Our healthcare landscape today is riddled with complexities. A value-based care system and improved health outcomes are a tall order given the rise in healthcare costs, chronic disease, and physician shortages. Perhaps that’s why so many healthcare providers are turning to next generation technologies, such as rules-based systems and artificial intelligence, to solve the healthcare crisis. 2018 promises to be a fortuitous year for this sector, as the North American health IT market is expected to reach $104 billion by 2020, according to a recent report by Markets and Markets. Health IT companies are already proposing innovative solutions to optimize health outcomes for patients while circumventing issues of physician shortages and in-facility patient congestion. While some of these solutions are still fledgling ideas just beginning to take flight in the public imagination, there are five key healthcare IT trends that are worth paying attention to this year.
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Integration could well be the watchword for medical technology this year. We may begin seeing healthcare technology companies start to augment rules-based engines with voice recognition and voice analysis for a more interactive and natural patient-consumer experience. Amazon is currently pioneering the smart speaker market, where the company sold an estimated 22 million of its Echo Smart speakers in 2017.
Perhaps the clearest medical application of a rules-based system with an integrated voice-activated interface is Healthtap’s Doctor AI, which the company built as a skill app on Amazon’s Alexa. When a user asks Alexa what a possible symptom(s) may likely indicate, Healthtap’s artificial intelligence technology parses data from the user’s medical records to consider probable causes, and then conveys that information to the user in a natural and conversational way. The app will then route the patient through one of several pathways of recommended action, such as scheduling an in-person office visit with the right specialist, depending on the patient’s response to the app’s follow-up questions. Healthtap’s Doctor AI app, when used with Amazon’s Alexa, makes high-quality and early-intervention healthcare immediately available for the elderly, the disabled, and the frail. With a smart, hands-free, voice-activated interface, the device and app duo ensure that people who cannot easily use their hands or eyes get the same access to healthcare as everyone else. Healthcare providers and medical technology companies may follow suit, creating rules-based systems with integrated voice technology to assist in triage and diagnostics.
Granting More Credence to AI
Apposite to a discussion of rules-based engine systems is the evolving role of AI in healthcare. This year may witness healthcare providers and medical technology companies beginning to experiment more boldly with AI, namely AI’s ability to make suggestions and tailor feedback based on learning. As AI collects individual patient data and begins to learn how they react differently to feedback, it can begin tailoring feedback that is personalized and predictive. Such feedback is the foundation upon which a preventive healthcare system is built. The healthcare industry is still cautious about letting AI make diagnoses and suggestions and not without reason. Deep learning applications are unable to explain how and why they arrived at the results that they did – even when they’re correct. Without this explanatory capacity, physicians are understandably hesitant to make decisions that could critically affect a patient’s health.
GE Healthcare, a leading imaging and monitoring provider, recently partnered with Roche Diagnostics, an in-vitro diagnostics leader, to create the industry’s first data-driven software that combines patients’ in-vivo and in-vitro diagnostics. By marrying a patient’s in-vitro diagnostic data including genomics, tissue pathology, and biomarkers with their imaging and monitoring data – and then adding data analytics and machine learning – clinicians will be able to access a comprehensive portfolio of patient information to make earlier, faster diagnoses and develop individualized treatment. Artificial intelligence stands to play a pivotal role in this new technology, providing actionable insights from multiple datasets which could not be visible to a human eye nor synthesized by human cognitive processes.
Medical Devices Increasingly Rely on Cellular Connectivity
According to Market Watch, global investment in Internet of Things (IoT) from the medical industry should top $410 billion by 2022, and much of this investment will come directly from cellular IoT markets. 2018 will see the emergence of the 5G network, which holds the potential to improve the medical industry with quicker service and greater support for IoT connected medical devices.
When quick diagnosis and early intervention are at stake, speed becomes paramount from an administrative and workflow perspective. 4G LTE networks are currently the fastest networks available, but the emerging 5G network promises to significantly trump the former’s speed. To put it in perspective, if it takes you about an hour to download a short HD movie on a 4G LTE network, it would take you a matter of seconds to download the same movie on a 5G network. The current lure of a mobile 5G network is potent, with both Verizon and AT&T promising to roll out the network in 2018. Currently, both 4G LTE and 5G networks make it possible to link an entire medical center across one cellular network, and to connect thousands of devices across one network without loss of data.
