Organizational Change in Health Institutions: Artificial Intelligence Anxiety of Internal and Surgical Branch Physicians
Öz
Objective: The integration of artificial intelligence applications into the health sector creates some concerns about the uncertainties in the process as well as facilitating factors in service delivery. This study investigates the interaction and changes with professional qualifications by examining AI anxiety, readiness for AI, and openness to organizational change among physicians in internal and surgical specialties.
Method: The study data were collected between September 1, 2024 and November 15, 2024 from 15 health institutions with the status of training and research hospitals on the Anatolian and European sides of Istanbul by online survey method. Valid measurement tools for data collection: Artificial Intelligence Anxiety Scale, Medical Artificial Intelligence Readiness Scale, and Organizational Openness to Change Scale were used. The distribution of variables was analyzed by Shapiro Wilk test. Differences between groups that did not show normal distribution were analyzed using Mann Whitney U and Kruskal Wallis H tests. Bonferroni correction was applied for multiple test corrections in intragroup comparisons.
Results: AI anxiety was generally moderate, with no difference between specialties. Regular follow-up of medical literature was positively correlated with decreased AI anxiety and increased readiness levels. Openness to organizational change was found to be high in both specialties.
Conclusion: AI anxiety and AI readiness are influenced by gender and following medical literature. Following academic literature and training programs are critical for building confidence in AI applications. Physicians' openness to organizational change is a facilitating factor for the best implementation of AI in clinical settings through hands-on training and scientific studies.
Anahtar Kelimeler
Artificial intelligent in medicine physician readiness technological adaptation Organizational Change Management education
Kaynakça
- 1. Waymel Q, Badr S, Demondion X, Cotten A, Jacques T. Impact of the rise of artificial intelligence in radiology: What do radiologists think? Diagn Interv Imaging. 2019;100(6):327–36.
- 2. Ahmed Z, Bhinder KK, Tariq A, Tahir MJ, Mehmood Q, Tabassum MS, et al. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey. Annals of Medicine and Surgery. 2022;76:1-7.
- 3. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.
- 4. Bhattacharya S, Pradhan BK, Bashar AM, Tripathi S, Semwal J, Marzo RR. Artificial intelligence enable healthcare: A hype, hope or harm. Journal of Family Medicine and Primary Care. 2019;8(11):3461-64.
- 5. Irfan F. Artificial intelligence: Help or hindrance for family physicians? Pak J Med Sci. 2021;37(1):288-91.
- 6. Guo J, Li B. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity. 2018;2(1) 174–81.
- 7. Hua D, Petrina N, Young N, Cho JG, Poon SK. Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artificial Intelligence in Medicine. 2024;147:102698.
- 8. Wubineh BZ, Deriba FG, Woldeyohannis MM. Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review. Urologic Oncology: Seminars and Original Investigations. 2024;42(3):48–56.
- 9. Mansoor MA, Ibrahim AF, Kidd N. The Impact of Artificial Intelligence on Internal Medicine Physicians: A Survey of Procedural and Non-procedural Specialties. Cureus. 2024;16(9):e69121. 10. De Simone B, Abu-Zidan FM, Gumbs AA, Chouillard E, Di Saverio S, Sartelli M, et al. Knowledge, attitude, and practice of artificial intelligence in emergency and trauma surgery, the ARIES project: an international web-based survey. World Journal of Emergency Surgery. 2022;17(10):1-8.
- 11. Yin M, Jiang S, Niu X. Can AI really help? The double-edged sword effect of AI assistant on employees’ innovation behavior. Comput Human Behav. 2024;150:107987.
- 12. Johnson-Mann CN, Loftus TJ, Bihorac A. Equity and Artificial Intelligence in Surgical Care. JAMA Surgery. American Medical Association; 2021;156(6):509–10.
- 13. Çalışkan A. Örgütsel Değişime Açıklık: Bir Ölçek Geliştirme Çalışması. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2022;14(2):191–202. 14. Terzi R. An adaptatıon of artificial intelligence anxiety scale into Turkish: Reliability and validity study research article an adaptatıon of artificial intelligence anxiety scale ınto Turkish: Reliability and validity study. International Online Journal of Education and Teaching. 2020;7(4):1501-15.
- 15. Wang YY, Wang YS. Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments. 2019;30(4):619–34.
