What if the patient was given a test, but it was actually a less expensive version than what the hospital billed for? Finding the answers to these kinds of questions can involve gathering and analyzing massive amounts of data from myriad sources. Traditionally, human fraud examiners have taken on these tasks, as they specialize in sifting through healthcare compliance and medical billing documents to determine what's legit and what isn't. Healthcare is built around helping people stay well or recover following illnesses and injuries, but like other industries it is susceptible to a range of fraud that can cause financial harm and drive up costs in the system. "Fraud, waste, and abuse is a huge contributor to unnecessary costs and the rise of spend within healthcare in the U.S.," says Arielle Trzcinski, senior analyst at Forrester.

Bill Condon is ready to take on a new monster of a project. After helping Disney's live-action "Beauty and the Beast" spin box-office gold, the 61-year-old director has been tapped to breathe new life into "Bride of Frankenstein." Universal Pictures announced on Monday that Condon's remake will hit theaters in February 2019, making it the second film in its newly coined "Dark Universe" series. Universal has long planned to draw on its own library of classic monster characters for a run of films. First up in the Dark Universe series is this summer's "The Mummy" reboot, starring Tom Cruise, Sofia Boutella and Russell Crowe, which debuts in theaters on June 9.

We introduce a synergetic approach incorporating psychological theories and data science in service of predicting human behavior. Our method harnesses psychological theories to extract rigorous features to a data science algorithm. We demonstrate that this approach can be extremely powerful in a fundamental human choice setting. In particular, a random forest algorithm that makes use of psychological features that we derive, dubbed psychological forest, leads to prediction that significantly outperforms best practices in a choice prediction competition. Our results also suggest that this integrative approach is vital for data science tools to perform reasonably well on the data. Finally, we discuss how social scientists can learn from using this approach and conclude that integrating social and data science practices is a highly fruitful path for future research of human behavior.