To say medicine has come a long way in the last few millennia is like saying the sun is warmer than a weenie in a picnic basket. Needless to say, we've still got a ways to go. And a new idea advocated by a trio of Stanford researchers may help us get there.
Modern medical practice is so far advanced from the crude practices of olden times that we simultaneously wince and scoff at the misguided procedures and bumbling diagnostics left in the dust by powerful tools, precise measurements and evidence-based judgments.
Count among modern medicine's proud achievements the randomized clinical trial, which, as I've written in a just-published feature, "On the Button," in our magazine, Stanford Medicine, works like this:
[A] number of participants are randomly assigned to one of two - sometimes more - groups. One group gets the drug or the procedure being tested; the other is given a placebo or undergoes a sham procedure. Ideally, the study is blinded - patients don’t know which option they’re getting - or even better, double-blinded - the investigators and their assistants don’t know, either. Once the trial’s active phase ends, rigorous statistical analysis determines whether the hypothesis, spelled out in advance of the trial, was fulfilled.
As I wrote, though, there's a gaping hole in the body of randomized clinical trials conducted to date:
In order to achieve meaningful results, investigators tend to select participants... who are a lot alike in terms of age, sex, ethnicity, medical conditions and treatment history. Yet the average patient walking into a doctor’s office seldom resembles a patient included in those trials.
Most of us are square pegs in the round hole of clinical-trial results. Medications that work great in one ethnic group can work dismally in another. Older people metabolize drugs more slowly than young people. Males and females can respond quite differently to the same drug. And people frequently are suffering from more than one medical problem.
Fortunately, new technologies may allow "all the rest of us" who don't neatly fit the description of the subjects for whom one or another clinical trial has shown that this or that procedure or prognosis or medicine is most appropriate to benefit from the real-life outcomes of medical practice as performed on people whose description - age, sex, ethnicity, constellation of conditions, and the like - closely matches our own.
In a 2014 Health Affairs paper, three Stanford faculty members - Nigam Shah, MBBS, PhD; Robert Harrington, MD; and Christopher Longhurst (who has since moved to UCSD) - proposed the use of sophisticated computer algorithms to quickly comb through millions of patients' electronic medical records for cases that most closely resemble that of the patient standing (or lying) in front of a physician right now, report the health outcomes of particular procedures or medications prescribed for those patients and, thus, give the physician the best possible guidance in the absence of clear-cut clinical trial results applicable to this particular patient.
With computational and telecommunication speeds accelerating, EMRs becoming more universal, and medical systems getting increasingly integrated and interconnected, we could be on the cusp of a paradigm shift that will bring personalized medicine to everybody.
Previously: Precision health: a special report from Stanford Medicine magazine, Push-button personalized treatment guidance for patients not covered by clinical-trial results, Widely prescribed heartburn drugs may heighten heart-attack risk and A new view of patient data: Using electronic medical records to guide treatment
Illustration by Harry Campbell
