Implementing a cirrhosis order set in a tertiary healthcare system: a theory-informed formative evaluation
Bibliographic record
Abstract
BACKGROUND: Standardized order sets are a means of increasing adherence to clinical practice guidelines and improving the quality of patient care. Implementation of novel quality improvement initiatives like order sets can be challenging. Before the COVID-19 pandemic, we conducted a formative evaluation to understand healthcare providers' perspectives on implementing clinical changes and the individual, collective and organizational contextual factors that might impact implementation at eight hospital sites in Alberta, Canada. METHODS: We utilized concepts from the Consolidated Framework for Implementation Research (CFIR) and Normalisation Process Theory (NPT) to understand the context, past implementation experiences, and perceptions of the cirrhosis order set. Eight focus groups were held with healthcare professionals caring for patients with cirrhosis. Data were coded deductively using relevant constructs of NPT and CFIR. A total of 54 healthcare professionals, including physicians, nurses, nurse practitioners, social workers and pharmacists and a physiotherapist, participated in the focus groups. RESULTS: Key findings revealed that participants recognized the value of the cirrhosis order set and its potential to improve the quality of care. Participants highlighted potential implementation challenges, including multiple competing quality improvement initiatives, feelings of burnout, lack of communication between healthcare provider groups, and a lack of dedicated resources to support implementation. CONCLUSIONS: Implementing a complex improvement initiative across clinician groups and acute care sites presents challenges. This work yielded insights into the significant influence of past implementation of similar interventions and highlighted the importance of communication between clinician groups and resources to support implementation. However, by using multiple theoretical lenses to illuminate what and how contextual and social processes will influence uptake, we can better anticipate challenges during the implementation process.
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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 | 0.169 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".