Individual-Level Digital Determinants of Health and Technology Acceptance of Patient Portals: Cross-Sectional Assessment
Bibliographic record
Abstract
BACKGROUND: Digital determinants of health (DDoH), including access to technological tools and digital health literacy, function independently as barriers to health. Assessment for DDoH is not routine within most health care systems, although addressing DDoH could help mitigate differential health outcomes and the digital divide. OBJECTIVE: This study aims to assess the role of individual-level DDoH factors on patient enrollment in and use of the patient portal. METHODS: We developed a multimodal, cross-sectional survey and deployed it to 11,424 individuals based on their preferred mode and language documented within the electronic medical record. Based on the Technology Acceptance Model, enrollment in and intent to use the patient portal were the outcomes of interest. Perceived usefulness and ease of use were assessed to determine construct validity, and exploratory investigations included individual-level DDoH, including internet and device access, availability of technological support, medical complexity, individual relationship with the health care system, and digital health literacy. Counts (n) and proportions (%) were used to describe response categories, and adjusted and unadjusted odds ratios are reported. RESULTS: This study included 1850 respondents (11,424 invited, 16.2% response rate), who were mostly female (1048/1850, 56.6%) and White (1240/1850, 67%), with an average age of 63 years. In the validation of the Technology Acceptance Model, measures of perceived ease of use (ie, using the patient portal will require a lot of mental effort; the patient portal will be very easy to use) and perceived usefulness (ie, the usefulness of the patient portal to send and receive messages with providers, schedule appointments, and refill medications) were positively associated with both enrollment in and intent to use the patient portal. Within adjusted models, perceived ease of use and perceived usefulness constructs, in addition to constructs of digital health literacy, knowing what health resources are available on the internet (adjusted odds ratio [aOR] 3.5, 95% CI 1.8-6.6), portal ease of use (aOR 2.8, 95% CI 1.6-5), and portal usefulness (aOR 2.4, 95% CI 1.4-4.2) were significantly associated with patient portal enrollment. Other factors associated with patient portal enrollment and intent to use included being comfortable reading and speaking English, reported use of the internet to surf the web or to send or receive emails, home internet access, and access to technology devices (computer, tablet, smartphone, etc). CONCLUSIONS: Assessing for and addressing individual-level DDoH, including digital health literacy, access to digital tools and technologies, and support of the relational aspects between patients, social support systems, and health care providers, could help mitigate disparities in health. By focusing efforts to assess for and address individual-level DDoH, an opportunity exists to improve digitally driven health care delivery outcomes like access and structural outcomes like bias built within algorithms created with incomplete representation across communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".