Optimising a person-centred approach to stopping medicines in older people with multimorbidity and polypharmacy using the DExTruS framework: a realist review
Bibliographic record
Abstract
BACKGROUND: Tackling problematic polypharmacy requires tailoring the use of medicines to individual circumstances and may involve the process of deprescribing. Deprescribing can cause anxiety and concern for clinicians and patients. Tailoring medication decisions often entails beyond protocol decision-making, a complex process involving emotional and cognitive work for healthcare professionals and patients. We undertook realist review to highlight and understand the interactions between different factors involved in deprescribing and to develop a final programme theory that identifies and explains components of good practice that support a person-centred approach to deprescribing in older patients with multimorbidity and polypharmacy. METHODS: The realist approach involves identifying underlying causal mechanisms and exploring how, and under what conditions they work. We conducted a search of electronic databases which were supplemented by citation checking and consultation with stakeholders to identify other key documents. The review followed the key steps outlined by Pawson et al. and followed the RAMESES standards for realist syntheses. RESULTS: We included 119 included documents from which data were extracted to produce context-mechanism-outcome configurations (CMOCs) and a final programme theory. Our programme theory recognises that deprescribing is a complex intervention influenced by a multitude of factors. The components of our final programme theory include the following: a supportive infrastructure that provides clear guidance around professional responsibilities and that enables multidisciplinary working and continuity of care, consistent access to high-quality relevant patient contextual data, the need to support the creation of a shared explanation and understanding of the meaning and purpose of medicines and a trial and learn approach that provides space for monitoring and continuity. These components may support the development of trust which may be key to managing the uncertainty and in turn optimise outcomes. These components are summarised in the novel DExTruS framework. CONCLUSION: Our findings recognise the complex interpretive practice and decision-making involved in medication management and identify key components needed to support best practice. Our findings have implications for how we design medication review consultations, professional training and for patient records/data management. Our review also highlights the role that trust plays both as a central element of tailored prescribing and a potential outcome of good practice in this area.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".