Participatory design of an improvement intervention for the primary care management of possible sepsis using the Functional Resonance Analysis Method
Bibliographic record
Abstract
BACKGROUND: Ensuring effective identification and management of sepsis is a healthcare priority in many countries. Recommendations for sepsis management in primary care have been produced, but in complex healthcare systems, an in-depth understanding of current system interactions and functioning is often essential before improvement interventions can be successfully designed and implemented. A structured participatory design approach to model a primary care system was employed to hypothesise gaps between work as intended and work delivered to inform improvement and implementation priorities for sepsis management. METHODS: In a Scottish regional health authority, multiple stakeholders were interviewed and the records of patients admitted from primary care to hospital with possible sepsis analysed. This identified the key work functions required to manage these patients successfully, the influence of system conditions (such as resource availability) and the resulting variability of function output. This information was used to model the system using the Functional Resonance Analysis Method (FRAM). The multiple stakeholder interviews also explored perspectives on system improvement needs which were subsequently themed. The FRAM model directed an expert group to reconcile improvement suggestions with current work systems and design an intervention to improve clinical management of sepsis. RESULTS: Fourteen key system functions were identified, and a FRAM model was created. Variability was found in the output of all functions. The overall system purpose and improvement priorities were agreed. Improvement interventions were reconciled with the FRAM model of current work to understand how best to implement change, and a multi-component improvement intervention was designed. CONCLUSIONS: Traditional improvement approaches often focus on individual performance or a specific care process, rather than seeking to understand and improve overall performance in a complex system. The construction of the FRAM model facilitated an understanding of the complexity of interactions within the current system, how system conditions influence everyday sepsis management and how proposed interventions would work within the context of the current system. This directed the design of a multi-component improvement intervention that organisations could locally adapt and implement with the aim of improving overall system functioning and performance to improve sepsis management.
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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 | 0.056 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".