Vol. 204 December 1, 2018 “Why Doctors Hate Computers”

December 2, 2018

 

Digitization promises to make medical care easier and more efficient. But are
screens coming between doctors and patients?
 – Atul Gawande

I wished I had thought of this title.
I wished I had written the article in the New Yorker that went with it. (1) But, it was written by a better writer, and a surgeon no less; a proceduralist, not a cognitive doctor like us pediatricians and internists. Atul Gawande nailed the reasons for the frustrations of most doctors in dealing with electronic medical records, including graphic points of special irritation with one specific computer behemoth, Epic.

Epic is the $100 million computer software system now in place in the Partners Health Care system serving 70,000 employees in 12 teaching hospitals with dozens of different medical/surgical specialities as well as thousands of office-based providers and their staff. In Epic I have learned the 6 different ways of using 13 different tabs or, worse still, those tiny little icons stuffed into the margin of the screen to get the information I need to see the next patient in a pediatric office. As I traverse the various and varied screens I usually am exposed to too much data and not enough information. It is clear to most of my colleagues and our staff that Epic is chiefly designed as an “optimizer of insurance reimbursement”; probably one reason that large hospital systems and their associated physician networks buy it. A recent Epic “upgrade” was so devoid of any upgrade in clinical relevance that it did nothing to dissuade our view of it as a “reimbursement optimization tool”.

One of Dr. Gawande’s insight as to why doctors have some much trouble liking the new way of computer documentation of everything is that computers do not handle “surprises” very well. In seeking a diagnosis and determining treatment, not all doctor’s questions and certainly not all patients’ answers can be accurately recorded with a simple click in a box. The computer thrives on all those clicks in all those boxes. Doctors do not. We often meander around in our conversations with a patient guided by chance comments or even subtle physical clues. If we elicit a “surprise” we can pursue it much more intelligently and enlightening than the computer can document it. In Gawande’s words computer programs are “brittle, bureaucratic, inflexible, designed for large data bases, rule-based, inflexible, and very difficult to adapt”; in short, unable to handle “surprises” easily. 

Defenders of Epic view their efforts as optimization of the medical care process – “reconfiguring various functions according to feedback from users.” An Epic VP labeled that as the “Revenge of the Ancillaries”. The “users” of an MRI or a X-ray request from a doctor are radiology techs or radiology department secretaries.  The questions they want answered may have little clinical importance but have multiplied within the computer screen requisition that now requires more data entry, more reading, and more in-the-box clicking by the doctor. Some computer programs allow the doctor to delegate ordering tasks, some don’t, and some, like Epic, allow delegating some tasks but not for others. Doctors who are now embracing the delegation of tasks by hiring nurse practitioners and physician assistants are confronting computer programs which are restricting delegation.

Studies have documented that doctors spend two hours in front of a computer for every one hour in front of a patient. In response a new “delegated person”, a medical scribe, has been hired by some doctors. A medical scribe is a non-physician that observes the doctor-patient visit and enters information into the computer freeing the doctor up to maximize the face-to-face patient interaction. (In Quality Management, aka Quality Assurance or Performance Improvement, we call this a “work around” – a human adaptation to bypass a problem in a operating system.)

The Clinical Director of the Partners Epic system defends its as being “for the patients, not the doctors.” Patients gain more access to their medical records like their lab test results, their medications, summary of their visits, and increased opportunity for communication with their physicians. Patient access to their medical record is via a “patient portal”; often touted as a successful way to build a practice and be a modern practitioner. Unfortunately the patient portal has not been the slam dunk it was expected to be. It certainly has not been in our pediatric practice. “Why Are Patient Portals Such Duds?” and other recent reviews describe some of both doctor and patient barriers to their adoption.

The Clinical Director of Partners Epic takes the long view that patients will eventually use the EMR as currently hoped and hyped. We shall see, and in the meantime I hope that fewer practicing primary care doctors experience “burn out” and that fewer new medical school graduates shun primary care practice.

References:
1. New Yorker Magazine, November 12, 2018, Atul Gawande

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Vol. 195 July 1, 2018 BIG DATA and a whiff of AI in health care

July 1, 2018

Hub thumbnail 2015

“When it comes to health data, Watson hasn’t been much help.”
-STATNEWS, Ross and Swetlitz. Bos Globe 6/18/18

This week all the newspapers (at least in Massachusetts) have been abuzz with the announcement that Atul Gawande, MD has been picked by three moneyed titans of innovation to head their new company to revolutionize health care. Optimism, promise, and hope is in the air! Kind of like when IBM presented Watson, its supercomputer, in 2015 as the tool to provide workable insights into the financial and clinical dilemmas of U.S. hospitals in 2015 via Watson Health.

How is that working out? Watson Health has access to data on tens of millions patients, in part by spending $9 billion to acquire other companies. It’s initial focus was on developing workable products in oncology, designed to help physicians individualize cancer treatments. “With these acquisitions, IBM will be one of the world’s leading health data, analytics, and insights companies, and the only one that can deliver the unique cognitive capabilities of the Watson platform”, said the general manager of Watson Health in 2015.

They (the newly merged companies) struggled with the basic step of learning about the different forms of cancer and the rapidly changing landscape of treatments. Last week Watson Health laid off people partly because, according to some, even Watson had difficulty in digesting all that data. “…They also don’t understand the generation of information, and how it is used, and whether they can do something different with it,” said Robert Burns, professor of health management at U Penn Wharton School. You can almost hear every primary care physician that is struggling to get their new EMR system to give him/her more information and less data cheering loudly in the background, “We couldn’t have said it better!”

The goal of a great deal of innovative technology in health care is “ “zero patient harm”. if Atul can’t do it all with his surgical checklists and Watson can’t do it all with data from tens of millions of patients , what/who can? How about Artificial Intelligence (AI), aka “machine learning”? AI and machine learning is the converting of data into information without the need for human programmers. For instance, if the computer views enough pictures of different dogs, it will learn to correctly identify a cocker spaniel. I think a real test of AI would be to see if it can recognize a Labradoodle,  or any other of the many poodle cross breeds. (Don’t you sometimes worry about the moral standards of poodles that seem to be eager to mate with any kind of passing breed?)

The building of knowledge from patterns in data, both visual and language, is labeled “computer vision”. In some medical studies “computer vision” is used to monitor actual bedside events and identify omissions or non-compliance in procedures. It has apparently improved rapidly beyond just identifying dogs or skin rashes because of “deep learning”: a type of machine learning that uses “multilayered neural networks whose hierarchical computational design is partly inspired by biologic neutron’s structure.” (1)  Got that? Think Google’s self-driving cars. “Computer vision may soon bring us closer to resolving a seemingly intractable mismatch between the growing complexity of intended clinician behavior and human vulnerability to error.” (2)

So, the effort to cut the Gordian knot of patient safety and cost-effective medicine continues. I suspect that the three titans of innovation have turned to Atul Gawande, a health care innovator who successfully uses clinical insight and re-education to effect change, because they recognize the limitations that are becoming more apparent in big data.

  1.  NEJM April 5, 2018 378:14; 1271-2
  2. Ibid.

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