Can AI Save Healthcare? (Part 1)

Artificial intelligence (AI) is a hot investment topic in healthcare. A recent report identified 106 AI healthcare startups, half with their initial funding in the last two years.1 In this frothy environment, offering “artificial intelligence” may have as much to do with marketing as with the mathematical algorithms that can effectively assist in making better decisions.2

The above picture of robots at their computers comes from Larry Birnbaum’s recent article, “Understanding the hype vs. reality around artificial intelligence.”3 He pragmatically states, “Today’s AI may be just ‘mere’ computer programs – lacking the sentience, volition, and self-awareness – but that does not negate their ability to serve as intelligent assistants for humans”.

Following Birnbaum’s pragmatism, I will limit my focus to how artificial intelligence (more intelligent compilation, interpretation, and presentation of data) can improve the effectiveness of healthcare. In this Conversation I’ll begin with my personal experience with AI as a physician and administrator. In the next Conversation, I will examine the big picture of healthcare and the pitfalls of expecting that artificial intelligence will necessarily improve the effectiveness of the health industry.

I was exposed to artificial intelligence in the late 1970’s when commercial EKG machines began to interpret electrocardiograms. Despite significant skepticism, by the early 1980’s the machine algorithms, followed by human expert overview, had made EKG interpretation both faster and more accurate – a clear increase in effectiveness.

The digitalization of pathology specimens over the last two decades is slowly replicating the experience with EKG’s. Although adoption has been irregular because of early technological challenges, set up expense, and regulations, the AI capacity to enhance the quality of final pathology reports is inevitable. AI is also revolutionizing the world of diagnostic imaging, again because, on average, it clearly enhances the reading speed and accuracy of radiologists.

In 2012-2014, I experienced a “total immersion” with AI during an electronic health record (EHR) installation in the 400-bed hospital where I had just started as Chief Medical Officer. Despite the positive aspects of EHR, living through this project was a nightmare. Yet after six months of 24/7 repair of initial snafus, processes such as ordering, discharge instructions, and the need to interpret physicians’ illegible handwriting had improved.

However, the challenges for physicians have been complex. One frustrating example (and yes, it was fixed) was the pop-up warning screens that would flash homilies such as, “Morphine is a respiratory depressant” each time a doctor ordered a drug with severe potential side effects. Such frequent reminders had the cumulative effect of Chinese water torture on our medical staff’s mental status.

The term for this phenomenon is “warning fatigue.” Although these admonitions were designed to improve safety, they were so poorly designed that for six months, our hospital was less safe than it had been.

To get a better flavor for the “warning effect”, imagine the following stop sign improvement: “Stop! Don’t forget to put your foot on the brake! You might kill someone!” Doctors were not amused.

The huge nationwide push for Electronic Health Records was part of the 2009 Obama stimulus package. The consensus of economists on the stimulus has been positive for both shortening and limiting the depth of the 2008 recession.4 However, the assessment of the effectiveness of the law’s investment to EHR installation is more controversial.

The $200,000 per bed investment left our hospital with what is already an antiquated mainframe solution (total installation, training, and customization costs – $80 million) with inflexible software requiring frequent upgrades. However, as noted above, there were also advantages. From a workflow perspective, the improvements were a mixed bag with some specialists experiencing improved processes while others experienced obstacles in effectiveness.

From an outpatient clinic perspective, EHR increased the productivity of non-physician staff. For physicians, the time with paperwork has noticeably increased, resulting in less face-to-face time with patients. Over the years, time has healed some of the initial angst, but the longer-term increase in physician job dissatisfaction stems at least in part from physicians’ unsatisfactory interface with EHR.5

Several years ago a UCSF professor wryly noted to a medical resident the good and bad of electronic records, “The good news is that now I always know where to find a resident; they are at the computer station. The bad news is that before, when I couldn’t find them, they were with their patients.”

We can learn from these AI experiences to create better designs for the future. However, as will be discussed in Part 3, achieving transformational healthcare improvements will require a major upgrade in the use of our own intelligence in conjunction with further improvements in AI.

Breakthrough To Better,
Carl

1Funding for healthcare AI is on the rise
2Algorithms to Live By
3Understanding the hype vs. reality around AI
4American Recovery and Reinvestment Act of 2009
5Biennial Physician Survey

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ALL POSTS:
Edition 1 – Solving a Well-Entrenched Problem
Edition 2 – A Case of Dr. Jekyll and Mr. Hyde
Edition 3 – Best marketing tagline of all time?
Edition 4 – Post-Truth Killed a President
Edition 5 – What’s an employer to do?
Edition 6 – Profiting From the Opioid Epidemic
Edition 7 – The Keys to Unlocking Better Decisions
Edition 8 – When Difficult Things Need to be Done Well
Edition 9 – Fixing Healthcare
Edition 10 – Beware of a Singing Cow
Edition 11 – Wise Reflections
Edition 12 – Warning: Reader Discretion Advised
Edition 13 – Can AI save healthcare? (Part 1)
Edition 14 – Can AI save healthcare? (Part 2)
Edition 15 – Can AI save healthcare? (Part 3)
Edition 16 – Embracing Reality to Improve Healthcare
Edition 17 – Everything I Needed To Know…
Edition 18 – The Eighth Circle of Hell
Edition 19 – So… What’s Our Solution?
Edition 20 – Protecting Integrity as a Core Strategy
Edition 21 – An Unadorned Legacy
Edition 22 – Time to Grow Up
Edition 23 – Against All Odds
Edition 24 – When Everyone Has Stopped Listening
Edition 25 – Focusing on What’s Important
Edition 26 – Don’t Give Up Your Shot
Edition 27 – Join the Goodhood
Edition 28 – Fixing Healthcare (Recycled)
Edition 29 – Taming the Healthcare Beast
Edition 30 – Leadership
Edition 31 – Better Health Requires Good Sense
Edition 32 – Little Decisions With Big Consequences
Edition 33 – Transformational Courage
Edition 34 – Transformational Courage – Part 2
Guest Post – Happy Thanksgiving! By Jeff Novick, RD
Edition 35 – Transformational Courage – Part 3

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  • […] – Wise Reflections Edition 12 – Warning: Reader Discretion Advised Edition 13 – Can AI save healthcare? (Part 1) Edition 14 – Can AI save healthcare? (Part […]

  • […] – Wise Reflections Edition 12 – Warning: Reader Discretion Advised Edition 13 – Can AI Save Healthcare? (Part 1) Edition 14 – Can AI Save Healthcare? (Part 2) Edition 15 – Can AI Save Healthcare? (Part […]

  • […] – Wise Reflections Edition 12 – Warning: Reader Discretion Advised Edition 13 – Can AI Save Healthcare? (Part 1) Edition 14 – Can AI Save Healthcare? (Part 2) Edition 15 – Can AI Save Healthcare? (Part […]

  • […] – Wise Reflections Edition 12 – Warning: Reader Discretion Advised Edition 13 – Can AI Save Healthcare? (Part 1) Edition 14 – Can AI Save Healthcare? (Part 2) Edition 15 – Can AI Save Healthcare? (Part […]

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  • […] Edition 11 – Wise Reflections Edition 12 – Warning: Reader Discretion Advised Edition 13 – Can AI save healthcare? (Part 1) Edition 14 – Can AI save healthcare? (Part 2) Edition 15 – Can AI save healthcare? (Part 3) […]

  • […] Edition 11 – Wise Reflections Edition 12 – Warning: Reader Discretion Advised Edition 13 – Can AI save healthcare? (Part 1) Edition 14 – Can AI save healthcare? (Part 2) Edition 15 – Can AI save healthcare? (Part 3) […]

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