Optimising polypharmacy management in primary care through general practitioner−pharmacist collaboration, informatics, and enhancing clinician engagement: the IMPPP cluster-randomised trial
Bibliographic record
Abstract
BACKGROUND: Polypharmacy is a major and growing challenge for patient safety and health outcomes, and associated with inefficient use of resources. Management requires balancing therapeutic benefits and risks, aligned with clinical and patient priorities. High-quality evidence supporting current guidance for the management of polypharmacy is scarce. The aim of the Improving Medicines use in People with Polypharmacy in Primary Care (IMPPP) study was to examine whether a complex intervention could reduce potentially inappropriate prescribing among patients experiencing polypharmacy in primary care. METHODS: A pragmatic, open-label, two-arm, parallel cluster-randomised trial was conducted in UK general practices providing National Health Service primary medical care. Practices were required to use the EMIS electronic health records system and have more than 4000 registered patients. For inclusion in the study, participants were required to be aged 18 years or older, prescribed at least five regular medications (ie, medicines recorded in the clinical system as repeat prescriptions, and thus available for recurrent ordering by patients without having to see a clinician) irrespective of when the drug was last issued, and with at least one indicator of potentially inappropriate prescribing identified by an informatics tool. Practices were randomly allocated to deliver the polypharmacy intervention or continue usual care. The complex intervention comprised a structured, collaborative, and patient-centred approach to medication review, supported by informatics, clinician training, performance feedback, and financial incentivisation. In each practice, adults receiving five or more regular medications, with at least one indicator of potentially inappropriate prescribing, were reviewed over a 6-month period. The primary outcome was number of indicators of potentially inappropriate prescribing at 26 weeks' follow-up, analysed on an intention-to-treat basis. The trial was registered with the ISRCTN registry (90146150) and is complete. FINDINGS: Between Jan 26, 2022, and June 20, 2022, 37 general practices were recruited. 33 745 patients were potentially eligible, and 11 022 of these were randomly sampled for study inclusion. 1471 were excluded after general practitioner screening, 9551 were invited to participate, and 1950 provided consent and were randomly assigned (944 patients from 18 practices assigned to usual care and 1006 patients from 19 practices assigned to the intervention). 1727 participants were enrolled in the trial (836 in the usual care group and 891 in the intervention group). Participants' median age was 73 years (IQR 66-79), 881 (51%) were male and 846 (49%) were female, and they had a median of four long-term conditions and a median of eight medications. There was no evidence of a difference in the primary outcome of number of potentially inappropriate prescribing indicators at the 26-week follow-up between the intervention and control groups after adjustment for pre-randomisation baseline (mean 2·3 in each group; difference in means -0·007 [95% CI -0·21 to 0·20]; p=0·95). There were 13 deaths and 99 people with one or more unplanned admissions in the intervention group, and 12 deaths and 91 people with one or more unplanned admissions in the control group. None of the admissions or deaths were deemed to be related to the intervention. INTERPRETATION: A complex medication optimisation intervention did not reduce potentially inappropriate prescribing in patients with polypharmacy. The findings are at odds with the current policy drive for digital health-care solutions, investment in clinical pharmacy staff, and promotion of structured medication reviews. Effective use of such strategies should be revisited accordingly. FUNDING: National Institute for Health and Care Research.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".