Patient and Caregiver Use of Patient Portal Features
Bibliographic record
Abstract
Context: Patient portals (PPs) are online healthcare platforms enabling patients access their electronic health records and offering different features to help patients manage their care such as viewing and entering health information, and permitting asynchronous communication between patients and their providers. The onset of COVID-19 propelled the rapid integration of PPs in primary care; however, little is known about the desirability of their various features. Objective: Describe the use, and anticipated use for those without PP access, of PPs in primary care (PC) and how these relate to the patient’s sociodemographic factors. Study Design and Analysis: Cross-sectional survey of virtual care in PC in patients and caregivers. Caregivers reported on behalf of their charge. Questions included the use of PP, or anticipated use if it didn’t offer any, of five PP features: communication with practice, viewing records, entering information (e.g. blood pressure) into record, receiving information from practice, and scheduling appointments. We report on the use of features and associations between patient sociodemographic factors combining patient and caregiver responses and PP use, categorized into use ‘at least one feature’ and ‘no feature’. Dataset: We used a section of a larger cross sectional survey on virtual care conducted Dec 2022-Mar 2023. Population: Ontario patients and caregivers 18+ years with at least one virtual PC appointment in the past 12 months. Outcome Measures: Use and anticipated use of PP features, and their associations with sociodemographic factors. Results: Patients(P), n=743; Caregivers(C), n=227. Respondents were 65+ years (P:9%;C:23% (i.e. in 23% their charge was >65), unemployed (P:19%;C:49%), and non-male (P:56%;C:46%). Respondents with PP access (P:58%;C:62%) report using at least one PP feature (P:98%;C:96%), and those without PP access report anticipated use of features (P:89%;C:92%). Amongst those with PP access, older individuals (>65 years:14% vs <65 years:1%); unemployed ((6%) vs employed (1%)); non-English speakers ((9%) vs English speakers (1%)) were significantly more likely to report not using any feature. Amongst those without PP access single individuals ((17%) vs not single (3%)) were significantly more likely to report not using any feature. Conclusions: The study provides an overview of the use and anticipated use of PP features among different demographic groups and highlights the need to enhance PP us
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".