Making patient values visible in healthcare: a systematic review of tools to assess patient treatment priorities and preferences in the context of multimorbidity
Bibliographic record
Abstract
OBJECTIVES: To identify studies of existing instruments available for clinicians to record overall patient preferences and priorities for care, suitable for use in routine primary care practice in patients with multimorbidity. To examine the data for all identified tools with respect to validity, acceptability and effect on health outcomes. DESIGN: Systematic Review. DATA SOURCES: MEDLINE, EMBASE and Cochrane databases, each with a predefined search strategy. ELIGIBILITY CRITERIA: Citations were included if they reported a tool used to record patient priorities or preferences for treatment, and quantitative or qualitative results following administration of the tool. RESULTS: Our search identified 189 potential studies of which 6 original studies and 2 discussion papers were included after screening for relevance. 5 of 6 studies (83%) were of cross-sectional design and of moderate quality. All studies reported on the usability of a tool in order to elicit patient preferences. No studies reported on changes to patient-specific healthcare outcomes as a consequence of recording preferences and priorities. 1 of 6 studies reported on eliciting patient preference in the context of multimorbidity. No studies incorporated patient preferences into an electronic medical record. CONCLUSIONS: Given the importance of eliciting patient priorities and preferences in providing patient-centred care in the context of multimorbidity and polypharmacy, we found surprisingly few relevant tools. Some aspects of the tools used for single-disease contexts may also be useful in the context of multimorbidity. There is an urgent need to develop ways to make patient priorities explicitly visible in the clinical record and medical decision-making and to test the effect on patient-relevant outcomes.
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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 | 0.023 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".