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march 3 , 2022
AI and Telehealth
AI and telemedicine in the service of health
Big Data and artificial intelligence are significant keywords in the medicine of the future. And the latest pandemic has only accelerated a multitude of trends towards it. Also known as telehealth, telemedicine uses modern communication tools (websites, apps, video calls etc.) to deliver healthcare services from doctors to patients without the necessity for both sides to be present in the same physical place like a hospital. According to the US CDC, use of telemedicine services skyrocketed by over 150% in the first quarter of 2020 compared to the same period in 2019. What are the assets of telemedicine and trends of its development in the future?

Most convincing benefits of telemedicine include: no transportation costs, access to a wider range of medical specialists, often a more flexible scheduling and more affordable services to patients without affecting their quality. It is also worth mentioning several trends which drive the future of telehealth: increased patient involvement, greater focus on providing services in the mental health sector, integrated data sharing (for example, many telemedicine apps begin to synchronise with fitness applications). Following all these trends and focusing on business needs, Magora created Ayadi - a telemedicine app that provides counselling services for patients suffering from mental disorders and successfully launched it on the Middle East market last year. This app allows to connect with qualified behavioural counsellor from the Middle East, and do it from smartphone by video or chat.
Artificial intelligence used in medicine
In order to support medical professionals in clinical settings and research, AI algorithms and other applications powered by AI are being used, improving health outcomes and patient experiences.
Currently, the most common roles for AI in medical settings are clinical decision-making and medical imaging analysis. Starting from x-ray pictures analysis following with apps created to define whether a mole is benign or not.
Indisputable benefit, AI never needs to rest. Machine learning algorithms could be used to observe the vital signs of patients and alert clinicians if certain risk factors deviate. While medical devices like heart monitors can track vital signs, AI can collect the data from those devices and look for more complex conditions and pathology.
Integrating medical AI into operative workflows can give providers valuable data while they're making important care decisions. A trained machine learning algorithm can help reduce research time by giving clinicians valuable search results with evidence-based insights about treatments and procedures while the patient is still in the room with them.
It is not unusual that the most important questions to your doctor come to your mind when the doctor's office has already closed. Driven by AI, chatbots can answer basic questions around-the-clock and give patients resources when their therapist isn’t open.

What’s in practice
To illustrate it, read more about Anova Onesource created by Magora - a platform for automating clinical trials that brings together Sites, Sponsors and Research Organisations. It enables rapid study activation, document management and patient enrollment.

There are a lot of potential ways AI can reduce costs across the healthcare industry. Some of the most promising opportunities include reducing medication errors, customised virtual health assistance, fraud prevention, and supporting more efficient administrative and clinical workflows.
Another example of a great Magora collaboration is InnovoCare, a new application which helps to calculate the true costs of healthcare services for medical centres.
In a nutshell, one of the biggest dilemmas of today has been either improving the quality of care or reducing costs for the insured. AI and Magora make both possible at the same time.

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