Comparing implementation strategies for optimizing depression care: A randomized control trial
Bibliographic record
Abstract
Abstract Importance Less than a third of depressed primary care patients experience clinical improvement, in part due to a lack of focus on treatment optimization (e.g., intensification). Objective To compare the impact of implementation and behavioral science informed system and multi-level strategies on population-wide treatment optimization in integrated/collaborative care model (CoCM) settings. Design Comparative effectiveness randomized controlled trial Setting 5 Primary care clinics with a mature integrated/CoCM Participants 44 primary care physicians and their patients with elevated depressive symptoms eligible for treatment optimization Exposures System-level strategy (i.e., enhanced usual care [EUC]) focused on staff and behavioral health provider (BHP) activation vs. multi-level strategy (intervention) involving BHP activation, primary care provider (PCP) behavioral support and a patient activation/psychoeducation tool (DepCare) Main outcomes and measures Patient optimization (e.g., filling a new, intensified/augmented, or previously nonadherent antidepressant and/or completing a new integrated/CoCM visit) during the 4 months following an index visit and PCP optimization (e.g., placing a referral for any integrated/CoCM service and/or initiating, intensifying, switching and/or combining antidepressant medications) at an index visit. We used multilevel logistic regression analysis (level 1 is the patient with an eligible visit, level 2 the PCP) to test our hypotheses. Odds ratios (ORs) and 95% CIs were based on these analyses. Results There were 605 eligible patients with 757 visits in the post-implementation period. The mean age was 48 (SD=17); 486 (80%) were female, 15% Black, 51% Hispanic and 32% Spanish speaking; 41% were on an antidepressant. Patient treatment optimization in the intervention vs. EUC arms was 39.1% vs. 44.9% (OR=0.78; 95% CI 0.50, 1.22, p =0.27). Pre- vs. post-implementation, patient treatment optimization increased from 30.0% to 39.1% (p=0.10) and 30.4% to 44.9% (p=0.001) in the intervention and EUC arms (p=0.22 for differential change). There were similar trends in PCP optimization behaviors. There was low fidelity to the DepCare tool. Conclusions and relevance Our study demonstrates little added benefit of a multi-level over a system-level strategy as it relates to treatment optimization, with only system-level strategies demonstrating pre-post improvements. Negative unintended impacts of multi-level, particularly clinician targeted, strategies should be explored. Key Points Question Is a theory-informed system-level strategy better than a multi-level strategy for improving population wide depression treatment optimization in integrated primary care settings? Findings In this comparative effectiveness randomized control trial of 2 implementation strategies for improving depression treatment optimization in integrated care settings, a multi-level strategy was no better than a system-level strategy for improving patient and clinician treatment optimization behaviors. Only the system-level strategy exhibited significant pre-post improvement in patient optimization. Meaning This is the first study to combine implementation and behavioral science to target treatment optimization in integrated care settings. We suggest that multi-level strategies that include clinician behavioral support may not be helpful and even harmful for improving population wide 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".