Structural Readiness in the Era of Machine Intelligence
DOI:
https://doi.org/10.5281/zenodo.20442574Keywords:
AI readiness assessment, Digital health infrastructure,Clinical workflow integration,Health data interoperability,Electronic health record (EHR) maturity,Data governance frameworks,Ethical AI in healthcare,Workforce AI competency,Change management in healthcare,Clinical decision support systems,Health information security,Regulatory compliance for AI,Algorithm transparency,Patient data quality,Organizational readiness for AI,AI adoption barriers,Health system innovation capacity,Human–AI collaboration,AI-enabled care delivery,Implementation science for AI in healthcare.Abstract
Artificial Intelligence (AI) is positioned to transform healthcare services and operations into intelligent systems, delivering innovative solutions to improve the quality of care while containing rising costs. However, AI readiness at all levels of the healthcare ecosystem must be examined in combination with real-world implementations that deliver actual transformation. Readiness is commonly defined as the state of being fully prepared for something. In a healthcare management context, readiness refers to the state of a healthcare organization in determining whether the required capabilities exist for the successful adoption of a new technology or concept. Towards AI integration in healthcare, the concept applies at three levels—technological, data, and skills readiness—and can be assessed using maturity frameworks that provide an indication of progress and direction, as well as stakeholder analysis.
While Technology Adoption Models assess potential uptake of AI solutions by health practitioners, these validations assess preparedness to adopt and/or innovate in AI solutions within the healthcare ecosystem. Emerging AI applications covering clinical needs from radiology to mental health support, or from product development to operational management, present recognition and acceptance challenges requiring stakeholder engagement. Stakeholder impact on the success of AI solutions can be assessed using readiness levels, grouping stakeholders into three categories: IDPC leaders responsible for implementation, users at the point of care who interact directly with patients, and patients, the users of the healthcare system and the ultimate beneficiaries or sufferers from the role out of the AI solution.
References
1. 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., & Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23, Article 84698.
2. 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.
3. Bakken, S. (2023). Perspectives on implementing models for decision support including AI-based clinical decision support. Journal of the American Medical Informatics Association, 30(8), 1329–1331.
4. Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? Journal of Arthroplasty, 33(8), 2358–2361.
5. Blease, C. R., Kaptchuk, T. J., Bernstein, M. H., & Mandl, K. D. (2024). Generative artificial intelligence in primary care: An online survey of UK general practitioners. BMJ Health and Care Informatics, 31(1), e100856.
6. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983.
7. Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J.-N., Laleh, N. G., Löffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., Wagner, S. J., & Kather, J. N. (2023). The future landscape of large language models in medicine. Communications Medicine, 3(1), 141.
8. Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., & Wynants, L. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, e078378.
9. Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., & SPIRIT-AI and CONSORT-AI Working Group. (2020). SPIRIT-AI extension: Guidelines for clinical trial protocols for interventions involving artificial intelligence. Nature Medicine, 26(9), 1351–1363.
10. Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.
11. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
12. Fehr, J., Citro, B., Malpani, R., Lippert, C., & Madai, V. I. (2024). A trustworthy AI reality-check: The lack of transparency of artificial intelligence products in healthcare. Frontiers in Digital Health, 6, Article 1267290.
13. Gama, F., Tavares, J., & Silva, D. (2022). Implementation frameworks for artificial intelligence translation into health care practice: A scoping review. Journal of Medical Internet Research, 24(1), e32215.
14. Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: Accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256.
15. Indian Council of Medical Research. (2023). Ethical guidelines for application of artificial intelligence in biomedical research and healthcare. ICMR.
16. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
17. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195.
18. Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madamba, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198.
19. Lee, P., Bubeck, S., & Petro, J. (2023). Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. New England Journal of Medicine, 388(13), 1233–1239.
20. Liao, F., Adelaine, S., Afshar, M. A., & Patterson, B. W. (2022). Governance of clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes. Frontiers in Digital Health, 4, Article 931439.
21. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.
22. Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K., & SPIRIT-AI and CONSORT-AI Working Group. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26(9), 1364–1374.
23. MacKay, C., Klement, W., Vanberkel, P., Lamond, N., Urquhart, R., & Rigby, M. (2023). A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions. Healthcare Analytics, 3, Article 100155.
24. Meskó, B., & Görög, M. (2020). A short guide for medical professionals in the era of artificial intelligence. npj Digital Medicine, 3(1), 126.
25. Meskó, B., & Topol, E. J. (2023). The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Medicine, 6(1), 120.
26. Murdoch, B. (2021). Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Medical Ethics, 22(1), 122.
27. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
28. Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. European Radiology Experimental, 2(1), 35.
29. Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43.
30. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38.
31. Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., He, Z., Wong, S. Y. S., Fang, P.-H., & Ming, W.-K. (2019). Artificial intelligence versus clinicians in disease diagnosis: Systematic review. JMIR Medical Informatics, 7(3), e10010.
32. Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. JAMA, 320(21), 2199–2200.
33. Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Goldbraikh, A. B., McLean, C. Y., Amin, M., Zhu, H., & Natarajan, V. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172–180.
34. Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023). Large language models in medicine. Nature Medicine, 29(8), 1930–1940.
35. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
36. Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.
37. Sonnet 4.6 Adaptive
