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Enhancing and Disaggregating Native Hawaiian and Pacific Islander (NHPI) Data Using Natural Language Processing and

Benjamin Viernes1, Qiwei Gan2, Elizabeth E Hanchrow

  • 1Center for Pacific Islander Veteran Health. US Dept of VA. Honolulu, HI, USA.

Studies in Health Technology and Informatics
|August 8, 2025
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Summary
This summary is machine-generated.

This study used natural language processing (NLP) to identify Native Hawaiian and Pacific Islander (NHPI) subgroups in health records. This improves data accuracy and supports health equity for NHPI populations.

Keywords:
DisparitiesEthnicityLexiconNLPNative Hawaiian and Pacific IslanderRaceTransformer

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Area of Science:

  • Health Informatics
  • Population Health
  • Health Equity Research

Background:

  • Native Hawaiian and Pacific Islander (NHPI) populations are frequently grouped into broad racial categories in healthcare data.
  • This aggregation obscures critical health disparities and hinders targeted interventions.
  • Electronic health records (EHRs) often lack detailed racial and ethnic subgroup data.

Purpose of the Study:

  • To address the underrepresentation of NHPI subgroups in EHRs.
  • To evaluate the utility of natural language processing (NLP) and an expanded race/ethnicity lexicon for identifying NHPI subgroups.
  • To improve the accuracy and granularity of race/ethnicity data for advancing health equity.

Main Methods:

  • Utilized natural language processing (NLP) algorithms.
  • Employed an expanded race/ethnicity lexicon to capture diverse NHPI subgroup identifiers.
  • Analyzed electronic health record (EHR) data to identify and classify NHPI documentation.

Main Results:

  • Demonstrated the capability of NLP to accurately classify documented NHPI subgroups.
  • Successfully disaggregated broad, legacy racial categories into specific NHPI populations.
  • Highlighted the potential for improved data granularity in EHRs.

Conclusions:

  • NLP offers a viable solution for enhancing the classification of NHPI subgroups in healthcare data.
  • More detailed subgroup data is crucial for addressing health disparities and promoting health equity among NHPI communities.
  • Standardized healthcare datasets can be improved by incorporating granular, NLP-derived demographic information.