Using Creative Problem Solving (TRIZ) in Improving the Quality of Hospital Services
Bibliographic record
Abstract
TRIZ is an initiative and SERVQUAL is a structured methodology for quality improvement. Using these tools, inventive problem solving can be applied for quality improvement, and the highest quality can be reached using creative quality improvement methodology. The present study seeks to determine the priority of quality aspects of services provided for patients in the hospital as well as how TRIZ can help in improving the quality of those services. This Study is an applied research which used a dynamic qualitative descriptive survey method during year 2011. Statistical population includes every patient who visited in one of the University Hospitals from March 2011. There existed a big gap between patients' expectations from what seemingly is seen (the design of the hospital) and timely provision of services with their perceptions. Also, quality aspects of services were prioritized as follows: keeping the appearance of hospital (the design), accountability, assurance, credibility and having empathy. Thus, the only thing which mattered most for all staff and managers of studied hospital was the appearance of hospital as well as its staff look. This can grasp a high percentage of patients' satisfaction. By referring to contradiction matrix, the most important principles of TRIZ model were related to tangible factors including principles No. 13 (discarding and recovering), 25 (self-service), 35 (parameter changes), and 2 (taking out). Furthermore, in addition to these four principles, principle No. 24 (intermediary) was repeated most among the others. By utilizing TRIZ, hospital problems can be examined with a more open view, Go beyond The conceptual framework of the organization and responded more quickly to patients ' needs.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".