The influence of artificial intelligence on communication in healthcare

Authors

DOI:

https://doi.org/10.31637/epsir-2024-312

Keywords:

Health, Artificial Intelligence, Effective Communication, Patients, Healthcare Professionals, Ethics, Data, Efficiency

Abstract

Introduction: Effective communication in healthcare is vital for quality care. Language and cultural barriers make it difficult, causing errors and costs. AI improves communication and treatment accuracy. Methodology: Literature review and research findings on AI in healthcare, focusing on its applications and challenges. Results: AI facilitates information interpretation and communication in healthcare, improving efficiency and diagnostic accuracy. Discussion: AI implementation faces cultural, technical, ethical and regulatory barriers, such as interoperability, data quality and privacy. Conclusions: Overcoming technical and ethical challenges is crucial. Interdisciplinary collaboration, transparency in algorithms and AI education are recommended to improve healthcare.

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Author Biography

Enrique Carvajal Zaera, Universidad CEU Fernando III

Associate Professor at CEU Fernando III University, Seville.
Degree in CEYE from the University of Seville and PhD from the Complutense University of Madrid, MA in European Studies from the University of Seville, MBA from IE in Madrid and GSMP from the University of Chicago. Associate Professor at the Antonio de Nebrija University of Madrid, European University of Madrid and EUSA of Seville.

References

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. y Escobar, G. (2018). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. https://doi.org/10.1377/hlthaff.2018.00069

Beam, A. L. y Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2017.18391

Bickmore, T. W., Schulman, D. y Sidner, C. (2018). Automated interventions for multiple health behaviors using conversational agents. Patient Education and Counseling, 92(2), 142-148. https://doi.org/10.1016/j.pec.2013.05.011

Blease, C., Kharko, A., Hägglund, M., DesRoches, C. y Samal, L. (2020). Artificial intelligence and the future of primary care: Exploring the role of chatbots in patient experience. Journal of Medical Internet Research, 22(8), e20702. https://doi.org/10.2196/20702

Char, D. S., Shah, N. H. y Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMsr1802290

Chen, J. H., Asch, S. M. y Liu, V. X. (2019). Machine learning and prediction in medicine—beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507-2509. https://doi.org/10.1056/NEJMp1814531

Chernick, M. R., Bennett, J. y Mulvey, E. P. (2020). Efficiency of the healthcare system: A systematic review. Journal of Health Economics, Policy, and Law, 15(3), 567-589. https://doi.org/10.1007/s40258-022-00785-2

Cirillo, D. y Valencia, A. (2019). Big data analytics for personalized medicine. Current Opinion in Biotechnology, 58, 161-167. https://doi.org/10.1016/j.copbio.2019.03.004

Doshi-Velez, F. y Kim, B. (2017). Towards a rigorous science of interpretable machine learning. https://arxiv.org/pdf/1702.08608

Epstein, R. M. y Street Jr, R. L. (2011). The values and value of patient-centered care. The Annals of Family Medicine, 9(2), 100-103. https://doi.org/10.1370/afm.1239

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corradp, G., Thrun, S. y Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/nm.3733

Floridi, L., Cowls, J., King, T. y Taddeo, M. (2018). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 24(6), 1729-1753. https://doi.org/10.1007/s11948-017-9901-7

Garg, A. X., Adhikari, N. K., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J. y Haynes, R. B. (2005). Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. JAMA, 293(10), 1223-1238. https://doi.org/10.1001/jama.293.10.1223

Gandhi, T. K., Weingart, S. N., Borus, J., Seger, A. C., Peterson, J., Burdick, E., Seger, D. L., Shu, K., Federico, F., Leape, L. L. y Bates, D. W. (2008). Adverse drug events in ambulatory care. New England Journal of Medicine, 348(16), 1556-1564. https://doi.org/10.1056/NEJMsa020703

Gawande, A. (2014). Being mortal: Medicine and what matters in the end. Metropolitan Books.

Goodfellow, I., Bengio, Y. y Courville, A. (2016). Deep Learning. MIT Press.

Goodman, K. W., Miller, R. A. y Wolf, G. (2020). Ethics and information technology: A case-based approach to a health care system in transition. Journal of Medical Ethics, 46(3), 172-178.

Guidotti, E., Arndt, A. y Browning, R. (2019). Impact of artificial intelligence on healthcare professional-patient relationship. Journal of Medical Ethics, 45(2), 98-102.

Ha, J. F. y Longnecker, N. (2010). Doctor-patient communication: A review. The Ochsner Journal, 10(1), 38-43.

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X. y Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36. https://doi.org/10.1038/s41591-018-0307-0

Househ, M., Kushniruk, A. y Borycki, E. (2019). Patient safety perspectives on health information technology and artificial intelligence in hospitals: A qualitative study. JMIR Human Factors, 6(2), e13358.

