In tele-medicine, systems that make use of machine-learning are crucial for clinical decision support (CDS). For such “smart” technology allows a computer to learn from past experiences; thereby enhancing a system’s capabilities. A clinical decision support system (CDSS) equipped with AI–specifically: an inference engine–is able to more expediently AND more accurately diagnose patients. It does this by finding patterns in clinical data. The “catch” is that a CDSS must not only incorporate AI technology; it must be seamlessly integrated with other emerging technologies like electronic health / medical records (EHR / EMR) and translational bio-informatics (TBI).
Cutting-edge TBI technology marries bio-medical informatics with machine-learning. The catch” is that, in order for it to be workable, it requires a universal language–as with GELLO (the object-oriented programming language established by Health Level 7) or CQL (Clinical Quality Language)–for all elements to be synchronized. The ultimate idea is to deploy medical services via the IoT (internet of things), a goal that is becoming more and more viable with emerging mobile / telecommunications technology. And the capabilities are being dramatically improved by AI technology (note especially the enhancements made possible by machine-learning).
Let’s take a look at three of the players at the forefront of this new realm:
ONE: Beam is a text-based tele-medicine service that enables billable medical treatment for primary care providers, thereby streamlining the reimbursement process for online medical visits. Physicians can capture reimbursements via the world’s first “intelligent text” platform for tele-medicine…thereby offering an end-to-end solution. The app directly handles the billing process and submits the claim to the insurance company.
TWO: GYANT offers tele-communication technology which makes use of AI to collect and analyze patients’ medical history. In just a matter of minutes, a user can be given professional advice or receive referrals to the appropriate resource. At the provider’s end, the AI creates the EMR (electronic medical record), thus streamlining the process by facilitating e-visits: remote diagnosis / treatment / monitoring / follow-up. AI enables much of this to be automated.
THREE: Nutrimedy bolsters the deployment of clinical nutrition using a B2B web-based platform, specially-designed for mobile devices. The system enables clinical dietitians to provide personalized care outside the hospital. Nutrimedy works with biotech, medtech, and healthcare outlets–delivering clinical nutrition-management directly to patients at home. This allows organizations to build personalized patient programs remotely–saving everyone time and money.
Tele-medicine is not necessarily a matter of REPLACING medical professionals; it’s about SUPPLEMENTING them. As with other “eHealth” technologies, CDSS drastically improves the performance of practitioners–empowering them to accomplish more in less time. In other words: it enables them to be more effective AND efficient in treating people. With recent advances, it is becoming increasingly clear that technology has the potential to improve the quality of healthcare while making it accessible to more people.
Real-time data can rapidly provide a snapshot of where and how fast the disease might be spreading, to deploy healthcare workers and equipment where they’re needed most. It’s all about catching cases as early as possible so the healthcare system doesn’t get overwhelmed at the peak of the resource-requirement curve.
By deeply analyzing the care that every patient receives, an inference engine could predict surges in cases that strain healthcare personnel and availability of supplies… then ascertain the best treatment strategies. The power of AI in controlling outbreaks depends on how effectively data can be anonymized. Only when people are assured of privacy will such technology be fully accepted by the general public. The corona-virus may be the trial by fire that tele-medicine finally needs to prove its worth.