6 Evident Ways AI is Transforming Telemedicine

ai in telemedicine

The application of Artificial Intelligence (AI) in telemedicine to allow doctors to make rich, data-driven, real-time decisions is a major factor in providing a better patient experience and improved health outcomes as physicians move more toward virtual care alternatives throughout the care continuum.

According to MIT research, 75% of healthcare facilities that used AI recognized an enhanced capacity to manage ailments, and 4/5 claimed it proactively helped reduce worker fatigue.

With Covid-19 placing a burden on both sectors (amount of clinical information and related patients, as well as increased professional workload), AI in healthcare is a strong strategy for the future of medical delivery.

Here are some of the ways that AI is transforming telemedicine.

1. Care for the Elderly

The proportion of the world’s aging population is increasing at an unprecedented rate, especially in developing countries like Europe, Japan, and China. Between 2000 and 2050, the proportion of the world’s population aged 60 and over will almost double, from roughly 12% to 22%. (from 605 million to 2 billion).

The number of persons aged 80 and older will triple throughout the same time. In the United States, 14.5 percent of the population is 65 or older, but that percentage is expected to rise to 20% by 2030.

The accelerated aging of the population will directly impact the social, economic, and health results of these developing countries. Healthcare delivery channels, in particular, must be redesigned to account for the frequency of chronic illnesses, comorbidities, and polypharmacy needs of the aged and geriatric patients.

Geriatric disorders such as cardiovascular atherosclerosis, osteoporosis disease, diabetes, dementia, osteoarthritis, and obesity need prompt diagnosis and ongoing treatment from a trained caregiver.

This is compounded by the fact that we are not adequately preparing doctors and caregivers to meet rising healthcare needs. By 2030, the United States will be short between 40,800 and 104,900 doctors.

Given the scenario, healthcare practitioners are beginning to automate various sections of treatment pathways using artificial intelligence (AI). From sophisticated surveillance of biometric data to early illness detection, AI may now be found at every stage of the treatment route.

AI is assisting patients and their families in comprehending the therapy process. AI is also assisting physicians in more effectively treating diseases.

2. Health monitoring at home

In the case of older individuals, constant surveillance and prompt diagnosis are always required. A medical diagnostic and consumer healthcare technology like biometric remote patient monitoring is applying device-level AI to enhance its platform.

Some companies use artificial intelligence to continually identify changes in activity and behavior patterns to detect health risks early.

AI is being used by voice-based virtual assistants like Amazon Echo and Orbita Health to help the elderly with medication adherence and care coordination. Voice-based virtual assistants are further optimized as nurses and cares for particular patient groups by companies.

3. Symptom Checker

There are several internet resources available for learning about certain medical disorders, symptoms, and treatments. Information shared through social networking sites, on the other hand, is sometimes ambiguous and might cause patients concern.

Those with symptoms that need immediate attention may be sent to the incorrect treatment route. A symptom checker may assist and enhance patient outcomes in this situation.

What is a Symptom Checker, and how does it work?

A symptom checker, when integrated with a hospital’s current care points, can evaluate a broad variety of patient complaints by severity level, removing the guesswork from the process. As a result, patients get appropriate treatment in the appropriate environment and at the appropriate time, avoiding needless visits to the emergency department.

While symptom checks aren’t intended to give a diagnosis or professional opinion, they may assist patients in determining the right plan of action and the best location for therapy.

These technologies are gaining popularity owing to their ability to raise apprehension, minimize needless visits to the doctor’s office, and solve the lack of access to healthcare facilities in rural places, with data scientists and medical specialists continually improving the precision of symptom checkers.

Doctors use EHR integration to check if the tool’s indicated diagnosis was right or not. AI technologies in telemedicine have the ability to continuously increase their diagnosis accuracy thanks to a closed-loop learning system that allows for a near-human level of learning.

What Is a Symptom Checker App and How Does It Work?

Natural language processing (NLP) is used by an AI symptom checker to interpret a patient’s free-text symptom report and then directs the patient through an appropriate symptom search.

Machine learning algorithms evaluate the patient inputs once the history of current illness (HPI) is obtained to give differential and treatment suggestions. The difference is usually presented in order of the severity of the problem and the amount of urgency.

For example, the symptom checker app may propose self-care for a headache, however for relevant ailments, it may switch to virtual treatment, specialized care, or clinic care. If the user submits any symptoms that signal a serious ailment such as a heart attack or stroke, advanced AI symptom checkers will break off the chat and quickly urge emergency treatment.

4. Patient monitoring using AI

Remote patient monitoring (RPM) is gradually becoming one of the most effective techniques for managing chronic diseases. RPM is the collecting and transfer of patient health data to clinicians outside of a traditional care environment using linked devices.

These monitoring devices keep track of vital signs including heart rate, blood pressure, and oxygen levels, and are particularly useful for keeping track of a patient’s health without having to see them often.

However, before the pandemic, broad RPM adoption was hampered by the minimal payment incentives available, and despite its enormous potential, it saw little usage. In the blink of an eye, everything changed.

