Objective risk assessment vs standard care for acute coronary syndromes—The Australian GRACE Risk tool Implementation Study (AGRIS): a process evaluation
Bibliographic record
Abstract
BACKGROUND: Structured risk-stratification to guide clinician assessment and engagement with evidence-based therapies may reduce care variance and improve patient outcomes for Acute Coronary Syndrome (ACS). The Australian Grace Risk score Intervention Study (AGRIS) explored the impact of the GRACE Risk Tool for stratification of ischaemic and bleeding risk in ACS. While hospitals in the active arm had a higher overall rate of invasive ACS management, there was neutral impact on important secondary prevention prescriptions/referrals, hospital performance measures, myocardial infarction and 12-month mortality leading to early trial cessation. Given the Grace Risk Tool is under investigation internationally, this process evaluation study provides important insights into the possible contribution of implementation fidelity on the AGRIS study findings. METHODS: Using maximum variation sampling, five hospitals were selected from the 12 centres enrolled in the active arm of AGRIS. From these facilities, 16 local implementation stakeholders (Cardiology advanced practice nurses, junior and senior doctors, study coordinators) consented to a semi-structured interview guided by the Theoretical Domains Framework. Directed Content Analysis of qualitative data was structured using the Capability/Opportunity/Motivation-Behaviour (COM-B) model. RESULTS: Physical capability was enhanced by tool usability. While local stakeholders supported educating frontline clinicians, non-cardiology clinicians struggled with specialist terminology. Physical opportunity was enhanced by the paper-based format but was hampered when busy clinicians viewed risk-stratification as one more thing to do, or when form visibility was neglected. Social opportunity was supported by a culture of research/evidence yet challenged by clinical workflow and rotating medical officers. Automatic motivation was strengthened by positive reinforcement. Reflective motivation revealed the GRACE Risk Tool as supporting but potentially overriding clinical judgment. Divergent professional roles and identity were a major barrier to integration of risk-stratification into routine Emergency Department practice. The cumulative result revealed poor form completion behaviors and a failure to embed risk-stratification into routine patient assessment, communication, documentation, and clinical practice behaviors. CONCLUSIONS: Numerous factors negatively influenced AGRIS implementation fidelity. Given the prominence of risk assessment recommendations in United States, European and Australian guidelines, strategies that strengthen collaboration with Emergency Departments and integrate automated processes for risk-stratification may improve future translation internationally.
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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 | 0.171 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".