Stanford team shows how doctors’ notes can spot problem drugs

When it comes to identifying potentially adverse reactions to prescription drugs, you might think doctors would be on the front lines. After all, they see a lot of patients for a lot of conditions and prescribe a lot of drugs, so who better to notice when certain prescriptions keep leading to the same side effects? And you’d be right — and wrong.

As individuals, doctors probably don’t see enough of any given adverse reaction to notice patterns emerging. But as a collection, their notes on patients’ medical records can provide valuable insights, as a group of Stanford researchers recently discovered. Using “18 years of patient data from 1.8 million patients [consisting of] 19 million encounters, 35 million coded ICD-9 diagnoses, and >11 million unstructured clinical notes,” the team was able to accurately identify interactions by analyzing the free-form text that doctors had entered about patients’ symptoms, conditions and prescription regimens.

A key aspect to being able to predict adverse interactions is understanding the relationships among the different sets of terminologies used in different medical fields. It’s a lot easier to spot patterns across hospitals or even an individual patients’ records when you know that a radiologist writing X is the same, or related to, an oncologist writing Y. We covered an earlier collaboration between the study’s leader, Nigam Shah, and medical-data startup Apixio around this very topic in 2011.

How Shah’s team developed its patient-feature matrix

Shah and his team hope their work can complement the current process for tracking drug reactions, the FDA’s Adverse Event Reporting System. Whereas that system requires doctors and patients to manually alert the FDA of potential adverse side effects, their method could highlight potential problems that no one noticed or took the time to report. I’d consider this similar to some early research by social medical sites such as PatientsLikeMe, whose users are producing lots of data about their conditions, drugs, dosages and side effects that could produce correlations ripe for controlled experiments.

A press release announcing the study’s publication highlights some of its future promise and current limitations:

“[T]he research team is working on refinements that will cull even more useful information from clinical notes, such as reports of reactions caused by drug combinations, the use of medications typically prescribed for one condition but found effective for treatment of a different health problem, or finding medical profiles of patients that fit a certain scenario. …

One downside is that most electronic health record systems are set up for patient care, not patient research, Goodman noted. In this study, the researchers mined a data system created for this kind of research, which isn’t widely available. The researchers used the Stanford Translational Research Integrated Database Environment, known as STRIDE.”

This is just one of many ways in which researchers are experimenting with big data concepts to help medical professionals make sense of more data than they could possibly analyze on their own. Other examples we’ve covered recently include an artificial intelligence model for prescribing safe, cost-effective treatments, the application of Google PageRank-like algorithms to map the spread of cancer cells throughout the body, and the use of graph data structures to organize highly complex sequencing data.

Feature image courtesy of Shutterstock user Maksym Dykha.