Evidence map›Paper›PMID 31928295›Full record

ArticleHealth informatics journal2020

Mental health professional perspectives on health data sharing: Mixed methods study.

Adela Grando, Julia Ivanova, Megan Hiestand, Hiral Soni, Anita Murcko, Michael Saks, David Kaufman, Mary Jo Whitfield, Christy Dye, Darwyn Chern and 1 more

Abstract read
In one paragraph

Article in Health informatics journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023
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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

11 authors.

Adela Grando
Megan Hiestand
Hiral Soni
Anita Murcko
Michael Saks
David KaufmanArizona State University, USA.
Mary Jo WhitfieldJewish Family and Children's Services, USA.
Christy Dye
Darwyn ChernPartners in Recovery, USA.
Jonathan MaupinArizona State University, USA.

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

This study explores behavioral health professionals' perceptions of granular data. Semi-structured in-person interviews of 20 health professionals were conducted at two different sites. Qualitative and quantitative analysis was performed. While most health professionals agreed that patients should control who accesses their personal medical record (70%), there are certain types of health information that should never be restricted (65%). Emergent themes, including perceived reasons that patients might share or withhold certain types of health information (65%), care coordination (12%), patient comprehension (11%), stigma (5%), trust (3%), sociocultural understanding (3%), and dissatisfaction with consent processes (1%), are explored. The impact of care role (prescriber or non-prescriber) on data-sharing perception is explored as well. This study informs the discussion on developing technology that helps balance provider and patient data-sharing and access needs.

Indexed as

Health Records, PersonalInformation DisseminationHealth PersonnelHumansMental HealthQualitative ResearchTrustconsentdata privacyhealth professional perceptionsinterviewmental health

Identifiers

PMID31928295
PMCPMC9310560

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.