Probability Maps

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Track Code: 2015-099 Short Description     Systems and methods for predicting the occurrence of diseases or other population characteristics within zip code regions. #healthcare #software #researchtool #method Abstract         Northwestern researchers have developed systems and methods (software and algorithms) that can accurately predict the occurrence of diseases from census and de-identified EHR data. Assessment of disease burdens in geographical areas is important for planning for use of public resources and other business activities. As these assessments are traditionally based on census data, predictions are only as accurate as the area covered by individual zip codes. This poses a challenge to the accurate prediction of disease burden in underpopulated areas, where the occurrence of a disease may differ at the level of streets or blocks. More detailed address information is typically associated with electronic health records (EHR); however, EHR data are often unavailable or cannot be shared for patient privacy reasons. This Northwestern technology is able to utilize statistical simulations to predict the occurrence of diseases with geographical resolution smaller than the area covered by zip codes (i.e., streets or blocks). The technology provides precise and reliable guidance to the planning of healthcare resources, bringing high value to public sectors as well as private companies in the healthcare industry. Tags   HEALTHCARE: software, RESEARCH TOOL: method Posted Date   Dec 22, 2016 10:50 AM Applications Public healthcare resources planning Disease control and prevention Targeted marketing & sales Academic research tool Advantages Improved accuracy and prediction Compatible with geographical information system (GIS) software Publications IP Status US provisional patent was filed.