US adults’ willingness to use a blockchain-based health information management application: A structural equation modeling approach

Background MediLinker is a blockchain-based application for identity verification and personal health information management. To prepare for its clinical deployment, the perceptions of health consumers toward MediLinker should be examined. Objective Guided by a conceptual model derived from the theory of reasoned action, privacy calculus, protection motivation theory, and regulatory uncertainty, the study identifies US adults' willingness to use MediLinker for health information management and the factors associated with this willingness. Methods We conducted a cross-sectional online survey in August 2022 among US adults recruited on Amazon Mechanical Turk. The analytic sample includes data from 913 respondents who nearly reflect US demographic characteristics by gender and race/ethnicity. Descriptive statistics were used to identify respondents' willingness to use MediLinker, while structural equation modeling (SEM) was used to identify direct and indirect factors associated with willingness. Results Most respondents were willing to use MediLinker to store and manage health information (76.7%), share health information with providers (79.3%), and provide consent for clinical research (78.4%). SEM results show that attitude towards MediLinker has a direct positive association with willingness to use MediLinker in general. We also identified several indirect predictors of willingness. For instance, perceived liability issues and perceived vulnerability have a negative indirect effect on willingness through perceived risks and attitude. On the other hand, perceived vulnerability and perceived severity have a positive indirect effect on willingness via perceived benefits and attitude. Conclusion Our sample of US adults is willing to use MediLinker for health information management. Moreover, our conceptual model shows direct and indirect pathways influencing US adults' willingness to use MediLinker. The results inform the deployment of blockchain-based health information management applications among health consumers. Overall, the study contributes to the literature on how healthcare consumers perceive blockchain technologies for medical decision making involving their online health information.

Authors

Institutions

Publication Details

Journal
Applied Clinical Informatics
Published
2026-07-30
DOI
https://doi.org/10.1055/a-2927-0791
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

US adults’ willingness to use a blockchain-based health information management application: A structural equation modeling approach

Anjum Khurshid, John Robert Bautista, Eric T. Meyer, Daniel Toshio Harrell
Applied Clinical Informatics
Blockchain Technology Applications and Security
article

US adults’ willingness to use a blockchain-based health information management application: A structural equation modeling approach

Anjum Khurshid, John Robert Bautista, Eric T. Meyer, Daniel Toshio Harrell
article en

Abstract

Background MediLinker is a blockchain-based application for identity verification and personal health information management. To prepare for its clinical deployment, the perceptions of health consumers toward MediLinker should be examined. Objective Guided by a conceptual model derived from the theory of reasoned action, privacy calculus, protection motivation theory, and regulatory uncertainty, the study identifies US adults' willingness to use MediLinker for health information management and the factors associated with this willingness. Methods We conducted a cross-sectional online survey in August 2022 among US adults recruited on Amazon Mechanical Turk. The analytic sample includes data from 913 respondents who nearly reflect US demographic characteristics by gender and race/ethnicity. Descriptive statistics were used to identify respondents' willingness to use MediLinker, while structural equation modeling (SEM) was used to identify direct and indirect factors associated with willingness. Results Most respondents were willing to use MediLinker to store and manage health information (76.7%), share health information with providers (79.3%), and provide consent for clinical research (78.4%). SEM results show that attitude towards MediLinker has a direct positive association with willingness to use MediLinker in general. We also identified several indirect predictors of willingness. For instance, perceived liability issues and perceived vulnerability have a negative indirect effect on willingness through perceived risks and attitude. On the other hand, perceived vulnerability and perceived severity have a positive indirect effect on willingness via perceived benefits and attitude. Conclusion Our sample of US adults is willing to use MediLinker for health information management. Moreover, our conceptual model shows direct and indirect pathways influencing US adults' willingness to use MediLinker. The results inform the deployment of blockchain-based health information management applications among health consumers. Overall, the study contributes to the literature on how healthcare consumers perceive blockchain technologies for medical decision making involving their online health information.

Applied Clinical Informatics
Harvard University (US), ISI Foundation (US), University of Missouri (US), The University of Texas at Austin (US), University of California, Berkeley (US)
Gender equality
Openalex Percentile: Top 3%
Blockchain Technology Applications and Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.