What is telehealth, and what’s it got to do with data analytics?

If you’re already familiar with the world of artificial intelligence, you might have stumbled across the concept of telehealth. It’s a word that’s often mistakenly used interchangeably in the media with telecare and telemedicine, so it’s understandable if you’re a little confused. In this blog post, we’ll unpack the difference between these three ideas, and establish what all of this has got to do with data analytics.

First things first: telehealth itself is something of an all-encompassing term, referring to health education, information and services in a relatively broad sense. Whilst telemedicine and telehealth are somewhat similar, telemedicine covers a narrower field, whilst telehealth is more extensive. Telemedicine is quite specific in terms of its scope, and generally refers to education or advice transmitted via telecommunications tools and technologies – this could be the digital transmission of medical imaging, remote diagnoses and video consultations with healthcare specialists. For example, telemedicine might involve remote clinical services such as patient consultation, whilst telehealth can also refer to remote non-clinical services.

Telecare, on the other hand, refers to technology that allows patients to maintain their independence whilst safely living in their own homes. This might be a patient companion app, for example, or remote monitoring software that determines whether or not an individual is moving.

Whilst all three of these developments are certainly interesting and valuable ideas, it might not immediately be clear how they relate to data analytics. Telehealth has existed for several years – it’s not a new concept introduced in response to the ongoing COVID-19 pandemic. That being said, the need for social distancing has prompted large investments in the telehealth sector, with remote medical services now considered an essential component of the healthcare system.

Harnessing the power of data analytics – and, specifically, predictive analytics – within telehealth means that healthcare providers are better equipped to deliver higher-quality services using these invaluable data-driven insights. By leveraging both new and existing knowledge, clinicians are able to reduce both initial hospitalization and readmittance rates through personalized telehealth engagement.

Optimizing patient adherence results in less pressure on the healthcare system as a whole: by tackling the root of the problem and determining how it is likely to progress through data-led insights, it’s a lot easier for clinicians to predict patient outcomes and provide a level of preventative care that actively improves the quality of life of their patients.

As with any medical development of this genre, a coordinated and informed approach is the best way to further integrate data analytics into the telehealth sector. By aggregating and analyzing patient-derived data, healthcare providers are able to establish best practices to ensure improved clinical care by accurately predicting which patients are most at-risk and require further investigation or support. Maintaining appropriate privacy policies is paramount throughout this process, and with the right ethical practices in place, the future for improved patient care through data analytics and telehealth services looks very bright.

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