Sağlık Hizmetlerinde Yapay Zekâ, Dijital Sağlık, Hata Azaltma ve Hasta Güvenliği
Özet
Bu bölümde yapay zekâ ve dijital sağlık teknolojilerinin modern sağlık hizmetlerinde yarattığı dönüşüm; dijital sağlık ekosistemi, Smart Hospital (akıllı hastane) modelleri ve hasta güvenliği ekseninde çok boyutlu olarak ele alınmaktadır. Çalışma, öncelikle yapay zekâ kavramının tarihsel gelişimini, Sağlık 4.0 ve Sağlık 5.0 yaklaşımlarını ve elektronik sağlık kayıtları, tele-sağlık, IoT tabanlı izlem sistemleri ile giyilebilir sensörler gibi dijital bileşenleri kuramsal bir çerçevede tartışmaktadır. Ardından klinik karar destek sistemleri, robotik cerrahi ve otonom sistemler gibi yapay zekâ uygulamalarının tanı doğruluğunu artırma, klinik hataları azaltma ve süreç verimliliğini yükseltme potansiyeli incelenmektedir.
Çalışmanın odak noktasını, yapay zekâ destekli dijital hastane (Smart Hospital) modeli ve bu modelin klinik ve operasyonel süreç optimizasyonundaki rolü oluşturmaktadır. Bu kapsamda dijital ikiz teknolojileri, yapay zekâ tabanlı erken uyarı sistemleri, hasta akış yönetimi, ameliyathane planlaması ve ilaç hatalarının önlenmesine yönelik algoritmaların hasta güvenliğine katkıları analiz edilmektedir. Bununla birlikte veri güvenliği, mahremiyet, algoritmik şeffaflık, dijital eşitsizlik ve çalışanların dijital yetkinlikleri gibi etik ve yönetsel tartışma alanlarına da özel vurgu yapılmaktadır. Sonuç olarak çalışma, yapay zekâ ve dijital sağlık teknolojilerinin doğru planlandığında klinik kaliteyi ve hasta güvenliğini güçlendiren, operasyonel verimliliği artıran stratejik araçlar olduğunu ortaya koymakta; sağlık yöneticileri ve politika yapıcılar için aşamalı teknoloji entegrasyonu, güçlü veri yönetişimi, etik çerçevelerin netleştirilmesi, Smart Hospital yatırımları ve dijital yetkinlik geliştirme programları gibi somut öneriler sunmaktadır.
Referanslar
Adler-Milstein, J., & Jha, A. K. (2017). HITECH act drove large gains in hospital electronic health record adoption. Health Affairs, 36(8), 1416–1422.
Albahri, A. S., et al. (2022). Systematic review of artificial intelligence techniques in smart hospitals: Privacy, security and ethical challenges. Computer Methods and Programs in Biomedicine, 221, 106874.
Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making, 17, 36.
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., ... & Shetty, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954–961.
Aspden, P., Wolcott, J., Bootman, J. L., & Cronenwett, L. R. (Eds.). (2007). Preventing medication errors. National Academies Press.
Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors, 13(12), 17472–17500.
Bates, D. W., & Singh, H. (2018). Two decades since To Err Is Human: An assessment of progress and emerging priorities in patient safety. Health Affairs, 37(11), 1736–1743.
Bruno, M. A., Walker, E. A., & Abujudeh, H. H. (2015). Understanding and confronting our mistakes: The epidemiology of error in radiology and strategies for error reduction. Radiographics, 35(6), 1668–1676.
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., ... & Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(8), 1301–1309.
Campbell, R. J., Guappone, K. P., Sittig, D. F., Dykstra, R. H., Ash, J. S., & Guappone, K. P. (2018). Computerized provider order entry adoption: Implications for clinical decision support and patient safety. Joint Commission Journal on Quality and Patient Safety, 44(10), 615–623.
Carayon, P., Wetterneck, T. B., Rivera-Rodriguez, A. J., Hundt, A. S., Hoonakker, P., Holden, R., & Gurses, A. P. (2014). Human factors systems approach to healthcare quality and patient safety. Applied Ergonomics, 45(1), 14–25.
Chang, J., Hsu, C. Y., & Chen, S. (2021). Cost–benefit analysis of smart hospital infrastructure: Evidence from IoT and AI-enabled services. International Journal of Medical Informatics, 149, 104431.
