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Related Experiment Video

Updated: Dec 24, 2025

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Pilot evaluation of sensitive data segmentation technology for privacy.

Adela Grando1, Davide Sottara2, Ripudaman Singh3

  • 1Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States.

International Journal of Medical Informatics
|April 12, 2020
PubMed
Summary
This summary is machine-generated.

Consent2Share (C2S) software for electronic health record (EHR) data segmentation showed significant differences in sensitivity classifications compared to healthcare providers. Further validation is needed before widespread adoption of this privacy tool.

Keywords:
Data privacyData segmentationElectronic medical records

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Area of Science:

  • Health Informatics
  • Health Data Privacy
  • Electronic Health Records (EHR)

Background:

  • Consent2Share (C2S) is an open-source software designed for granular segmentation of electronic health record (EHR) data.
  • No formal evaluations of Consent2Share have been published to date.

Purpose of the Study:

  • To formally evaluate Consent2Share's performance in segmenting EHR data.
  • To compare the data segmentation classifications of Consent2Share with those of healthcare providers.

Main Methods:

  • Structured EHR data from 36 patients with behavioral health conditions were extracted.
  • Both Consent2Share and healthcare providers classified EHR data based on value sets and sensitivity.
  • Descriptive statistics and Chi-square analysis were used to compare classifications.

Main Results:

  • Significant differences were found between Consent2Share and provider sensitivity classifications (p < 0.0001).
  • Agreement was 56.0%, with 31.2% disagreements and 12.8% partial agreements.
  • Disagreements often occurred when Consent2Share classified data as not sensitive, while providers deemed it sensitive, particularly for behavioral health and genetic data.

Conclusions:

  • Consent2Share requires further validation before widespread clinical use.
  • Study outcomes can guide improvements in EHR data segmentation logic and personalized consent engines.