The feasibility of an interactive voice response system (IVRS) for monitoring patient safety after discharge from the ED
Bibliographic record
Abstract
BACKGROUND: Return ED visits are frequent and may be due to adverse events: adverse outcomes related to healthcare received. An interactive voice response system (IVRS) is a technology that translates human telephone input into digital data. Use of IVRS has been explored in many healthcare settings but to a limited extent in the ED. We determined the feasibility of using an IVRS to assess for adverse events after ED discharge. METHODS: This before and after study assessed detection of adverse events among consecutive high-acuity patients discharged from a tertiary care ED pre-IVRS and post-IVRS over two 2-week periods. The IVRS asked if the patient was having a health problem and if they wanted to speak to a nurse. Patients responding yes received a telephone interview. We searched health records for deaths, admissions to hospital and return ED visits. Three trained emergency physicians independently determined adverse event occurrence. We analysed the data using descriptive statistics. RESULTS: Of 968 patients studied, patients' age, sex, acuity and presenting complaint were comparable pre-IVRS and post-IVRS. Postimplementation, 393 (81.7%) of 481 patients had successful IVRS contact. Of these, 89 (22.6%) wanted to speak to a nurse. A total of 37 adverse events were detected over the two periods: 10 patients with 10 (6.5%) adverse events pre-IVRS and 16 patients with 27 (16.9%) adverse events post-IVRS. In the postimplementation period, the adverse events of seven patients were detected by the IVRS and five patients spontaneously requested assistance navigating post-ED care. CONCLUSIONS: This was a successful proof-of-concept study for applying IVRS technology to assess patient safety issues for discharged high-acuity ED patients.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".