Implementation of an emergency department virtual follow-up care process in a community-based hospital: a quality improvement initiative
Bibliographic record
Abstract
During the COVID-19 pandemic, patients were apprehensive to seek acute care resulting in delayed diagnoses of serious conditions and reduction in emergency room (ER) visits by 50% in the Fraser Health Authority. Patients who did present to the ER left prior to their results being available and some refused admission and critical treatments.At the Chilliwack General Hospital ER, a virtual care clinic was established to follow-up on patients after their initial ER visit, providing test results and ensuring patients are not clinically deteriorating at home. Specific criteria were created for safe referral to virtual follow-up. For 2 hours daily, an ER physician contacts selected patients by telephone to provide a virtual follow-up based on the patients' needs.Through the emergency department virtual care (EVC) pilot project, from May 14 to August 31, 2020, on average 58 telehealth visits were conducted weekly, with 19% of visits reaching unattached patients without a regular primary care provider. A patient survey revealed that 75% of respondents were very satisfied or satisfied with telephone virtual care as a follow-up to their emergency department (ED) visit, while 95% would like to continue to receive telephone follow-up care. Additionally, based on a physician survey, 80% of providers were satisfied or very satisfied with the overall EVC experience. The majority (80%) would like to continue to provide the service. One patient was referred for a virtual care follow-up for imaging results that did not meet the referral criteria; the patient was diagnosed with a perforated appendicitis. They had an atypical presentation of abdominal pain and their care was delayed by several hours than if they were to present to the ED for in-person follow-up. The process and referral criteria may require minor modification and must be followed strictly to ensure safety and efficiency in providing telehealth follow-up in the acute care setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".