Telehealth is an expanding industry with prominent use cases to make the patient care process easier for both providers and patients. The telemedicine sector is growing at a rapid rate with global estimates that the market will reach $130 billion by the year 2025. Currently, the telemedicine industry sits at a market capitalization of roughly $31.8 billion with global leaders like Teladoc, Lemonaid Health, Doctor on Demand and many other major players leading the way. The substantial increase in adoption and market-size can be accredited to technological advancements, rising health-care costs, and the new-found ability for providers to monitor chronic-care patients from the comfort of their own home. Studies show that about 76% of hospitals in the United States are now integrated with telehealth systems. The beauty of telemedicine is the ability to bypass the traditional doctor’s office visit. Instead of waiting in the provider's office for 2 hours for an acute concern surrounded by a plethora of similarly sick individuals, you can receive quality care whenever, wherever. The Telemedicine company, Lemonaid Health is currently integrating Artificial Intelligence into their repertoire by providing their patients with access to an AI doctor. This intelligent bot can make diagnoses or if the symptoms are too complicated for the bot to process, recommend that the patient seek out a higher echelon of care. It will be interesting to see if other telemedicine companies will adopt this model and if machine learning will be good enough to limit the need for medical providers in the future.
Data Use Cases
The advancement of new technology in the healthcare field provides medical professionals with the proper tools and resources that they need to manage the daily influx of patients. With each new patient comes a new set of data and providers must find a way to manage their patient’s data and make sense of it. Machine Learning can give providers a new way to analyze masses of raw patient data and offer interesting insights.
The medical field is an ever-evolving community with new medical knowledge entering the field at alarming rates. With this daily inrush of new information, it is nearly impossible for physicians to keep up with the newfound knowledge. Artificial Intelligence systems can process all this unstructured information, condense it, and make it understandable to both humans and machines. Medical data is expected to double every 73 days by the year 2020. This can help prevent the huge problem of physician burn-out, which is entirely possible when providers are expected to see their daily panel of patients, chart all of the patient’s information, and keep up with new medical discoveries in the community. It is important to develop a low information diet for medical providers or the sheer amount of new material will overwhelm any normal person. Humans are much more susceptible to error when fatigue is involved. To avoid this problem, using available technological resources to create more free time is not only, effective, but necessary.
AI Chatbots
One new form of machine learning that is gradually being adopted in telehealth is the use of AI chatbots. AI chatbots are smarter, faster, and able to display more human-like qualities than ever before. The bots, which are available to patients 24/7 can make sense of pertinent data in a matter of seconds. The current use case for AI chatbots is simply checking on patient symptoms and matching them with an accurate diagnosis. This method isn’t always accurate as most patients aren’t always sure of their symptoms or, in a worse case, they could be lying or exaggerating their symptoms for some type of personal gain. Chatbots can also be used to monitor a patient’s symptoms remotely, collecting relevant quantitative data and passing the information on to the individual doctor managing the chronic-care patient. Other companies use machine learning to recommend a patient to an appropriate medical provider. This takes the pain out of the selection process. It becomes exceedingly hard to select a provider in the healthcare field when nearly 20,000 doctors graduate from a medical school in the United States every year. There are not many tools for selecting a provider based on your need’s, but machine learning could automate this process and make it much easier for the patient. When a patient fills out an online questionnaire and expresses their current symptoms, machine learning is used to match patients with an able and qualified provider based off the patient and the provider's information, on file.
AI can be Used to Form Second Opinions
There are some second opinion companies that use machine learning to analyze their symptoms and give patients clarification on their diagnosis or provide them with an entirely new diagnosis. It is estimated that around 15-20% of patients are misdiagnosed by medical providers. These companies take human error out of the equation. One company claims that 28% of the company’s patients resulted in either a change or correction of diagnosis.
AI can be Used to Determine Patient Priority
AI is also able to determine the priority of patients. Patient priority is typically categorized in four priority categories. Computers are able to distinguish what is important and what is not much faster than a human, making the mortality rate lower for many treatment facilities. AI can determine whether a patient should be categorized as critical so that they can receive quality care quicker and faster than a patient who might only be sustaining minor injuries. It can become difficult for medical providers working in inpatient settings to determine a patient's priority and then determine whether to treat the patient in-house or to send them to a medical center more equipped to handle the patient's needs. AI provides health care professionals with unique insights to make the decision process easier in order to benefit the patient in need.
The use cases for machine learning will keep expanding as these systems get faster and more responsive. The ability to integrate machine learning with Telehealth companies can automate the entire healthcare process, challenging traditional ways of thinking, and the way that patients receive medical care. For example, a patient could use a service like Teladoc for symptoms of nasal congestion, frontal headaches, post-nasal drip, coughing, and fever. The virtual provider may use a bot to sift through the patient’s data to effectively diagnose the patient with a sinus infection. The bot can then provide the doctor with an appropriate treatment plan including medications with the proper dosage. Once the medication has been signed off, they can then send the medication directly to the patient’s doorstep bypassing the need to visit an actual pharmacy. The bot is then able to track a patient’s response to the treatment plan keeping the provider updated, in real-time. This makes the entire process for patients and providers much simpler. This process creates more time for everybody whilst giving the provider a sense of security knowing that his diagnosis and treatment plan is backed up by a machine that has access to an insurmountable amount of knowledge and data. As technology keeps getting more effective, it will be easier to trust mathematics, which is backed by data, than it is to trust a human being going off prior knowledge and instinct.
