A Randomized, Controlled Pragmatic Trial of Telephonic Medication Therapy Management to Reduce Hospitalization in Home Health Patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a telephonic medication therapy management (MTM) service on reducing hospitalizations among home health patients. SETTING: Forty randomly selected, geographically diverse home health care centers in the United States. DESIGN: Two-stage, randomized, controlled trial with 60-day follow-up. All Medicare- insured home health care patients were eligible to participate. Twenty-eight consecutive patients within each care center were recruited and randomized to usual care or MTM intervention. The MTM intervention consisted of the following: (1) initial phone call by a pharmacy technician to verify active medications; (2) pharmacist-provided medication regimen review by telephone; and (3) follow-up pharmacist phone calls at day seven and as needed for 30 days. The primary outcome was 60-day all-cause hospitalization. DATA COLLECTION: Data were collected from in-home nursing assessments using the OASIS-C. Multivariate logistic regression modeled the effect of the MTM intervention on the probability of hospitalization while adjusting for patients' baseline risk of hospitalization, number of medications taken daily, and other OASIS-C data elements. PRINCIPAL FINDINGS: A total of 895 patients (intervention n = 415, control n = 480) were block-randomized to the intervention or usual care. There was no significant difference in the 60-day probability of hospitalization between the MTM intervention and control groups (Adjusted OR: 1.26, 95 percent CI: 0.89-1.77, p = .19). For patients within the lowest baseline risk quartile (n = 232), the intervention group was three times more likely to remain out of the hospital at 60 days (Adjusted OR: 3.79, 95 percent CI: 1.35-10.57, p = .01) compared to the usual care group. CONCLUSIONS: This MTM intervention may not be effective for all home health patients; however, for those patients with the lowest-risk profile, the MTM intervention prevented patients from being hospitalized at 60 days.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".