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HOW TO DECIDE:

A physician’s roundabout, intuitive way // determining diagnosis, dosage, prognosis through experience, trial and error // or a fleet of formulas // applying rules to data, expediting answers // and streamlining care?

Algorithms: Logical Medicine

By Meera Lee Sethi // Photographs by Mauricio Alejo // SUMMER 2010
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algorithms

Mauricio Alejo for Proto

To nonmathematicians, the word algorithm (from a latinized version of the name of the ninth-century Persian astronomer who wrote a treatise on calculation) may seem arcane and off-putting, its definition difficult to pin down. Yet the thing itself, if not the term, pops up everywhere. Across the spectrum of human activity, algorithms run vital decision-making processes behind the scenes. If you’ve taken out a home mortgage, an algorithm was applied to your financial records to determine how to price your loan. If you were stranded after the eruption of Iceland’s Eyjafjallajökull volcano, algorithms were responsible for the rerouting of thousands of planes and crews to get you home. If you own a Volvo S60 sedan, algorithms are used to scan for pedestrians in your path and hit the brakes even if you don’t. In every modern industry, including medicine, algorithms rule.

An algorithm is any step-by-step procedure that applies rules to data, producing an unambiguous solution to a problem, and there is now a vast universe of clinical examples. The Medical Algorithm Project (MedAL), which stores peer-reviewed algorithms in an online database, contains more than 14,400. These tools can help physicians make diagnoses, choose treatments, calculate dosages, predict outcomes and monitor side effects. More are being developed every day.

Like their counterparts in mathematics, medical algorithms take myriad shapes. They can look like equations, scales, truth tables, checklists, scoring systems or decision trees. The simplest are performed with pen and paper, and the answers they provide may seem intuitive, something experienced physicians might come up with on their own, at least when dealing with familiar conditions. The widely used body mass index calculation, for instance, uses a straightforward ratio—mass in kilograms divided by the square of height in meters—to produce a number that physicians can use to see where a patient falls in a range from dangerously thin to morbidly obese.

Other clinical algorithms, however, are more complex and can help specialists keep up with a knowledge base that’s expanding exponentially. These formulas are computerized and often sift huge amounts of data and alternative approaches before reaching conclusions. For example, the algorithms that drive automated external defibrillators analyze the pattern of a patient’s heart rhythm to determine the number and strength of shocks required to restore normal functioning.

But simple or complicated, and despite their proliferation in textbooks, journals and, increasingly, electronic databases, most formal algorithms don’t get used. To critics such as Herbert L. Fred of the University of Texas Health Science Center, that’s a good thing. Fred, a professor of internal medicine, has written that algorithms lead physicians to interact with numbers, not patients, and has urged medicine to “give algorithms back to the mathematicians.” But advocates, including John Svirbely, medical director of laboratories at the McCullough-Hyde Memorial Hospital in Oxford, Ohio, and co-founder of MedAL, argue that algorithms save time, money and lives—or would, if they were integrated into everyday practice.

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Algorithms in Action

In many cases, physicians rely on medical algorithms as part of an ongoing process of patient assessment and care.

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1. “Why Physicians Do Not Follow Some Guidelines and Algorithms,” by David N. Osser, Drug Benefit Trends, Dec. 6, 2009. Osser explores the reasons behind the pervasive neglect of psychopharmacological algorithms, including issues of work flow and time management, and a preference for relying on personal clinical experience.

2. “Thinking About Diagnostic Thinking: A 30-Year Perspective,” by Arthur S. Elstein, Advances in Health Sciences Education, September 2009. The author reviews several types of diagnostic errors caused by faulty clinical reasoning and argues that algorithms can help remedy deficiencies in human judgment.

3. “The Limited Role of Expert Guidelines in Teaching Psychopharmacology,” by Carl Salzman, Academic Psychiatry, June 2005. Salzman reviews formal decision-making tools in psychopharmacology, arguing that algorithms overemphasize new drugs and lack utility in diagnosing patients with complex symptoms.

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