Evidence map›Paper›PMID 40094152›Full record

ArticleFrontiers in digital health2025

Consent mechanisms and default effects in health information exchange in Japan.

Atsushi Ito, Fumihiko Nakamura

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Atsushi ItoFaculty of Public Policy, Kyoto Prefectural University, Kyoto, Japan.
Fumihiko NakamuraFaculty of Engineering, Kitami Institute of Technology, Hokkaido, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Health information exchange (HIE) is an information system that efficiently shares patient information across medical institutions. However, traditional consent methods, represented by opt-in and opt-out, face a trade-off between efficiency and ethical, making it difficult to fundamentally improve consent rates. To address this issue, we focused on default settings and proposed an innovative approach called the "two-step consent model," which leverages the advantages of existing models using utility theory. We evaluated the acceptability of this method. Methods: An online survey was conducted with 2,000 participants registered with Japan's largest internet survey company. We compared and analyzed the consent rates of the opt-in, opt-out, and two-step consent models. Results: The opt-in model had a 29.5% consent rate, maximizing patient autonomy but increasing the burden and reducing efficiency. The opt-out model had a 95.0% consent rate but raised concerns among half of the respondents. The two-step consent model had a 68.5% consent rate, demonstrating its cost-effectiveness compared with traditional models. Discussion: The two-step consent model, involving implicit and explicit consent when needed, ensures efficient consent acquisition while respecting patient autonomy. It is a cost-effective policy option that can overcome the ethical issues associated with the opt-out model. Introducing methods that leverage both opt-in and opt-out advantages is expected to address HIE stagnation. Conclusion: The two-step consent model is expected to improve consent rates by balancing the efficiency and quality of consent acquisition. To achieve this, patient education is crucial for raising awareness and understanding of HIE and its consent methods.

Indexed as

consent mechanismdefault settingshealth information exchangeonline surveyutility-based consent rate model

Identifiers

PMID40094152
PMCPMC11908376

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.