individualized, intuitive approach that lies at the center of modern medicine is flawed—it causes more mistakes than it prevents. There’s ample support for this conclusion from studies outside medicine. Over the past four decades, cognitive psychologists have shown repeatedly that a blind algorithmic approach usually trumps human judgment in making predictions and diagnoses. The psychologist Paul Meehl, in his classic 1954 treatise,
Clinical Versus Statistical Prediction
, described a study of Illinois parolees that compared estimates given by prison psychiatrists that a convict would violate parole with estimates derived from a rudimentary formula that weighed such factors as age, number of previous offenses, and type of crime. Despite the formula’s crudeness, it predicted the occurrence of parole violations far more accurately than the psychiatrists did. In recent articles, Meehl and the social scientists David Faust and Robyn Dawes have reviewed more than a hundred studies comparing computers or statistical formulas with human judgment in predicting everything from the likelihood that a company will go bankrupt to the life expectancy of liver-disease patients. In virtually all cases, statistical thinking equaled or surpassed human judgment. You might think that a human being and a computer workingtogether would make the best decisions. But, as the researchers point out, this claim makes little sense. If opinions agree, no matter. If they disagree, the studies show that you’re better off sticking with the computer’s judgment.
What accounts for the superiority of a well-developed computer algorithm? First, Dawes notes, human beings are inconsistent: we are easily influenced by suggestion, the order in which we see things, recent experience, distractions, and the way information is framed. Second, human beings are not good at considering multiple factors. We tend to give some variables too much weight and wrongly ignore others. A good computer program consistently and automatically gives each factor its appropriate weight. After all, Meehl asks, when we go to the store, do we let the clerk eyeball our groceries and say, “Well, it looks like seventeen dollars’ worth to me”? With lots of training, the clerk might get very good at guessing. But we recognize the fact that a computer that simply adds up the prices will be more consistent and more accurate. In the Swedish study, as it turned out, Ohlin rarely made obvious mistakes. But many EKGs are in the gray zone, with some features suggesting a healthy heart and others suggesting a heart attack. Doctors have difficulty estimating faithfully which way the mass of information tips, and they are easily influenced by extraneous factors, such as what the last EKG they came across looked like.
It is probably inevitable that physicians will have to let computers take over at least some diagnostic decisions. One network, PAPNET, has already gained mainstream use in the screening of digitized Pap smears—microscopic scrapings taken from a woman’s cervix—for cancer or precancerous abnormalities, which is a job usually done by a pathologist. Researchers have completed more than a thousand studies on the use of neural networks in nearly every field of medicine. Networks have been developed to diagnose appendicitis, dementia, psychiatric emergencies, and sexually transmitted diseases. Others can predict success from cancer treatment, organ transplantation, and heart valve surgery. Systems have been designedto read chest X rays, mammograms, and nuclear-medicine heart scans.
In the treatment of disease, parts of the medical world have already begun to extend the lesson of the Shouldice Hospital concerning the advantages of specialized, automated care. Regina Herzlinger, a professor at the Harvard Business School, who introduced the term “health-care focused factory” in her book
Market-Driven Health Care
, points to other examples, including the Texas Heart Institute
Clinical Versus Statistical Prediction
, described a study of Illinois parolees that compared estimates given by prison psychiatrists that a convict would violate parole with estimates derived from a rudimentary formula that weighed such factors as age, number of previous offenses, and type of crime. Despite the formula’s crudeness, it predicted the occurrence of parole violations far more accurately than the psychiatrists did. In recent articles, Meehl and the social scientists David Faust and Robyn Dawes have reviewed more than a hundred studies comparing computers or statistical formulas with human judgment in predicting everything from the likelihood that a company will go bankrupt to the life expectancy of liver-disease patients. In virtually all cases, statistical thinking equaled or surpassed human judgment. You might think that a human being and a computer workingtogether would make the best decisions. But, as the researchers point out, this claim makes little sense. If opinions agree, no matter. If they disagree, the studies show that you’re better off sticking with the computer’s judgment.
What accounts for the superiority of a well-developed computer algorithm? First, Dawes notes, human beings are inconsistent: we are easily influenced by suggestion, the order in which we see things, recent experience, distractions, and the way information is framed. Second, human beings are not good at considering multiple factors. We tend to give some variables too much weight and wrongly ignore others. A good computer program consistently and automatically gives each factor its appropriate weight. After all, Meehl asks, when we go to the store, do we let the clerk eyeball our groceries and say, “Well, it looks like seventeen dollars’ worth to me”? With lots of training, the clerk might get very good at guessing. But we recognize the fact that a computer that simply adds up the prices will be more consistent and more accurate. In the Swedish study, as it turned out, Ohlin rarely made obvious mistakes. But many EKGs are in the gray zone, with some features suggesting a healthy heart and others suggesting a heart attack. Doctors have difficulty estimating faithfully which way the mass of information tips, and they are easily influenced by extraneous factors, such as what the last EKG they came across looked like.
It is probably inevitable that physicians will have to let computers take over at least some diagnostic decisions. One network, PAPNET, has already gained mainstream use in the screening of digitized Pap smears—microscopic scrapings taken from a woman’s cervix—for cancer or precancerous abnormalities, which is a job usually done by a pathologist. Researchers have completed more than a thousand studies on the use of neural networks in nearly every field of medicine. Networks have been developed to diagnose appendicitis, dementia, psychiatric emergencies, and sexually transmitted diseases. Others can predict success from cancer treatment, organ transplantation, and heart valve surgery. Systems have been designedto read chest X rays, mammograms, and nuclear-medicine heart scans.
In the treatment of disease, parts of the medical world have already begun to extend the lesson of the Shouldice Hospital concerning the advantages of specialized, automated care. Regina Herzlinger, a professor at the Harvard Business School, who introduced the term “health-care focused factory” in her book
Market-Driven Health Care
, points to other examples, including the Texas Heart Institute
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