Screening by Health Care Systems for Barriers to Patient Engagement With Digital Health Care: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Digital health tools, including patient portals, telemedicine, and mobile health apps, are increasingly a core part of health care. Digital readiness, encompassing both digital access and literacy, is crucial for enabling patients to effectively engage with the increasing number of digital health tools. Despite growing recognition of digital readiness as a health-related social need, little is known about digital readiness screening practices. OBJECTIVE: We aimed to assess the extent of digital readiness screening and the organizational factors associated with screening. METHODS: From January to May 2024, we administered an online survey to a convenience sample of clinicians or informatics leaders from US health care systems. Our primary outcome was whether the respondent reported that their organization screened for digital readiness (yes vs no), and the secondary outcome was self-reported barriers to screening. We asked respondents to report characteristics related to their health system, including health system type, geographic area, payers accepted, patient population characteristics, screening practices for health-related social needs (eg, screening for food insecurity), and awareness of digital inclusion policies and programs. Using bivariate logistic regression models, we examined organizational characteristics associated with screening for digital readiness. RESULTS: Of 144 total respondents, 64 (44%) reported screening patients for digital readiness. Organizations serving uninsured patients had lower odds of screening (odds ratio [OR] 0.32, 95% CI 0.14-0.72). Less than half of respondents to the digital readiness survey (47/99, 47%) were familiar with any digital readiness-related policy, but screening was more likely when respondents were familiar with at least one policy or program promoting equitable digital readiness (OR 6.6, 95% CI 2.4-20.6). Screening for other health-related social needs was not associated with digital readiness screening. The most frequently cited barriers to screening for digital readiness were lack of resources to address digital access (n=45, 45%), lack of resources to implement screening (n=42, 42%), and lack of time (n=41, 41%). CONCLUSIONS: Digital readiness screening has had limited adoption in US health care systems, particularly in settings serving the populations most likely to experience challenges with digital access or literacy. The limited adoption of digital readiness screening likely reflects lower awareness of digital readiness as a social need and a lack of infrastructure to support its uptake, such as standardized screening questions or a workforce trained on how to screen for and intervene on barriers to digital readiness. Low awareness of digital equity policies that might incentivize digital readiness screening further hinders adoption. Without increased adoption of digital readiness screening and/or interventions to mitigate barriers to digital readiness, digital health tools are unlikely to be accessible to or benefit all populations. Multilevel interventions, including policy changes and workforce training, are likely necessary to increase the adoption of digital readiness screening and mitigation efforts that address barriers to digital exclusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".