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Short Communication Volume 7 Issue 3
Incorporating artificial intelligence in palliative care: opportunities and challenges
Utria Munive Jesus
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Physician, Specialist, and masters in clinical Epidemiologist, University Foundation of Health Sciences, Colombia
Correspondence: Jesús Utria Munive, Faculty of Medicine, University Foundation of Health Sciences – FUCS, Castellana Bogota, Colombia, Tel +57 3017300133
Received: July 15, 2024 | Published: July 26, 2024
Citation: Utria MJ. Incorporating artificial intelligence in palliative care: opportunities and challenges. Hos Pal Med Int Jnl. 2024;7(3):81-82. DOI: 10.15406/hpmij.2024.07.00244
Abstract
Artificial Intelligence (AI) is transforming the field of palliative care, offering innovative solutions that improve patients' quality of life and optimize clinical practice. Using technologies such as machine learning and natural language processing, AI makes it possible to analyze large amounts of clinical data to provide valuable insights and decision-making support. This article aims to examine the current applications of AI in palliative care, assess its potential benefits, analyze the associated ethical challenges, and explore prospects in this rapidly evolving field. In conclusion, AI offers unprecedented opportunities to improve palliative care, but it is crucial to address ethical and equity challenges to ensure fair benefits. The future of palliative care will depend on our ability to balance technological innovation with human values.
Keywords: palliative care, artificial intelligence, intelligence, hospice care, palliative medicine
Abbreviations
AI, Artificial intelligence
Artificial intelligence in palliative care
The integration of Artificial Intelligence (AI) in palliative care is revolutionizing end-of-life care, providing innovative solutions to enhance patient quality of life and optimize clinical practice. This article delves into the current applications of AI in palliative care, its potential benefits, associated ethical challenges, and prospects in this rapidly evolving field. Current Applications of AI in Palliative Care: AI has proven to be a promising tool in pain monitoring and management. Petrova et al.1 reviewed the implementation of electronic palliative care coordination systems incorporating AI elements to enhance patient care. Their study found that these systems can improve communication and coordination by 23%, leading to more effective management of pain and other symptoms. These systems utilize machine learning algorithms to analyze pain patterns and recommend personalized interventions, enabling a more initiative-taking and effective approach to pain management in palliative patients.1,2
In the realm of clinical decision-making support, Guo et al.3 analyzed 995 publications on AI in healthcare, highlighting its potential to improve diagnostic accuracy and decision-making in complex clinical settings such as palliative care. AI-based decision support systems can analyze large volumes of clinical data and provide evidence-based recommendations, improving the accuracy of clinical decisions by up to 30% in some cases. This is particularly valuable in palliative care, where decisions often involve balancing multiple complex factors.3,4 Potential Benefits: Data-driven personalization of care is one of the most significant benefits AI offers in palliative care. Rajkumar et al. discuss how machine learning can enhance diagnostic and treatment accuracy in medicine, including potential applications in palliative care. AI algorithms can analyze large clinical datasets to identify patterns and trends that may not be evident to human physicians, allowing for more personalized and effective care. Studies have shown that implementing AI systems for treatment personalization can improve clinical outcomes by up to 20% compared to traditional approaches.5
Furthermore, AI has demonstrated significant potential in reducing the workload of healthcare professionals in palliative care. Topol examines how AI can automate routine tasks, allowing healthcare professionals to spend more time on direct patient care. For example, AI systems can reduce the time spent on clinical documentation by up to 45%, freeing up valuable time for direct patient interaction. This is particularly important in palliative care, where the quality of human interaction is fundamental to providing compassionate, patient-centered care.6 Ethical Challenges and Considerations: Despite these benefits, the implementation of AI in palliative care raises significant ethical challenges. Patient data privacy and security are primary concerns, as highlighted by Price and Cohen in their analysis of privacy challenges in the era of medical big data. AI systems require access to copious amounts of sensitive patient data, posing risks of privacy breaches and misuse of information. Studies have shown that up to 70% of patients express concerns about the security of their data in AI systems, underscoring the need for robust security measures and transparent privacy policies.7,8
