Email Between Patient and Provider: Assessing the Attitudes and Perspectives of 624 Primary Health Care Patients
Bibliographic record
Abstract
BACKGROUND: Email between patients and their health care providers can serve as a continuous and collaborative forum to improve access to care, enhance convenience of communication, reduce administrative costs and missed appointments, and improve satisfaction with the patient-provider relationship. OBJECTIVE: The main objective of this study was to investigate the attitudes of patients aged 16 years and older toward receiving email communication for health-related purposes from an academic inner-city family health team in Southern Ontario. In addition to exploring the proportion of patients with a functioning email address and interest in email communication with their health care provider, we also examined patient-level predictors of interest in email communication. METHODS: A cross-sectional study was conducted using a self-administered, 1-page survey of attitudes toward electronic communication for health purposes. Participants were recruited from attending patients at the McMaster Family Practice in Hamilton, Ontario, Canada. These patients were aged 16 years and older and were approached consecutively to complete the self-administered survey (N=624). Descriptive analyses were conducted using the Pearson chi-square test to examine correlations between variables. A logistic regression analysis was conducted to determine statistically significant predictors of interest in email communication (yes or no). RESULTS: The majority of respondents (73.2%, 457/624) reported that they would be willing to have their health care provider (from the McMaster Family Practice) contact them via email to communicate health-related information. Those respondents who checked their personal email more frequently were less likely to want to engage in this electronic communication. Among respondents who check their email less frequently (fewer than every 3 days), 46% (37/81) preferred to communicate with the McMaster Family Practice via email. CONCLUSIONS: Online applications, including email, are emerging as a viable avenue for patient communication. With increasing utility of mobile devices in the general population, the proportion of patients interested in email communication with their health care providers may continue to increase. When following best practices and appropriate guidelines, health care providers can use this resource to enhance patient-provider communication in their clinical work, ultimately leading to improved health outcomes and satisfaction with care among their patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".