Every Patient Tells a Story (34 page)

The rapidly growing ability of computers to store vast amounts of information seemed to fit perfectly with the needs of medicine, in particular the challenges of medical diagnosis. It was obvious that medical knowledge was also growing exponentially. In a 1976 article, a group of doctors working on a computer simulation of “clinical cognition” estimated that a practicing
doctor draws on a store of at least two million medical facts. And it was clear that this mountain of knowledge would only grow larger with time. Using a computer “brain” to augment and support human brains in the often bedeviling work of diagnosing illness seemed to Szolovits a logical and technologically feasible goal.

During these heady times Szolovits began a series of conversations with physicians about collaborating to design a computer to help doctors meet the demands of the rapidly expanding universe of medical knowledge. He was surprised by what he found. One conversation in particular with a highly respected senior physician in a university teaching hospital stands out from those days. After listening to Szolovits describe the possibilities of, for instance, entering a set of symptoms into a computer that would then generate a list of likely diagnoses, the physician interrupted him.

“Son,” he said, raising his bare hands in front of Szolovits, “these are the hands of a surgeon, not a typist.” And he turned on his heel and walked away.

It was an early indication that the application of computers to medical diagnosis might not be as straightforward as Szolovits had thought.

Flash forward thirty years.

By 2006, Szolovits was a full professor at MIT. An energetic man with just the barest hint of middle-aged thickening and a salt-and-pepper beard, Szolovits heads the group at the Massachusetts Institute of Technology devoted to designing computers and systems of artificial intelligence to address problems of medical decision making and diagnosis. Every fall he shares his ideas and insights into this world in a graduate student seminar called Bio-medical Decision Support. I had read about this course and wanted to see what the future of diagnostic software was going to look like.

I visited at the end of the semester, when students presented their final projects. Sitting in a hard plastic chair in the classroom, I watched as PowerPoint slides whizzed by, accompanied by rapid-fire, acronym-studded sentences. One group presented a new technique to look for “interesting hits” amid vast databases; another presented a user-friendly interface for a Web-based electronic medical records program; a third presented a program
that bolsters the privacy of genetic test data. One group exceeded their fifteen-minute slot to describe an elegant program for identifying potentially harmful interactions between prescription drugs that performs better than the current state-of-the-art software.

All of the projects seemed to improve or expand the boundaries of one or another aspect of health care delivery. Indeed, after the presentations Szolovits chatted with the team who’d created the drug interaction program because not only did it appear to be publishable, it might also be something the students could turn into a business opportunity.

And yet something was missing. Despite the title of the course, none of the projects addressed the issue that had beckoned so alluringly to Szolovits thirty years ago—the task of improving clinical diagnosis with computers.

In his office after the class, Szolovits leaned back in his chair, musing.

“Thirty years ago we thought we could identify all of the best practices in medicine, create a system that would make diagnosis faster and easier, and bring it all to doctors via a computer,” he said. Twenty years ago he wrote a paper for the
Annals of Internal Medicine
that proclaimed artificial intelligence techniques would eventually give the computer a major role as an expert consultant to the physician. And today? Szolovits sighed. “As it turns out, it’s simply not possible.” It might be an interesting idea, but there’s no market for it. Doctors aren’t interested in buying it and so companies aren’t interested in designing and building it. “Rather than trying to bring the average doctors up to a level of being super-diagnosticians, the emphasis and attention has shifted toward bringing below-average doctors up to current standards and helping even good doctors avoid doing really stupid things. That turns out to provide greater benefits to patients. Plus, there is a financial model for it.”

Szolovits ticked off some of the major reasons that most doctors today still rely on their own brains and the brains of their colleagues when making a diagnosis rather than a computerized diagnostic aid.

First, computers can’t collect the data from the patients themselves. These machines excel in data crunching, not data collecting. Physicians must collect the data and then enter it into the program. And the programs themselves
don’t make this easy. There are many ways of describing a patient’s symptoms and physical exam findings, but most computers don’t have adequate language skills to understand. You’re left with long pull-down lists of every possible symptom variation or using terms that the computer simply doesn’t recognize.

There are technical difficulties as well. Doctors, laboratories, and hospitals all use different kinds of computer software. No single system can interface with the huge variety of software used to store patient data. Once again the physician must provide the data if she wants it to be considered. Then there are financial difficulties. Who is going to pay the doctor or hospital to invest in this kind of software? Szolovits noted that hospitals don’t get reimbursed for
understanding
things, they get reimbursed for
doing
things.

But perhaps the greatest difficulty lies in persuading doctors themselves to use this kind of software. When confronted with a confusing clinical picture, it is often faster and easier for doctors to do what doctors have always done—ask for help from other doctors.

For these and many other reasons, the medical community has yet to embrace any particular computerized diagnostic support system. The dream of a computer system that can “think” better, faster, and more comprehensively than any human doctor has not been realized. For all their limitations, well-trained human beings are still remarkably good at sizing up a problem, rapidly eliminating irrelevant information, and zeroing in on a “good-enough” decision.

This is why human chess players held out for so long against computer opponents whose raw computational and memory abilities were many orders of magnitude better than those of a human brain. Humans devise shortcut strategies for making decisions and drawing conclusions that are simply impossible for computers. Humans are also extraordinarily good at pattern recognition—in chess, skilled players are able to size up the entire board at a glance and develop a feel, an intuition, for potential threats or opportunities.

It took decades and millions of dollars to create a computer that was as good as a human at the game of chess. It is a complex game requiring higher
order thinking but is two-dimensional and based on clear, fixed rules using pieces that never vary. The diagnosis of human beings, in contrast, is four-dimensional (encompassing the three spatial dimensions and the fourth dimension of time), has
no
invariable rules, and involves “pieces” (bodies), no two of which are exactly the same.

