AI-Powered Assistants for Patients:
Transforming the Healthcare Experience

promo
20 November 2024
5 minutes read
Medically Reviewed by: Dr. Danielle Kelvas, MD

Recent advances in AI have led to healthcare undergoing a digital transformation journey. AI is now being used to streamline multiple areas of healthcare, including roles in diagnostics, drug discovery, predictive medicine, and administration (1). In addition to assisting medical professionals, AI also plays an important role in improving patient satisfaction. This blog discusses how AI tools can be an asset to healthcare organizations, improving patient care and efficiency.

Frame 1171276847123

Benefits of AI Assistants for Patients

Decreased Wait Times

Long waiting times are a common complaint from patients in all areas of healthcare – from waiting in emergency departments to waiting to be discharged from the hospital. AI is able to analyze large amounts of historical and real-time data in order to organize appointment schedules whilst adjusting to changing factors(2). AI can also optimize patient discharge through predicting discharge 24 hours in advance, allowing discharge processes to begin in advance(3). For example, IT Medical has produced an AI-powered discharge management tool that results in an 11% reduction in average length of stay, improving both patient satisfaction and saving the hospital money(4).

Access to Care

AI-powered assistants for patients can provide care in the form of Virtual Health Assistants, Medical Assistant Chatbots, and Patient Portals. These provide services such as answering patient queries and offering personalized health advice(2). The 24/7 nature of AI-powered assistants results in patients receiving personalized answers to questions in real-time, rather than having to wait to book an appointment – and so providing instant alleviation of worries(5). Patients often fail to ask all of their questions to medical professionals due to worry about pushback or that they are wasting their time(6). The use of AI-powered assistants allays these concerns, whilst providing the information that the patient needs. In addition, AI-powered assistants are not limited geographically, meaning that they can provide care regardless of how far patients are situated from a hospital. They can also tailor any advice and information to the patient’s level of health literacy, thus increasing understanding and patient engagement(2).

Improved Follow-up

AI-powered assistants, such as Virtual Health Assistants, can aid in patient follow-up. They provide tailored information about the patient’s health condition, translating complex medical terms into simpler language to ensure that the information being provided is at a level that the patient can understand(7). Following a new disease diagnosis or a stay in hospital, patients are often given new medications to manage the disease symptoms. However, many forget to take their medication – one study estimates that 62% of those with chronic illnesses have forgotten medication(8). Virtual Health Assistants can provide reminders of when to take medications and can optimize medication schedules for individual patients, ensuring that they remain on top of their symptoms and that the drug is working effectively(9),(10).

Fewer Unnecessary Hospital Visits

AI-powered assistants have the potential to significantly reduce unnecessary hospital visits, reducing overcrowding and strain on the system. The tailoring of information to an individual’s health literacy level will ensure that patients are making informed decisions(7). Patients are able to use AI-powered assistants to ask urgent questions about symptoms or medication side effects. These questions will be instantly answered by the assistant, serving to alleviate worries and offer information on whether a hospital visit is necessary(5). For example, a symptom-assessing technology tested with the NHS found that 12.8% of people reported that the use of this technology would have saved them an unnecessary visit to the doctor(11). A study in the US using the same technology found that the recommendations were equivalent to using a nurse-staffed triage phone line(12).

Medication Reminders

Medication reminders help patients adhere to their medication schedule ensuring that symptoms remain under control and do not escalate (which may require further hospital visits)(9). The use of wearable devices, such as glucose trackers, can monitor vital signs and offer instant personalized healthcare recommendations. For example, a glucose tracker may advise an optimized insulin dose based on blood sugar readings to ensure effective glycemic control and reduce the chances of complications requiring hospitalization(13),(14). Virtual chronic disease assistants can further offer lifestyle recommendations to reduce hospital visits based on data obtained from wearables, such as recommending dietary adjustments and exercise routines15 or providing cognitive behavioral therapy(16).

 

Remote Patient Monitoring img1

Considerations

Though offering many benefits, both in improving patient care and streamlining operations, aspects of AI need to be carefully considered before implementation. Firstly, ethical issues arise from AI requiring access to large amounts of patient data. It is therefore crucial that AI-powered assistants comply with data privacy and confidentiality rules{17}. In addition, usability and trust – both by patients and medical professionals – must be considered. In older patients (65+), 59% preferred a doctor visit over virtual care, demonstrating a level of reluctance to move away from exclusively human care{18}. Older patients also struggle with the use of the technology. It is therefore important that doctor-led patient care is maintained, and the use of AI assistants to support this care is carefully explained to ensure its proper use.

Conclusion

AI-powered assistants are transforming the healthcare experience of patients. Patients face many challenges to receiving optimal healthcare and making their own informed decisions, including lack of access, low health literacy, and poor disease management. AI-powered assistants can help in overcoming these challenges through their 24/7 availability regardless of geographical location, translation of complex medical information to a level compatible with patient understanding, and provision of optimized medication schedules with incorporated reminders to ensure medication is taken correctly. Not only do these features improve patient education and satisfaction with healthcare but they also assist busy medical professionals through decreasing the number of unnecessary hospital visits and time spent answering questions instead of treating patients. Ultimately, AI assistants will improve patient care and operational efficiency, leading to improved satisfaction.

