Engaging patients in de-implementation interventions to reduce low-value clinical care: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Many decisions regarding health resource utilization flow through the patient-clinician interaction. Thus, it represents a place where de-implementation interventions may have considerable effect on reducing the use of clinical interventions that lack efficacy, have risks that outweigh benefits, or are not cost-effective (i.e., low-value care). The objective of this systematic review with meta-analysis was to determine the effect of de-implementation interventions that engage patients within the patient-clinician interaction on use of low-value care. METHODS: MEDLINE, EMBASE, and CINAHL were searched from inception to November 2019. Gray literature was searched using the CADTH tool. Studies were screened independently by two reviewers and were included if they (1) described an intervention that engaged patients in an initiative to reduce low-value care, (2) reported the use of low-value care with and without the intervention, and (3) were randomized clinical trials (RCTs) or quasi-experimental designs. Studies describing interventions solely focused on clinicians or published in a language other than English were excluded. Data was extracted independently in duplicate and pertained to the low-value clinical intervention of interest, components of the strategy for patient engagement, and study outcomes. Quality of included studies was assessed using the Cochrane Risk of Bias tool for RCTs and a modified Downs and Black checklist for quasi-experimental studies. Random effects meta-analysis (reported as risk ratio, RR) was used to examine the effect of de-implementation interventions on the use of low-value care. RESULTS: From 6736 unique citations, 9 RCTs and 13 quasi-experimental studies were included in the systematic review. Studies mostly originated from the USA (n = 13, 59%), targeted treatments (n = 17, 77%), and took place in primary care (n = 10, 45%). The most common intervention was patient-oriented educational material (n = 18, 82%), followed by tools for shared decision-making (n = 5, 23%). Random effects meta-analysis demonstrated that de-implementation interventions that engage patients within the patient-clinician interaction led to a significant reduction in low-value care in both RCTs (RR 0.74; 95% CI 0.66-0.84) and quasi-experimental studies (RR 0.61; 95% CI 0.43-0.87). There was significant inter-study heterogeneity; however, intervention effects were consistent across subgroups defined by low-value practice and patient-engagement strategy. CONCLUSIONS: De-implementation interventions that engage patients within the patient-clinician interaction through patient-targeted educational materials or shared decision-making tools are effective in decreasing the use of low-value care. Clinicians and policymakers should consider engaging patients within initiatives that seek to reduce low-value care. REGISTRATION: Open Science Framework (https://osf.io/6fsxm).
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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 | 0.035 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.054 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".