Implementation of a clinical practice guideline for schizophrenia in a specialist mental health center: an observational study
Bibliographic record
Abstract
BACKGROUND: In mental health settings, implementation of and adherence to clinical practice guidelines (CPGs) is low. Strategies are needed to overcome barriers and facilitate successful implementation of CPGs into standard care. The goals of this study were to develop a framework for the implementation of a CPG for schizophrenia for hospitalized service users in a mental health care facility, and to monitor adherence to the guideline. METHODS: An eight-step framework was developed based on project management principles: 1) the Appraisal Guideline for Research and Evaluation (AGREE) tool was used to rate and select a CPG; 2) an algorithm was created from the guideline; 3) a gap analysis identified clinical services and processes not conforming with the CPG recommendations; 4) a governance structure was created; 5) a modified Delphi process determined key outcome and process adherence metrics; 6) a project charter was developed; 7) clinical informatics ensured that systems and tools were in place to support the CPG; and 8) therapeutic services were realigned to match the requirements of the CPG within specified fiscal constraints. Percent adherence to the identified process adherence metrics was calculated before (March 2014) and for 12 months after implementation (April 2014-March 2015). RESULTS: The National Institute of Health and Care Excellence guideline scored highest on AGREE and was used to develop the algorithm. Cognitive behavior therapy for psychosis (CBT-P), art therapy and carer assessments were identified as gaps in care. Clinical global impression - Schizophrenia score was identified as the primary service user outcome variable and antipsychotic polypharmacy, metabolic monitoring, CBT-P referral and supported employment/vocational services referral as the primary process adherence measures. Adherence to guidance for metabolic monitoring (March 2014, 76.7 %; March 2015, 81.6 %), CBT-P referral (March 2014, 6.5 %; March 2015, 11.4 %) and vocational rehabilitation referral (March 2014, 36.6 %; March 2015, 49.1 %) were increased after CPG implementation. There was an initial increase in adherence to antipsychotic monotherapy (March 2014, 53.4 %; November 2014, 62.7 %), which decreased back toward baseline (March 2015, 55.1 %). CONCLUSIONS: The eight-step framework was used to implement a CPG process, though further quality improvements initiatives may be needed to improve adherence.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".