Victims of domestic abuse can hide the truth from doctors, but they leave clues in their medical records that a computer program has now learned to follow.

The program could save lives by acting as an early warning system for domestic violence, flagging up possible cases of abuse to doctors months or even years before they would otherwise be detected.

“You are potentially able to detect high abuse risk years ahead of time: you don’t wait for a very bad thing to happen,” says Ben Reis at Children’s Hospital Boston and Harvard Medical School, who led the new study.

Though domestic abuse occurs in up to 16 per cent of US couples every year, it’s notoriously difficult for doctors and nurses to spot, Reis says. Victims and abusers frequently make excuses for emergency room visits – saying that an injury caused by a partner’s assault was due to a fall, for instance. Studies have shown that some go so far as to visit different hospitals to avoid a pattern of injuries getting noticed.


Doctors are trained – or required, in some hospitals – to be on the lookout for domestic violence, but isolated emergency room visits make it difficult for them to spot patterns that could be a sign of domestic violence.

Trails of violence

To make these patterns more apparent, Reis and his colleagues Isaac Kohane and Kenneth Mandl turned to the medical histories of 561,000 people over six years in a single US state. About 19,000 of these people were known to have been domestically abused. To protect confidentiality, the team did not identify the state where the people lived.

Their program started by searching two-thirds of the records for differences between the histories of people who were abused by their partners and those of people who were not known to have been abused.

“Unsupervised, you tell us, computer: what are the risk factors? What are the things highly associated with future diagnosis of abuse?” says Reis, summarising his approach.

New clues

The program produced a set of rules based on the differences it found. These were mainly based on patterns of injuries and bouts of mental illness – signs of abuse that doctors already look for.

But the program also found new clues, including some that pick out victims of one sex but not the other. Alcoholism, for instance, is a red flag for abuse in women, but not men, because it is less common among women in general. In contrast, depression and other mood disorders are a strong predictor of abuse in men, but less so for women.

To see how useful the rules could be for detecting domestic violence, the team fed the remaining medical histories to the program. They found that on average it detected abuse earlier in people’s records than their doctors had.

“Sometimes doctors are blinded because they don’t have access to 10 years of medical history,” he says. “We’re trying to empower the doctors to make the decision.”

The usefulness of the program varied greatly between patients. In some, the program flagged abuse six years earlier than doctors had, but in others the warning came just before doctors noticed – or not at all.

Reis’s team could also tweak the sensitivity of the software to lengthen or shorten this window. Setting the program to tolerate higher rates of “false positives” – instances where the program flags up abuse, even though none was ever detected by a doctor – meant it also caught more cases of real abuse earlier. Under one scenario, a 10 per cent false-positive rate turned up two-thirds of abuses an average of two years before being reported.

Journal reference: BMJ, DOI: 10.1136/bmj.b3677