AI-Powered Assistants
for Enhanced Patient Care

Patient engagement is essential for optimal outcomes in healthcare. However, there are perennial barriers to ensuring patients are educated and involved in their own medical care. These barriers include geographical distance from medical practices, lack of medical literacy, distrust of healthcare institutions, and lack of clinician time and resources⁽¹⁾,⁽²⁾. AI is revolutionizing healthcare in many ways, such as improving the diagnosis of different diseases. Recently, AI tools are being implemented to enhance patient engagement and lessen the burden on healthcare providers. While these tools are likely to have important and enduring benefits, there are several barriers to their effective implementation ⁽³⁾,⁽⁴⁾. This blog discusses the need for, and benefits of, AI-powered assistants in healthcare and the current challenges to their implementation.

The Need for AI-powered Solutions

Patient engagement ensures that patients adhere to their treatment plans, attend appointments, and engage with regular screening programs. Lack of engagement leads to poorer outcomes and higher rates of readmission, which puts further strain on healthcare providers and the healthcare system more generally ⁽⁵⁾,⁽⁶⁾. Furthermore, biases and the underserving of certain populations continue to contribute to disparities in healthcare access, quality, and outcomes⁽⁷⁾.

The Power of AI Tools in Healthcare: Virtual Healthcare Assistants

AI-powered tools include virtual assistants, chatbots, and patient portals, each playing distinct and important roles in improving healthcare infrastructure. While this article only touches on virtual healthcare assistants, the full-length white paper provides more information on the importance of chatbots and patient portals.

Virtual health assistants are AI-powered tools designed to provide personalized healthcare support. They offer interactive, spoken, or written information to help patients manage symptoms, make decisions about when to seek care, schedule appointments, and adhere to treatment plans. By integrating data from wearable devices, such as heart rate and sleep monitors, these assistants also deliver tailored advice and help to guide clinical decision-making. Specialized healthcare assistants cater to specific conditions. For example, virtual mental health assistants monitor mood, sleep, and stress levels, while diabetes-focused assistants help to track blood glucose levels⁽⁷⁾,⁽⁸⁾,⁽⁹⁾.

Let’s examine how virtual healthcare assistants help overcome common barriers to patient engagement while reducing the workloads of clinicians and healthcare administrators.

Geographical Barriers: 

The Problem: Individuals living in rural areas frequently encounter geographical barriers to healthcare access, often requiring them to take more time off work and travel longer distances for appointments⁽¹⁰⁾. This problem is compounded by poorer public transportation infrastructure in these areas. Furthermore, individuals in rural areas may have lower levels of health literacy, which acts as another barrier to engaging fully with the healthcare system.

The Solution: AI-powered virtual assistants help tackle this issue by providing patients with 24/7 remote access to healthcare information, regardless of their distance from the physical healthcare provider⁽²⁾.

Lack of Health Literacy:

The Problem: Many individuals lack sufficient understanding of medicine and biology, and clinicians cannot communicate essential information effectively within short timescales, such as during checkups. This leads to poorer education about their health and, as a result, less engagement.

The Solution: AI-powered virtual assistants can answer patient questions at any time and tailor responses to different levels of health literacy. Patients can ask as many follow-up questions as they like without worrying about wasting clinician time, which has the added benefit of reducing the patient education burden on healthcare providers⁽²⁾.

Bias:

The Problem: Subconscious bias affects healthcare, as clinicians may unknowingly treat individuals differently based on clinically irrelevant patient characteristics. This can lead to poorer patient outcomes, particularly in populations that may already suffer due to inequalities within the healthcare system⁽⁷⁾.

The Solution: AI-powered virtual assistants carry a significantly reduced risk of bias. However, as much of healthcare research is based on white populations, more inclusive research is required to ensure bias does not become an issue with AI-powered healthcare solutions⁽¹¹⁾. 

Barriers to Implementing AI-powered Tools in Healthcare

While implementing AI-powered tools in healthcare has many benefits, several barriers prevent their integration. Firstly, many patients and healthcare professionals do not have experience interacting with AI. While technological barriers are problematic, individuals may not trust their healthcare to AI systems. Furthermore, there are several ethical and security issues to overcome when integrating AI into healthcare. Central to this are issues with entrusting AI systems with increasingly vast volumes of patient data⁽¹²⁾. Lastly, face-to-face communication remains the gold standard for patient-doctor interaction. It is important that AI serves as a tool to support, rather than replace, doctor-led patient care⁽²⁾.

Conclusion

AI-powered virtual healthcare assistants offer promising solutions to many of the challenges that hinder patient engagement and burden healthcare providers. By overcoming barriers such as geographical distance, health literacy, and bias, these tools enhance patient accessibility and reduce the strain on clinicians. However, successful implementation requires addressing issues related to technological trust, ethical concerns, and data security. As AI continues to evolve, it is crucial to integrate these tools thoughtfully, ensuring they complement, rather than replace, the vital role of human healthcare providers in delivering personalized, compassionate care.

The full-length whitepaper on this topic provides additional details, including the benefits of AI-powered assistants for clinicians and healthcare administrators.

Contact a member of our team today to discover how AI and machine learning can transform your organization’s healthcare infrastructure.

References

1. Johnson AM, Brimhall AS, Johnson ET, et al. A systematic review of the effectiveness of patient education through patient portals. JAMIA Open. 2023;6(1):ooac085. doi:10.1093/jamiaopen/ooac085

2. Bickmore T, Giorgino T. Health dialog systems for patients and consumers. Journal of Biomedical Informatics. 2006;39(5):556-571. doi:10.1016/j.jbi.2005.12.004

3. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi:10.7861/futurehosp.6-2-94

4. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689. doi:10.1186/s12909-023-04698-z

5. Joynt KE, Sarma N, Epstein AM, Jha AK, Weissman JS. Challenges in Reducing Readmissions: Lessons from Leadership and Frontline Personnel at Eight Minority-Serving Hospitals. Jt Comm J Qual Patient Saf. 2014;40(10):435-437. doi:10.1016/s1553-7250(14)40056-4

6. Baryakova TH, Pogostin BH, Langer R, McHugh KJ. Overcoming barriers to patient adherence: the case for developing innovative drug delivery systems. Nat Rev Drug Discov. 2023;22(5):387-409. doi:10.1038/s41573-023-00670-0

7. Vela MB, Erondu AI, Smith NA, Peek ME, Woodruff JN, Chin MH. Eliminating Explicit and Implicit Biases in Health Care: Evidence and Research Needs. Annu Rev Public Health. 2022;43:477-501. doi:10.1146/annurev-publhealth-052620-103528

8. Mitsea E, Drigas A, Skianis C. Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review. Behavioral Sciences. 2023;13(12):1008. doi:10.3390/bs13121008

9. Guan Z, Li H, Liu R, et al. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med. 2023;4(10):101213. doi:10.1016/j.xcrm.2023.101213

10. Maganty, Byrnes, Hamm, et al. Barriers to rural health care from the provider perspective. RRH. Published online May 17, 2023. doi:10.22605/RRH7769

11. Mittermaier M, Raza MM, Kvedar JC. Bias in AI-based models for medical applications: challenges and mitigation strategies. npj Digit Med. 2023;6(1):113, s41746-023-00858-z. doi:10.1038/s41746-023-00858-z

12. Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 2023;15(10):e46454. doi:10.7759/cureus.46454