Different Ways AI is Impacting Telemedicine
1 year ago
Artificial intelligence and machine learning are no longer technologies used in research labs only. They are now available on the consumer front with use cases influencing our everyday tasks on a larger scale.
Be it Uber, Tiktok or Airbnb, all modern-age startups are leveraging AI and ML for providing personalized user experience and delivering their services in a manner that users engage more.
While use cases of AI are ever-evolving, AI has been more centralized around healthcare - especially telemedicine - since the pandemic hit us.
Telemedicine - what we experienced before the pandemic - is no longer the same in 2021, due to AI and ML impacting utilities of telemedicine solutions greatly.
Let us reveal 5 different ways AI and ML are pushing the boundaries of telemedicine!
Our other useful resources:
- Accuro EMR integration
- White-label telemedicine software for providers
- Telemedicine for pregnancy care
- Build app for caregiver
1. Symptom checker
When someone wakes up with a sore throat and fever, he gets confused over his symptoms as he could not review whether he is having common flu or COVID-19.
And many times his confusion leads him to a panicked situation.
But in such a non-life-threatening situation, he should not rush to a hospital. He can evaluate his symptoms and know what causes symptoms from his smartphone itself.
An AI-enabled symptom checker asks users to answer a few questions about their symptoms and determines whether a user is having common flu or COVID-19 with the help of sophisticated AI algorithms.
Symptom checker integrated with a telemedicine solution also suggests users whether they require medical consultation or not.
Babylon by Telus Health is the best example here. It has an integrated AI-enabled symptom checker within its telemedicine app.
2. Remote patient monitoring with advanced alert
Many new telemedicine startups prefer to connect a remote patient monitoring system with a telemedicine platform to not make healthcare providers record biometric values or vital body signs of patients manually.
With such an integrated solution, healthcare providers receive real-time biometric data of patients - blood pressure, heart rate, oxygen level and glucose level - directly on a telemedicine platform itself.
Role of AI and machine learning in remote patient monitoring is to keep an eye on patients’ biometric value, analyze it in real-time and alert a caretaker if AI algorithms find worsening values of vital body signs.
It also analyzes historic and real-time data of patients’ biometric value and finds out crucial early signs of chronic illness and alerts providers.
With early detection of diseases, healthcare providers can plan the care accordingly and avoid any further complications.
3. In-video gesture and voice analysis for clinical purpose
In an in-person visit, a healthcare provider can easily know the severity of symptoms and pain a patient is going through by physically examining him.
But while in a virtual meeting, healthcare providers could only ask questions about how severe symptoms and pain are. They cannot physically examine patients.
And this results in an ineffective care plan prepared by a healthcare provider as he is not fully aware of the physical health of patients.
An AI and ML algorithm can play a major role here. It can analyze physical behaviour and voice of a patient while he is on a video call with a physician.
And based on this analysis, it estimates real pain a patient is going through and severity of his symptoms.
Physiotherapists providing online rehabilitation therapy can leverage this use case of AI and ML in telemedicine to its full potential. See following tweet.
Me and @ColellaTracey are delighted that our project "Multimodal AI for Delivering Home-based #Cardiac #Telerehabilitation" is selected for the 2020-21 Cohort for the #Entrepreneurship for #Cardiovascular Health Opportunities (#ECHO). Thanks @Soror_Sha @trogersresearch judges 1/2 pic.twitter.com/S5En3n5l9J— Shehroz Khan (@Shehroz_SKhan) September 30, 2020
4. Evidence-based clinical assessment and decision-making
Healthcare providers work in intense environments where they are prone to errors.
If they diagnose poorly or prescribe a wrong drug, patient outcomes get neutralized.
So, rather than using humans for crucial clinical decisions, it is a much safer and reliable idea to use AI technology for clinical decision-making and assessment.
AI and ML algorithms analyze patients’ clinical data including lab results, previous prescriptions, medical notes & allergies and assist healthcare providers to diagnose patients, prepare a care plan and prescribe the right drug.
It majorly uses patients’ clinical data stored on EMR/EHR and a dataset of other patients having similar symptoms, their care plans, and patient outcomes associated with specific care plans to help providers in clinical decision-making.
5. Personal health management with a virtual assistant
Medical treatment does not end with a prescription. Patients need to take medical drugs on time. They need to follow a diet plan. They need to follow a home exercise program. They need to track their medical progress.
But since every patient gets involved in their daily life chores after getting a prescription, they could not manage food, prescription, and medical goals the way it is supposed to be managed.
And such ignorance due to a busy lifestyle many times leads a common illness to a life-threatening medical emergency.
But with hand-picked telemedicine app features working on AI and machine learning technologies, patients can look after themselves more effectively and easily.
- It reminds users to take medicine and do workouts.
- It allows users to keep track of food they take and the nutrition value of each food item.
- Based on personal health data patients are storing, a telemedicine app lets patients know whether their medical condition is improving or not.
- Virtual assistant of telemedicine solution answers all health queries of patients and provides them with personalized health information, training sessions and education.
Conclusion: CEO’s Note
Talking about AI is a piece of cake. But executing AI at consumer front requires guts! It’s not like you start an AI project and fail. It’s only like you fail to start an AI project.
If not executed the right way, AI turns into a threat - a threat to the authenticity of healthcare information and compliance-readiness of your tool.
But we practice AI like a medical surgery - with no scope for error & a second chance. Being resilient to AI is the only way to master AI.
Let’s have a productive discussion around your telemedicine platform and how and where AI fits in there.