IBM Watson Health’s stumble and possible fall

This Editor hadn’t thought about or seen news about IBM Watson Health in over a year…and likely, neither did you. Granted, our minds have been Otherwise Engaged, but for the company that was supposed to dominate AI and health analytics, it’s notable that TTA’s last two articles mentioning Watson Health was 25 April 2019, on a report that its Drug Discovery unit was being cut back as the latest in a series of executive cutbacks and lawsuits (MD Anderson on a failed oncology initiative), and 14 Feb 2020 on 3M’s lawsuit on unauthorized use of their software.

The New York Times in an investigative piece (may be paywalled or require signup for limited access), brings us up to date on what is happening at IBM Watson, and it’s not bright for Watson Health. IBM, like so many other companies, badly underestimated the sheer screaming complexity of health data. Their executives believed they could translate the big win on the “Jeopardy!” game show in 2011, based on brute computing power, into mastery of healthcare data and translation into massive predictive models. The CEO at the time called it their ‘moon shot’. Big thinkers such as Clayton Christensen chimed in. IBM managers sang its praises to all in healthcare who would listen. This Editor, on a gig at a major health plan in NJ that was ‘thinking big’ at the time and used IBM consultants extensively, in 2012 was able to bring in speakers from Watson for an internal meeting.

But we haven’t been on the moon since 1972 (though probes have visited Mars). Since the big push in 2011-12, it’s been one stumble after another. According to the Times:

  • The bar was set much too high with oncology. Watson researchers knew early on in their research at the University of North Carolina School of Medicine that their genetic data was filled with gaps, complexity, and messiness. The experience was similar with Memorial Sloan-Kettering Cancer Center. The products growing out of the UNC and MSKCC research, Watson for Genomics and Watson for Oncology, were discontinued last year. These were in addition to the MD Anderson Cancer Center initiative, Oncology Expert Advisor for treatment recommendations, that was kicked to the curb [TTA 22 Feb 2017] after $62 million spent. At the same time, IBM’s CEO was proudly announcing at HIMSS17 that they were betting the company on multiple new initiatives. 
  • Watson Health, formed in 2015, bought leading data analytics companies and then didn’t know what to do with them. TTA noted in August 2018 that Phytel, Explorys, and Truven Health Analytics were acquired as market leaders with significant books of business–and then shrank after being ‘bluewashed’. HISTalk, in its review of the Times article, noted that along with Merge Healthcare, IBM spent $4 billion for these companies. IBM’s difficulties in crunching real doctor and physical data were well known in 2018 with revealing articles in IEEE Spectrum and Der Spiegel

Six years later, Watson Health has been drastically pared back and reportedly is up for sale. Smaller, nimbler companies have taken over cloud computing and data analytics with AI and machine learning solutions that broke problems down into manageable chunks and business niches.

What’s recoverable from Watson? Basic, crunchy AI. Watson does natural language processing very well, as well as or better than Amazon, Google, and Microsoft. Watson Assistant is used by payers like Anthem to automate customer inquiries. Hardly a moonshot or even clinical decision support. For business, Watson applications automate basic tasks in ‘dishwashing’ areas such as accounting, payments, technology operations, marketing, and customer service. The bottom line is not good for IBM; both areas bring in a reported $1 billion per year but Watson continues to lose money. 

A sobering, mercifully hype-free view of AI in healthcare

Way up there on the Peak of Inflated Expectations in the Gartner Hype Cycle is that two-letter creature, AI. Artificial Intelligence has been invoked in multiple tech fields, and Microsoft in the US currently is running 30 second commercials about how AI is “making tomorrow today” but without much explanation as to how.

If AI’s current puffery makes you dizzy, long-time observer of the Healthcare Scene Anne Ziegler’s article in Hospital EMR and EHR might stabilize the whirlies. In direct and brief terms, she classifies the realities of healthcare AI adoption in three areas:

  1. Lack of Transparency. How does AI reach its conclusions in making ‘good decisions’? Sometimes the logic of the conclusion is obvious, but often it is not, and what you get is physician and clinician bypass–and suspicion.
  2. That Old Monkey Wrench Tossed into Existing Processes. It’s taken a long time for organizations to fully integrate their EHR inputs and documentation. Throwing in an AI implementation even in a limited sense may require more adjustments than the outcomes are worth.
  3. It’s Too, Tooooo Much Data. Healthcare organizations do not suffer from a paucity of data. AI feeds on data. Sounds like a good match, doesn’t it. Except that a lot of this data isn’t usable without filtering and mining, and that takes a lot of processing. The future may have more advanced data processing and indexing tech to do that, but right now even natural language processing to identify useful information is rare in the field.

Widespread AI use in healthcare is, despite the IBM Watson Health hype, a long way off. In healthcare, the rubber must meet the road of patient care and clinical practicality to be useful to us with Non-Artificial Intelligence. Problems We Need To Address Before Healthcare AI Becomes A Thing