- 16. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study. BMC Med Educ. 2021;21(112):1-9.
- 17. Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR, Wahl B. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3:e000798.
- 18. Pedro AR, Dias MB, Laranjo L, Cunha AS, Cordeiro J V. Artificial intelligence in medicine: A comprehensive survey of medical doctor’s perspectives in Portugal. PLoS One. 2023;18(9):e0290613.
- 19. Recht MP, Dewey M, Dreyer K, Langlotz C, Niessen W, Prainsack B, et al. Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol. 2020;30(6):3576–84.
- 20. Hopson S, Mildon C, Hassard K, Urie PM, Della Corte D. Equipping Future Physicians with Artificial Intelligence Competencies through Student Associations. International Medical Education. 2024;3(4):388–94. 21. Triantafyllopoulos L, Feretzakis G, Tzelves L, Sakagianni A, Verykios VS, Kalles D. Evaluating the interactions of Medical Doctors with chatbots based on large language models: Insights from a nationwide study in the Greek healthcare sector using ChatGPT. Comput Human Behav. 2024;161:108404.
- 22. Eroğlu S.G, Alga E. Üniversite Çalışanlarının Örgütsel Değişime Açıklıkları ile Örgütsel Ataletleri Arasındaki ilişki Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2019; 23 (3):1251-1271.
- 23. Başer A, Altuntaş SB, Kolcu G, Özceylan G. Artificial Intelligence Anxiety of Family Physicians in Turkey. Progress in Nutrition. 2021;23(2):c2021275.
Öz
Amaç: Yapay zeka uygulamalarının sağlık sektörüne entegrasyonu hizmet sunumunda kolaylaştırıcı unsurların yanı sıra süreçteki belirsizlikler de birtakım endişeler yaratmaktadır. Bu çalışma dahili ve cerrahi uzmanlık alanlarındaki doktorlar arasında yapay zeka kaygısını, yapay zekaya hazır olma ve örgütsel değişime açıklık durumlarının incelenerek mesleki nitelikleri ile olan etkileşimi ve değişimi araştırmaktadır.
Yöntem: Çalışma verileri 1 Eylül 2024 - 15 Kasım 2024 tarihleri arasında online anket yöntemiyle İstanbul’da Anadolu ve Avrupa yakasında bulunan 15 eğitim araştırma hastanesi statüsünde olan sağlık kurumlarından toplanmıştır. Veri toplamada geçerli ölçme araçları olan: Yapay Zeka Kaygı Ölçeği, Tıbbi Yapay Zeka Hazırlık Bulunuşluk Ölçeği ve Örgütsel Değişime Açıklık ölçeği kullanılmıştır. Değişkenlerin dağılımı Shapiro Wilk testi ile incelenmiştir. Normal dağılım göstermeyen gruplar arasındaki farklar Mann Whitney U ve Kruskal Wallis H testleri ile analiz edilmiştir. Grup içi karşılaştırmalarda çoklu test düzeltmesi için Bonferroni düzeltmesi uygulanmıştır.
Bulgular: Yapay zeka kaygısı genel olarak orta düzeydeyken uzmanlık alanları arasında bir farklılık tespit edilmemiştir. Tıbbi literatürün düzenli takibi, yapay zeka kaygısının azalması ve hazır olma seviyelerinin artması ile pozitif korelasyon göstermiştir. Her iki uzmanlık alanında örgütsel değişime açıklığın yüksek olduğu belirlenmiştir.
Sonuç: Yapay zeka kaygısı ve yapay zekaya hazır bulunuşluk cinsiyet ve tıbbi literatürü takip etme faktörlerinden etkilenmektedir. Akademik literatürü takip etme ve eğitim programları yapay zeka uygulamalarına olan güveni oluşturmak için kritik öneme sahiptir. Hekimlerin örgütsel değişime açık olması; uygulamalı olarak yapılacak eğitimlerin ve bilimsel çalışmaların yapay zekanın klinik ortamlarda en iyi şekilde uygulanmasında kolaylaştırıcı bir etkendir.
Anahtar Kelimeler
Tıpta yapay zeka Eğitim hekimin hazır bulunuşluğu teknolojik adaptasyon organizasyonel değişim yönetimi
Kaynakça
- 1. Waymel Q, Badr S, Demondion X, Cotten A, Jacques T. Impact of the rise of artificial intelligence in radiology: What do radiologists think? Diagn Interv Imaging. 2019;100(6):327–36.
