Patient Safety and Artificial Intelligence in Healthcare: A Bibliometric Analysis

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.

Anahtar Kelimeler

• Patient Safety
• artificial intelligence
• bibliometric analysis

Sağlık Hizmetlerinde Hasta Güvenliği ve Yapay Zekâ: Bibliyometrik Bir Analiz

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.

Anahtar Kelimeler

• Hasta güvenliği
• yapay zeka
• bibliyometrik analiz

Kaynakça

1. 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
2. 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
3. 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
4. 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
5. 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
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
7. 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
8. 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

9. 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
10. 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
11. 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
12. 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
13. DSÖ. (2019). Patient safety. https://www.who.int/news-room/facts-in-pictures/detail/patient-safety, Erişim Tarihi: 20.09.2024.
14. DSÖ. (2021). Global strategy on digital health 2020-2025. Içinde World Health Organization. https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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.
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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.
44. 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
45. 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
46. TÜYZE. (2024). Erişim Adresi: https://tuyze.tuseb.gov.tr/kurumsal/hakkimizda, Erişim tarihi: 24.09.2024
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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