Evidence map›Paper›PMID 32644138›Full record

ArticleJAMA network open2020

Health Policy and Privacy Challenges Associated With Digital Technology.

David Grande, Xochitl Luna Marti, Rachel Feuerstein-Simon, Raina M Merchant, David A Asch, Ashley Lewson, Carolyn C Cannuscio

Abstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

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

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  5. Open forum infectious diseases · 2026
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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

7 authors.

David GrandeLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Xochitl Luna MartiCenter for Public Health Initiatives, University of Pennsylvania, Philadelphia.
Rachel Feuerstein-SimonCenter for Public Health Initiatives, University of Pennsylvania, Philadelphia.
Raina M MerchantLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
David A AschLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Ashley LewsonDepartment of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis.
Carolyn C CannuscioLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.

Funding

Patient Preferences for Collecting and Repurposing Genetic, Consumer and Health Care InformationR01HG009655 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI GRANDE, DAVID T · 2017 to 2020
$1.9M
NHGRI NIH HHS R01 HG009655
6 · The paper itself

Abstract

Importance: Digital technology is part of everyday life. Digital interactions generate large amounts of data that can reveal information about the health of individual consumers (the digital health footprint). Objective: Τo describe health privacy challenges associated with digital technology. Design, Setting, and Participants: For this qualitative study, In-depth, semistructured, qualitative interviews were conducted with 26 key experts from diverse fields in the US between January 1 and July 31, 2018. Open-ended questions and hypothetical scenarios were used to identify sources of digital information that contribute to consumers' health-relevant digital footprints and challenges for health privacy. Participants also completed a survey instrument on which they rated the health relatedness of digital data sources. Main Outcomes and Measures: Health policy challenges associated with digital technology based on qualitative responses to expert interviews. Results: Although experts' ratings of digital data sources suggested a possible distinction between health and nonhealth data, qualitative interviews uniformly indicated that all data can be health data, particularly when aggregated across sources and time. Five key characteristics of the digital health footprint were associated with health privacy policy challenges: invisibility (people are unaware of how their data are tracked), inaccuracy (data in the digital health footprint can be inaccurate), immortality (data have no expiration date and are aggregated over time), marketability (data have immense commercial value and are frequently bought and sold), and identifiability (individuals can be readily reidentified and anonymity is nearly impossible to achieve). There are virtually no regulatory structures in the US to protect health privacy in the context of the digital health footprint. Conclusions and Relevance: The findings suggest that a sector-specific approach to digital technology privacy in the US may be associated with inadequate health privacy protections.

Indexed as

Computer SecurityDigital TechnologyConfidentialityHealth PolicyHumansInformation ManagementNeeds AssessmentQualitative ResearchUnited States

Identifiers

PMID32644138
PMCPMC7348687

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.