providers. Patient Education and Counseling, 92(2), 205–210. https://doi.org/10.1016/j.pec.2013.04.014
  • Kurutkan, M. N., Orhan, F., ve Kaygısız, P. (2017). Bibliometric analysis of patient safety literature example of thesis and articles in Turkish. Health Care Academician Journal, 4(4), 253. https://doi.org/10.5455/sad.13-1513948006
  • Kurutkan, M. N., Usta, E., Orhan, F., ve Simsekler, M. C. E. (2015). Application of the IHI Global Trigger Tool in measuring the adverse event rate in a Turkish healthcare setting. International Journal of Risk ve Safety in Medicine, 27(1), 11–21. https://doi.org/10.3233/JRS-150639
  • Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., ve Coiera, E. (2018). Conversational agents in healthcare : A systematic review. Journal ofthe American Medical Informatics Association, 25(July), 1248–1258. https://doi.org/10.1093/jamia/ocy072
  • Lim, W. M., ve Kumar, S. (2024). Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Global Business and Organizational Excellence, 43(2), 17–26. https://doi.org/10.1002/joe.22229
  • Matheny, M. E., Whicher, D., ve Thadaney Israni, S. (2020). Artificial intelligence in health care: A report from the national academy of medicine. JAMA, 323(6), 509–510. https://doi.org/10.1001/jama.2019.21579
  • Miller, D. D., Facp, C. M., ve Brown, E. W. (2018). Artificial intelligence in medical practice: The question to the answer ? The American Journal of Medicine, 131(2), 129–133. https://doi.org/10.1016/j.amjmed.2017.10.035
  • Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., ve Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de la Información, 29(1), 1–20. https://doi.org/10.3145/epi.2020.ene.03
  • Palojoki, S., Mäkelä, M., Lehtonen, L., ve Saranto, K. (2017). An analysis of electronic health record–related patient safety incidents. Health Informatics Journal, 23(2), 134–145. https://doi.org/10.1177/1460458216631072
  • Ratwani, R. M., Bates, D. W., ve Classen, D. C. (2024). Patient safety and artificial intelligence in clinical care. JAMA Health Forum, 5(2), e235514. https://doi.org/10.1001/jamahealthforum.2023.5514
  • Ryu, J. Y., Kim, H. U., ve Lee, S. Y. (2018). Deep learning improves prediction of drug – drug and drug–food interactions. PNAS, 115(18), E4304–E4311. https://doi.org/10.1073/pnas.1803294115
  • Salas, M., Petracek, J., Yalamanchili, P., Aimer, O., ve Kasthuril, D. (2022). The use of artificial intelligence in pharmacovigilance : A systematic review of the literature. Pharmaceutical Medicine, 36(5), 295–306. https://doi.org/10.1007/s40290-022-00441-z
  • Shaikh, A. K., Alhashmi, S. M., Khalique, N., Khedr, A. M., Raahemifar, K., ve Bukhari, S. (2023). Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digital Health, 9. https://doi.org/10.1177/20552076221149296
  • Sonmez, S. C., Sevgi, M., Antaki, F., Huemer, J., ve Keane, P. A. (2024). Generative artificial intelligence in ophthalmology: Current innovations, future applications and challenges. British Journal of Ophthalmology, 1–6. https://doi.org/10.1136/bjo-2024-325458
  • Tang, R., Zhang, S., Ding, C., Zhu, M., ve Gao, Y. (2022). Artificial intelligence in intensive care medicine: Bibliometric analysis. Journal of Medical Internet Research, 24(11), 1–16. https://doi.org/10.2196/42185
  • Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Rees, G., Laing, C., Baker, C. R., Peterson, K., Hassabis, D., King, D., Suleyman, M., … Mohamed, S. (2020). A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 572(7767), 116–119. https://doi.org/10.1038/s41586-019-1390-1.
  • Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(January), 44–56. https://doi.org/10.1038/s41591-018-0300-7
  • Tubaishat, A. (2019). The effect of electronic health records on patient safety: A qualitative exploratory study. Informatics for Health and Social Care, 44(1), 79–91. https://doi.org/10.1080/17538157.2017.1398753
  • TÜYZE. (2024). Erişim Adresi: https://tuyze.tuseb.gov.tr/kurumsal/hakkimizda, Erişim tarihi: 24.09.2024
  • Uygun İlikhan, S., Özer, M., Tanberkan, H., ve Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Sciences, 54(3), 483–492. https://doi.org/10.55730/1300-0144.5814
  • Wahl, B., Cossy-gantner, A., Germann, S., ve Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health : How can AI contribute to health in resource-poor settings ?, BMJ Glob Health, 1–7. https://doi.org/10.1136/bmjgh-2018-000798
  • Walsh, M. N., ve Rumsfeld, J. S. (2017). Leading the digital transformation of healthcare. Journal of the American College of Cardiology, 70(21), 2719–2722. https://doi.org/10.1016/j.jacc.2017.10.020
  • Wang, J., Liang, Y., Cao, S., Cai, P., ve Fan, Y. (2023). Application of artificial intelligence in geriatric care: Bibliometric analysis. Journal of Medical Internet Research, 25, 1–16. https://doi.org/10.2196/46014
  • Whipple, E. C., E. Dixon, B., ve J. McGowan, J. (2013). Linking health information technology to patient safety and quality outcomes: a bibliometric analysis and review. Informatics for Health and Social Care, 38(1), 1–14. https://doi.org/10.3109/17538157.2012.678451
  • Yilmaz Muluk, S. (2024). Enhancing musculoskeletal injection safety: Evaluating checklists generated by artificial intelligence and revising the performed checklist. Cureus, 16(5). https://doi.org/10.7759/cureus.59708
  • Zhao, D., Zhang, W., Liu, Y., ve Yan, Z. (2024). Post-marketing safety concerns with lumateperone: a pharmacovigilance analysis based on the FDA adverse event reporting system (FAERS) database. Frontiers in Pharmacology, 15(May), 1–11. https://doi.org/10.3389/fphar.2024.1389814
  • Zhou, L., Sun, Y., Wang, J., Huang, H., Luo, J., Zhao, Q., ve Xiao, M. (2024). Trends in patient safety education research for healthcare professional students over the past two decades: a bibliometric and content analysis. Medical Education Online, 29(1). https://doi.org/10.1080/10872981.2024.2358610

