Prioritizing quality measure concepts at the interface of behavioral and physical healthcare
Bibliographic record
Abstract
OBJECTIVE: Integrated healthcare models can increase access to care, improve healthcare quality, and reduce cost for individuals with behavioral and general medical healthcare needs, yet there are few instruments for measuring the quality of integrated care. In this study, we identified and prioritized concepts that can represent the quality of integrated behavioral health and general medical care. DESIGN: We conducted a literature review to identify candidate measure concepts. Experts then participated in a modified Delphi process to prioritize the concepts for development into specific quality measures. SETTING: United States. PARTICIPANTS: Expert behavioral health and general medical clinicians, decision-makers (policy, regulatory and administrative professionals) and patient advocates. MAIN OUTCOME MEASURES: Panelists rated measure concepts on importance, validity and feasibility. RESULTS: The literature review identified 734 measures of behavioral or general medical care, which were then distilled into 43 measure concepts. Thirty-three measure concepts (including a segmentation strategy) reached a predetermined consensus threshold of importance, while 11 concepts did not. Two measure concepts were 'ready for further development' ('General medical screening and follow-up in behavioral health settings' and 'Mental health screening at general medical healthcare settings'). Among the 31 additional measure concepts that were rated as important, 7 were rated as valid (but not feasible), while the remaining 24 concepts were rated as neither valid nor feasible. CONCLUSIONS: This study identified quality measure concepts that capture important aspects of integrated care. Researchers can use the prioritization process described in this study to guide healthcare quality measures development work.
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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 | 0.174 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".