A Natural Language Processing Approach to Acquire Accurate Health Provider Directory Information








Abstract

Introduction Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining provider directory data and keeping it up to date is challenging. The objective of this study is to determine the feasibility of using NLP techniques to combine disparate resources and acquire accurate information on health providers. Methods Publically available state licensure lists in Connecticut were obtained along with National Plan and Provider Enumeration System (NPPES) public use files. Connecticut licensure lists textual information of each health professional who is licensed to practice within the state. A NLP-based system was developed based on Healthcare Provider Taxonomy code, location, and name and address information to identify textual data within the state and federal records. Qualitative and quantitative evaluation were performed, and the recall and precision were calculated. Results We identified nurse midwives, nurse practitioners, and dentists in the State of Connecticut. The recall and precision were 0.95 and 0.93 respectively. Using the system, we were able to accurately acquire 6,849 of the 7,177 records of health provider directory information. Conclusion The authors demonstrated that the NLP based approach was effective at acquiring health provider information. Furthermore, the NLP-based system can be re-applied on the updated information further reducing processing burdens as data changes.


Modules


Algorithms


Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL