HarrisLogic uses data to identify prisoners that require intensive treatment for mental illness.

The company's analytics can predict who will return to prison within six months with a high degree of accuracy.

By cutting back on treatment costs and recidivism, HarrisLogic has saved Dallas County $30 million over the course of four years.

The majority of America's prison population is mentally ill, but there are few ways to effectively diagnose and care for prisoners on a massive scale.

In Dallas, the technology and clinical services company HarrisLogic is attempting to solve this problem using data-driven tools to determine the appropriate behavioral health services for prisoners. By pooling information from jails, police departments, emergency services, mental health and social services, courts, and hospitals, the company has saved Dallas County $30 million over the course of four years.

This process of aggregating data across agencies, then using it for predictive modeling and analytics, could have major implications for prisons across the country. While mental health care in US prisons is notoriously inadequate, even prisons that offer decent care tend to treat all prisoners the same, according to Hudson Harris, the company's chief engagement officer.

It once took the Dallas County Criminal Justice Department four to six weeks to identify mentally ill prisoners. HarrisLogic now knows within 15 minutes when a prisoner is booked into prison. The company then quickly contacts the prisoner's public defender and case provider to obtain consent for an evaluation.

On average, HarrisLogic evaluates 350 prisoners each month. Half of these prisoners go through the company's Jail Diversion Program, which devises a care plan based on the prisoner's individual history and needs. These evaluations, combined with the company's robust database, help determine which prisoners require higher levels of care, such as inpatient services. By reducing the likelihood that a prisoner will receive unnecessary treatment, HarrisLogic says it has reduced higher-level care costs by 25% in the Dallas County prison system.

The company has also piloted a program to reduce the likelihood that a prisoner will commit a future offense. Using predictive analytic software from SAP, the company says it can predict who will return to prison within six months with 72% accuracy, and who won't return with 99% accuracy.

These are some big claims, but the idea that appropriate care can reduce recidivism is well-supported. "If you book into jail with a mental health condition, your odds of returning are 67%," says Harris. "We worked on quantifying the variables and factors that were driving people to come back."

In many cases, he says, formerly homeless prisoners with mental health conditions are more likely to return to the system. But to avoid sweeping assumptions, the company combines its knowledge of a prisoner's criminal and mental health history with other factors that might influence one's behavior, such as economic conditions.

This information is helpful in not only identifying individuals that require extra care, but also in identifying people that may be predisposed to more severe forms of mental illness. With this knowledge at hand, the company has successfully reduced the average daily jail population in Dallas County by 20%.

There are reasons to be skeptical of algorithms, which often fail to account for human bias or changes in patterns and behavior. Criminals with long arrest records, for instance, may come from neighborhoods where arrests are more frequent.

But there's still a need to quantify mental health among prisoners. "There are a lot of issues around the morality of data, what you're doing with it, how you're helping people," says Harris. "[But] what we found is, even without the predictive [analytics], having evidence-based treatment had a tremendous impact on the quality of care."