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. 2019 Apr 4;19(Suppl 3):80.
doi: 10.1186/s12911-019-0788-x.

Facilitating accurate health provider directories using natural language processing

Matthew J Cook  1   2   3 , Lixia Yao  4 , Xiaoyan Wang  5   6   7
Affiliations

Facilitating accurate health provider directories using natural language processing

Matthew J Cook et al. BMC Med Inform Decis Mak. .

Abstract

Background: 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 natural language processing (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, 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 6849 of the 7177 records of health provider directory information.

Conclusions: The authors demonstrated that the NLP- based approach was effective at acquiring health provider information. Furthermore, the NLP-based system can always be applied to update information further reducing processing burdens as data changes.

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Conflict of interest statement

Ethics approval and consent to participate

The data analyzed in this study was public information and was considered as non-human subjects research. An ethics approval was waived by the corresponding IRB.

Consent for publication

Written informed consent for publication was obtained from all research participants.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Provider directory data in health care. Arrows indicate flow of provider directory data into the various health care areas for a variety of different purposes
Fig. 2
Fig. 2
A NLP-based system to acquire and update the directory information of health providers
Fig. 3
Fig. 3
A specialty taxonomy module built to extract provider type taxonomies for certified nurse midwives, nurse practitioners, and dentists
Fig. 4
Fig. 4
An entity recognition module implemented to acquire information on location, name and address from the disparate resources
See this image and copyright information in PMC

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