What distinguishes the 5G network from its predecessors, especially where added value to healthcare is concerned, lies in its enhanced capacity to support IoT connected medical devices. 5G contains a computing model that pulls insights from data with billions of devices. A recent report written by the Haas School of Business, U.C. Berkeley, takes a focused look at this distinction. According to the authors, “the phrase that most pithily captures the impact of 5G within the healthcare sector is the ‘personalization of healthcare.’” They go on to explain that “…the much greater ability to continuously gather patient-specific data and the ability to process, analyze and quickly return processed information and recommended courses of action to the patient will give patients greater ability to manage conditions on their own.” In effect, 5G networks will better support continuous monitoring and enable patients to better manage their chronic conditions, while simultaneously catalyzing the move towards a preventive healthcare system.
Increased Telemedicine Usage for Remote Patient Triage
The close of the last year witnessed an increasing amount of health systems beginning to use telemedicine and mHealth to screen emergency department patients, a trend that will only continue to grow in 2018. Already, healthcare companies are using rules-based systems with data sets licensed from research groups such as the Mayo Clinic and the American Heart Association to triage for diagnostic purposes. Applied Pathways recently announced the launch of Curion® Go, a symptom self-triage solution leveraging content from Mayo Clinic. With Curion® Go, people can access health guidance 24/7 by following a series of relevant triage questions based on the symptoms being experienced.
Wisconsin-based startup EmOpti recently developed a telemedicine platform that is now being used in eight hospitals across four different health systems. Patients who are admitted to the emergency room and are waiting to be seen by a doctor can first be seen by another doctor or physician assistant in a remote “command center” via a secure video-conferencing technology platform. The remote physician or assistant examines the patient with the help of on-site triage nurses and can order tests or prescribe medications. Physician shortages and patient congestion are a hallmark of emergency rooms; triage via telemedicine could help physicians better screen patients and see to those who require urgent care first. TeleMedCo, a Florida startup, has also developed a real-time communications platform that utilizes IBM’s machine-learning solution to engage in primary patient care in the emergency room and in urgent care environments. Their solution assists doctors by overseeing non-urgent care patients from diagnosis to prescription or treatment recommendations. The solution is built to code and bill for the entire patient visit, while also alerting doctors if immediate human intervention is required based on the information, analysis, and rules built into the software. Telemedicine triage continues to illustrate how automation can help expedite the more routine facets of healthcare while increasing physician efficiency and optimizing health outcomes.
Pioneering of Robotic Surgery
Telemedicine triage is just one growing trend in the automation of healthcare this year; robotic surgery is fast becoming another. The da Vinci robot has perhaps garnered the most attention in the medical sphere; the device allows surgeons to deftly manipulate robotic limbs in order to perform surgeries with high precision and minimal invasion that would be impossible to do with the human hand alone. Surgery represents the apex of high-risk patient procedures – not only is it invasive, it is extremely dependent on the physical state of the surgeon at the time of the procedure (I.e. hand tremors, lack of concentration, fatigue, and vision problems). The da Vinci robot blends the best of two worlds – a human surgeon’s intelligence, foresight, and sound judgement with the technical precision and mastery that only a machine can offer. Still, in the most complex surgical procedures today, such as neurosurgery, where extremely sensitive maneuvers are necessary, robots are less dexterous than human surgeons. This may well change as our technology evolves.
While today’s robotic surgery is still performed with complete surgeon oversight, the future could see machine learning becoming a large part of robotic surgery, with robots learning how to perform entire procedures autonomously after “training” with living surgeons. Verb Surgical, founded in 2015, hopes to build robots that can learn from one another and advise surgeons on best practices. The robots would collect data and videos of every procedure they perform, and this data would then be fed to machine-learning algorithms for analysis to determine what works best. Eventually, Verb Surgical hopes the robots could start helping surgeons to determine sick tissue from healthy, or what to do in unexpected situations. Robotic surgery powered by artificial intelligence is still just a whispered suggestion for obvious reasons – surgeries are by very nature complicated and unpredictable – and robots may never have the same exposure to repetitive feedback as seen in industrial robotics.