A Randomised Controlled Trial of Medication Liaison Services—Patient Outcomes
Bibliographic record
Abstract
Objectives: To evaluate the effects of a Medication Liaison Service (MLS) on quality of medication‐related information associated with hospital admission, risk of drug misadventure and other patient outcomes, and health resource utilisation. Method: Patients were randomly selected from eight acute wards (servicing 12 medical and five surgical units) and an orthopaedic pre‐admission clinic at two major hospitals. Subjects were randomly assigned to an intervention (MLS) or control group. For each patient in the MLS group a clinical pharmacist attached to the MLS contacted the general practitioner and community pharmacist in order to prepare a comprehensive medication history. Prior to discharge, the MLS clinical pharmacist, in consultation with the hospital medical staff, prepared a discharge communication which included a list of medications on admission and discharge, corresponding therapy changes, intended duration of therapy, sources of supply, allergies and adverse drug reactions, new therapeutic devices, medication‐related problems and actions required by the general practitioner. This information was forwarded to the patient's general practitioner and community pharmacist within 24 hours of discharge. Patient outcomes were evaluated 30 days after discharge using a mailed questionnaire and review of medical files. Results: Of 240 patients recruited, 113 were randomised to the MLS group and 127 to the control group. During hospital admission a significantly greater proportion of MLS subjects experienced a clinical pharmacist intervention (MLS 68% vs control 44%) and more subjects had at least one change to their therapy (MLS 97% vs control 90%), with more interventions (mean MLS 1.43 [SD 1.5] vs control 0.77 [SD 1.1]) and more medication changes per subject (mean MLS 3.75 [SD 2.6] vs control 3.10 [SD 2.3]). There was no significant effect on length of stay or mortality. In the 30 days following discharge there were fewer réadmissions per subject in the MLS group (mean 0.12 [SD 0.4] vs 0.46 [SD 1.9]) but this difference did not quite reach statistical significance (p = 0.055). The MLS group had significantly fewer healthcare professional visits per subject (mean MLS 7.54 [SD 7.4] vs control 9.94 [SD 10]). In an assessment of functional health status, MLS subjects improved for 7 of 8 factors (significantly so for two), and tended to improve more than controls in all factors except vitality. Conclusion: MLS was associated with improved patient outcomes and a greater number of inpatient clinical pharmacy interventions and medication changes intended to optimise therapy. There was also a tendency for reduced readmissions, a significant decrease in community healthcare professional visits, and significant improvements in functional health status score.
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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 | 0.005 | 0.006 |
| 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) | 0.001 | 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 teacher head, 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".