The Revolutionizing Applications of AI in Telehealth

Thanks to Covid-19, remote healthcare services are no longer yet to happen; telemedicine is now a welcomed asset in the industry. And the integration of machine learning and artificial intelligence in telehealth is bringing medical care to the next level. The exciting fact is that 3 out of 4 clinics that used AI report improvements in their ability to treat diseases.

So how exactly is AI in telemedicine changing how we provide and receive care? Let’s discuss this in detail.

AI in Telehealth: Market Overview

The telehealth market has been experiencing significant growth worldwide and is expected to increase from $45.5B in 2019 to $175.5B in 2026 globally. And it is a good change in the industry’s market value considering the boost of Covid-19 and the growing life expectancy, which provokes an unprecedented demand for healthcare.

Global telemedicine market size in 2019 and a forecast for 2026
Figure 1. Global telemedicine market size in 2019 and a forecast for 2026

Telemedicine is revolutionizing the patient-doctor relationship and streamlining consultations. As a result, patients and healthcare facilities have been gaining more trust and interest in telehealth — just 1 in 10 Americans was using telemedicine before Covid-19 compared to 76% of Americans being interested in using it now. 

And when powered by healthcare AI technologies, which now appear hand in hand more often, telehealth provides extended capabilities, like more accurate and effective care at a more affordable cost and with fewer hospital visits. 

Benefits of Telemedicine Powered by AI

Telehealth has been growing at such a rapid pace for a reason — it provides benefits to patients, doctors and medical facilities. And AI in telemedicine enables clinics to provide even better care. Here are four advantages and reasons for adopting AI-powered telehealth solutions.

1. Convenience 

The undeniable benefit of telemedicine is its convenience to doctors and patients. Remote monitoring allows taking care of patients using digital diagnostics, with no adverse effect on the treatment process and care. There is no need to commute to the healthcare facility for a check-up — the doctor is available from the comfort of your home. The same applies to doctors, who can accept patients from their homes or while traveling.

How care is extended beyond hospital walls
Figure 2. How care is extended beyond hospital walls

Thus, telehealth makes medicine fast, accessible 24/7 and free of challenge. And it does not take away the human factor — it is the same face-to-face interaction but with no hassle. 

2. Cost-Effectiveness

The growing inflation is making medicine less affordable, especially for US citizens. But telehealth appears less expensive than in-person visits, notably $108 and $155 on average per consultation, respectively. That makes telehealth not only more convenient but also more affordable for patients.

3. Better Medical Care

Chatbots, predictive analytics, remote monitoring, digital diagnostics, clinical decision support systems — all these AI-powered medical and telehealth solutions are bringing medical care to the next level. Patients get a personalized approach with their preferences taken into account. 

Remote monitoring can preserve the quality of life during treatment or chronic disease management since there is no need to go to the doctor’s office as often. Additionally, patients get assistance like medication adherence reminders and simplified appointment scheduling in a single telehealth solution. 

4. Easier Clinic Management

Because medical services are available via phone and video, telemedicine apps reduce the need for hospital visits, decreasing their number over time. But still, some patients need to come to the clinic, which is when AI in telemedicine comes in handy. 

While telemedicine solutions handle patients online, AI analyzes busy hours at the facility, patient waiting times and other data related to providing patient care on-site. 

In particular, this helps medical staff make informed decisions that are better by 60% than without AI integration. So with real-time big data analysis and ML algorithms, clinics can more efficiently manage the patient inflow and high-priority cases.

Examples of AI-Enabled Telemedicine Solutions

Over the past several years, more and more medical staff and patients have gained access to innovative software that enables them to work with AI in telehealth. AI-supported devices and technologies have been making a difference in the quality of care facilities provide — here are their most common use cases.

Remote Health Monitoring

Artificial intelligence is used to monitor patients with no human interaction. AI-powered devices track the patient’s vital signs, including their pulse, blood pressure, body temperature, blood oxygen and respiratory rate. The data is collected via sensors and other devices that send the information to AI software.

