Artificial Intelligence in Geriatric Patient Follow-up: Impact on Physician Workload and Clinical Applications

Abstract

Purpose: The rapid advancements in artificial intelligence (AI) have significantly influenced healthcare, especially in geriatric patient monitoring, a complex process due to multimorbidity, polypharmacy, and frailty. This study aims to evaluate the role of AI applications in geriatric monitoring and examine their potential impact on reducing physicians' workload. Methods: A comprehensive literature review was conducted, covering current AI-supported patient monitoring systems, clinical decision support tools, and workflow automation. Areas such as diagnosis, treatment planning, and real-time health monitoring via wearable technologies were examined in detail. Results: AI-supported systems have been shown to facilitate early diagnosis, optimize treatment planning, and improve clinical decision-making. These systems enhance patient outcomes while reducing administrative burdens on physicians. Remote monitoring and predictive analytics enable timely interventions, potentially reducing hospital admissions and emergency visits. Furthermore, AI-based automation can take over routine tasks, increasing clinical workflow efficiency. Conclusion: The integration of AI into geriatric patient monitoring offers the potential to improve healthcare efficiency, enhance patient safety, and reduce physicians’ workload. However, ethical concerns, data privacy, and AI system reliability must be carefully addressed. Future research should focus on developing user-friendly AI systems and evaluating their long-term clinical effectiveness. Total words:191

Keywords

• Geriatrics
• Artificial Intelligence
• Clinical Decision Support
• Physician Workload
• Healthcare Automation
• Digital Health.

Ethical Statement

This study has not been presented at any scientific meeting and has not been published elsewhere. The authors of this study have no financial support or conflict of interest in relation to the manuscript.

Geriatrik Hasta Takibinde Yapay Zeka: Hekim İş Yüküne Etkisi ve Klinik Uygulamaları

Abstract

Amaç: Yapay zekâ (YZ) alanındaki hızlı gelişmeler, geriatrik hasta takibi gibi karmaşık süreçleri etkileyerek sağlık hizmetlerinde önemli dönüşümlere yol açmıştır. Bu çalışmanın amacı, YZ uygulamalarının geriatrik hasta takibindeki rolünü değerlendirmek ve hekim iş yükünü azaltmadaki potansiyel etkilerini incelemektir. Yöntem: Güncel YZ destekli hasta izleme sistemleri, klinik karar destek araçları ve iş akışı otomasyonu gibi konuların yer aldığı kapsamlı bir literatür taraması yapılmıştır. Tanı, tedavi planlaması ve giyilebilir sağlık teknolojileri aracılığıyla gerçek zamanlı sağlık izlemesi gibi uygulama alanları detaylı şekilde incelenmiştir. Bulgular: YZ destekli sistemlerin erken tanıyı kolaylaştırdığı, tedavi planlarını optimize ettiği ve klinik karar verme süreçlerini iyileştirdiği saptanmıştır. Bu sistemler hasta sonuçlarını iyileştirirken hekimlerin idari yükünü de azaltmaktadır. Uzaktan izleme ve öngörücü analizlerle erken müdahale sağlanabilmekte, hastane yatışları ve acil başvurular azaltılabilmektedir. Ayrıca YZ tabanlı otomasyonun rutin görevleri devralarak klinik iş akışını daha verimli hâle getirdiği görülmüştür. Sonuç: YZ’nin geriatrik hasta takibine entegrasyonu, sağlık hizmetlerinde verimliliği artırma, hasta güvenliğini geliştirme ve hekim iş yükünü azaltma potansiyeline sahiptir. Ancak etik sorunlar, veri gizliliği ve sistem güvenilirliği konularına dikkat edilmelidir. Gelecekte kullanıcı dostu sistemlerin geliştirilmesi ve uzun vadeli klinik etkinliğin değerlendirilmesine yönelik çalışmalara ihtiyaç vardır. Toplam Sözcük: 183

Keywords

• Geriatri
• Yapay Zeka
• Klinik Karar Destek
• Hekim İş Yükü
• Sağlık Otomasyonu
• Dijital Sağlık.

Ethical Statement

Bu çalışma herhangi bir bilimsel toplantıda sunulmamış ve başka bir yerde yayınlanmamıştır. Bu çalışmanın yazarlarının makale ile ilgili herhangi bir finansal desteği veya çıkar çatışması yoktur.

References

1. 1. Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. arXiv Preprint. 2021;arXiv:2111.08441.
2. 2. Efendioğlu EM. Artificial intelligence in geriatric medicine. Exp Appl Med Sci. 2024.
3. 3. Aprahamian I, Morley JE. To drug or not to drug: the geriatrician dilemma of polypharmacy. J Nutr Health Aging. 2020;24:809–11.
4. 4. Quazi S, Saha RP, Singh M. Applications of artificial intelligence in healthcare. J Exp Biol Agric Sci. 2022;10(1):211–26.
5. 5. Mittal A, Afsar A, Tayal A, Shetty M. Artificial intelligence and healthcare. MAMC J Med Sci. 2023;9:81–7.
6. 6. Jeyaraj PD, Narayanan T. Role of artificial intelligence in enhancing healthcare delivery. Int J Innov Sci Mod Eng. 2023.
7. 7. Haque N. Artificial intelligence and geriatric medicine: New possibilities and consequences. J Am Geriatr Soc. 2023;71:2028–31.
8. 8. Nigar N. AI in remote patient monitoring. arXiv Preprint. 2024;arXiv:2407.17494.

