Interventions for Improving Outcomes in Patients With Multimorbidity in Primary Care and Community Setting: A Systematic Review
Bibliographic record
Abstract
Abstract BackgroundMultimorbidity, defined as the co-existence of two or more chronic conditions, presents significant challenges to patients, healthcare providers and health systems. Despite this, there is ongoing uncertainty about the most effective ways to manage patients with multimorbidity. This review aimed to determine the effectiveness of interventions designed to improve outcomes in people with multimorbidity in primary care and community settings, compared to usual care.MethodsWe searched eight databases and two trials registers up to 9th September 2019. Two review authors independently screened and selected studies, extracted data, evaluated study quality and judged the certainty of the evidence (GRADE). Interventions were grouped by their predominant focus into care-coordination/self-management support, self-management support and medicines management. Main outcomes were health related quality of life (HRQoL) and mental health. Meta-analyses were conducted, where possible, but the synthesis was predominantly narrative.ResultsWe included 16 RCTs with 4,753 participants, the majority being older adults with at least three conditions. There were eight care-coordination/self-management support studies, four self-management support studies and four medicines management studies. There was little or no evidence of an effect on primary outcomes of HRQoL (MD 0.03, 95% CI -0.01 to 0.07, I 2 = 39%) and mental health or on secondary outcomes with a small number of studies reporting that care coordination may improve patient experience of care and self-management support may improve patient health behaviours. Overall the certainty of the evidence was graded as low due to significant variation in study participants and interventions. Conclusions There are remaining uncertainties about the effectiveness of interventions for people with multimorbidity, despite the growing number of RCTs conducted in this area. Our findings suggest that future research should consider patient experience of care, optimising medicines management and targeted patient health behaviours such as exercise.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".