Predictive Analytics – Highlighting the line of Danger for A Child In Need

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The New York Times  By  Dan Hurley

“The [team] linked many dozens of data points — just about everything known to the county about each family before an allegation arrived — to predict how the children would fare afterward. What they found was startling and disturbing: 48 percent of the lowest-risk families were being screened in, while 27 percent of the highest-risk families were being screened out. Of the 18 calls to C.Y.F. between 2010 and 2014 in which a child was later killed or gravely injured as a result of parental maltreatment, eight cases, or 44 percent, had been screened out as not worth investigation.

According to Rachel Berger, a pediatrician who directs the child-abuse research center at Children’s Hospital of Pittsburgh and who led research for the federal Commission to Eliminate Child Abuse and Neglect Fatalities, the problem is not one of finding a needle in a haystack but of finding the right needle in a pile of needles. “All of these children are living in chaos,’ she told me. ‘How does C.Y.F. pick out which ones are most in danger when they all have risk factors? You can’t believe the amount of subjectivity that goes into child-protection decisions. That’s why I love predictive analytics.

It’s finally bringing some objectivity and science to decisions that can be so unbelievably life-changing.'”