Improving Medication Prescribing-Related Outcomes for Vulnerable Elderly In Transitions on High Risk Medications (IMPROVE-IT HRM): A Pilot Randomized Trial Protocol
Bibliographic record
Abstract
ABSTRACT Rationale Transitions in, through, and out of hospital define the highest risk periods for patient safety. Hospitalized senior high-cost health care users taking high risk medications, are a large group of patients, usually highly complex with polypharmacy, and at high risk of serious adverse medication events. We will assess whether an expert Clinical Pharmacology Toxicology (CPT) medication management intervention during hospitalization with follow-up post-discharge and communication with circle of care, is feasible and can decrease drug therapy problems amongst this group. Design Pragmatic pilot randomized trial at SJHH with 1:1 patient-level concealed randomization with blinded outcome assessment and data analysis. Participants Adults 65 years of age and older, admitted to Internal Medicine services for more than 2 days, who are high-cost users defined as at least one other hospitalization in the prior year, taking 5 or more chronic medications including at least one high risk medication. Intervention CPT consult service identifies medication target(s), completes consult, including priorities for improving prescribing negotiated with the patient, starts the care plan, ensures a detailed discharge medication reconciliation and circle-of-care communication, and sees the patient at least twice after hospital discharge via integrated virtual visits to consolidate the care plan in the community. Control group receives usual care as provided by admitting services. Outcomes Include a) Feasibility Outcomes and b) Clinical Outcomes including the number of drug therapy problems improved, medication appropriateness and safety, the quality and coordination of transitions in care, quality of life, and health care utilization and costs by 3-month follow-up. Impact If results support feasibility of ramp-up and promising clinical outcomes, a follow-up definitive trial will be organized using a developing national platform and medication appropriateness network. RESEARCH QUESTION Our detailed research question is ‘In a randomized pilot trial, can an expert Clinical Pharmacology team coordinate and improve medication management during the very high-risk transition period from hospitalization through post-hospital discharge follow-up for senior high-cost users of healthcare taking high risk medications, meeting key feasibility outcomes while improving patient-important outcomes and health care costs sufficiently to warrant a large subsequent trial?’
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".