Patient Safety and Artificial Intelligence in Healthcare: A Bibliometric Analysis

Yıl 2025, Cilt: 24 Sayı: 2, 764 - 782
https://doi.org/10.17755/esosder.1563082

Öz

The objective of this study is to conduct a bibliometric analysis of publications on the use of artificial intelligence in healthcare and its impact on patient safety. A search of the Web of Science was conducted on 24/09/2024 for research and comprehensive articles on healthcare published in English covering the topic of artificial intelligence in patient safety from 1991 to 2024. The analyses were performed using the open-source R-based bibliometrix tool and Biblioshiny web user interface. A total of 1273 publications on patient safety and artificial intelligence were identified between 1991 and 2024. The articles included in the study were published in 692 journals from 2267 institutions in 61 countries and were authored by 7908 individuals. The number of publications increased rapidly after 2019, representing 85.46% of all publications. The United States of America is the country with the highest number of publications on this subject, while the Cureus Journal of Medical Science is the journal with the highest number of publications. The author with the highest number of publications on the subject is Bates D. W. A co-occurrence network analysis with keywords revealed six clusters. Guidelines on patient safety and artificial intelligence, effectiveness, and computer-aided detection emerged as trending topics. In the context of this research, it is recommended that international collaboration be prioritised in order to facilitate future research on the application of artificial intelligence in patient safety and to establish a framework for the utilisation of artificial intelligence in this field.

