A dynamic model of the systemic causes for patient treatment delays in emergency departments
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on the development of a qualitative systems model developed to understand why average emergency department (ED) length of stay (LOS) was rapidly increasing while the number of ED visits was relatively constant. The paper's focus was to identify systemic causes for poor patient flow so that the model could then be used to evaluate improvement options using a more complete view of the causal structure for the ED delays. Design/methodology/approach In this case study, a disciplined system dynamics approach was used that included development of a dynamic hypothesis, causal loop and stock and flow diagramming, interviews with system experts, and data collection and analysis. Findings Results support the dynamic hypothesis that an aging population and shortages of resources to treat chronically ill patients (among other dynamics) were causing longer average LOS. Older and sicker patients were consuming more ED resources and causing less acute patients to leave without being seen or to avoid visiting the ED in the first place. In essence, the ED was acting as a safety valve for the wider health care system as many parts of this wider system became overloaded. Practical implications Owing to the systemic causes for the patient treatment delay problem in the ED, simple local solutions are unlikely to be effective. The system model can be used as a basis to understand the underlying dynamics of the systemic causes for poor patient flow and identify robust and long‐term solutions. Originality/value The paper presents a process for developing a dynamic model to engage the various participants in a health care system in understanding the causes for delays and poor patient flow. The modeling approach can be used as a means for health care managers/administrators to identify improvement options that address the systemic problems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".