Classen, D. C., Phansalkar, S., & Bates, D. W. (2011). Critical drug–drug interactions for use in electronic health records systems with computerized physician order entry: Review of leading approaches. Journal of Patient Safety, 7(2), 61–65.
Cresswell, K., Williams, R., Sheikh, A., & Bates, D. (2022). Impacts of the national electronic health record system on patient safety: A mixed-methods evaluation. Journal of the American Medical Informatics Association, 29(3), 472–482.
Cvach, M. (2012). Monitor alarm fatigue: An integrative review. Biomedical Instrumentation & Technology, 46(4), 268–277.
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6, 54.
Dorsey, E. R., & Topol, E. J. (2020). Telemedicine 2020 and the next decade. The Lancet, 395(10227), 859.
European Commission. (2021). Digital health in the EU: Transforming health and care in the digital single market.
European Commission. (2022). Digital health and care: Overview of the digital health ecosystem in Europe.
European Commission. (2023). Towards a human-centric digital transformation: Society 5.0 and Health 5.0.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
Fernandes, E., Rahmati, A., Jung, J., Prakash, A., & Mao, Z. M. (2017). Security implications of smart IoT devices in healthcare. IEEE Security & Privacy, 15(5), 52–60.
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. Fukuyama, M. (2018). Society 5.0: Aiming for a new human-centered society. Japan SPOTLIGHT, 27(5), 47–50.
Goldstein, B. A., Navar, A. M., Pencina, M. J., & Ioannidis, J. P. A. (2017). Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review. Journal of the American Medical Informatics Association, 24(1), 198–208.
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015). A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine, 7(299), 299ra122.
Holden, R. J. (2022). What stands in the way of technology-mediated patient safety improvements? A human factors and ergonomics perspective. Healthcare, 10(4), 677.
Hong, Y. A., Zhou, Z., Fang, Y., & Shi, L. (2021). The digital divide and health disparities in China: Evidence from a national survey and policy implications. Journal of Medical Internet Research, 23(7), e27012.
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2015). The internet of things for health care: A comprehensive survey. IEEE Access, 3, 678–708.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., ... & Dudley, J. T. (2021). Artificial intelligence in cardiology. Journal of the American College of Cardiology, 77(4), 445–457.
Koonin, L. M., Hoots, B., Tsang, C. A., Leroy, Z., Farris, K., Jolly, T., ... & Harris, A. M. (2020). Trends in the use of telehealth during the emergence of the COVID-19 pandemic — United States, January–March 2020. MMWR, 69(43), 1595–1599.
Krick, T., Huter, K., Domhoff, D., Schmidt, A., Rothgang, H., & Wolf-Ostermann, K. (2021). Digital technology and nursing care: A scoping review on acceptance, effectiveness and efficiency studies of informal and formal care technologies. BMC Health Services Research, 21, 157.
Kruse, C. S., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2017). Evaluating barriers to adopting telemedicine worldwide: A systematic review. Journal of Telemedicine and Telecare, 24(1), 4–12.
Kwak, L., et al. (2020). Outcomes of robotic vs laparoscopic or open surgery: A meta-analysis. Surgical Endoscopy, 34(4), 129–141.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., ... & Keane, P. A. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297.
Majnarić, L. T., Babič, F., O’Sullivan, S., & Holzinger, A. (2021). Digital transformation of clinical practice and AI-based decision support systems: Ethical challenges in the implementation of telemedicine. Journal of Personalized Medicine, 11(7), 631.
Marcus, H. J., Hughes-Hallett, A., Payne, C. J., Yang, G. Z., & Darzi, A. (2022). The future of surgical robotics: Flexible, adaptive, and autonomous. Surgical Innovation, 29(1), 54–61.
Marr, B. (2022). Future of healthcare: How AI, big data and digital technologies are changing the world. Wiley.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1956). A proposal for the Dartmouth summer research project on artificial intelligence. Dartmouth College.
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94.
Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Medicine, 3, 65.
Mongan, J., Moy, L., & Kahn, C. E. (2020). Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers. Radiology: Artificial Intelligence, 2(2), e200029.
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172.
Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLoS ONE, 13(8), e0203316.
Musen, M. A., Middleton, B., & Greenes, R. A. (2021). Clinical decision-support systems. In E. Shortliffe & J. Cimino (Eds.), Biomedical informatics (pp. 643–674). Springer.
Omar, A., et al. (2022). Health 5.0: A new paradigm for digital health systems. IEEE Access, 10, 12345–12360.
