Patient Perspectives on the Digitization of Personal Health Information in the Emergency Department: Mixed Methods Study During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Although the digitization of personal health information (PHI) has been shown to improve patient engagement in the primary care setting, patient perspectives on its impact in the emergency department (ED) are unknown. OBJECTIVE: The primary objective was to characterize the views of ED users in British Columbia, Canada, on the impacts of PHI digitization on ED care. METHODS: This was a mixed methods study consisting of an online survey followed by key informant interviews with a subset of survey respondents. ED users in British Columbia were asked about their ED experiences and attitudes toward PHI digitization in the ED. RESULTS: A total of 108 participants submitted survey responses between January and April 2020. Most survey respondents were interested in the use of electronic health records (79/105, 75%) and patient portals (91/107, 85%) in the ED and were amenable to sharing their ED PHI with ED staff (up to 90% in emergencies), family physicians (up to 91%), and family caregivers (up to 75%). In addition, 16 survey respondents provided key informant interviews in August 2020. Interviewees expected PHI digitization in the ED to enhance PHI access by health providers, patient-provider relationships, patient self-advocacy, and postdischarge care management, although some voiced concerns about patient privacy risk and limited access to digital technologies (eg, smart devices, internet connection). Many participants thought the COVID-19 pandemic could provide momentum for the digitization of health care. CONCLUSIONS: Patients overwhelmingly support PHI digitization in the form of electronic health records and patient portals in the ED. The COVID-19 pandemic may represent a critical moment for the development and implementation of these tools.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".