Integration model of total quality management and six sigma in hospital quality management
Bibliographic record
Abstract
Implementation of quality management is very important for hospitals to improve processes, solve problems, and reduce variations and errors in service, including through the implementation of popular Total Quality Management (TQM) and Six Sigma (SS) as new quality management strategies to increase profitability, effectiveness and efficiency of the organization's operations to meet customer’s needs. This study aims to develop an integrated hospital quality management model from the practice of TQM and SS to provide synergy in improving hospital performance. The study design was cross sectional through a survey using a questionnaire on 863 respondents, namely all employees ranging from doctors to administrative personnel at 8 hospitals. The TQM and SS practice integration model identified as “Quality Management Alliance Model (QMA)” consists of 6 variable constructs, namely: Management Practice (MP); TQM Infrastructure Practice (IPTQM); SS Infrastructure Practice (IPSS); Core Practice TQM (CPTQM); Core Practice SS (CPSS); and Hospital Performance (KRS) with 12 structural equations hypothesized. Data analysis are performed using Structural Equation Model through 2 tests, namely analysis of measurement models using confirmatory factor analysis (CFA) second order approach and structural model analysis. The results of the first order confirmatory factor analysis (CFA) analysis, after issuing invalid indicators (SLF≤0.5 and t≤1.96), obtained constructs of latent variables with models fit, valid, and reliable. Then in the second order CFA analysis on the overall model after being simplified through LVS (latent variable score) the study obtained construct model fit, valid and reliable. The results of the structural model analysis obtained a model fit with 11 structural equations that are positively and significantly related (t> 1.96). This study proves that the QMA model is feasible and can be applied to measure the implementation of hospital quality management. Hospital management is recommended to implement the QMA Model optimally to improve performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".