Measurement-Based Care in a Remote Intensive Outpatient Program: Pilot Implementation Initiative
Bibliographic record
Abstract
BACKGROUND: The ongoing mental health crisis, especially among youth, has led to a greater demand for intensive treatment at the intermediate level, such as intensive outpatient programs (IOPs). Defining best practices in remote IOPs more broadly is critical to understanding the impact of these offerings for individuals with high-acuity mental health service needs in the outpatient setting. Measurement-based care (MBC), or the routine and systematic collection of patient-reported data throughout the course of care to make meaningful changes to treatment, is one such practice that has been shown to improve patient outcomes in mental health treatment. Despite the literature linking MBC to beneficial clinical outcomes, the adoption of MBC in clinical practice has been slow and inconsistent, and more research is needed around MBC in youth-serving settings. OBJECTIVE: The aim of this paper is to help bridge these gaps, illustrating the implementation of MBC within an organization that provides remote-first, youth-oriented IOP for individuals with high-acuity psychiatric needs. METHODS: A series of 2 quality improvement pilot studies were conducted with select clinicians and their clients at Charlie Health, a remote IOP program that treats high-acuity teenagers and young adults who present with a range of mental health disorders. Both studies were carefully designed, including thorough preparation and planning, clinician training, feedback collection, and data analysis. Using process evaluation data, MBC deployment was repeatedly refined to enhance the clinical workflow and clinician experience. RESULTS: The survey completion rate was 80.08% (3216/4016) and 86.01% (4218/4904) for study 1 and study 2, respectively. Quantitative clinician feedback showed marked improvement from study 1 to study 2. Rates of successful treatment completion were 22% and 29% higher for MBC pilot clients in study 1 and study 2, respectively. Depression, anxiety, and psychological well-being symptom reduction were statistically significantly greater for MBC pilot clients (P<.05). CONCLUSIONS: Our findings support the feasibility and clinician acceptability of a rigorous MBC process in a real-world, youth-serving, remote-first, intermediate care setting. High survey completion data across both studies and improved clinician feedback over time suggest strong clinician buy-in. Client outcomes data suggest MBC is positively correlated with increased treatment completion and symptom reduction. This paper provides practical guidance for MBC implementation in IOPs and can extend to other mental health care settings.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".