Telemedicine solutions today and possibilities for growth in the future

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Telehealth

By Henry Bell, Head of Product at Vendorland

Many startups are working to create solutions to facilitate long-distance communication between doctors and their patients. By 2025, telemedicine applications are projected to generate $20 billion in revenue.

With proper implementation, telemedicine solutions can give more people access to basic healthcare. In the past, people could only seek medical help in their vicinity. Now the times have changed. Anyone with an internet connection can seek the most qualified advice available on various platforms. Accessibility is one of the biggest advantages of telemedicine.

Most solutions are usually provided in the form of an app, video conferencing tool, or chatbot. Many of these solutions use machine learning to power their core features. We’ll talk about specific applications and challenges in each field in later sections of this article.

Problems faced by the health industry today

For now, let’s look at challenges faced by the health industry and try to predict how telemedicine (powered by Machine Learning) can help resolve them.

Shortage of professionals

The ratio of patients for every doctor is very high. Even in large cities, the demand for qualified doctors is high, while the supply is low. The situation is even worse in rural areas, where finding any doctor can be a problem.

Since they are swamped with patients, doctors have to prioritize efficiency over effectiveness. They’re unable to spend enough time talking to patients to provide the most accurate diagnosis. During these talks, patients often reveal bits of information that are crucial for administering the treatment.

Solution: AI-powered telemedicine can help in numerous ways. First, it can facilitate information collection for making an accurate diagnosis. Chatbots powered with machine learning can also provide assistance when doctors aren’t available. Lastly, telemedicine solutions can make healthcare more accessible, as long as the patient has internet access.

Time constraints

Standards in health industries are always changing. Most doctors don’t have time to keep up with all the discoveries. 

Solution: Telemedicine applications can use machine learning to incorporate new knowledge into their diagnosis and treatment suggestions.

High cost

The cost of healthcare in the US is one of the highest in the world. This is mainly caused by large overhead costs and competitive pressure. In some cases, patients without insurance might not be able to afford the treatment at all. 

Solution: Automatization can make healthcare more efficient and increase its overall quality as well. In most cases, machine learning companies can reduce the cost of healthcare. AI-powered telemedicine solutions are often designed to cut out the middleman, thus paving the way for more direct communication between health experts and the patient. 

Geographic restrictions

In some areas of the world, access to any doctor, let alone a knowledgeable one is uncertain. Telemedicine has been prevalent in the US, which has a large rural population, for this exact reason. As long as people have internet access, they can get remote help from some of the best professionals in the field. 

During the pandemic, telemedicine has become especially important. Hospitals were already swamped with patients. Thanks to the efficiency of telemedicine, a lot of patients were able to receive diagnosis and treatment recommendations. 

Based on data

Healthcare professionals have always relied on data to make clinical decisions. Thanks to the emergence of machine learning companies, we have access to tools that we can use to analyze the data. As the role of telemedicine and AI grows, the standard operating practices in the industry are going to change as well. 

In later sections of this article, we’ll review specific applications of machine learning technology.

Machine Learning in Telemedicine – Applications

Telemedicine venues today are more concerned with facilitating communication between healthcare professionals and patients. Unfortunately, Machine Learning doesn’t play as big of a role as we think it should. ML in telemedicine is mostly limited to chatbots and image-based data analysis. Still, thanks to machine learning companies’ efforts, the technology has come a long way and is set to develop even further.

Chatbots

Chatbots have started as simple rule-based tools and developed into complex tools that heavily rely on machine learning to analyze users’ input and return relevant responses. Chatbots are useful for diagnosing common diseases. For rare diseases, such as cancer, chatbots can be useful in collecting the data and helping the doctor in diagnosing the disease.

Rule-based chatbots are very simple tools that can be effective for collecting data. Such tools can answer a fixed set of questions and return one of the pre-written responses based on users’ input. Rule-based chatbots are very useful for collecting data but are limited in their capabilities as diagnostic tools. 

Chatbots that utilize NLP have wider-ranging capabilities and features. NLP stands for natural language processing, which is an AI-based technology. The user can ask any question he or she wants and the chatbot will understand it. These chatbots analyze not only the actual input but the user’s intent and context in which the question was asked as well. The potential for advanced chatbots is immense. They have access to a large pool of information and can analyze it within seconds. 

Diagnoses

AI has been used in the clinical decision-making process for a while now. Machine learning companies have successfully replicated this practice for telemedicine solutions.

Thanks to machine learning, patients can get diagnosed through chatbots as well as video conferencing apps. AI-powered telemedicine tools are the most effective in scanning images

Treatment recommendations

Diagnosis accuracy can be difficult to measure. That’s where Reinforcement Learning comes in. Thanks to RL, telemedicine tools can learn from their mistakes and improve diagnostic accuracy in the future. Thanks to this technology, rare conditions can get diagnosed early on and start receiving correct treatments right away.  

Conclusion

It is unlikely that customer-facing technologies like chatbots are going to replace doctors entirely. Most people like the personal touch that doctors can provide. Telemedicine and in-person consultations are two different, but equally viable options.

The efficiency of telemedicine solutions can be a big draw. People are always going to need the mental and physical health advice provided by telemedicine solutions. 

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