Examining Health Care Provider Experiences With Patient Portal Implementation: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Health systems are increasingly offering patient portals as tools for patients to access their health information with the goal of improving engagement in care. However, understanding health care providers' perspectives on patient portal implementation is crucial. OBJECTIVE: This study aimed to understand health care providers' experiences of implementing the MyChart patient portal, perspectives about its impact on patient care, clinical practice, and workload, and opportunities for improvement. METHODS: Using an explanatory sequential mixed methods approach, we conducted a web-based questionnaire and semistructured individual interviews with health care providers at a large Canadian community hospital, 6 months after MyChart was first offered to patients. We explored perspectives about the impact of MyChart on clinical practice, workload, and patient care. Data were analyzed using descriptive statistics and thematic analysis. RESULTS: In total, 261 health care providers completed the web-based questionnaire, and 15 also participated in interviews. Participants agreed that patients should have access to their health information through MyChart and identified its benefits such as patients gaining a greater understanding of their own health, which could improve patient safety (160/255, 62%). While many health care providers agreed that MyChart supported better patient care (108/258, 42%), there was limited understanding of features available to patients and expectations for integrating MyChart into clinical routines. Concerns were raised about the potential negative impacts of MyChart on patient-provider relationships because sensitive notes or results could be inappropriately interpreted (109/251, 43%), and a potential increase in workload if additional portal features were introduced. Suggested opportunities for improvement included support for both patients and health care providers to learn about MyChart and establishing guidelines for health care providers on how to communicate information available in MyChart to patients. CONCLUSIONS: While health care providers acknowledged that MyChart improved patients' access to health information, its implementation introduced some friction and concerns. To reduce the risk of these challenges, health systems can benefit from engaging health care providers early to identify effective patient portal implementation strategies.
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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 | 0.041 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".