Patient Portal Implementation and Uptake: Qualitative Comparative Case Study
Bibliographic record
Abstract
BACKGROUND: Giving patients access to their health information is a provincial and national goal, and it is critical to the delivery of patient-centered care. With this shift, patient portals have become more prevalent. In Alberta, the Alberta Health Services piloted a portal (MyChart). There was a need to identify factors that promoted the use of this portal. Furthermore, it was imperative to understand why there was variability in uptake within the various clinics that participated in the pilot. OBJECTIVE: This study aims to identify potential factors that could improve the uptake of MyChart from the perspectives of both users and nonusers at pilot sites. We focused on factors that promoted the use of MyChart along with related benefits and barriers to its use, with the intention that this information could be incorporated into the plan for its province-wide implementation. METHODS: A qualitative comparative case study was conducted to determine the feasibility, acceptability, and initial perceptions of users and to identify ways to increase uptake. Semistructured interviews were conducted with 56 participants (27 patients, 21 providers, 4 nonmedical staff, and 4 clinic managers) from 5 clinics. Patients were asked about the impact of MyChart on their health and health care. Providers were asked about the impact on the patient-provider relationship and workflow. Managers were asked about barriers to implementation. The interviews were recorded, transcribed verbatim, and entered into NVivo. A thematic analysis was used to analyze the data. RESULTS: Results from a comparison of factors related to uptake of MyChart in 5 clinics (2 clinics with high uptake, 1 with moderate uptake, 1 with low uptake, and 1 with no uptake) are reported. Some theoretical constructs in our study, such as intention to use, perceived value, similarity (novelty) of the technology, and patient health needs, were similar to findings published by other research teams. We also identified some new factors associated with uptake, including satisfaction or dissatisfaction with the current status quo, performance expectancy, facilitating conditions, behavioral intentions, and use behavior. All these factors had an impact on the level of uptake in each setting and created different opportunities for end users. CONCLUSIONS: There is limited research on factors that influence the uptake of patient portals. We identified some factors that were consistent with those reported by others but also several new factors that were associated with the update of MyChart, a new patient portal, in the clinics we studied. On the basis of our results, we posit that a shared understanding of the technology among patients, clinicians, and managers, along with dissatisfaction with nonportal-based communications, is foundational and must be addressed for patient portals to support improvements in care.
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".