Technological dependency is another major challenge in implementing AI in palliative care. Sujan et al. discuss the human factors and risks associated with integrating AI into patient care, emphasizing the importance of maintaining the "human touch" in palliative care. There is a risk that excessive reliance on AI systems could lead to a decrease in empathy and compassion in patient care. Studies have shown that up to 40% of healthcare professionals express concerns about the potential dehumanization of care due to excessive dependence on technology.9 Additionally, equity in access to these advanced technologies is a crucial ethical consideration in implementing AI in palliative care. Mills addresses the barriers to accessing AI in palliative care, noting that existing healthcare disparities could be exacerbated if not adequately addressed. Studies have shown that access to AI technologies in healthcare can vary significantly based on socio-economic and geographic factors, with some populations being up to 50% less likely to benefit from these innovations. This raises critical issues of justice and equity in the distribution of the benefits of AI in palliative care.10
Future of AI in Palliative Care: The future of AI in palliative care is promising, with emerging innovations that have the potential to significantly transform end-of-life care. He et al. emphasize the importance of practical implementation of AI technologies in medicine, highlighting the need for interdisciplinary collaboration to develop effective and ethically sound solutions. It is anticipated that in the next 5-10 years, there will be a significant increase in the use of AI systems for crisis prediction in palliative patients, optimization of treatment plans, and continuous emotional support.11 Collaboration between technologists and healthcare professionals will be fundamental to developing effective, patient-centered AI solutions in palliative care. Sendak et al. demonstrate the importance of this collaboration in their study on AI implementation in clinical settings. The authors found that AI projects developed with interdisciplinary teams were 40% more likely to be successfully implemented in clinical practice than those developed without such collaboration. This underscores the need for a holistic approach that combines technical expertise with clinical knowledge and an understanding of the unique needs of palliative care patients.12
Conclusion
AI offers unprecedented opportunities to improve palliative care, from pain management to emotional support and clinical decision-making. The potential benefits include greater diagnostic and treatment accuracy, a significant reduction in healthcare professionals' workload, and more effective personalization of care. However, it is crucial to address ethical and equity challenges to ensure these innovations benefit all patients equally. The future of palliative care will depend on our ability to balance technological innovation with fundamental human values of compassion and dignity. Interdisciplinary collaboration between technologists, healthcare professionals, ethicists, and patient representatives will be key to developing AI solutions that are not only technically advanced but also ethically sound and patient centered.
Acknowledgments
None.
Conflicts of interest
The authors declare that there are no conflicts of interest.
References
- Petrova M, Riley J, Abel J, et al. Crash course in EPaCCS (electronic palliative care coordination systems): 8 years of successes and failures in patient data sharing to learn from. BMJ Supportive Palliative Care. 2018;8(4):447.
- Wilson PM, Ramar P, Philpot LM, et al. Effect of an artificial intelligence decision support tool on palliative care referral in hospitalized patients: a randomized clinical trial. J Pain Symptom Manage. 2023;66(1):24–32.
- Guo Y, Hao Z, Zhao S, et al. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res. 2020;22(7):e18228.
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- Alvin R, Jeffrey D, Isaac K. Machine learning in medicine. New England Journal of Medicine. 2019;380(14):1347–1358.
- Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
- Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37–43.
- Sarkar A, Singh P, Varkey M. Healthcare artificial intelligence in India and ethical aspects. AI, Consciousness and The New Humanism. 2024. pp. 107–150.
- Sujan M, Furniss D, Grundy K, et al. Human factors challenges for the safe use of artificial intelligence in patient care. BMJ Health Care Inform. 2019;26(1):e100081.
- Mills J, Fox J, Damarell R, et al. Palliative care providers’ use of digital health and perspectives on technological innovation: a national study. BMC Palliat Care. 2021;20(1):124.
- He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36.
- Sendak MP, Gao M, Brajer N, et al. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med. 2020;3(1):41.
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