An Intelligent Interface for Patient Diagnosis by HealthTap

HealthTap, an organization that’s hard to categorize, really should appear in more studies of modern health care. Analysts are agog over the size of the Veterans Administration’s clientele, and over a couple other major institutions such as Kaiser Permanente–but who is looking at the 104,000 physicians and the hundreds of millions of patients from 174 countries in HealthTap’s database?

HealthTap allows patients to connect with doctors online, and additionally hosts an enormous repository of doctors’ answers to health questions. In addition to its sheer size and its unique combination of services, HealthTap is ahead of most other health care institutions in its use of data.

I talked with founder and CEO Ron Gutman about a new service, Dr. AI, that triages the patient and guides her toward a treatment plan: online resources for small problems, doctors for major problems, and even a recommendation to head off to the emergency room when that is warranted. The service builds on the patient/doctor interactions HealthTap has offered over its six years of operation, but is fully automated.

Somewhat reminiscent of IBM’s Watson, Dr. AI evaluates the patient’s symptoms and searches a database for possible diagnoses. But the Dr. AI service differs from Watson in several key aspects:

  • Whereas Watson searches a huge collection of clinical research journals, HealthTap searches its own repository of doctor/patient interactions and advice given by its participating doctors. Thus, Dr. AI is more in line with modern “big data” analytics, such as PatientsLikeMe does.

  • More importantly, HealthTap potentially knows more about the patient than Watson does, because the patient can build up a history with HealthTap.

  • And most important, Dr. AI is interactive. Instead of doing a one-time search, it employs artificial intelligence techniques to generate questions. For instance, it may ask, “Did you take an airplane flight recently?” Each question arises from the totality of what HealthTap knows about the patient and the patterns found in HealthTap’s data.

The following video shows Dr. AI in action:

A well-stocked larder of artificial intelligence techniques feed Dr. AI’s interactive triage service: machine learning, natural language processing (because the doctor advice is stored in plain text), Bayesian learning, and pattern recognition. These allow a dialog tailored to each patient that is, to my knowledge, unique in the health care field.

HealthTap continues to grow as a platform for remote diagnosis and treatment. In a world with too few clinicians, it may become standard for people outside the traditional health care system.

About the author

Andy Oram

Andy Oram

Andy Oram writes and edits documents about many aspects of computing, ranging in size from blog postings to full-length books. Topics cover a wide range of computer technologies: data science and machine learning, programming languages, Web performance, Internet of Things, databases, free and open source software, and more. My editorial output at O'Reilly Media included the first books ever published commercially in the United States on Linux, the 2001 title Peer-to-Peer (frequently cited in connection with those technologies), and the 2007 title Beautiful Code. He is a regular correspondent on health IT and health policy for HealthcareScene.com. He also contributes to other publications about policy issues related to the Internet and about trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business.

1 Comment

  • I love to hear about these innovative advances, but their needs to be a dose of caution. Are these systems giving medical advice, based on medical decision making? Is the software filtering information based on their own internal algorithms? If so then these systems should only be described in the context of their performance and error rates. Much more validation is required before these systems can be trusted by the public. The consumer needs to be aware of the risks and FDA requirements for these systems and further consider the information in full context.

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