Kaynakça

  • Aasvang, E. K., ve Meyhoff, C. S. (2023). The future of postoperative vital sign monitoring in general wards: Improving patient safety through continuous artificial intelligence-enabled alert formation and reduction. Current Opinion in Anaesthesiology, 36(6), 683–690. https://doi.org/10.1097/ACO.0000000000001319
  • Abdullah, K. H. (2022). Bibliometric analysis of safety behaviour research. Asian Journal of Behavioural Sciences, 4(2), 19–33. https://doi.org/10.55057/ajbs.2022.4.2.2
  • Alonso, A., ve Siracuse, J. J. (2023). Protecting patient safety and privacy in the era of artificial intelligence. Seminars in Vascular Surgery, 36(3), 426–429. https://doi.org/10.1053/j.semvascsurg.2023.06.002
  • Aria, M., ve Cuccurullo, C. (2017). Bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Bates, D. W., Levine, D., Syrowatka, A., Kuznetsova, M., Craig, K. J. T., Rui, A., Jackson, G. P., ve Rhee, K. (2021). The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digital Medicine, 4(1), 54. https://doi.org/10.1038/s41746-021-00423-6
  • Begoli, E., Bhattacharya, T., ve Kusnezov, D. (2019). The need for uncertainty quantification in machine-assisted medical decision making. Nature Machine Intelligence, 1(1), 20–23. https://doi.org/10.1038/s42256-018-0004-1
  • Çayırtepe, Z., ve Şenel, A. C. (2022). Risk management ın ıntensive care units with artificial ıntelligence technologies: Systematic review of prediction models using electronic health records. Journal of Basic and Clinical Health Sciences, 6(3), 958–976. https://doi.org/10.30621/jbachs.993798
  • Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., ve Sun, J. (2015). Doctor AI: Predicting clinical events via recurrent neural networks. JMLR workshop and conference proceedings, 56, 301–318. http://www.ncbi.nlm.nih.gov/pubmed/28286600%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5341604
  • Choudhury, A., ve Asan, O. (2020). Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Medical Informatics, 8(7), 1–30. https://doi.org/10.2196/18599
  • Clancy, C. M. (2010). Common formats allow uniform collection and reporting of patient safety data by patient safety organizations. American Journal of Medical Quality, 25(1), 73–75. https://doi.org/10.1177/1062860609352438
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., ve Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Doyon, O., ve Raymond, L. (2024). Surveillance and patient safety in nursing research: A bibliometric analysis from 1993 to 2023. Journal of Advanced Nursing, 80(2), 777–788. https://doi.org/10.1111/jan.15793
  • DSÖ. (2019). Patient safety. https://www.who.int/news-room/facts-in-pictures/detail/patient-safety, Erişim Tarihi: 20.09.2024.
  • DSÖ. (2021). Global strategy on digital health 2020-2025. Içinde World Health Organization. https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf
  • Durfee, W., ve Iaizzo, P. (2018). Medical device innovation handbook (Edition 9). Institute for Engineering in Medicine and Earl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, USA. z.umn.edu/mdih
  • Ehrmann, S., Ibarra-Estrada, M., Perez, Y., Pavlov, I., McNicholas, B., Roca, O., Mirza, S., Vines, D., Garcia-Salcido, R., Aguirre-Avalos, G., Trump, M. W., Nay, M., ve Dellamonica, J. (2020). Awake prone positioning for COVID-19 acute hypoxaemic respiratory failure: a randomised, controlled, multinational, open-label meta-trial. Lacent Respiratory Medicine, 9(January), 1387–1395. https://doi.org/10.1016/ S2213-2600(21)00356-8
  • Ferrara, M., Bertozzi, G., Fazio, N. Di, Aquila, I., Fazio, A. Di, Maiese, A., Volonnino, G., Frati, P., ve Russa, R. La. (2024). Risk management and patient safety in the artificial intelligence era : A Systematic Review. Healthcare, 12, 1–15. https:// doi.org/10.3390/healthcare12050549
  • Fosso Wamba, S., ve Queiroz, M. M. (2023). Responsible artificial intelligence as a secret ingredient for digital health: Bibliometric analysis, insights, and research directions. Information Systems Frontiers, 25(6), 2123–2138. https://doi.org/10.1007/s10796-021-10142-8
  • Guo, Y., Hao, Z., Zhao, S., Gong, J., ve Yang, F. (2020). Artificial intelligence in health care: Bibliometric analysis. Journal of Medical Internet Research, 22(7), e18228. https://doi.org/10.2196/18228
