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When we think of breakthroughs in healthcare, we often conjure images of heroic interventions — the first organ transplantation, robotic surgery, and so on. But in fact many of the greatest leaps in human health have come from far more prosaic interventions — the safe disposal of human excrement through sewage and sanitation, for example, or handwashing during births and caesarians. 

We have a similar opportunity in medicine now with the application of artificial intelligence and machine learning. Glamorous projects to do everything from curing cancer to helping paralyzed patients walk through AI have generated enormous expectations. But the greatest opportunity for AI in the near term may come not from headline-grabbing moonshots but from putting computers and algorithms to work on the most mundane drudgery possible. Excessive paperwork and red-tape is the sewage of modern medicine. An estimated 14% of wasted health care spending — $91 billion — is the result of inefficient administration. Let’s give AI the decidedly unsexy job of cleaning out the administrative muck that’s clogging up our medical organizations, sucking value out of our economy, and literally making doctors ill with stress.

Here’s just one example of the immediate opportunity: Each year, some 120 million faxes still flow into the practices of the more than 100,000 providers on the network of athenahealth, the healthcare technology company where I’m CEO. That’s right: faxes. Remember those?

In healthcare, faxes remain the most common method that practitioners use to communicate with each other, and therefore often contain important clinical information: lab results, specialist consult notes, prescriptions and so on. Because most healthcare fax numbers are public, doctors also receive scores of pizza menus, travel specials, and other “junk faxes.” Faxes don’t contain any structured text — so it takes medical practice staff an average of two minutes and 36 seconds to review each document and input relevant data into patient records. Through a combination of machine learning and business-process outsourcing that has automated the categorizing of faxes, we’ve reduced time-per-fax for our practices to one minute and 11 seconds. As a result, last year alone we managed to eliminate over 3 million hours of work from the healthcare system.

And that’s just the beginning for our AI team. Next year, we hope to reduce the time it takes to import data from a fax into a patient record to 30 seconds. And we’re developing software that can scan lab results and flag urgent findings for human attention and an algorithm that can help automatically schedule high-risk patients for routine follow-ups.

Reading faxes and scheduling appointments don’t exactly quicken the pulse. But here’s why this sort of work is so important. First, we are in the midst of a burnout crisis among U.S. physicians. They’re crushed by administrative overload and feel they are becoming box-tickers rather than clinicians. Patients, too, feel overwhelmed by the cumbersome work required to chase referrals and ensure basic clinical information follows them through the health system.

Applying AI to the work that doctors detest presents a path to redemption for the health IT industry. For too many doctors, once-hyped technology such as electronic health records have become part of the problem and added to – rather than mitigated – overload and burnout. We need to rebuild confidence in the promise of technology to free up provider time and enhance care delivery. Will algorithms and AI cause new, unforeseen tensions with physicians in the future? Perhaps. That’s always a risk with new technology. But in my experience, most doctors don’t fear automation, they fear a loss of autonomy. Using AI to relieve scut work will allow them to focus again on what they love most and where they create the most value: the patient encounter.

Finally, focusing artificial intelligence on the goal of eliminating the mundane annoyances of modern medicine might one day lay the groundwork for curing cancer (and other AI moonshots). A recent report by JASON — the elite group of scientists who advise the U.S. government on matters of science and technology — found that poor data management remains a key obstacle to the clinical application of AI. Much heavy lifting remains to be done to improve the data on which the future of AI relies. We need to bust data out of silos so it can be easily accessed, queried, and analyzed. Using AI to correctly identify, categorize and share information will lay the groundwork for future, breakthrough analyses.

Most healthcare executives are still unsure of their AI strategy. They sense that AI will be a game changer, but they’re not sure how. I love that healthcare has heroic ambitions for a promising new technology, even after years of high-tech disappointment. But while we shoot for the moon, let’s clean up the muck that’s bogging us down today, unleashing our potential to transform healthcare.

from HBR.org http://ift.tt/2FfB9ld