A Novel Digital Platform Approach to Enhance Enterprise-Wide Patient Portal Adoption
Bibliographic record
Abstract
Background: Patient portals provide a simplified route for health providers to share medical information with individual patients, and are incentivized under Meaningful Use. Currently there are numerous friction factors in the onboarding process of patient portals that limits patients signing up for them. As a result, health systems are spending significant resources to drive the adoption of patient portals, with limited success. Objective: To evaluate the effectiveness of a innovative rules-driven digital patient engagement strategy for patients to sign up for online patient portal at an academic medical center. Methods: Rx.Universe is a digital platform integrated into provider EMR systems that enables physicians to directly “prescribe” mobile health applications and/or digital care bundles to patients. Rx.Universe Bulk Prescription feature was used to prescribe—via SMS—a direct link to the MyChart login page with the patient’s unique code and personal information embedded within the link. This removed several key barriers to adoption: patients needing to copy and paste access codes, fill in their personal data and complete this process within 30 days—after which their unique access code expires. The Rx.Universe engagement dashboard displayed the total number of patients who received prescribed messages, the number of unsuccessful prescriptions, prescriptions opened in the first 24 hours, and total prescriptions opened. Results: We digitally prescribed MyChart Activation to 23,485 patients under the care of Yale-New Haven Hospital over a period of 2 days. Of these prescriptions, 21,997 (93.66%) were successfully delivered and 1488 (6.33%) failed to be delivered because of incorrect cell phone numbers in EHR. Of the prescriptions successfully prescribed, 2170 (9.86%) were clicked within 24 hours of being prescribed with a total of 2378 (10.81%) clicked within a week. Conclusions: Digital Medicine Platforms offer new channel for onboarding and following up patients through customized digital care plans. The power of this approach in removing barriers for patients is highlighted by the fact that Yale-New Haven Hospital met their yearly MyChart adoption target through this campaign within a week. Furthermore, the data could be assessed and acted upon in real-time as opposed to the usual weeks. This technology can be extended to close the care gaps for hospitals and Accountable Care Organizations (ACO) in a scalable manner for a subpopulation, with manual processes reserved for patients unable to be reached in an automated fashion.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".