Evidence map›Paper›PMID 32278288›Full record

ArticleInternational journal of medical informatics2020

Pilot evaluation of sensitive data segmentation technology for privacy.

Adela Grando, Davide Sottara, Ripudaman Singh, Anita Murcko, Hiral Soni, Tianyu Tang, Nassim Idouraine, Michael Todd, Mike Mote, Darwyn Chern and 2 more

Abstract read
In one paragraph

Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. FHIR Granular Sensitive Data Segmentation.Applied clinical informatics · 2025
    Article
  3. Article
  4. Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Adela GrandoBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States. Electronic address: agrando@asu.edu.
Davide SottaraMayo Clinic, Rochester, MN, United States.
Ripudaman SinghSchool of Computing, Informatics and Decision Systems Engineering, Tempe, AZ, United States.
Anita MurckoBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States.
Hiral SoniBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States.
Tianyu TangUniversity of Arizona, College of Medicine, Tucson, AZ, United States.
Nassim IdouraineBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States.
Michael ToddCollege of Nursing and Health Innovation, Arizona State University, Phoenix, United States.
Mike MoteHealth Current, Phoenix, AZ, United States.
Darwyn ChernPartners in Recovery, Phoenix, AZ, United States.
Christy DyePartners in Recovery, Phoenix, AZ, United States.
Mary Jo WhitfieldJewish Family and Children's Services, Phoenix, AZ, United States.

Funding

My Data Choices, evaluation of effective consent strategies for patients with behavioral health conditionsR01MH108992 · NIMH · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI GRANDO, MARIA ADELA · 2016 to 2020
$2.0M
NIMH NIH HHS R01 MH108992
6 · The paper itself

Abstract

backgroundConsent2Share (C2S) is an open source software created by the Office of the National Coordinator Data Segmentation for Privacy initiative to support electronic health record (EHR) granular segmentation. To date, there are no published formal evaluations of Consent2Share.

methodStructured data (e.g. medications) codified using standard clinical terminologies (e.g. RxNorm) was extracted from the EHR of 36 patients with behavioral health conditions from study sites. EHRs were available through a health information exchange and two sites. The EHR data was already classified into data types (e.g. procedures and services). Both Consent2Share and health providers classified EHR data based on value sets (e.g. mental health) and sensitivity (e.g. not sensitive. Descriptive statistics and Chi-square analysis were used to compare differences between data categorizations.

resultsFrom the resulting 1,080 medical records items, 584 were distinct. Significant differences were found between sensitivity classifications by Consent2Share and providers (χ2 (2, N = 584) = 114.74, p = <0.0001). Sensitivity comparisons led to 56.0 % of agreements, 31.2 % disagreements, and 12.8 % partial agreements. Most (97.8 %) disagreements resulted from information classified as not sensitive by Consent2Share, but sensitive by provider (e.g. behavioral health prevention education service). In terms of data types, most disagreements (57.1 %) focused on procedures and services information (e.g. ligation of fallopian tube). When considering value sets, most disagreements focused on genetic data (100.0 %), followed by sexual and reproductive health (88.9 %).

conclusionsThere is a need to further validate Consent2Share before broad use in health care settings. The outcomes from this pilot study will help guide improvements in segmentation logic of tools like Consent2Share and may set the stage for a new generation of personalized consent engines.

Indexed as

Electronic Health RecordsPrivacyFemaleHealth Information ExchangeHumansPilot ProjectsSoftwareData privacyData segmentationElectronic medical records

Identifiers

PMID32278288
PMCPMC7229704

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.