What does the rise of artificial intelligence mean for the future of telehealth?

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Telehealth is much more than two video cameras, two screens, a doctor and a patient. Combined with technologies such as Artificial Intelligence (AI), it can revolutionize the way we bridge the gap to make healthcare more accessible. Per report, 76% of U.S hospitals are integrated with telehealth systems and almost every state has some form of coverage for telehealth services.

AI can provide timely interventions and keep patients out of the hospital while ensuring the quality of healthcare delivery. The rise of machine learning is enabling ‘AI engines’ to even take over much of the care provided by primary care doctors. Furthermore, the advent of artificial intelligence as a platform for improved healthcare provides unparalleled possibilities for improving outcomes of clinical staff, for lowering the cost of care and for changing the health for large populations of patients.

Consequently, there has been a push towards universal electronic health record systems and the systematic collection of patient information through the emergence of information technology in healthcare. Listed below are few ways to imply how AI is expected to play a role in revolutionizing telehealth. 

1. Intelligent bots monitoring patient’s symptoms 

The use of AI chatbots is slowly and steadily being adopted in the telehealth practices through natural language processing (NLP). They are smarter and can monitor patient’s symptoms remotely, can collect relevant quantitative data and pass the information on to the individual doctor managing the chronic-care patient. David Thompson, MD, CEO of Health Navigators is a pioneer of NLP for healthcare. In a Telehealth Secrets talk, Dr. Thompson shares how NLP lays the foundation for AI digital health in use cases from Microsoft, MDLIVE and others.

Florida-based telemedicine provider, MDLIVE has added artificial intelligence (AI) into its virtual healthcare services with its new interactive chatbot named “Sophie”. It focuses on enhancing the platform user experience by creating a customized, conversational registration process for each individual patient.  

The objective of this initiative is to provide accessibility, ease, efficiency, and reduce costs compared to any physical patient monitoring. The use of this technology can result in considerable improvements, including coordinating care more effectively, reducing costs, enhancing the experience between patients and physicians and improving overall consumer experience. 

In addition to virtual care, AI further strikes a balance between technology and human interaction by initiating a more personalized experience for the patient. 

2. AI-powered diagnosis through medical imaging

Hospitals possess a wealth of medical scans, categorized, collected and stored in medical institutions. These massive collections of image data can be used to train AI systems not only to perform the collection and categorization of scans but also to analyze medical images and identify a myriad of health conditions. 

AI is on its way globally to make a significant global impact with which doctors can diagnose a potential disease by applying machine learning methods to large datasets of disease populations. Furthermore, this beauty of telemedicine along with AIA.I has the ability to bypass the traditional doctor’s office visit. 

With the upsurge of telemedicine apps and tools, doctors and physicians find it convenient and flexible to scale their clinical practice. Through telemedicine a wider range of patients can automate reminders and patients can be remotely monitored and diagnosed.  

A well-known existing area of telediagnosis is teledermatology. It lends itself well to automation through AI. FDNA, a digital health company located in Boston, trains a machine-learning algorithm to detect and diagnose rare genetic diseases from images of patients’ faces. Through FDNA, you can send pictures of faces to an algorithm via telemedicine to help detect genetic disease. Likewise, a recent study has shown that a computer algorithm using convolutional neural networks has performed at par in accurately diagnosing melanoma. 

3. Virtual consultations and data-driven personalization 

It is the most widespread application of big data in medicine. EHR data is used with the intent to improve care, perform quality improvement, increase patient engagement, create new knowledge, conduct research in a “real-world, and facilitate personalized care and decision-making. 

Eventually, the goal is to create a continual learning healthcare infrastructure with real-time knowledge production and create an ecosystem that is preventive, predictive, and personalized. 

Take for instance, Ada Health, a telemedicine app that is an AI-powered database uses machine learning and AI to provide personalized diagnosis support. It helps physicians and clinicians to recognize potential conditions by asking questions to the user via a chat interface. Art Papier, the CEO of VisualDX (the company behind Ada), also talks more about their AI technologies in this short talk.

The algorithm of the application is trained on a large database of thousands of medical conditions and symptoms. In addition, after downloading the app, the user has to fill a form for an initial survey assessment. It aids in developing a patient profile. Furthermore, depending upon the responses to the app’s questions the survey is compared to a larger pool of similar cases to suggest possible symptoms through AI. 

Apart from this, EHRs can trigger warnings to track prescriptions of the patient and be sure that the patient is following doctors’ orders as well as set reminders about when a patient is supposed to get a new lab test. 

4. Resolving logistic challenges in telemedicine with AI

When you glance at telemedicine through the lens of logistics, some of the solutions presently resolving logistical issues in other industries such as the shipping sector are also enticing for the telemedicine space. 

When it comes to telemedicine, AI would be able to direct queries to the doctor with the best outcomes for the symptoms of a patient, rather than just referring them to the very first available doctor. Understanding the AI can make a huge difference, from taking massive amount of data, understanding complex constraints and then routing physicians’ time around in the network is a best way to solve logistical issues.So, AI is used to reduce long hospital wait times and other administrative headaches. It routes questions related to patients to the doctor with the best outcomes for a patient’s symptoms.

The actual strength of AI is that it allows computers to search more intelligently and sort through all of the facility, possible doctors, consultation, time, space combinations for the patients. So instead of aimlessly stumbling around, artificial intelligence encourages the machine to map the most efficient path to the right solution. This set of rosters ensures consultation availability and low wait times, as well as ensuring doctors are well utilized by specialty. 

Conclusion

With countless health issues as well as growing geriatric population, AI-enabled telehealth facilities are providing healthcare solutions that play a vital role in not only operating the telemedicine industry but also ensuring the quality as well as optimal patient care. 

The advent of AI driven telemedicine is laying down a path that streamlines accessibility of healthcare facilities remotely in the most efficient manner possible. The conjugation of artificial intelligence and machine learning with telehealth platforms, portals and apps is going to see a widespread increase in adoption in the days to come, a change that will stem with its proven track record of benefiting both patients and healthcare providers. 

Photo by Alex Knight from Pexels

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