While devices can collect data automatically, some AI-powered systems may require patients to enter data manually at specific intervals. 

Remote patient monitoring
Figure 3. Remote patient monitoring

Next, the data is analyzed based on machine learning algorithms to identify whether there are any signs of health risk. The medical staff gets an alert if a patient’s condition has changed and their case needs more attention and further monitoring.

Remote health monitoring software and telehealth app integration can target a patient’s overall health or specific condition monitoring. For example, Somatix is designed for healthcare facilities and nursing homes. The software analyzes gestures in real-time through a wearable to identify increased risks of falls for the elderly. EarlySense, in turn, tracks patients’ vital signs via a sensor under their mattress to identify early signs of deterioration.

Enhanced Diagnosis

Cloud-based servers allow AI to access patient data to provide diagnoses and treatment recommendations. As electronic health records (EHRs) contain the medical history of the patients and their current symptoms, AI can come up with potential diagnoses based on that.

Besides, AI in telemedicine provides access to test and lab results and generates recommendations depending on them. Telepathology also enables the interpretation of X-rays and scans, contributing to the diagnosis.

Similarly, the software can assist clinicians in the decision-making process on patient diagnosis. Clinical decision support systems apply AI to offer the most appropriate diagnosis and recommendations for every patient based on given information and similar previous cases, leaving the right to make the final decision to the clinician.

Personalized Treatment Plans

Treatment plans may be expected to be similar for patients with the same conditions. But digital diagnostics powered by AI and ML can help take into account details that allow personalizing patient treatment plans. 

For example, artificial intelligence can make the treatment process a bit less of a challenging experience for each patient based on the individual’s medical history, preferences, and particular needs. That includes the location of where they are going to receive treatment, the type of treatment and other factors. 

So in combination with ML algorithms and real-time data analysis, AI can determine the most effective and most suitable treatment plan for a patient, whether on-site or remote treatment.

Patient Engagement

Chatbots and natural language processing are significant assets of AI applications in telemedicine. Digital diagnostics conducted through these technologies contribute to patient engagement and reduce administrative workload.

Chatbots manage a big part of administrative tasks and patient care — they provide service to patients with mild health concerns without clinician involvement or clinic visits.

In particular, chatbots log symptoms, conduct screening, answer common questions, direct to valuable resources and schedule appointments. Besides, chatbots can send reminders for medication intake, upcoming screening, medical tests and appointments.

Uses of chatbots in the healthcare industry
Figure 4. Uses of chatbots in the healthcare industry

At the same time, natural language processing allows transcribing remote patient-doctor visits held via phone or video calls. The transcribed data is automatically added to the patient record, while AI analyzes the most significant parts to provide further recommended actions.

Early Disease Detection

Artificial intelligence can analyze patient data like current symptoms, vital signs, EHRs and medical history to identify small changes in their well-being that can signal disease development. Besides, digital pathology assists in interpreting images like X-ray scans and CT and informs clinicians of early signs of diseases.

Altogether, early disease detection can help prevent the development of severe conditions because it allows getting care and acting upon the risks when the issue is still treatable. And combined with remote monitoring, patients can manage their early spotted conditions and prevent disease development.

Managing Chronic Diseases 

AI is beneficial for patients with chronic diseases like diabetes or heart conditions. By tracking a patient’s vital signs, telemedicine apps like Myia provide personalized recommendations, predict complications, track treatment and send out reminders to at-home patients. 

ML algorithms, in turn, can identify early signs of complications and alert doctors to take measures and prevent disease development. This way, digital diagnostics and other healthcare AI technologies help manage chronic diseases with no additional hassle like regular clinic visits or testing.

Medical Training

Because of the specifics of medical training, AI and telehealth app integration greatly help those aspiring to become doctors. There are learning platforms like Coursera that adapt to each learner and tailor the course to make the learning process easier for them.