Peters, B. S., Armijo, P. R., Krause, C., Choudhury, S. A., & Oleynikov, D. (2018). Review of emerging surgical robotic technology. Surgical Endoscopy, 32(4), 1636–1655.
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLoS Medicine, 13(2), e1001953.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
Ransbotham, S., Kiron, D., LaFountain, B., & Patil, D. J. (2021). The divergent AI maturity of companies. MIT Sloan Management Review, 62(1), 1–25.
Razzak, M. I., Imran, M., & Xu, G. (2019). Big data analytics for preventive medicine. Neural Computing and Applications, 32(9), 4417–4451.
Reason, J. (2000). Human error: Models and management. BMJ, 320(7237), 768–770.
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Scott, K. R., Karem, P., Shifflett, K., & Brooks, M. (2021). Telehealth use in primary care during the COVID-19 pandemic. BMC Primary Care, 22, 214.
Shahid, N., Rappon, T., & Berta, W. (2021). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS ONE, 14(2), e0212356.
Shah, S. A., Yang, X., Wang, L., & Zhao, Y. (2020). Industry 4.0 and Health 4.0: The role of digital twins in smart healthcare. IEEE Access, 8, 141206–141220.
Shaw, J., Jamieson, T., Agarwal, P., Griffin, B., Wong, I., & Bhatia, R. S. (2018). Virtual care policy recommendations for patient-centred primary care. NPJ Digital Medicine, 1, 49.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604.
Shillan, D., Sterne, J. A. C., Champneys, A., & Gibbison, B. (2019). Use of machine learning to analyse routinely collected intensive care unit data: A systematic review. Critical Care, 23, 284.
Shortliffe, E. H. (1976). Computer-based medical consultations: MYCIN. Elsevier.
Singh, H., Meyer, A. N. D., & Thomas, E. J. (2017). The frequency of diagnostic errors in outpatient care: Estimations from three large observational studies involving US adult populations. BMJ Quality & Safety, 23(9), 727–731.
Slight, S. P., et al. (2019). The impact of electronic prescribing on medication errors and adverse drug events: A systematic review. Journal of the American Medical Informatics Association, 26(8–9), 1022–1035.
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17.
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2019). Wearable photoplethysmographic sensors—past and present. Electronics, 3(2), 282–302.
The Joint Commission. (2022). Improving communication in healthcare: Sentinel event alert on communication failures.
Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020). Digital technology and COVID-19. Nature Medicine, 26(4), 459–461.
Top, M., & Gider, Ö. (2023). Smart hospitals and digital transformation in healthcare management. Health Policy and Technology, 12(2), 100754.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Tortorella, G. L., Fogliatto, F. S., Mac Cawley Vergara, A., Vassolo, R., & Sawhney, R. (2021). Healthcare 4.0: Trends, challenges and research directions. Production Planning & Control, 32(16), 1280–1297.
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome, 14(4), 337–339.
Vaidya, S. R., Hsueh, P. H., & Tsai, C. T. (2019). Clinical decision support systems for medication dosing in critically ill patients. Journal of Critical Care, 50, 173–180.
van Dijk, J. (2020). The digital divide. Polity.
Verma, S., & Gupta, A. (2023). Digital transformation in healthcare: A managerial perspective. International Journal of Health Policy and Management, 12, 1–10.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.
World Health Organization. (2017). Medication without harm: WHO’s third global patient safety challenge.
World Health Organization. (2021). Global patient safety action plan 2021–2030: Towards eliminating avoidable harm in health care.
Yang, G. Z., Cambias, J., Cleary, K., Daimler, E., Drake, J., Dupont, P. E., ... & Hawkes, D. (2017). Medical robotics—Regulatory, ethical, and legal considerations for increasing levels of autonomy. Science Robotics, 2(4), eaan 4487.
Zeng, Z., Chen, P. J., & Lew, A. A. (2020). From High-touch to High-tech: COVID-19 drives the digitalization of smart tourism.
Zheng, B., Dexter, F., & Macario, A. (2020). Scheduling surgical cases into operating room: Systematic review. Anesthesiology, 132(5), 1326–1345.
Zhu, X., Zhou, X., Zhang, Y., & Sun, J. (2020). Predicting adverse drug reactions of multiple drugs based on graph convolutional network. IEEE Journal of Biomedical and Health Informatics, 24(9), 2633–2642.
