Bibliographic record
Abstract
BACKGROUND: Health systems in many jurisdictions struggle to reduce Emergency Department congestion and improve patient flow across the continuum of care. Flow is often described as a systemic issue requiring a "system approach"; however, the implications of this idea remain poorly understood. Focusing on a Canadian regional health system whose flow problems have been particularly intractable, this study sought to determine what system-level flaws impede healthcare organizations from improving flow. METHODS: This study drew primarily on qualitative data from in-depth interviews with 62 senior, middle and departmental managers representing the Region, its programs and sites; quantitative analysis of key flow indicators (1999-2012) and review of ~700 documents furnished important context. Examination of the interview data revealed that the most striking feature of the dataset was contradiction; accordingly, a technique of dialectical analysis was developed to examine observed contradictions at successively deeper levels. RESULTS: Analysis uncovered three paradoxes: "Many Small Successes and One Big Failure" (initiatives improve parts of the system but fail to fix underlying system constraints); "Your Innovation Is My Aggravation" (local innovation clashes with regional integration); and most critically, "Your Order Is My Chaos" (rules that improve service organization for my patients create obstacles for yours). This last emerges when some entities (sites/hospitals) define their patients in terms of their location in the system, while others (regional programs) define them in terms of their needs/characteristics. As accountability for improving flow was distributed among groups that thus variously defined their patients, local efforts achieved little for the overall system, and often clashed with each other. These paradoxes are indicative of a fundamental antagonism between the system's parts and the whole. CONCLUSION: An accretion of flow initiatives in all parts of the system will never add up to a system approach, and may indeed perpetuate the paradoxes. What is needed is a coherent strategy of defining patient populations by needs, analyzing their entire trajectories of care, and developing consistent processes to better meet those needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".