Supporting the use of patient portals in mental health settings: a scoping review
Bibliographic record
Abstract
With the increased use of patient portals in acute and chronic care settings as a strategy to support patient care and improve patient-centric care, there is still little known about the impact of patient portals in mental health contexts. The purposes of this review were to: 1) identify the critical success factors for successful patient portal implementation and adoption among end-users that could be utilized in a mental health setting; 2) uncover what we know about existing mental health portals and their effectiveness for end-users; and 3) determine what indicators are being used to evaluate existing patient portals for end-users that may be applied in a mental health context. This scoping review was conducted through a search of six electronic databases including Medline, EMBASE, PsycINFO, and CINAHL for articles published between 2007 and 2021. A total of 31 articles were included in the review. Critical success factors of patient portal implementation included those related to education, usefulness, usability, culture, and resources. Only two patient portals had articles published related to their effectiveness for end-users (one in Canada and the other in the United States). More than 100 measures of process (n = 73) and outcome (n = 59) indicators were extracted from the studies and mapped to the Benefits Evaluation Framework. Patient portals carry great potential to improve patient care, but more attention needs to be given to ensure they are being evaluated through the development and implementation phases with the end-users in mind. Further understanding of process indicators relating to use are essential for long-term patient adoption of portals to obtain their potential benefits.
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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 | 0.026 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".