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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Data Management

Background:

  • Accurate health provider directory information is crucial for healthcare operations, including data exchange, quality reporting, and care delivery.
  • Maintaining up-to-date provider data is a significant challenge in the healthcare industry.
  • Existing methods for provider data management are often labor-intensive and prone to inaccuracies.

Purpose of the Study:

  • To assess the feasibility of employing Natural Language Processing (NLP) techniques for consolidating diverse data sources to obtain accurate health provider information.
  • To develop and evaluate an NLP-based system for improving the accuracy and completeness of health provider directories.
  • To determine the effectiveness of NLP in automating the acquisition and maintenance of health provider data.

Main Methods:

  • Utilized publicly available state licensure lists (Connecticut) and National Plan and Provider Enumeration System (NPPES) data.
  • Developed an NLP-based system focusing on healthcare provider taxonomy codes, location, name, and address for data matching.
  • Conducted qualitative and quantitative evaluations, calculating recall and precision metrics to assess system performance.

Main Results:

  • Successfully identified nurse midwives, nurse practitioners, and dentists in Connecticut.
  • Achieved high performance metrics with a recall of 0.95 and precision of 0.93.
  • Accurately acquired 6849 out of 7177 (95.4%) health provider directory records using the NLP system.

Conclusions:

  • The NLP-based approach proved effective in acquiring accurate health provider information from multiple sources.
  • The developed NLP system offers a scalable solution for updating provider information, thereby reducing processing burdens.
  • This technology has the potential to significantly improve the reliability and efficiency of health provider directory management.