Using an interactive voice response system to improve patient safety following hospital discharge
Bibliographic record
Abstract
BACKGROUND: Patients often experience complications when transitioning from hospital to home. These complications are frequently related to poor monitoring. An interactive voice response system (IVRS) could improve post-discharge monitoring. OBJECTIVE: To determine the feasibility and utility of an IVRS to monitor patients following hospital discharge. DESIGN: Prospective cohort study at an academic health sciences centre. PATIENTS: Consecutive internal medicine patients who had a touch-tone telephone, spoke English, had no cognitive impairments and were discharged home. MEASUREMENTS: Feasibility was defined as the proportion of patients reached by the IVRS and the proportion completing an IVRS-based survey. Utility was defined as the percentage of patients whose outcomes could have been changed by the IVRS. METHODS: We programmed the IVRS to call patients and administer a simple survey 48 hours after discharge. The survey's objective was to identify all patients with new health problems. Such patients were telephoned by a nurse to clarify and address the problem. RESULTS: We enrolled 77 patients who were predominantly male (68%), elderly (median age 65 years) and chronically ill (median number of co-morbidities = 3). The IVRS reached 45 of the 77 patients (58.4%). Forty patients (51.9%) answered all questions on the survey. Twenty patients (26%, 95% CI 17%-37%) indicated new or worsening symptoms, problems with their medications, or requested to talk to the clinic nurse. For 10 patients (13%, 95% CI 7%-22%), the IVRS could have made a difference in their outcome. CONCLUSION: Using an IVRS, we were able to identify several important new health concerns arising following hospital discharge. Subtle changes could increase the feasibility and utility of IVRS technology in improving post-discharge outcomes.
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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".