Lean in Healthcare: What is Required to Support a Successful Hospital Lean Improvement Programme?
Bibliographic record
Abstract
Abstract \n \nThe NHS has committed to reduce variation in practice, increase productivity and expand integration of services in healthcare, (NHS England, 2019). To achieve these outcomes adequate improvement capacity and capability is required. A systematic lean approach to improvement appears to be delivering sustained success in healthcare organisations in Canada and the United States (Toussaint & Berry, 2013; Kaplan et al, 2014) and now in the UK (KPMG, 2018; BBC News, 2019) and the NHS is therefore investing in this type of programme, (Health Service Journal (HSJ), 2015). \nThis study seeks to understand what constitutes a systemic approach to lean improvement, how this is integrated into the wider work of healthcare and what the barriers and enablers are in an acute hospital setting. A case study methodology was used with semi-structured interviews and the main findings were \n•\tSystematic features that impacted on improvement in everyday work included knowledge and implementation of the improvement vision, lean leadership and in particular empowerment of teams to own and sustain change. \n•\tBarriers and enablers were identified, with varying views on the presence of resistance as a barrier or whether this was a feature of conflicting priorities and time commitment rather than a lack of motivation. \n•\tOther barriers included maintaining momentum for change, and hospital internal support functions that did not always support improvement. The participants identified the central QI team as being essential to momentum and support but believed this should diminish as local improvement capability and capacity increased. \nThis study has generated learning around the nature of systemic lean improvements, including the importance of understanding reasons for resistance and impact of leadership, training and engagement. This contributes to the existing literature on lean improvement and the learning can be applied to develop the improvement programme in the case study hospital and similar organisations.
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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 | 0.052 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".