Why is machine learning important in healthcare?
In the last couple decades, the Internet has presented us with several pseudo-doctors guised as search engines that we have used to feverishly (pun-intended) self-diagnosis, self-triage, and research symptoms whenever we have felt a sickness coming on. But the proliferation of medical information to the general public comes at a price and is often misinterpreted when not combined with the advice of a trained physician, resulting in misdiagnosis and unnecessary anxiety.
In the last four years, machine learning and AI have taken the health industry by storm. In 2014, the AI healthcare market was valued at $600 million and in 2018, an Accenture report[ii] was released at HIMMS, estimating that the market may increase eleven-fold by 2021 to $6.6 billion.
In 1976, Maxmen predicted that artificial intelligence would bring about the end of physicians[iii]. While the possibility of living in a physician-less world seems unlikely, this megatrend is fundamentally changing how consumers find and receive care and how health providers administer care.
What is Buoy Health doing?
Buoy Health is a Boston-based, Series A startup that is using machine learning and AI to develop an online symptom and cure tracker that uses intelligent algorithms to help patients diagnosis and triage themselves. For the past five years, Buoy Health has been developing algorithms that analyze thousands of real world data points drawn from 18,000 clinical papers covering 5 million patients to resemble the dynamic and nuanced experience of chatting with a doctor[iv],[v]. Their hope is to use AI to augment decision making for doctors and help improve the gap between a patient googling for systems online and having to go in to see a physician.
As shown in Exhibit 1, the Buoy algorithm interacts with a patient like a provider would, asking individualized follow up questions and eliminating potential disease choices based on their answers. As a patient inputs symptoms, Buoy is dynamically picking 1 of 30,000 possible questions to ask the patients based on which one, from a statistical perspective, is going to reduce the uncertainty of what they have the most. At the end, Buoy will spit out potential diagnosis (3 max) and different triage options. Sharing personal information like sex and age eliminates potential options that a patient never could simply by Googling their symptoms. Not only will this result in better diagnosis of patients, but patients will be able to triage themselves better and come better prepared when they see their physician. It can even affect reduce unnecessary costs like ER visits or urgent care.
Exhibit 1: Sample screenshot of Buoy Health chatbot[vii]
What can Buoy do in the future?
Now that Buoy has developed an initial working application, they now need to work on 1) refining their algorithms based on their continuous influx of data and 2) gaining credibility in the industry. Because of the complexity of healthcare, the users are usually not the ones that pay for services. Therefore, because their business model will most likely depend on developing partnerships with payers and employers, they will need to follow these steps to strengthen their ability to forge partnerships with payers, employers, and patients, ultimately fueling their business model.
With several million patients using Buoy every month, they now have a growing data set that can be used to continually train and test their algorithms, adding to its accuracy and building out its capability of the types of conditions that it can diagnosis. Buoy Health recently partnered with Boston Children’s Hospital to improve the way parents diagnose their children[vi]. Not only will this be new data to help improve their algorithms for diagnosing children, but it will also give them access to a whole network of new providers to gain input.
Questions moving forward
Machine learning and AI have the potential to improve care delivery, streamline processes, decrease costs, and improve decision making. The possibilities are endless and the only questions are how the changes will manifest. How will the role of providers transition from being the main decision maker for data and instead become a voice in a cognitive-computing fueled cycle that can make more personalized, accurate decisions for large populations? How can professions such as radiologists, anesthesiologists and pathologists remain relevant as these technologies permeate the industry?
[i] The Medical Futurist. “Could AI Solve the Human Resources Crisis in Healthcare?” https://medicalfuturist.com/could-a-i-solve-the-human-resources-crisis-in-healthcare. August 2, 2018. Accessed November 2018.
[ii] Dale Van DeMark, HIT Consultant. “AI and Machine Learning is Shaping the Future of Healthcare Delivery”. https://hitconsultant.net/2018/06/27/ai-machine-learning/. June 27, 2018. Accessed November 2018.
[iii] C. David Naylor, American Medical Association. “One the Prospects for a Deep Learning Health Care System.” http://www.fsk.it/attach/Content/News/6636/o/jama_naylor_2018.pdf. August 30, 2018. Accessed November 2018.
[vi] PRNewswire. “Buoy Health Partners With Boston Children’s Hospital To Improve The Way Parents Currently Assess Their Children’s Symptoms Online”. https://www.prnewswire.com/news-releases/buoy-health-partners-with-boston-childrens-hospital-to-improve-the-way-parents-currently-assess-their-childrens-symptoms-online-300693055.html. August 8, 2018. Access November 2018.
[vii] Avery Hartmans, Business Insider. “This easy-to-use app eliminates the scary feeling of looking up your health symptoms online”. https://www.businessinsider.com/buoy-app-health-symptom-checker-photos-2017-5#i-added-one-more-symptom-feeling-foggy-headed-then-buoy-asked-me-about-my-lifestyle-and-any-other-unusual-feelings-or-symptoms-just-like-a-regular-doctor-would-5. May 2, 2017. Accessed November 2018.