Abstract
Every year more than 800,000 children in the U.S. spend time in foster care with 35% being on psychotropic medication. An increasing ratio of foster care children per case worker makes it challenging to balance their multiple roles during the lifecycle of a case. Although there are review boards for identifying cases that require special attention, scanning through all the unstructured data is time-consuming and is further complicated by poorly written, or incomplete, case note entries from the overwhelmed case workers. As part of a larger comprehensive study of a technology-centric foster care organization, we investigate a challenging nationwide problem of over-prescription of psychotropic drugs, which can lead to unnecessary deaths. We demonstrate how text mining techniques can significantly reduce the administrative requirements for auditing case notes by providing a smaller sample that is manageable yet be sensitive to exclusion of children showing evidence of psychotropic drug use. This should significantly lighten the load for case managers as they investigate possible cases.
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Castillo, A., Castellanos, A., Tremblay, M.C. (2014). Improving Case Management via Statistical Text Mining in a Foster Care Organization. In: Tremblay, M.C., VanderMeer, D., Rothenberger, M., Gupta, A., Yoon, V. (eds) Advancing the Impact of Design Science: Moving from Theory to Practice. DESRIST 2014. Lecture Notes in Computer Science, vol 8463. Springer, Cham. https://doi.org/10.1007/978-3-319-06701-8_21
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DOI: https://doi.org/10.1007/978-3-319-06701-8_21
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