The Power of AI in Telehealth
The biggest topic in digital health at the moment: the use of AI and how it will change our lives, improve our jobs while simultaneously making careers obsolete. ChatGPT recently turned one year old and the amount of thought pieces, opinions and ideas is dizzying.
Regardless, AI has been groundbreaking in doing tasks much more quickly than I could myself. With the right data and information, it can also make healthcare much faster. And since i'm always interested in all improvements in telehealth, AI can and will have an effect on virtual care.
1. Enhancing Diagnostics and Decision-Making: Machine learning algorithms can analyze vast amounts of medical data, including patient records, lab results, and imaging studies. Clinicians can make more accurate and timely diagnoses. In scenarios where time is of the essence, such as emergency consultations, AI can aid in swift decision-making by providing valuable insights based on historical data and patterns. In telehealth, whether at home or at a hospital, this is still true. It will especially helpful to parse through data from RPM devices and ensure we are finding the most relevant flags.
2. Personalized Treatment Plans: The same data can be used to make medicine more precise by considering a patient's medical history, genetics, and lifestyle. I can see it being connected to other apps and figuring out health factors based on movement, nutrition, social media activity, and following our shopping patterns. All of this is fascinating. Better personalized data can help with better at home care - this will also lead to MORE care being able to be delivered at the home.
3. Predictive Analytics for Preventive Care: Somewhat related to the above point but by analyzing patient data, AI algorithms can identify patterns that indicate potential health risks. This proactive approach enables healthcare providers to intervene early, preventing the escalation of health issues and reducing the overall burden on the healthcare system. This can also help with population health and monitoring for potential outbreaks.
4. Telemonitoring and Chronic Disease Management: Chronic disease care from home can be improved significantly with AI. RPM and home devices continuously collect and analyze patient data, and can provide real-time feedback to clinicians as well as patients. This not only ensures prompt intervention in case of any concerning developments but also empowers patients to actively manage their conditions with insights and guidance from their healthcare team. Patients want better information about their healthcare. Clinicians want better and relevant data from all the various medical devices we now have access to. This is where AI can help...and save time.
5. Efficient Communication: AI-powered language processing tools enhance communication in telemedicine by facilitating efficient transcription services, language translation, and even sentiment analysis. We have to inform patients about this - I can see issues with privacy if patients don't realize there is an ambient AI but the benefits are worth it. So much can be lost in translation and having it dictated directly can make sure we see all of what was communicated.
There is something to be said on still making sure AI can be empathatic if we are using it instead of a human clinicians. Matt Sakumoto and I wrote a paper on how to do that for chat telehealth that uses AI. Patients still want the feeling of human interaction which AI can be programmed to do (sounds illogical but most of us just need to feel like we are connecting to a human for this to work).
6. Overcoming Healthcare Disparities: This is not a given but potentially, if the data entered is improved, this will take out the unconscious (and sometimes conscious) biases that lead to healthcare disparities. We should do better. We all want to do better. Perhaps having something completely data driven will help. If we can do that over telehealth by taking the information the AI recommends, it would improve this. This has already happened - chat and patient surveys that use algorithms do the same. With a true AI solution, it can go even further.
The integration of AI into telemedicine is going to improve our ability to use virtual care significantly. There are still some things that need to happen including creating a real generative AI, ensuring the data gathered is 'clean' to avoid furthering already biased processes, and figuring out how the privacy may work. There has been already more thought about this than when I started in telehealth. We all have a better understanding that tech solutions need some standardization and formalizing. It is still exciting and i'm looking forward to using more it in my work.