- 2. Ahmed Z, Bhinder KK, Tariq A, Tahir MJ, Mehmood Q, Tabassum MS, et al. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey. Annals of Medicine and Surgery. 2022;76:1-7.
- 3. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.
- 4. Bhattacharya S, Pradhan BK, Bashar AM, Tripathi S, Semwal J, Marzo RR. Artificial intelligence enable healthcare: A hype, hope or harm. Journal of Family Medicine and Primary Care. 2019;8(11):3461-64.
- 5. Irfan F. Artificial intelligence: Help or hindrance for family physicians? Pak J Med Sci. 2021;37(1):288-91.
- 6. Guo J, Li B. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity. 2018;2(1) 174–81.
- 7. Hua D, Petrina N, Young N, Cho JG, Poon SK. Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artificial Intelligence in Medicine. 2024;147:102698.
- 8. Wubineh BZ, Deriba FG, Woldeyohannis MM. Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review. Urologic Oncology: Seminars and Original Investigations. 2024;42(3):48–56.
- 9. Mansoor MA, Ibrahim AF, Kidd N. The Impact of Artificial Intelligence on Internal Medicine Physicians: A Survey of Procedural and Non-procedural Specialties. Cureus. 2024;16(9):e69121. 10. De Simone B, Abu-Zidan FM, Gumbs AA, Chouillard E, Di Saverio S, Sartelli M, et al. Knowledge, attitude, and practice of artificial intelligence in emergency and trauma surgery, the ARIES project: an international web-based survey. World Journal of Emergency Surgery. 2022;17(10):1-8.
- 11. Yin M, Jiang S, Niu X. Can AI really help? The double-edged sword effect of AI assistant on employees’ innovation behavior. Comput Human Behav. 2024;150:107987.
- 12. Johnson-Mann CN, Loftus TJ, Bihorac A. Equity and Artificial Intelligence in Surgical Care. JAMA Surgery. American Medical Association; 2021;156(6):509–10.
- 13. Çalışkan A. Örgütsel Değişime Açıklık: Bir Ölçek Geliştirme Çalışması. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2022;14(2):191–202. 14. Terzi R. An adaptatıon of artificial intelligence anxiety scale into Turkish: Reliability and validity study research article an adaptatıon of artificial intelligence anxiety scale ınto Turkish: Reliability and validity study. International Online Journal of Education and Teaching. 2020;7(4):1501-15.
- 15. Wang YY, Wang YS. Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments. 2019;30(4):619–34.
- 16. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study. BMC Med Educ. 2021;21(112):1-9.
- 17. Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR, Wahl B. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3:e000798.
- 18. Pedro AR, Dias MB, Laranjo L, Cunha AS, Cordeiro J V. Artificial intelligence in medicine: A comprehensive survey of medical doctor’s perspectives in Portugal. PLoS One. 2023;18(9):e0290613.
- 19. Recht MP, Dewey M, Dreyer K, Langlotz C, Niessen W, Prainsack B, et al. Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol. 2020;30(6):3576–84.
- 20. Hopson S, Mildon C, Hassard K, Urie PM, Della Corte D. Equipping Future Physicians with Artificial Intelligence Competencies through Student Associations. International Medical Education. 2024;3(4):388–94. 21. Triantafyllopoulos L, Feretzakis G, Tzelves L, Sakagianni A, Verykios VS, Kalles D. Evaluating the interactions of Medical Doctors with chatbots based on large language models: Insights from a nationwide study in the Greek healthcare sector using ChatGPT. Comput Human Behav. 2024;161:108404.
- 22. Eroğlu S.G, Alga E. Üniversite Çalışanlarının Örgütsel Değişime Açıklıkları ile Örgütsel Ataletleri Arasındaki ilişki Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2019; 23 (3):1251-1271.
- 23. Başer A, Altuntaş SB, Kolcu G, Özceylan G. Artificial Intelligence Anxiety of Family Physicians in Turkey. Progress in Nutrition. 2021;23(2):c2021275.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Hizmetleri ve Sistemleri (Diğer) |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 17 Haziran 2025 |
| Gönderilme Tarihi | 9 Ocak 2025 |
| Kabul Tarihi | 15 Mayıs 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 17 Sayı: 2 |