9. 9. Dubey A, Tiwari A. Artificial intelligence and remote patient monitoring in US healthcare market: a literature review. J Market Access Health Policy. 2023;11.
10. 10. Pedersini P, Tovani-Palone M. Artificial intelligence in geriatric rehabilitation. Top Geriatr Rehabil. 2024;40:95–8.
11. 11. Petrauskas V, Jasinevicius R, Damulevičienė G, Liutkevičius A, Janavičiūtė A, Lesauskaite V, et al. Explainable artificial intelligence-based decision support system for assessing the nutrition-related geriatric syndromes. Appl Sci. 2021;11(1):1–15.
12. 12. Madden K, Maher D, Montero-Odasso M, Lam R. Unmet needs for geriatric medicine and care of the elderly physicians work force in Canada. Can Geriatr J. 2021;24:162–3.
13. 13. Auerbach D, Levy D, Maramaldi P, Dittus R, Spetz J, Buerhaus P, et al. Optimal staffing models to care for frail older adults in primary care and geriatrics practices in the US. Health Aff (Millwood). 2021;40(9):1368–76.
14. 14. Koç M. Artificial intelligence in geriatrics. Turk J Geriatr. 2023.
15. 15. Petrauskas V, Damulevičienė G, Dobrovolskis A, Dovydaitis J, Janavičiūtė A, Jasinevicius R, et al. XAI-based medical decision support system model. Int J Sci Res Publ. 2020;10(7).
16. 16. Lukkien DRM, Stolwijk NE, Askari SI, Hofstede BM. Artificial intelligence-assisted decision making in long-term care: qualitative study on prerequisites for responsible innovation. JMIR Aging. 2024;7:e51189.
17. 17. Thakur U, Varma A. Psychological problem diagnosis and management in the geriatric age group. Cureus. 2023;15:e45023.
18. 18. Pawar AB, Mary S. Artificial intelligence in medicine and healthcare. Stud Health Technol Inform. 2020;272:1–9.
19. 19. Golden A. Theoretical framework for an artificial intelligence-based comprehensive geriatric assessment. Innov Aging. 2023;7:877.
20. 20. Shaik T, Tao X, Higgins N, Li L, Gururajan R, Zhou X, et al. Remote patient monitoring using artificial intelligence: current state, applications, and challenges. Wiley Interdiscip Rev Data Min Knowl Discov. 2023;13(5):e1494.
21. 21. Ghasemi A, Naeimaeyi Aali M. Clinical reasoning and artificial intelligence. Ann Mil Health Sci Res. 2023;21(1):e123456.
22. 22. Nyiramana MP. The role of artificial intelligence in clinical decision support systems. Res Invent J Public Health Pharm. 2024;9(2):45–50.
23. 23. Suárez YS, Alawi AM, Ricardo SEL. Hospital processes optimization based on artificial intelligence. Lat Am J Artif Intell (LatIA). 2023;1(1):1–6.
24. 24. Rathore Y, Sinha U, Haladkar JP, Mate NR, Bhosale SA, Chobe S. Optimizing patient flow and resource allocation in hospitals using AI. In: 2023 Int Conf Artif Intell Innov Healthc Ind (ICAIIHI). 2023. p.1–6.
25. 25. Tilala MH, Chenchala PK, Choppadandi A, et al. Ethical considerations in the use of artificial intelligence and machine learning in health care: a comprehensive review. Cureus. 2024;16:e55521.
26. 26. Naik N, Hameed B, Shetty D, et al. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Front Surg. 2022;9:876312.
27. 27. Moon G, Yang JH, Son YN, Choi EK, Lee I. Ethical principles and considerations concerning the use of artificial intelligence in healthcare. Korean J Med Ethics. 2023;26(1):55–65.
28. 28. Veluru CS. Impact of artificial intelligence and generative AI on healthcare: security, privacy concerns and mitigations. J Artif Intell Cloud Comput. 2024;3(1):10–7.
29. 29. Prajapati M, Upadhyay AK, Rezaie M, Dongradive J. A comparative study on AI-driven anonymization techniques for protecting personal data. Int J Multidiscip Res. 2024;14(2):95–104.
30. 30. Shamszare H, Choudhury A. Clinicians’ perceptions of artificial intelligence: focus on workload, risk, trust, clinical decision making, and clinical integration. Healthcare (Basel). 2023;11(3):456.
31. 31. Huang KA, Choudhary HK, Kuo PC. Artificial intelligent agent architecture and clinical decision-making in the healthcare sector. Cureus. 2024;16:e60021.
32. 32. Sorte S, Rawekar A, Rathod SB. Understanding AI in healthcare: perspectives of future healthcare professionals. Cureus. 2024;16:e59000.
33. 33. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
34. 34. Wong A, Wang M, Mentis HM. Designing health information technologies for older adults: a review of recent literature. Am J Geriatr Psychiatry. 2020;28(10):1036-1045.
35. 35. Bickmore TW, Caruso L, Clough-Gorr K, Heeren T. "It's just like you talk to a friend": relational agents for older adults. Interacting with Computers. 2005;17(6):711-735.
36. 36. Batara L, Vogel L. How should specialist physicians prepare for the AI revolution? CMAJ. 2020;192:E595.
37. 37. AlZaabi A, AlMaskari S, AlAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? Digit Health. 2023;9:1–8.