ArticleJournal of medical Internet research2016
Electronic Health Record Patient Portal Adoption by Health Care Consumers: An Acceptance Model and Survey.
Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it, 210 citations in OpenAlex.
- A Process Related View on the Usage of Electronic Health Records from the Patients' Perspective: A Systematic Review.Journal of medical systems · 2022Pooled it
- Providing Mobile Patient Access to Their Electronic Secondary Care Patient Record in Adults With Cystic Fibrosis: Results of a Prospective, Parallel, Randomized Open-Pilot Quantitative Study.JMIR formative research · 2025Trial
- Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.Internal and emergency medicine · 2024Trial
- Article
- Extending the Information Systems Success Model to Explain E-Health Adoption in Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Empathy-amplified social influence and gender-sensitive thresholds in doctors' adoption of internet hospitals: a mixed-methods study in China.BMC health services research · 2026Article
- Understanding patient perception of digital value co-creation in electronic health record through clustering approach.Scientific reports · 2025Article
- A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study.JMIR human factors · 2024Article
- Pilot implementation of a telehealth course for health professions students.BMC medical education · 2024Article
- A holistic view of facilitators and barriers of electronic health records usage from different perspectives: A qualitative content analysis approach.Health information management : journal of the Health Information Management Association of Australia · 2024Article
- How do opt-in versus opt-out settings nudge patients toward electronic health record adoption? An exploratory study of facilitators and barriers in Austria and France.BMC health services research · 2024Article
- Intention to use personal health record system and its predictors among chronic patients enrolled at public hospitals in Bahir Dar city, northwest Ethiopia: using modified UTAUT2 model.Frontiers in medicine · 2024Article
- A Computationally-guided Qualitative Analysis to Understand User Experiences with Different Types of Mobile Personal Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
- Authors' Reply: "The Problem of Investigating Causal Relationships Between Cognitive and Evaluative Variables".Journal of medical Internet research · 2023Article
- Personal Health Record (PHR) Experience and Recommendations for a Transformation in Saudi Arabia.Journal of personalized medicine · 2023Article
- Healthcare professionals' knowledge, attitude and its associated factors toward electronic personal health record system in a resource-limited setting: A cross-sectional study.Frontiers in public health · 2023Article
- Paving the way for increased e-health record use: elaborating intentions of Gen-Z.Health systems (Basingstoke, England) · 2023Article
- Telehealth during the pandemic: Patient perceptions and policy implications.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2023Article
- The Impact of Social Influence on the Intention to Use Physician Rating Websites: Moderated Mediation Analysis Using a Mixed Methods Approach.Journal of medical Internet research · 2022Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe future of health care delivery is becoming more citizen centered, as today's user is more active, better informed, and more demanding. Worldwide governments are promoting online health services, such as electronic health record (EHR) patient portals and, as a result, the deployment and use of these services. Overall, this makes the adoption of patient-accessible EHR portals an important field to study and understand.
objectiveThe aim of this study is to understand the factors that drive individuals to adopt EHR portals.
methodsWe applied a new adoption model using, as a starting point, Ventkatesh's Unified Theory of Acceptance and Use of Technology in a consumer context (UTAUT2) by integrating a new construct specific to health care, a new moderator, and new relationships. To test the research model, we used the partial least squares (PLS) causal modelling approach. An online questionnaire was administrated. We collected 360 valid responses.
resultsThe statistically significant drivers of behavioral intention are performance expectancy (beta=.200; t=3.619), effort expectancy (beta=.185; t=2.907), habit (beta=.388; t=7.320), and self-perception (beta=.098; t=2.285). The predictors of use behavior are habit (beta=0.206; t=2.752) and behavioral intention (beta=0.258; t=4.036). The model explained 49.7% of the variance in behavioral intention and 26.8% of the variance in use behavior.
conclusionsOur research helps to understand the desired technology characteristics of EHR portals. By testing an information technology acceptance model, we are able to determine what is more valued by patients when it comes to deciding whether to adopt EHR portals or not. The inclusion of specific constructs and relationships related to the health care consumer area also had a significant impact on understanding the adoption of EHR portals.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.