Besides, health information technology (HIT) platforms like Medical Realities allow getting hands-on experience through virtual reality (VR) training. The training helps prepare practitioners-to-be for medical tasks, procedures and experiences, including patient interactions, through immersive simulations without any real-life losses.

Overview of Population Health

AI in telehealth helps see the bigger picture of population health by collecting and analyzing individual patient data. For example, Innovaccer offers a vulnerability index for population health supported by AI to get insights into the well-being of bigger patient groups. 

Other AI applications help identify at-risk groups that need monitoring for particular health conditions. These include using predictive analytics to identify health risks by collecting data from various sources, like patient surveys, EHRs and research.

Preventing Medical Staff Burnout

Interacting with patients, performing medical procedures, spending a lot of time at work on gadgets and long work days — all these increase the doctors’ levels of anxiety, fatigue and dissatisfaction. In turn, these lead to burnout in medical specialists. 

Medical staff burnout statistics
Figure 5. Medical staff burnout statistics

Of course, burnout affects medical staff’s performance at work, the quality of care they provide to patients and the quality of their lives. But telehealth app integration with AI help decreases the chance of burnout and other issues with emotional well-being at medical facilities.

In particular, healthcare AI technologies handle many administrative tasks, like filling in EHRs and collecting information on patient’s symptoms, which allows doctors to spend less time using devices. At the same time, AI helps detect signs of burnout and take measures before it occurs. 

It also allows managing the schedule to prevent overworking, like determining the busiest hours and distributing workload accordingly or predicting how many patients a doctor can handle until they feel exhausted.

Telehealth Issues and Challenges

Since the application of telemedicine is still relatively new to the field, some challenges limit us from taking full advantage of AI in telehealth. Here are the key issues telehealth is facing.

Autonomous vs. Augmented AI

The issue with AI-powered telehealth is whether it can handle decision-making automation completely or can only augment the process. In other words, it is a question of whether the software should leave the final decision regarding predictions and recommendations on patients’ condition to clinicians or cover the entire decision-making process.

Ethical Issue

The ethical concern implies responsibility for the decisions based on AI and ML algorithms. It is up for discussion who will be responsible if incorrect predictions or digital pathology misinterpretation lead to wrong decisions, mainly whether it will be the clinician, the AI application vendor or the clinic.

5 ethical issues of AI in healthcare
Figure 6. 5 ethical issues of AI in healthcare

Data Preparation

A universal data preparation strategy should be established to meet the requirements of AI in telehealth. That includes related processes like data gathering, cleansing and annotation to keep the information clean and ready for use in ML models. Proper data preparation is challenging but crucial to enhance the accuracy of AI predictions.

Data Security

Because patient data is sensitive, data security is a significant challenge in telehealth app integration. That includes developing software according to HIPAA and other compliances to regulate access to patient data for internal and external stakeholders. The regulations should also consider guidelines for secure data storage and exchange.

Rules for protecting sensitive patient health information
Figure 7. Rules for protecting sensitive patient health information

AI Governance

The implementation of AI in telehealth implies that governance practices should be adopted. These cover various kinds of model testing, including those with adversarial datasets, to confirm correct model performance and eliminate faulty models from retraining. Regular model monitoring and retraining are other governance practices to be established.

Hybrid-Cloud Architecture

Telemedicine applications require a cloud-native design to support their secure functioning. The hybrid-cloud architecture allows storing parts of the healthcare AI technologies and data it uses in the cloud while storing other parts of data on-premise. Naturally, this complex architecture should be capable of keeping technical and patient data protected.

Conclusion

Telehealth has been transforming the healthcare industry at a rapid pace. And artificial intelligence helps significantly change the quality of care provided through telemedicine. It is a matter of time before the world transitions from traditional healthcare systems to telemedicine — we can help your healthcare infrastructure take this step today.

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