A tale of two patients – How did the pandemic impact patients’ usage of health portals
Bibliographic record
Abstract
Objective: The study examines whether patient health portal usage significantly increased during the COVID-19 pandemic between 2019 and 2022.Methods: In order to measure patient usage of patient portals before and during the first year of the COVID-19 pandemic, this study used the Health Information National Trends Survey results for 2019, 2020, 2021, and 2022. It was compared, using a least square regression model, to see if there was a significant relationship between increased use of telehealth, the usage of health portals, and the number of times seen by a regular healthcare provider.Results: The number of patients who saw their health care provider thrice a year and used their patient portal pre- and postpandemic increased. However, the overall increase in patients using their portals before and during the first two years of the pandemic remains below 50%.Conclusions: Overall, the pandemic increased patients’ use of telemedicine but only significantly increased their usage of patient portals for those patients who saw their provider three or more times a year. These findings indicate that more interaction with providers might impact future portal usage.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".