Are you ready to move to AI-powered healthcare solutions? Contact our team to discover how we can incorporate machine learning and data science into your current infrastructure to enhance patient outcomes and streamline organizational efficiency.

References

  1. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095.

  2. Batra P, Dave DM. Revolutionizing Healthcare Platforms: The Impact of AI on Patient Engagement and Treatment Efficacy. IJSR. 2024;13(2):273-280. doi:10.21275/SR24201070211

  3. Kovoor JG, Bacchi S, Gupta AK, Stretton B, Malycha J, Reddi BA, Liew D, Beltrame JF, Zannettino AC, Jones KL, Horowitz M, Dobbins C, Hewett PJ, Trochsler MI, & Maddern GJ. The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery. ANZ Journal of Surgery. 2023;93(9), 2119-2124. doi: 10.1111/ans.18546

  4. IT Medical. Patient discharge management system. IT Medical; 2024. Accessed 22 October 2024. https://itmedical.com/patient-discharge-management-system/

  5. Velibor BOŽİĆ B. The Role of Artificial Intelligence in Increasing the Health Literacy of Patients. Published online June 29, 2024. doi:10.5281/ZENODO.12593108

  6. Ianwarne N, Newbould J, Burt J, Campbell JL, & Roland M. Wasting the doctor’s time? A video-elicitation interview study with patients in primary care. Social Science & Medicine. 2017;176, 113-122.doi:10.1016/j.socscimed.2017.01.025

  7. Bickmore T, Giorgino T. Health dialog systems for patients and consumers. J Biomed Inform. 2006 Oct;39(5):556-71. doi: 10.1016/j.jbi.2005.12.004.

  8. Piña IL, Di Palo KE, Brown MT, Choudhry NK, Cvengros J, Whalen D, Whitsel LP, Johnson J. Medication adherence: Importance, issues and policy: A policy statement from the American Heart Association. Prog Cardiovasc Dis. 2021 Jan-Feb;64:111-120. doi: 10.1016/j.pcad.2020.08.003.

  9. Babel A, Taneja R, Mondello Malvestiti F, Monaco A, Donde S. Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases. Front Digit Health. 2021 Jun 29;3:669869. doi: 10.3389/fdgth.2021.669869.

  10. Tan BKJ, Teo CB, Tadeo X, Peng S, Soh HPL, Du SX, Luo VWY, Bandla A, Sundar R, Ho D, Kee TW, Blasiak A. Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial. Front Digit Health. 2021 Apr 12;3:635524. doi: 10.3389/fdgth.2021.635524.

  11. Miller S, Gilbert S, Virani V, Wicks P. Patients’ Utilization and Perception of an Artificial Intelligence-Based Symptom Assessment and Advice Technology in a British Primary Care Waiting Room: Exploratory Pilot Study. JMIR Hum Factors. 2020 Jul 10;7(3):e19713. doi: 10.2196/19713.

  12. Morse KE, Ostberg NP, Jones VG, Chan AS. Use Characteristics and Triage Acuity of a Digital Symptom Checker in a Large Integrated Health System: Population-Based Descriptive Study. J Med Internet Res. 2020 Nov 30;22(11):e20549. doi: 10.2196/20549.

  13. Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. Sensors (Basel). 2020 Jul 10;20(14):3870. doi: 10.3390/s20143870.

  14. Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med. 2023 Oct 17;4(10):101213. doi: 10.1016/j.xcrm.2023.101213.

  15. Oh YJ, Zhang J, Fang ML, et al. A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. Int J Behav Nutr Phys Act 2021;18, 60. doi:10.1186/s12966-021-01224-6

  16. Mitsea E, Drigas A, Skianis C. Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review. Behav Sci (Basel). 2023 Dec 10;13(12):1008. doi: 10.3390/bs13121008.

  17. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care – Addressing Ethical Challenges. N Engl J Med. 2018 Mar 15;378(11):981-983. doi: 10.1056/NEJMp1714229.

  18. PWC. PwC’s 2024 US Healthcare Consumer Insights and Engagement Survey. PwC, 2024. Accessed 25 October 2024. https://www.pwc.com/us/en/industries/health-industries/library/healthcare-consumer-insights-survey.html

Whitepaper

AI-Powered Assistants
for Enhanced Patient Care

Read now

Contact Us

Here's what you can expect to happen next:

  1. After reviewing your project’s requirements, one of our experts will get in touch to schedule a call.
  2. Upon request, we can promptly arrange an NDA to ensure the highest level of privacy.
  3. You will receive a detailed project proposal with estimates, timelines, and our expert opinion.

    * This is a required fields.
    No more than 3 files may be attached up to 15MB
    each. Formats: doc, docx, pdf, ppt, pptx.
    The information you submit to us by filling out this contact form will be processed in accordance with the Privacy Policy.

    Thank you
    for your message

    Your details have been submitted and we will get in touch with you shortly