CEO change at GE may mean delay or cancellation of GE Healthcare spinoff–for good or ill

October 3, 2018 | by

The well-publicized and unvarnished dumping of GE‘s CEO John Flannery after only 13 months has led a leading research analyst to predict that the planned GE Healthcare spinoff will be delayed or even halted. Analyst Jim Corridore of CFRA stated on CNBC that incoming CEO Lawrence Culp, a recent board member who was CEO of Danaher, a scientific, industrial and healthcare conglomerate, may decide that the division should stay. 

At $19 billion in revenue with a profit of $3.4 billion, 15.8 percent of GE’s total sales and 43.2 percent of its operating profit in 2017, the wisdom of a GEHC spinoff always seemed doubtful. The selloff was in line with Mr. Flannery’s strategy of refocusing on GE’s industrial and energy business. However, this was not going terrifically well, at least in the BOD’s view, with a sluggish turnaround, shares dropping off the S&P 500 and the Dow Jones Industrial Average, projections of missing year-end targets, activist investor Nelson Peltz hovering, and exacerbated by problems at GE Power with its new line of natural gas-fired power turbines. Perhaps a few were doubly offended by the selloff of the corporate jets (relative pennies) as well as the expensive and frankly hard-to-justify corporate HQ move from Connecticut to Boston.

Mr. Culp is apparently well-thought of, having retired after a highly successful 14-year run at Danaher, but he has his work cut out for him. He will also need to quickly judge whether to continue the GEHC spinoff process or bring the cattle back into the fold, as the drive was well underway down the trail. Somehow, spinning off 40 percent of your operating profit seems strategically foolish given a plummeting share price.

A jaundiced opinion. Perhaps as an outsider, Mr. Culp can change the ‘death star’ culture at GE. This Editor, in her brief encounter with GEHC as part of an acquired company (Living Independently Group, developer of QuietCare, circa 2008-9) found their business practices and many of their people to be both ruthless and self-referential to the point of stumbling blindness. The LIG acquisition was part of an ill-considered and perhaps ego-driven experiment by GEHC’s CEO at the time to get into home, remote monitoring, and assisted living health, a developmental, small-scale, early-stage area. It was obvious that GE’s vaunted methodology and hospital-based acute care experience were worse than useless when it came to understanding what is still a developmental area. The home health businesses were sold, closed, or (in the case of QuietCare), spun off into a joint venture. That CEO and a few other people leveraged it well; LIG’s employees, shareholders, and others at GEHC did not. 

As Star Wars fans know, Death Stars are destroyed in the final reel.

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

October 3, 2018 | by

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