In addition, of course, humans have a set of diagnostic tools that computers may never equal—five independent and exquisitely powerful sense organs. At a glance, a doctor can take in and almost immediately process reams of information about a patient—their posture, skin tone, quality of eye contact, aroma, voice quality, personal hygiene, and hints and clues so subtle they defy verbal description. A computer, in contrast, has only words and numbers, typed in by a human, that inadequately represent a living, breathing, and immensely complicated patient.

Despite the challenges, Szolovits was among those who first attempted to develop computer programs to diagnose medical conditions. Dozens of prototype models were created and tested in a laboratory setting. But most foundered when attempts were made to scale them up, move them into a clinical setting, or make a profit on them. Computers lacked the necessary memory and processing speeds to make vast databases rapidly usable. Until the advent of the World Wide Web, programs had to be distributed via diskettes, or as part of a dedicated computer, or via dial-up modem connections. All of these challenges slowed momentum in the field.

But even systems that have embraced more recent technological improvements have not seen wide success. A case in point is one of the earlier attempts to use computers to improve diagnosis. In 1984 a team of computer scientists from MIT’s Laboratory for Computer Science teamed up with a group of doctors from Massachusetts General Hospital, just across the river. They worked for two years to develop an electronic medical reference system and an aid to diagnosis. In 1986 the program, dubbed DXplain, was launched with a database of information on five hundred diseases. National distribution of DXplain with an expanded database of about two thousand
diseases began in 1987 over a precursor to the Internet—a dedicated computer network using dial-up access. Between 1991 and 1996, DXplain was also distributed as a stand-alone version that could be loaded on an individual PC. Since 1996, Internet access to a Web-based version of DXplain has replaced all previous methods of distribution. The program has been continually expanded over the years and is now available to about 35,000 medical personnel, almost all of them in medical schools and teaching hospitals where the program is used as an educational tool.

DXplain and other first-generation diagnostic decision support software programs use compiled knowledge bases of syndromes and diseases with their characteristic symptoms, signs, and laboratory findings. Users enter the data from their own patients by selecting from a menu of choices, and the programs use Bayesian logic or pattern-matching algorithms to suggest diagnostic possibilities.

“There was a lot of work in the 1980s on using computers in diagnostic problem solving and then, in the 1990s, it sort of petered out,” says Eta Berner, a professor of Health Informatics at the University of Alabama. Berner may have been part of the reason this work petered out. In 1994 she and a group of thirteen other physicians tested four of the most widely used programs in a paper published in the
New England Journal of Medicine
. They collected just over one hundred difficult cases from specialists from around the country. They entered the data from each of the patients into each of the four databases. All four programs correctly diagnosed 63 out of the 105 cases included in the study. Overall the four programs provided the correct diagnosis anywhere from 50 to 70 percent of the time—a solid C performance at best.

The authors of the study concluded that the programs tested might be somewhat helpful in clinical settings: “The developers of these systems intend these programs to serve a prompting function, reminding physicians of diagnoses they may not have considered or triggering their thinking about related diagnostic possibilities.” But as their study showed, many times the programs would not provide the answers that the doctors are looking for. “The field was sort of a wasteland for a while,” Berner explained, but then added, “Now it’s picking up again.”

Consulting an Expert System

One of the difficulties of diagnostic software systems like DXplain is that they try to cover all areas of medicine. Other systems that have been developed as specialized “expert systems” are used by doctors when a case presents a particular type of diagnostic challenge.

Dr. Frank Bia is the medical director of AmeriCares, an international relief organization. He’s also a specialist in infectious disease—particularly tropical disease—and until recently a professor of medicine at Yale. He uses a program called GIDEON (Global Infectious Disease and Epidemiology Network) when he sees patients who are sick and have recently returned from other countries. Not long ago he described a case where GIDEON provided clues to a very difficult diagnosis.

It was the early hours of the morning. A twenty-one-year-old woman was moaning softly in her hospital bed. Beside her an IV dripped fluid into her slender arm. Her mother sat next to the bed, her stylish clothes rumpled from her night-long vigil and her face heavy with fatigue.

She’d been brought to the emergency room of this small Connecticut hospital late one night, pale and feverish. “She’s been like this for two weeks,” the mother told the young physician who entered the room. “And no one can figure out why.”

Her daughter had always been very healthy. She’d recently spent a month on a research trip to Africa without any health issues. It wasn’t until two weeks after her return to Wesleyan College that she began to feel hot and sweaty. Just standing up made her light-headed. A lengthy nap brought some relief but, by the next day, she realized that she was feverish, so she went to the infirmary.

“I told them I thought it might be malaria,” the patient explained to the doctor in a barely audible voice. “The teacher told us it was common where we were in Tanzania.” And she hadn’t always taken the preventative medicine while she was there. The school nurse thought it was probably the flu. But when the young woman didn’t get better over the next several days, the nurse referred her to an infectious disease specialist in town. Maybe it
was
malaria. Since she had been in an area rife with this mosquito-borne illness, the specialist started her on a week of quinine and doxycycline.

She took a full seven-day course, but the medicine didn’t help. Over the next few days she developed a cough so violent it made her vomit. She had abdominal pain that made even standing difficult. And she had terrible diarrhea. When she made yet another trip to the infirmary, they called an ambulance to take her to a hospital nearby.

Fadi Hammami, the doctor on duty that morning, listened quietly to the story. He told me later: “I didn’t want to miss this diagnosis. She probably had picked up something in Africa; I just had to figure out what it was.”