IVR Technology Use by Patients with Health Failure: Utilization Patterns and Compliance
Bibliographic record
Abstract
Heart failure (HF) is the leading cause of cardiovascular morbidity and health care utilization inCanada. Much of the cost for HF is related to hospitalization, strategies to decrease cost need tofocus on avoiding unnecessary readmissions to the hospital. Interactive voice response (IVR) is anautomated telephony system that leverages existing telephone lines to monitor patients post-discharge from a hospital, for early intervention. Limited evidence exists on the pattern of use andsuccess of IVR technology among patients with heart failure and how IVR impacts theircompliance. This study explores the pattern of IVR use by HF patients in the IVR program at theUniversity of Ottawa Heart Institute (UOHI), describes their characteristics and IVR patterns ofuse in relation to occurrence of symptoms, compliance behavior (e.g., weighing themselves,medication compliance) and service utilization (i.e., hospital readmission). The system is based onan algorithm that triggers automated telephone calls to patients at a predetermined time for 3months after discharge. A total of 902 HF patients were considered with a mean age of 70 years(59.4% male). Over the 12 weeks, results showed an overall increase in medication adherence anda decrease in symptom occurrence, weight gain and readmission rates. The highest compliancerate in this study was found in medication adherence and the lowest was found in the variableassociated with exercise. The risk of readmission for patients who completed the IVR call,answered all the questions and listened to the educational prompts was lower than the patients whowere called back by nurses. These results suggest that IVR calls do have a positive impact on HFpatients. The increased use of IVR in remote patient monitoring will allow for a cheaper and moreaccessible form of at home monitoring. Leveraging IVR technology to support other conditions,especially during a pandemic, may be beneficial for patients to avoid unnecessary visits to thehospital and complications due to delay in seeking care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".