Business Process Redesign in Healthcare: Towards a Structured Approach
Bibliographic record
Abstract
This paper focuses on the potential contribution of Business Process Redesign to society’s demand for decreasing costs of healthcare. Our focus is on the reduction of throughput times and service times by exploiting business process redesign techniques, i.e. rules of thumb that aim to optimise the business process by improving its tasks, its routing structure, the resource organisation, etc. We define a redesign approach based on a set of existing redesign heuristics (Reijers, 2003) and apply this approach in a mental healthcare case. We show seven alternative redesigns for an intake process and evaluate their impact on throughput times and service times. Our conclusion is that the approach is feasible and results in a fruitful input for the organisation in question. This result is in line with results from the evolutionary approach of (Buchanan, 1998).The application of best practices in the mental healthcare setting shows its potency in this specific context and very similar settings. A next necessary step towards a wider application in healthcare seems to be a more structured method on how to select or combine an effective set of best practices for a specific medical context.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".