Electronically Screening Discharge Summaries for Adverse Medical Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Detecting adverse events is pivotal for measuring and improving medical safety, yet current techniques discourage routine screening. The authors hypothesized that discharge summaries would include information on adverse events, and they developed and evaluated an electronic method for screening medical discharge summaries for adverse events. DESIGN: A cohort study including 424 randomly selected admissions to the medical services of an academic medical center was conducted between January and July 2000. The authors developed a computerized screening tool that searched free-text discharge summaries for trigger words representing possible adverse events. MEASUREMENTS: All discharge summaries with a trigger word present underwent chart review by two independent physician reviewers. The presence of adverse events was assessed using structured implicit judgment. A random sample of discharge summaries without trigger words also was reviewed. RESULTS: Fifty-nine percent (251 of 424) of the discharge summaries contained trigger words. Based on discharge summary review, 44.8% (327 of 730) of the alerted trigger words indicated a possible adverse event. After medical record review, the tool detected 131 adverse events. The sensitivity and specificity of the screening tool were 69% and 48%, respectively. The positive predictive value of the tool was 52%. CONCLUSION: Medical discharge summaries contain information regarding adverse events. Electronic screening of discharge summaries for adverse events using keyword searches is feasible but thus far has poor specificity. Nonetheless, computerized clinical narrative screening methods could potentially offer researchers and quality managers a means to routinely detect adverse events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it