Computer model helps diagnose abuse

Tapping commonly available electronic health records, researchers from Children’s Hospital Boston have shown that predictive computer models could help doctors diagnose domestic abuse an average of 10 to 30 months earlier, by highlighting subtle patterns that are easy to miss.

Medical records, if carefully examined, often contain clues that hint at possible abuse. But with patient visits to doctors typically lasting less than 10 minutes, doctors often lack the time and resources to carefully review and interpret information from multiple visits over many years.

The researchers, led by Dr Ben Reis of the Children’s Hospital Informatics Program (CHIP) and the Division of Emergency Medicine at Children’s, analysed six years of insurance claims for hospitalisations and emergency-department visits by more than 560,000 patients over 18.

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 did not, 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 the predictive model, which was tested on the remaining third of the patients.

Reis and his CHIP colleagues Dr Kenneth Mandl and Dr Isaac Kohane found that the model was then able to identify these patients an average of 10 to 30 months before the diagnosis was made.

Reis said: ‘This is an important step towards the goal of predictive medicine. This decision-support tool can help doctors identify abuse earlier by reminding them to perform standardised screening when certain patterns appear. By providing doctors with this additional safety net, we’re hoping to minimise the chances that a high-risk patient falls through the cracks.’