  • Habli, I., Lawton, T., ve Porter, Z. (2020). Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/BLT.19.237487
  • He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., Jolla, L., Jolla, L., Jolla, L., Tongren, B., Hospital, B. T., Ophthalmology, B., ve Science, V. (2020). The practical implementation of artificial intelligence technologies in medicine. Nat Med, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0.
  • Howell, M. D. (2024). Generative artificial intelligence, patient safety and healthcare quality: A review. BMJ Quality ve Safety, 1, bmjqs-2023-016690. https://doi.org/10.1136/bmjqs-2023-016690
  • Huang, H. C., Wang, C. H., Chen, P. C., ve Lee, Y. Der. (2019). Bibliometric analysis of medication errors and adverse drug events studies. Journal of Patient Safety, 15(2), 128–134. https://doi.org/10.1097/PTS.0000000000000205
  • Jabali, A. K., Waris, A., Khan, D. I., Ahmed, S., ve Hourani, R. J. (2022). Electronic health records: Three decades of bibliometric research productivity analysis and some insights. Informatics in Medicine Unlocked, 29(February), 100872. https://doi.org/10.1016/j.imu.2022.100872
  • Jimma, B. L. (2023). Artificial intelligence in healthcare: A bibliometric analysis. Telematics and Informatics Reports, 9(June 2022). https://doi.org/10.1016/j.teler.2023.100041
  • Johnson, E. A., Dudding, K. M., ve Carrington, J. M. (2024). When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nursing Inquiry, 31(1), 1–8. https://doi.org/10.1111/nin.12583
  • Kaplan, B. (2001). Evaluating informatics applications - Clinical decision support systems literature review. International Journal of Medical Informatics, 64(1), 15–37. https://doi.org/10.1016/S1386-5056(01)00183-6
  • Kreps, G. L., ve Neuhauser, L. (2013). Artificial intelligence and immediacy: Designing health communication to personally engage consumers and providers. Patient Education and Counseling, 92(2), 205–210. https://doi.org/10.1016/j.pec.2013.04.014
  • Kurutkan, M. N., Orhan, F., ve Kaygısız, P. (2017). Bibliometric analysis of patient safety literature example of thesis and articles in Turkish. Health Care Academician Journal, 4(4), 253. https://doi.org/10.5455/sad.13-1513948006
  • Kurutkan, M. N., Usta, E., Orhan, F., ve Simsekler, M. C. E. (2015). Application of the IHI Global Trigger Tool in measuring the adverse event rate in a Turkish healthcare setting. International Journal of Risk ve Safety in Medicine, 27(1), 11–21. https://doi.org/10.3233/JRS-150639
  • Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., ve Coiera, E. (2018). Conversational agents in healthcare : A systematic review. Journal ofthe American Medical Informatics Association, 25(July), 1248–1258. https://doi.org/10.1093/jamia/ocy072
  • Lim, W. M., ve Kumar, S. (2024). Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Global Business and Organizational Excellence, 43(2), 17–26. https://doi.org/10.1002/joe.22229
  • Matheny, M. E., Whicher, D., ve Thadaney Israni, S. (2020). Artificial intelligence in health care: A report from the national academy of medicine. JAMA, 323(6), 509–510. https://doi.org/10.1001/jama.2019.21579
  • Miller, D. D., Facp, C. M., ve Brown, E. W. (2018). Artificial intelligence in medical practice: The question to the answer ? The American Journal of Medicine, 131(2), 129–133. https://doi.org/10.1016/j.amjmed.2017.10.035
  • Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., ve Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de la Información, 29(1), 1–20. https://doi.org/10.3145/epi.2020.ene.03
  • Palojoki, S., Mäkelä, M., Lehtonen, L., ve Saranto, K. (2017). An analysis of electronic health record–related patient safety incidents. Health Informatics Journal, 23(2), 134–145. https://doi.org/10.1177/1460458216631072
  • Ratwani, R. M., Bates, D. W., ve Classen, D. C. (2024). Patient safety and artificial intelligence in clinical care. JAMA Health Forum, 5(2), e235514. https://doi.org/10.1001/jamahealthforum.2023.5514
  • Ryu, J. Y., Kim, H. U., ve Lee, S. Y. (2018). Deep learning improves prediction of drug – drug and drug–food interactions. PNAS, 115(18), E4304–E4311. https://doi.org/10.1073/pnas.1803294115