The deployment of remote technological solutions was greatly hastened by Covid-19, which required a major paradigm change in the delivery of care.

During the pandemic, medical resources were stretched to their limits like never before, and non-essential visits were postponed or completed through telemedicine to relieve the load on clinicians, lessen the danger for more susceptible patients, and delay the spread.

As a consequence, many healthcare organizations (HCOs) have used RPM to allow chronic illness treatment without in-person visits. CMS reacted by announcing broad regulatory reforms, including increased reimbursement for telehealth and RPM.

While these temporary payments have now been altered, RPM will continue to get significantly more funding in the post-pandemic period.

5. Appointment and health reminders

For a smooth experience for both patients and employees, use AI-powered two-way appointment reminders. Optimize the timing and cadence of your reminders with appointment and health reminder, and enable patients to respond and reschedule immediately from an SMS.

  • 79 percent fewer no-shows
  • Each year, 3,480 hours of staff time are saved.
  • Improve patient comfort.

6. Accurate Diagnoses

Years of medical training are important to accurately diagnose diseases. Even yet, diagnostics may be a lengthy and time-consuming procedure. The demand for expertise in many disciplines considerably outnumbers the available supply. This puts professionals under a lot of pressure, and it often causes life-saving patient diagnoses to be delayed.

Machine Learning algorithms, especially Deep Learning algorithms, have lately made significant progress in autonomously identifying illnesses, lowering the cost and increasing the accessibility of diagnostics. Development of 

How do machines learn to diagnose themselves?

AI algorithms can learn to recognize patterns in the same way that physicians do. Algorithms, on the other hand, need a large number of actual instances — several thousand – in addition to learning. Machines can’t read between the lines in textbooks, therefore these examples must be carefully digitized.

As a result, AI is especially useful in situations when the diagnostic data a doctor reviews has already been digitized.

For example:

  • Using electrocardiograms and cardiac MRI scans to assess the risk.
  • CT scans may be used to detect lung cancer or strokes.
  • Cardiac death or other heart disorders.
  • Finding diabetic retinopathy signs in eye photographs.
  • Identifying skin lesions in skin photos and classifying them.

Because there is so much excellent data accessible in many circumstances, algorithms have become as good as professionals at diagnosing.

The difference is that the algorithm can arrive at results in a fraction of a second and it can be easily replicated anywhere in the globe. Soon, everyone and anywhere will have access to the same high-quality radiological tests from top experts at a reasonable cost.

AI diagnostics that are more sophisticated are on the way

AI use in diagnostics is only getting started; more ambitious systems combine many data sources (CT, genomes, MRI and proteomics, patient information, and even handwritten files) to diagnose a disease or its course.

7. Assisting physicians in accurate information/fact finding 

Global healthcare systems have a basic issue in providing accurate and timely diagnosis. Each year, an estimated 5 percent of outpatients in the U. S. obtain inaccurate diagnoses.

When assessing patients with significant medical diseases, these mistakes are especially widespread, with an estimated 20 percent of these patients getting misdiagnosed at the primary care level and one in every three among these misdiagnoses leading to substantial patient harm.

Machine learning and AI have become strong tools for tackling complicated issues in a variety of disciplines in recent years.  Machine learning aided diagnosis, in particular, has the potential to transform healthcare by utilizing large amounts of patient data to give exact and customized diagnoses. 

Here are some examples:

Kethan Solution

This iOS application that Arkenea designed and developed is powered by artificial intelligence and assists medical practitioners in quickly identifying implants that appear on patient x-rays. This saves time, money, and the risk of human mistake that comes with manual identification and verification.

Livongo Health is using a Big Data-Based Diabetes Care Approach

Livongo Health is using big data to assist individuals in better managing their health issues and improving patient outcomes.

Their blood glucose meters, blood pressure cuffs, and scales are used by hundreds of thousands of individuals. The extra benefit is that these gadgets capture data and transfer it to a bigger database, which the corporation then uses to provide insights for its subscribers.

This trend has also prompted the company to develop a reinforcement learning platform, which allows them to analyze data and produce a range of customized messages to deliver to its customers.

With the replies they get, they learn about members’ behavior and finally figure out what works best for them. This is a solid start, in our opinion!

Final Thought

AI deployment in telehealth applications is gaining traction. The most common AI applications in telemedicine include remote patient monitoring, data analysis and cooperation, and intelligent diagnosis and help.

The potential of AI may be used to complement doctors’ skills to diagnose and treat patients, reduce physician burnout, and improve the overall patient experience.

Due to the continuing public health issue, the emphasis on AI and telehealth remains a significant push for healthcare executives looking to stay competitive by improving physician processes and uncovering predictive potential via patient data analysis.

Are you seeking an AI-based telemedicine application that has been specifically developed for your needs? Arkenea is a well-known name in the field of custom healthcare software development, with over a decade of experience developing medical software for a diverse variety of clients.

Contact our qualified team now to discuss your requirements.

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