Jha, S. y Topol, E. J. (2018). Information and artificial intelligence. Journal of the American College of Radiology, 15(3), 509-511. https://doi.org/10.1016/j.jacr.2017.12.025

Jha, A. K., Doolan, D., Grandt, D., Scott, T. y Bates, D. W. (2019). The use of health information technology in seven nations. International Journal of Medical Informatics, 76(1), 1-11. https://doi.org/10.1016/j.ijmedinf.2006.09.009

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. y Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H. Ma, S., Wang, Y., Dong, Q., Shen, H. y Wang, Y. (2021). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 6(2), 146-157. https://doi.org/10.1136/svn-2017-000101

Jurafsky, D. y Martin, J. H. (2020). Speech and Language Processing (3rd ed.). Pearson.

Kohn, L. T., Corrigan, J. M. y Donaldson, M. S. (2000). To err is human: Building a safer health system. National Academy Press.

Krizhevsky, A., Sutskever, I. y Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.

Kurtz, S., Silverman, J., Draper, J. y Benson, J. (2017). Marrying content and process in clinical method teaching: Enhancing the Calgary-Cambridge guides. Academic Medicine, 92(1), 74-78. https://doi.org/10.1097/ACM.0000000000001266

LeCun, Y., Bengio, Y. y Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

McGlynn, E. A., McDonald, K. M., Cassel, C. K. y Bell, D. S. (2020). Improving patient care with health information technology. The New England Journal of Medicine, 383(2), 176-178.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. y Floridi, L. (2019). The ethics of algorithms: Mapping the debate. Big Data y Society, 6(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

Obermeyer, Z. y Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181

Obermeyer, Z., Powers, B., Vogeli, C. y Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

Ong, E. y Coiera, E. (2020). Evaluating the effectiveness of a chatbot for improving patient care in a primary care setting. Journal of Medical Internet Research, 22(4), e16235. https://doi.org/10.2196/16235

Patel, V., Arocha, J. F. y Kaufman, D. R. (2019). Diagnostic reasoning and decision making in medicine: A cognitive approach. In Healthcare Systems Engineering (pp. 93-106). Springer.

Price, W. N. y Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0316-4

Rajkomar, A., Dean, J. y Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1815788

Reddy, S., Allan, S., Coghlan, S. y Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497. https://doi.org/10.1093/jamia/ocz192

Rudin, C. y Carlson, D. (2019). The frontiers of fairness in machine learning. https://arxiv.org/abs/1906.00554

Russell, S. y Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Shah, N. H., Milstein, A. y Bagley, S. C. (2019). Making machine learning models clinically useful. JAMA, 322(14), 1351-1352. 10.1001/jama.2019.9233

Siciliano, B. y Khatib, O. (2016). Springer Handbook of Robotics. Springer.

Smith, M., Saunders, R., Stuckhardt, L. y McGinnis, J. M. (Eds.). (2013). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: National Academies Press.

Steinhauer, T. J., Osterhage, K. y Heidel, R. E. (2018). Comprehensive patient medical history and improving collaboration among multidisciplinary teams. Journal of Interprofessional Care, 32(6), 774-782.

Suchman, A. L., Markakis, K., Beckman, H. B. y Frankel, R. (1997). A model of empathic communication in the medical interview. JAMA, 277(8), 678-682.

Tempfer, C. B., Jirecek, S. y Zeisler, H. (2017). Tabar's Cyclopedic Medical Dictionary. F.A. Davis Company.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7

Torrey, W. C., Bond, G. R., McHugo, G. J. y Swain, K. (2019). Evidence-based practice implementation in community mental health settings: The relative importance of key domains of implementation activity. Administration and Policy in Mental Health and Mental Health Services Research, 46(2), 219-227.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. y Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008. https://doi.org/10.48550/arXiv.1706.03762

Vayena, E., Blasimme, A. y Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689.

Wang, F., Casalino, L. P. y Khullar, D. (2020). Deep learning in medicine—promise, progress, and challenges. JAMA Internal Medicine, 179(3), 293-294. https://doi.org/10.1001/jamainternmed.2018.7117

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F. y Jung, K. (2018). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340.

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W. y Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. https://arxiv.org/abs/1609.08144

Yu, K. H., Beam, A. L. y Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731. https://doi.org/10.1038/s41551-018-0305-z

Published

2024-07-04

How to Cite

Carvajal Zaera, E. (2024). The influence of artificial intelligence on communication in healthcare. European Public & Social Innovation Review, 9, 1–19. https://doi.org/10.31637/epsir-2024-312

Issue

Section

Research articles