Implementation Strategies to Improve Engagement With a Multi-Institutional Patient Portal: Multimethod Study
Bibliographic record
Abstract
BACKGROUND: Comprehensive multi-institutional patient portals that provide patients with web-based access to their data from across the health system have been shown to improve the provision of patient-centered and integrated care. However, several factors hinder the implementation of these portals. Although barriers and facilitators to patient portal adoption are well documented, there is a dearth of evidence examining how to effectively implement multi-institutional patient portals that transcend traditional boundaries and disparate systems. OBJECTIVE: This study aims to explore how the implementation approach of a multi-institutional patient portal impacted the adoption and use of the technology and to identify the lessons learned to guide the implementation of similar patient portal models. METHODS: This multimethod study included an analysis of quantitative and qualitative data collected during an evaluation of the multi-institutional MyChart patient portal that was deployed in Southwestern Ontario, Canada. Descriptive statistics were performed to understand the use patterns during the first 15 months of implementation (between August 2018 and October 2019). In addition, 42 qualitative semistructured interviews were conducted with 18 administrative stakeholders, 16 patients, 7 health care providers, and 1 informal caregiver to understand how the implementation approach influenced user experiences and to identify strategies for improvement. Qualitative data were analyzed using an inductive thematic analysis approach. RESULTS: Between August 2018 and October 2019, 15,271 registration emails were sent, with 67.01% (10,233/15,271) registered for an account across 38 health care sites. The median number of patients registered per site was 19, with considerable variation (range 1-2114). Of the total number of sites, 55% (21/38) had ≤30 registered patients, whereas only 2 sites had over 1000 registered patients. Interview participants perceived that the patient experience of the portal would have been improved by enhancing the data comprehensiveness of the technology. They also attributed the lack of enrollment to the absence of a broad rollout and marketing strategy across sites. Participants emphasized that provider engagement, change management support, and senior leadership endorsement were central to fostering uptake. Finally, many stated that regional alignment and policy support should have been sought to streamline implementation efforts across participating sites. CONCLUSIONS: Without proper management and planning, multi-institutional portals can suffer from minimal adoption. Data comprehensiveness is the foundational component of these portals and requires aligned policies and a key base of technology infrastructure across all participating sites. It is important to look beyond the category of the technology (ie, patient portal) and consider its functionality (eg, data aggregation, appointment scheduling, messaging) to ensure that it aligns with the underlying strategic priorities of the deployment. It is also critical to establish a clear vision and ensure buy-ins from organizational leadership and health care providers to support a cultural shift that will enable a meaningful and widespread engagement.
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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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".