Validity of AHRQ patient safety indicators derived from ICD-10 hospital discharge abstract data (chart review study)
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: To assess if the Agency for Healthcare Research and Quality patient safety indictors (PSIs) could be used for case findings in the International Classification of Disease 10th revision (ICD-10) hospital discharge abstract data. DESIGN: We identified and randomly selected 490 patients with a foreign body left during a procedure (PSI 5-foreign body), selected infections (IV site) due to medical care (PSI 7-infection), postoperative pulmonary embolism (PE) or deep vein thrombosis (DVT; PSI 12-PE/DVT), postoperative sepsis (PSI 13-sepsis)and accidental puncture or laceration (PSI 15-laceration) among patients discharged from three adult acute care hospitals in Calgary, Canada in 2007 and 2008. Their charts were reviewed for determining the presence of PSIs and used as the reference standard, positive predictive value (PPV) statistics were calculated to determine the proportion of positives in the administrative data representing 'true positives'. RESULTS: The PPV for PSI 5-foreign body was 62.5% (95% CI 35.4% to 84.8%), PSI 7-infection was 79.1% (67.4% to 88.1%), PSI 12-PE/DVT was 89.5% (66.9% to 98.7%), PSI 13-sepsis was 12.5% (1.6% to 38.4%) and PSI 15-laceration was 86.4% (75.0% to 94.0%) after excluding those who presented to the hospital with the condition. CONCLUSIONS: Several PSIs had high PPV in the ICD administrative data and are thus powerful tools for true positive case finding. The tools could be used to identify potential cases from the large volume of admissions for verification through chart reviews. In contrast, their sensitivity has not been well characterised and users of PSIs should be cautious if using them for 'quality of care reporting' presenting the rate of PSIs because under-coded data would generate falsely low PSI rates.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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 it