Healthcare Epidemiology is <i>the</i> Paradigm for Patient Safety
Bibliographic record
Abstract
I was honored to receive the 2001 Lectureship Award from the Society for Healthcare Epidemiology of America (SHEA). It was my intent during the talk to review our field and implications that some of the new initiatives called “patient safety” have for our expertise. This article is based on the SHEA Lectureship that was given April 1, 2001, at the SHEA Annual Meeting in Toronto, Ontario, Canada. This article consists of four sections. First, I review lessons learned from colleagues during the 33 years that I have been associated with the field of hospital epidemiology and infection control, since my first days at the Centers for Disease Control and Prevention (CDC). Second, I explore issues raised by the Institute of Medicine (IOM) report on patient safety, adverse events, and medical errors, evaluating research that went into the extrapolation of the numbers of preventable deaths that this report highlighted. Those deaths gained everyone's attention. Third, I review the field of healthcare epidemiology, highlighting the three decades of success in our field in enhancing the safety of patients, improving their outcomes, and making a difference in the quality of medical care received in the United States. Finally, I discuss the challenges that hospital epidemiology currently faces and the opportunities that come with the expertise we have developed during more than 30 years.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".