Kevin Murnane reports in Forbes, "Machine learning's ability to produce actionable results from unstructured data is clearly demonstrated in a study published in the April 2016 volume of The Journal of Biomedical Informatics that used machine learning algorithms to identify cancer diagnoses from free text pathology reports. The results provide strong evidence that machine learning techniques can bypass a central bottleneck that interferes with turning unstructured data into useful information." He continues, "Medical practitioners routinely create clinical reports on their patients that contain a wealth of potentially useful and valuable information. Many benefits could be realized if the information in these reports could be compiled and easily accessed to produce actionable results. For example, the dangerous levels of lead in Flint, Michigan's water supply might have been discovered sooner if individual doctors' reports noting unusually high lead levels in children's blood had been gathered in an accessible format at an earlier date."