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Using data from two-thirds of the patients, a computer model was trained to differentiate those who ultimately received a diagnosis of abuse from those who didn't, based solely on their history of visits. The variables associated with abuse (such as a higher number of annual visits, mental health diagnoses, and visits for injury) were used to create a predictive model, which was tested on the remaining third of the patients.
Reis and his CHIP colleagues Kenneth Mandl, MD, MPH and Isaac Kohane, MD PhD, found that the model was able to identify these patients an average of 10 to 30 months before the diagnosis was made, with high sensitivity and specificity.
"This is an important step towards the goal of Predictive Medicine," says Reis. "This decision support tool can help doctors identify abuse earlier by reminding them to perform standardized screening when certain patterns appear. By providing doctors with this additional safety net, we're hoping to minimize the chances that a high-risk patient falls through the cracks."
The analysis also found some gender differences in the statistical signs associated with abuse. Alcoholism, poisoning and injuries from external causes were more predictive of abuse in women than in men, while psychoses, affective disorders, and other mental conditions were more predictive in men than in women.
Reis notes that the database used may underestimate the true incidence of abuse, which is sometimes undiagnosed or simply not recorded in medical records. "The data set we used to train and evaluate the predictive model has all the challenges of real-world data," says Reis. "The important finding here is that in spite of these real-world limitations, the model was able to produce useful and reliable results."
Reis also developed a color-coded visual display to help physicians quickly process large amounts of information, integrating the patient's entire diagnostic history into one easy-to-digest view (see accompanying images) and displaying an alert if the history is suggestive of abuse. "Our goal is to communicate an overview of a patient's longitudinal history to the doctor in 10 seconds," says Reis. "We're hoping doctors with access to this kind of view will be able to provide better, more-informed care, including detecting certain medical conditions earlier."
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