Quantifying care, qualifying experiences: a systematic review of measurement-based care in psychiatry from patient and provider perspectives
Bibliographic record
Abstract
BACKGROUND: Measurement based care (MBC) is a patient-centered approach that is gaining popularity in healthcare systems, particularly in mental health settings. However, attitudes towards MBC vary among mental health clinicians and patients, leading to variable implementation. OBJECTIVE: This systematic review synthesises clinician and patient perspectives on the benefits and drawbacks of measurement-based care (MBC) in psychiatry. STUDY SELECTION AND ANALYSIS: We searched Ovid MEDLINE, EMBASE, EBM Reviews, APA PsychINFO and CINAHL databases from inception to January 2024. After screening 1644 titles and abstracts, 48 full papers were reviewed, and 24 studies were ultimately included. Quality assessment was conducted using the Mixed Methods Appraisal Tool, and key patterns were extracted using thematic analysis. FINDINGS: The review reflects opinions of 901 patients and 2831 clinicians across various settings. Patients valued MBC for enhancing communication, self-awareness and reducing stigma. However, they expressed concerns about the adequacy of measures in reflecting their clinical state and uncertainty about how responses influence treatment decisions. Clinicians appreciated MBC for improving patient involvement, tracking treatment response and enhancing communication efficiency. Concerns included inadequate capture of clinical complexity, potential reporting biases, time constraints, insufficient training and concerns with respect to data usage and privacy. CONCLUSIONS AND CLINICAL IMPLICATIONS: While patients and clinicians recognise significant benefits, including enhanced communication, improved insight and more structured clinical decision-making, they also identify important limitations. These include concerns about the adequacy of scales to capture complex clinical presentations, potential impacts on the therapeutic alliance and increased administrative burden. Moving forward, successful integration of MBC into routine care will require addressing these challenges through improved clinician training, clear guidelines for interpretation, greater transparency with respect to how data will be used and more seamless integration with existing clinical workflows. PROSPERO REGISTRATION NUMBER: PROSPERO CRD420250651562.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".