A Processing of Natural Language Approach for Acquiring Reliable Healthcare Service Directory Data
Abstract
Introduction Accurate provider directory information is critical in health care, including health information exchange, health benefits exchange, quality reporting, and reimbursement and delivery of treatment. Maintaining and updating provider directory data is difficult. The goal of this project is to see if it is possible to use NLP approaches to merge diverse data and obtain correct information about health providers. Methods Connecticut state licensure listings were received, as were public usage information from the National Plan and Provider Enumeration System (NPPES). Connecticut licensure includes textual information for each health practitioner licenced to practise in the state. To identify textual data, an NLP-based system was constructed using the Healthcare Provider Taxonomy code, location, and name and address information.
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