Objective: Sharing health research data is essential for accelerating the translation of research into actionable knowledge that can impact health care services and outcomes. Qualitative health research data are rarely shared due to the challenge of deidentifying text and the potential risks of participant reidentification. Here, we establish and evaluate a framework for deidentifying qualitative research data using automated computational techniques including removal of identifiers that are not considered HIPAA Safe Harbor (HSH) identifiers but are likely to be found in unstructured qualitative data. Materials and Methods: We developed and validated a pipeline for deidentifying qualitative research data using automated computational techniques. An in-depth analysis and qualitative review of different types of qualitative health research data were conducted to inform and evaluate the development of a natural language processing (NLP) pipeline using named-entity recognition, pattern matching, dictionary, and regular expression methods to deidentify qualitative texts. Results: We collected 2 datasets with 1.2 million words derived from over 400 qualitative research data documents. We created a gold-standard dataset with 280K words (70 files) to evaluate our deidentification pipeline. The majority of identifiers in qualitative data are non-HSH and not captured by existing systems. Our NLP deidentification pipeline had a consistent F1-score of ∼0.90 for both datasets. Conclusion: The results of this study demonstrate that NLP methods can be used to identify both HSH identifiers and non-HSH identifiers. Automated tools to assist researchers with the deidentification of qualitative data will be increasingly important given the new National Institutes of Health (NIH) data-sharing mandate.

Original languageEnglish
Article numberooab069
JournalJAMIA Open
Issue number3
StatePublished - Jul 1 2021


  • data sharing
  • deidentification
  • natural language processing
  • qualitative research data


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