Evidence map›Paper›PMID 39813672›Full record

ArticleJournal of medical Internet research2025

From Theory to Practice: Viewpoint on Economic Indicators for Trust in Digital Health.

Felix Gille, Laura Maaß, Benjamin Ho, Divya Srivastava

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Felix Gille *University of Zurich, Digital Society Initiative, Zurich, Switzerland.ORCID 0000-0002-2847-4633
Laura Maaß *SOCIUM Research Center on Inequality and Social Policy, University of Bremen, Bremen, Germany.ORCID 0000-0001-7354-8120
Benjamin Ho *Department of Economics, Vassar College, Poughkeepsie, NY, United States.ORCID 0000-0001-9728-6444
Divya Srivastava *Department of Health Services Research Management, AI and Digital Health Lab (Centre for Healthcare Innovation Research), City St George's University, London, United Kingdom.ORCID 0000-0001-5135-3592

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

User trust is pivotal for the adoption of digital health systems interventions (DHI). In response, numerous trust-building guidelines have recently emerged targeting DHIs such as artificial intelligence. The common aim of these guidelines aimed at private sector actors and government policy makers is to build trustworthy DHI. While these guidelines provide some indication of what trustworthiness is, the guidelines typically only define trust and trustworthiness in broad terms, they rarely offer guidance about economic considerations that would allow implementers to measure and balance trade-offs between costs and benefits. These considerations are important when deciding how best to allocate scarce resources (eg, financial capital, workforce, or time). The missing focus on economics undermines the potential usefulness of such guidelines. We propose the development of actionable trust-performance-indicators (including but not limited to surveys) to gather evidence on the cost-effectiveness of trust-building principles as a crucial step for successful implementation. Furthermore, we offer guidance on navigating the conceptual complexity surrounding trust and on how to sharpen the trust discourse. Successful implementation of economic considerations is critical to successfully build user trust in DHI.

Indexed as

TelemedicineTrustArtificial IntelligenceCost-Benefit AnalysisDigital HealthHumansAIartificial intelligencedigital healthdigital health innovationeconomic evaluationeconomicshealth datamedical appspublic trusttrust

Identifiers

PMID39813672
PMCPMC11780289

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.