Evidence map›Paper›PMID 31726102›Full record

SynthesisJournal of biomedical informatics2020

State of the art and a mixed-method personalized approach to assess patient perceptions on medical record sharing and sensitivity.

Hiral Soni, Adela Grando, Anita Murcko, Sabrina Diaz, Madhumita Mukundan, Nassim Idouraine, George Karway, Michael Todd, Darwyn Chern, Christy Dye and 1 more

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of biomedical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
–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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. FHIR Granular Sensitive Data Segmentation.Applied clinical informatics · 2025
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  5. Review
  6. Article
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  14. Review
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  17. Article
  18. Pilot evaluation of sensitive data segmentation technology for privacy.International journal of medical informatics · 2020
    Article
  19. Article
  20. Article
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

11 authors.

Hiral SoniBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, United States.
Adela GrandoBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, United States. Electronic address: agrando@asu.edu.
Anita MurckoBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, United States.
Sabrina DiazKinesiology, College of Health Solutions, Arizona State University, Phoenix, United States.
Madhumita MukundanBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, United States.
Nassim IdouraineBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, United States.
George KarwayBiomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, United States.
Michael ToddCollege of Nursing and Health Innovation, Arizona State University, Phoenix, United States.
Darwyn ChernPartners in Recovery, Phoenix, United States.
Christy DyePartners in Recovery, Phoenix, United States.
Mary Jo WhitfieldJewish Family and Children's Services, Phoenix, 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

objectiveSensitive health information possesses risks, such as stigma and discrimination, when disclosed. Few studies have used a patient's own electronic health records (EHRs) to explore what types of information are considered sensitive andhow such perceptions affect data sharing preferences. After a systematic literature review, we designed and piloted a mixed-method approach that employs an individual's own records to assess content sensitivity and preferences for granular data sharing for care and research.

methodsA systematic literature review of methodologies employed to assess data sharing willingness and perceptions on data sensitivity was conducted. A methodology was designed to organize and categorize sensitive health information from EHRs. Patients were asked permission to access their EHRs, including those available through the state's health information exchange. A semi-structured interview script with closed card sorting was designed and personalized to each participant's own EHRs using 30 items from each patient record. This mixed method combines the quantitative outcomes from the card sorting exercises with themes captured from interview audio recording analysis.

resultsEight publications on patients' perspectives on data sharing and sensitivity were found. Based on our systematic review, the proposed method meets a need to use EHRs to systematize the study of data privacy issues. Twenty-five patients with behavioral health conditions, English and Spanish-speaking, were recruited. On average, participants recognized 82.7% of the 30 items from their own EHRs. Participants considered mental health (76.0%), sexual and reproductive health (75.0%) and alcohol use and alcoholism (50.0%) sensitive information. Participants were willing to share information related to other addictions (100.0%), genetic data (95.8%) and general physical health information (90.5%).

conclusionThe findings indicate diversity in patient views on EHR sensitivity and data sharing preferences and the need for more granular and patient-centered electronic consent mechanisms to accommodate patient needs. More research is needed to validate the generalizability of the proposed methodology.

Indexed as

Information DisseminationPrivacyElectronic Health RecordsHumansPerceptionResearch DesignBehavioral healthCard sortingData sharingElectronic medical recordsPatient preferencesSensitive data

Identifiers

PMID31726102
PMCPMC6952579

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.