Six ways not to improve patient flow: a qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Although well-established principles exist for improving the timeliness and efficiency of care, many organisations struggle to achieve more than small-scale, localised gains. Where care processes are complex and include segments under different groups' control, the elegant solutions promised by improvement methodologies remain elusive. This study sought to identify common design flaws that limit the impact of flow initiatives. METHODS: This qualitative study was conducted within an explanatory case study of a Canadian regional health system in which multitudinous flow initiatives had yielded no overall improvement in system performance. Interviews with 62 senior, middle and departmental managers, supplemented by ∼700 documents on flow initiatives, were analysed using the constant comparative method. RESULTS: ; flawed initiatives reflected failure to consider one or more of these essential elements. Many initiatives focused narrowly on process, failing to consider that the intended population was poorly defined or the needed capacity inaccessible; some introduced capacity for an intended population, but offered no process to link the two. Moreover, interveners were unable to respond effectively when a bottleneck moved to another part of the system. Errors of population, capacity and process, in different combinations, generated six 'formulae for failure'. CONCLUSIONS: Typically, flawed initiatives focused on too small a segment of the patient journey to properly address the impediments to flow. The proliferation of narrowly focused initiatives, in turn, reflected a decentralised system in which responsibility for flow improvement was fragmented. Thus, initiatives' specific design flaws may have their roots in a deeper problem: the lack of a coherent system-level strategy.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it