  • Salas, M., Petracek, J., Yalamanchili, P., Aimer, O., ve Kasthuril, D. (2022). The use of artificial intelligence in pharmacovigilance : A systematic review of the literature. Pharmaceutical Medicine, 36(5), 295–306. https://doi.org/10.1007/s40290-022-00441-z
  • Shaikh, A. K., Alhashmi, S. M., Khalique, N., Khedr, A. M., Raahemifar, K., ve Bukhari, S. (2023). Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digital Health, 9. https://doi.org/10.1177/20552076221149296
  • Sonmez, S. C., Sevgi, M., Antaki, F., Huemer, J., ve Keane, P. A. (2024). Generative artificial intelligence in ophthalmology: Current innovations, future applications and challenges. British Journal of Ophthalmology, 1–6. https://doi.org/10.1136/bjo-2024-325458
  • Tang, R., Zhang, S., Ding, C., Zhu, M., ve Gao, Y. (2022). Artificial intelligence in intensive care medicine: Bibliometric analysis. Journal of Medical Internet Research, 24(11), 1–16. https://doi.org/10.2196/42185
  • Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Rees, G., Laing, C., Baker, C. R., Peterson, K., Hassabis, D., King, D., Suleyman, M., … Mohamed, S. (2020). A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 572(7767), 116–119. https://doi.org/10.1038/s41586-019-1390-1.
  • Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(January), 44–56. https://doi.org/10.1038/s41591-018-0300-7
  • Tubaishat, A. (2019). The effect of electronic health records on patient safety: A qualitative exploratory study. Informatics for Health and Social Care, 44(1), 79–91. https://doi.org/10.1080/17538157.2017.1398753
  • TÜYZE. (2024). Erişim Adresi: https://tuyze.tuseb.gov.tr/kurumsal/hakkimizda, Erişim tarihi: 24.09.2024
  • Uygun İlikhan, S., Özer, M., Tanberkan, H., ve Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Sciences, 54(3), 483–492. https://doi.org/10.55730/1300-0144.5814
  • Wahl, B., Cossy-gantner, A., Germann, S., ve Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health : How can AI contribute to health in resource-poor settings ?, BMJ Glob Health, 1–7. https://doi.org/10.1136/bmjgh-2018-000798
  • Walsh, M. N., ve Rumsfeld, J. S. (2017). Leading the digital transformation of healthcare. Journal of the American College of Cardiology, 70(21), 2719–2722. https://doi.org/10.1016/j.jacc.2017.10.020
  • Wang, J., Liang, Y., Cao, S., Cai, P., ve Fan, Y. (2023). Application of artificial intelligence in geriatric care: Bibliometric analysis. Journal of Medical Internet Research, 25, 1–16. https://doi.org/10.2196/46014
  • Whipple, E. C., E. Dixon, B., ve J. McGowan, J. (2013). Linking health information technology to patient safety and quality outcomes: a bibliometric analysis and review. Informatics for Health and Social Care, 38(1), 1–14. https://doi.org/10.3109/17538157.2012.678451
  • Yilmaz Muluk, S. (2024). Enhancing musculoskeletal injection safety: Evaluating checklists generated by artificial intelligence and revising the performed checklist. Cureus, 16(5). https://doi.org/10.7759/cureus.59708
  • Zhao, D., Zhang, W., Liu, Y., ve Yan, Z. (2024). Post-marketing safety concerns with lumateperone: a pharmacovigilance analysis based on the FDA adverse event reporting system (FAERS) database. Frontiers in Pharmacology, 15(May), 1–11. https://doi.org/10.3389/fphar.2024.1389814
  • Zhou, L., Sun, Y., Wang, J., Huang, H., Luo, J., Zhao, Q., ve Xiao, M. (2024). Trends in patient safety education research for healthcare professional students over the past two decades: a bibliometric and content analysis. Medical Education Online, 29(1). https://doi.org/10.1080/10872981.2024.2358610
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Politika ve Yönetim (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Dilek Şahin 0000-0003-0865-7763

Erken Görünüm Tarihi 27 Mart 2025
Yayımlanma Tarihi
Gönderilme Tarihi 7 Ekim 2024
Kabul Tarihi 22 Aralık 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 24 Sayı: 2

Kaynak Göster

APA Şahin, D. (2025). Sağlık Hizmetlerinde Hasta Güvenliği ve Yapay Zekâ: Bibliyometrik Bir Analiz. Elektronik Sosyal Bilimler Dergisi, 24(2), 764-782. https://doi.org/10.17755/esosder.1563082

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Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.

ESBD Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Türk Patent ve Marka Kurumu tarafından tescil edilmiştir. Marka No:2011/119849.