Improving healthcare quality in the United States healthcare system: A scientific management approach
Bibliographic record
Abstract
The US healthcare system has been facing pressures from stakeholders to reduce costs and improve quality. The purpose of this paper is to develop a conceptual model to illustrate the approaches used in healthcare quality management (Continuous Quality Improvement/Total Quality Management, Lean, and Six Sigma) weaved into the underlying framework of scientific management theory. This paper employs scientific management theory to explain the healthcare quality tenets that influence the quality of care in our healthcare organizations. The father of scientific management, Frederick Taylor, and other key contributors collectively created scientific management principles, which are widely used for quality improvement purposes both in the engineering and the healthcare field. Healthcare quality is also discussed with examples of the application of scientific management principles. Shared themes between scientific management principles and healthcare quality tenets, as given in CQI/TQM, Six Sigma-Lean, and Donabedian Model, were developed. To understand the three pillars of quality (structure, process, outcome) in relation to the underpinnings of scientific management principles, we incorporated insights of scientific management theory into Donabedian’s healthcare quality model. It is recommended that selection of personnel play a more significant role among human resources practices in organizations; strategy formulation must include a careful assessment of organizations’ strengths and weaknesses with regard to continuous quality improvement, with organizations striving to achieve standardization to attain efficiency and reduce costs.
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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 | 0.026 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".