Approaches for Reducing Medical Errors and Increasing Patent Safety: TRM, Quality and 5QS Method
Bibliographic record
Abstract
The goal in any country should be to deliver safe and high-quality health care to patients in all clinical settings. Despite the best intentions, however, a high rate of largely preventable adverse events and medical errors occur that cause harm to patients. Medical errors are one of many Nations´ leading causes of death and injury. In USA, between 50,000 to 100,000 people die in U.S. hospitals each year and 1 000 000 excess injures as the result of medical errors (MEs) and adverse events (AEs). 23% of Europeans argue that they have been directly affected by a medical errors personally or in the family. over 3000 people die in Sweden, 185,000 case are associated with an adverse event in Canada and almost 11% of total deaths in Australia are caused by medical errors. Mixed up test results, injuries suffered during childbirth, infections following surgery, and incorrect drug dosages are just a few of the harmful medical errors. This means that more people die from medical errors than from motor vehicle accidents, breast cancer, or AIDS. One question is How many patients need to die before the media, government, county councils and care planners start to take serious actions to prevent such lose of people because of the medical errors? Most of the published academic studies in the services sector have looked only at the link between services quality and satisfaction. Some studies have been conducted to investigate the link between technical and functional quality dimensions and the level of patient‘s safety, medical errors and patient satisfaction. Very few of the identified studies have empirically examined how the atmosphere, interaction and infrastructure might prevent the medical errors and impact overall patient‘s quality perception and satisfaction. Total relationship management (TRM) emphasizes the totality and the holistic nature of a relationship which includes internal and external factors, functions and resources inside and outside any health care organization/institution. TRM includes 5 generic quality dimensions (5 Qs) and measurements. 5Qs will be used in this study to identify the shortcoming of a health care institution to reduce the medical errors which lead to the increase of the patient safety and physicians and patients relationship and satisfaction. The result can be used by the hospitals to reengineer and redesign their quality management processes and the future direction of their more effective healthcare quality strategies.
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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 | 0.071 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".