Effect of Medication Management at Home via Pharmacist-Led Home Televisits: Protocol for a Cluster Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Older adults are more likely to have multiple chronic conditions, be prescribed multiple medications, and be more susceptible to adverse drug reactions (ADRs) to their medications. In addition, older adults often use over-the-counter medications and supplements, further complicating their medication regimens. Complex medication regimens are potentially harmful to older adults. Interventions aimed at reducing medication discrepancy in the ambulatory clinic setting, such as reviews of medication lists and the implementation of "brown bag" reconciliation, continue to be challenging, with limited success. Pharmacist-led interventions to improve appropriate medication use in older adults have demonstrated effectiveness in reducing ADRs. Video visits have the potential to provide direct visualization of medications in older adults' homes, thereby reducing medication discrepancy and increasing medication adherence. Pharmacist-led management of older adults' medication regimens may improve appropriate medication use in older adults. OBJECTIVE: The objective of this study is to examine the effect of pharmacist-led medication through home televisits compared to usual care on appropriate medication use, medication discrepancies, medication adherence, and ADRs. METHODS: We will conduct a 2-site cluster randomized controlled trial (RCT). The intervention will be a pharmacist-led home televisit including medication reconciliation and assessment of actual medication use. The cluster RCT was iteratively adapted after a pilot test. The primary outcome of medication appropriateness of the intervention will be measured using the STOPP (Screening Tool of Older Persons' Prescriptions) criteria for potentially inappropriate medications (PIMs) at 6 months. Medication lists obtained will be compared against electronic medical records (EMRs) by a clinician to establish discrepancies in medications. The clinician will review medications using the validated Medication Appropriateness Index (MAI). RESULTS: This project has been peer-reviewed and selected for support by the Veterans Affairs (VA) Health Services Research Service. The pilot phase of the study was completed December 2021 with 20 veterans and was primarily informed by the Steinman model of the prescribing process adapted to include system- and provider-level factors. The last date of enrollment was August 6, 2021. We anticipate the completion of the ongoing trial in spring 2025. The first results are expected to be submitted for publication in 2025. CONCLUSIONS: The cluster RCT will provide evidence on medication management through televisits. If found effective in improving the use of medications, the intervention has the potential to impact older adults with multiple chronic conditions and polypharmacy. TRIAL REGISTRATION: ClinicalTrials.gov NCT04340570; https://clinicaltrials.gov/study/NCT04340570. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/65141.
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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 | 0.038 | 0.037 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.012 |
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".