Doctor Evidence has launched DIF—the Data Impact Factor—technology for identifying data cohorts, across time and various reported data sets
| SANTA MONICA, Calif., /PRNewswire/ -- Doctor Evidence has launched DIF—the Data Impact Factor—technology for identifying data cohorts, across time and various reported data sets. As technology progresses, it has become possible to move from past paradigms, like the Journal Impact Factor, which is an index of the relative influence of leading journals. Today’s innovative technologies can assign actual weights, prominence, and influence factors to individual data cohorts across all research or within a selected topic area. DIF embraces the increased capabilities for micro-tagging of clinical trial research and subsequent representations of that data. In the DOC Search artificial intelligence (AI) platform, Doctor Evidence has developed a technology that identifies common individual data elements occurring across disparate reporting formats, like clinical studies, registries, drug labels, news, and press releases. The DOC Search technology links publications and meeting abstracts back to the clinical trial registrations and subsequent publications following cohorts over time and across various analyses. Harnessing this interconnectivity supplies metrics to the impact of the data elements. The iconic Framingham Heart Study provides a great example of how DIF could be applied in practice. The Framingham Study drove some 1,200 papers in leading medical journals. There is a Framingham formula for predicting risk of coronary heart disease, the Framingham Risk Score, which has subsequently been revised to include cerebrovascular events, peripheral artery disease, and heart failure to estimate overall cardiovascular disease risk. These and other points of influence have driven much of cardiology for decades, all stemming from the Framingham research. If we had a Data Impact Factor and score it would help us understand this significant impact on research, drug development, drug coverage, and treatment benefits. Despite the fact that the Framingham population was predominantly Caucasian and treatment naïve, the results have been applied more broadly in diverse settings. Today, we can link data, identify it in various formats and expressions and, therefore, use it more precisely to improve the health of patients with more similar characteristics and conditions. In this early era of precision medicine, identification of these limits to data generalizability will prove to be quite valuable in the application of important research results. “We are moving to an age where the reliance on and search for quality has moved from ‘proxy orientations,’ like housings, or in this case Journals, to the actual source data inside of the house being measured for impact,” said Bob Battista, CEO of Doctor Evidence. “Given the increased democratization of the tools of analysis that are available today, DIF embodies inspections of individual data elements, and particularly their valuable use with real-world data for purposes like identifying unmet needs and creating synthetic control arms in places where traditional methods are not possible, like oncology. DIF enables the scientific community to see, and use, the impact of data on a value story while using the wealth of data available in real-world settings.” About Doctor Evidence: About DOC Search:
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