12 Ways Artificial Intelligence Will Transform Health Care

September 14, 2016 David Ollier Weber

You may be familiar with the name Robert Wachter, M.D. He’s written six books and hundreds of journal articles; he chairs the department of medicine at the University of California, San Francisco; and he’s a leading advocate for patient safety. One health care magazine this year anointed him the nation’s most influential physician-executive. He’s perhaps best known for having coined the term “hospitalist,” and for having defined and promoted hospital medicine as a recognized primary care subspecialty.

Artificial intelligence is what enables a digital device to see and recognize objects (e.g., read a bar code), understand and reply to normal speech (à la “OK, Google”), make decisions and even learn to change its thinking and behavior as it analyzes the gazillions of data bits in the distributed memory known as the cloud (viz. IBM’s Watson). AI infuses the modern health care system.

Many of the nation’s smaller hospitals will close, avers Wachter, because they will be left behind in the race for quality by competitors quicker to adapt to cognitive computing and cloud-based AI technology. In the wired environment, geography won’t matter much.

Same for most small independent physician practices.

If you feel sick enough to need urgent care, you will go to a conveniently located clinic, probably in a local mall or chain pharmacy. There you’ll be seen by a nurse practitioner working “at the top of her license” and able to take into account your entire medical history by pulling up your universally accessible, privacy protected, electronic health record, or EHR.

You won’t have to leave home to get advice about how to treat many worrisome conditions. Simply dial your health care provider on a smartphone and send a picture or a video of, say, your child’s inflamed ear. A computer will read the image and recommend how to proceed. (Thanks to machine learning, the computer’s better at pattern recognition than the human eye/brain. The same goes for decoding X-rays, skin rashes and biopsy slides. Wachter predicts that radiology, dermatology and pathology are three of the medical fields most likely to be swept under by “the digital tsunami.”)

Similarly, most people with chronic conditions will be cared for at home by nurse educators and doctors who pop in frequently — just not in the flesh. They’ll chat with you via the television set, laptop, tablet computer or smartphone, acting on data from implantable, wearable or external sensors. Shut-ins may be watched over by furry AI-imbued robots that double as caregiver/companions. (See Part 1 of this series.)

If you do need to be in a hospital, you will be very, very sick — or there for major surgery or for diagnosis of some puzzling, rare or extremely complex condition. Most people who are infirmed will be cared for in less expensive, more comfortable settings.

Thriving hospitals will be huge and “bristling with technology.” There will be no such thing as an ICU; every single room in the facility (and they will all be single rooms) will be a self-contained ICU.

Nurse staffing ratios will be adjusted constantly according to the individual patient’s need as determined by AI risk-monitoring and treatment algorithms. Each room will feature a large video screen on the wall for display of the patient’s EHR and for interactive tutorials using computer voice recognition and prompts. A variety of built-in cameras will enable close-up examinations of the patient or wide-angle views of those in the room during remote specialist consults. Clinicians won’t have to be physically present (but they’ll have to be licensed nationally or even internationally).

Most physician orders and notes will be entered into the EHR through natural language voice recognition software while the kindly doctor looks you in the eye. Each patient will control his or her own EHR, a digital compendium of “clinician-generated notes and data with patient-generated information and preferences.” Redundant information requirements will be eliminated.

Alerts will be calibrated to clearly distinguish life-threatening problems from minor anomalies — a blurring that plagues clinicians in today’s hospital environment and a major contributor to the errors that inspired Wachter’s book.

Physicians will be aided in differential diagnosis and evidence-based treatment by cognitive computing systems like IBM’s Watson. Artificial intelligence applied to cloud-dwelling Big Data will assist clinicians by juxtaposing the individual patient’s characteristics — down to the last nucleotide in her genome — against billions of anonymous, equally detailed patient histories and the latest findings in world medical research (which will no longer rely on expensive and elaborate clinical trials.) Easy-to-read dashboards will track adherence to each step of the tailor-made care plan.

A new health professional will emerge “akin to an air traffic controller.” Not a physician but under the supervision of a physician, his or her task will be to “understand the data, put it in context and act on it.”

A Happy Ending

Some of this may sound uncomfortable. But to Wachter (and, he adds, every single one of the nearly 100 people he interviewed for this book about what’s wrong with medical technology today), it’s an upbeat, “thrilling” prospect.

To explore how medicine has arrived at its current, suboptimal stage of AI implementation, Wachter went to Washington, D.C., to talk with former and current federal health officials about the background to CMS technology mandates; he went to Rochester, Minn., to learn how the Mayo Clinic is refining clinical IT; he visited Verona, Wis., where EPIC, the most widely used EHR system nationwide, is designed and programmed; he traveled to Poughkeepsie, N.Y., to discuss the development of IBM’s Watson cognitive supercomputer; and he talked with frustrated doctors, nurses, pharmacists and back-office personnel who struggle to wring the best out of their amazing yet maddening technological tools.

Then he sat down with Capt. Chesley Sullenberger, the seasoned pilot who made a successful emergency landing on the Hudson River in 2009, about how the airline industry has adapted technology and cockpit instrumentation to make flying “massively safer than it was 15 or 20 years ago.”

Which is what, he’s confident, AI ultimately will do for health care. To be sure, the pace of improvement “won’t be uniform,” he cautions. “It’s not like technology comes in and takes over medicine tomorrow. Things will evolve in ways that’ll be pretty interesting and unpredictable.”

AI = Augmented Intelligence

Anil Jain, M.D., is an internist and medical informatics specialist at the Cleveland Clinic.  He was instrumental in developing a Big Data analytics platform that now mines EHR clinical and billing information gathered from about 50 million patients, or 15 percent of the U.S. population, at 360 U.S. hospitals. (It has generated more than 60 articles in medical research journals already, he notes.) In 2009, it was spun off as a private company, Explorys, with Jain as its senior vice president and chief medical officer. Explorys was acquired earlier this year by IBM’s new Watson Health division.

“A lot of fears we as doctors have,” observes Jain, “is that we’re going to have others [e.g., artificially intelligent computers] making decisions for us. But our goal at Watson Health is to empower and enable physicians to make better decisions using cognitive computing’s capabilities in areas where our human brains need a lot of help.”

“Instead of saying ‘AI’ stands for ‘artificial’ intelligence,” he proposes, “we should think of it as ‘augmented’ intelligence. It starts with data, but it ends with insights that transform the organization. And what every CEO, CFO and CMO ought to be thinking about is how to [adapt to it].”

Questions C-suite leaders need to be asking themselves, suggests Jain, include:

• How can we aggregate all the data input generated within our four walls to do right for our patients? How can we draw insights from the data to take actionable steps to improve quality of care? How can we serve up the data to make each and every member of the care team operate at the highest level? How can we do all this cost-effectively?
• Are we capable of both the descriptive analytics that tell us how good we are at taking care of patients and the predictive analytics that will let us take on more risk-based contracts?
• Is our EHR system open to the cloud? Can it incorporate third-party apps? Is it user-friendly?
• Are we paying license fees that are proportional to the benefits we get out of our databases? Do they update every day? Are our vendors really leveraging all the advances in AI technology?

Are You Ready?

Austrian economist Joseph Schumpeter noted in the 1940s that companies that fail to produce value will be destroyed — a phenomenon he termed “creative destruction.” Health care has been artificially shielded from that business model, writes Wachter. But AI may be the lance that finally pierces the shield.

“The incentives to buy high-functioning technology systems,” he argues, “will be the same as for other businesses in competitive markets: namely, the price for not doing so will be swift Schumpeterian death in the marketplace.”

 

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