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Record W4200259495 · doi:10.2196/33356

Predictors of Online Patient Portal Use Among a Diverse Sample of Emerging Adults: Cross-sectional Survey

2021· article· en· W4200259495 on OpenAlexvenueno aff
Julie Wright, Julie E. Volkman, Suzanne G. Leveille, Daniel J. Amante

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsPatient portaleHealthHealth literacyCross-sectional studyHealth careMedicineHealth Information National Trends SurveyLiteracyLogistic regressionFamily medicinePsychologyHealth informationPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Health self-management is increasingly being influenced by emerging health information technologies (IT), especially online patient portals. Patient portals provide patients with direct access to their health information, electronic tools to manage their health, and additional opportunities to engage with their care team. Previous studies have found that patient portal use is highest among patients with high eHealth literacy, the ability to find health information from electronic sources and apply the knowledge gained to solve a health problem. The role of eHealth literacy on patient portal use appears to be especially strong among older adults with chronic diseases. The use of patient portals among emerging adults (ages 18-29) is much less understood. Although generally healthy, emerging adults are more regular IT users and just beginning to independently navigate the health care system. A good understanding of how emerging adults are using online patient portals and what factors, including eHealth, impact portal use is lacking. OBJECTIVE: The aim of this study is to describe patient portal use and explore the predictors of portal use among a diverse sample of emerging adults. METHODS: A cross-sectional survey study that used convenience sampling was conducted at two universities. Data on demographics, health care encounters, eHealth literacy, patient engagement, and use of patient portal features (administrative and clinical) were obtained via self-report and summarized. Logistic regression models were used to examine factors associated with portal use. RESULTS: Of the 340 emerging adults, 257 (76%) were female, 223 (65%) White, 156 (47%) low income, and 184 (54%) reported having patient portal access. Of those reporting access, 142 (77%) used at least 1 portal feature and 42 (23%) reported using none. Significant predictors were patient engagement (odds ratio [OR] 1.08, 95% CI 1.04-1.13, P=.001) and total encounters (OR 1.23, 95% CI 1.05-1.44, P=.009) but not eHealth literacy. Hispanic and Asian emerging adults were more likely to be frequent users of clinical portal features than White emerging adults (Hispanic: OR 2.97, 95% CI 1.03-8.52, P=.04; Asian: OR 4.28, 95% CI 1.08-16.89, P=.04). CONCLUSIONS: We found that about half of emerging adults had access to a patient portal. Among those with access, a majority reported using at least one portal feature. Factors associated with increased portal use included increased patient engagement and total clinical encounters. Self-reported eHealth literacy was not associated with patient portal use in this diverse sample of emerging adults. This may have been due to high overall eHealth literacy levels in this population of frequent IT users. There may also be racial/ethnic differences that are important to consider, as we found Hispanic and Asian emerging adults reported more frequent portal use than White emerging adults. Interventions to promote patient portal use among emerging adults should include strategies to increase awareness of portal access and engagement among patients with fewer clinical encounters, with a focus on preventative health management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.174
GPT teacher head0.525
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2021
Admission routes1
Has abstractyes

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