Feasibility and Effectiveness of an Urgent Care–Community Partnership to Reduce Disparities in Patient Portal Uptake: Quality Improvement Project
Bibliographic record
Abstract
Background: Patient portals demonstrate significant potential for improving health care engagement but face critical adoption challenges. Disparities persist across different demographic groups, creating a digital divide in health care access. Targeted training strategies, particularly personalized and one-on-one approaches, show promise in increasing portal utilization. Innovative solutions, like community health workers specializing in digital navigation, offer a potential pathway to reduce enrollment barriers. The key challenge remains developing a scalable, cost-effective training model. Objective: Our quality improvement (QI) project aimed to assess the feasibility and effectiveness of a collaborative effort between a free community-based digital navigation program and an urgent care clinic in facilitating patient access to their portal. Methods: We created the Digital Health Equity Navigation Training (DHENT) program to improve patient portal access and usage. The program used a train-the-trainer model to scale up patient portal training across the community. DHENT trainers partnered with urgent care physicians to enroll patients in the portal. Physicians briefly explained portal benefits and referred interested patients for DHENT assistance. Trainers then contacted patients by phone to help with enrollment and navigation. We employed 3 Plan-Do-Study-Act cycles to understand the feasibility of the collaboration. We used descriptive statistics to describe participant characteristics and referral processes. Results: The collaboration was marginally successful, exceeding referral targets by 27.7% (115/90). Most patients were under 60 years old (94/115, 81.7%) and White (78/115, 67.8%). There was a significant delay in contact, averaging 37 days. While 4.8% (5/104) of patients accessed the portal with DHENT trainer assistance, 9.6% (10/104) had already signed up independently after their urgent care visit. Conclusions: Overall, we found our partnership had a moderate impact, and only a low dose of intervention and resources were needed.
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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 | 0.066 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".