Validating administrative data for the detection of adverse events in older hospitalized patients
Bibliographic record
Abstract
UNLABELLED: Older hospitalized patients are at risk of experiencing adverse events including, but not limited to, hospital-acquired pressure ulcers, fall-related injuries, and adverse drug events. A significant challenge in monitoring and managing adverse events is lack of readily accessible information on their occurrence. PURPOSE: The objective of this retrospective cross-sectional study was to validate diagnostic codes for pressure ulcers, fall-related injuries, and adverse drug events found in routinely collected administrative hospitalization data. METHODS: All patients 65 years of age or older discharged between April 1, 2009 and March 31, 2011 from a provincial academic health sciences center in Canada were eligible for inclusion in the validation study. For each of the three types of adverse events, a random sample of 50 patients whose records were positive and 50 patients whose records were not positive for an adverse event was sought for review in the validation study (n=300 records in total). A structured health record review was performed independently by two health care providers with experience in geriatrics, both of whom were unaware of the patient's status with respect to adverse event coding. A physician reviewed 40 records (20 reviewed by each health care provider) to establish interrater agreement. RESULTS: A total of 39 pressure ulcers, 56 fall-related injuries, and 69 adverse drug events were identified through health record review. Of these, 34 pressure ulcers, 54 fall-related injuries, and 47 adverse drug events were also identified in administrative data. Overall, the diagnostic codes for adverse events had a sensitivity and specificity exceeding 0.67 (95% confidence interval [CI]: 0.56-0.99) and 0.89 (95% CI: 0.72-0.99), respectively. CONCLUSION: It is feasible and valid to identify pressure ulcers, fall-related injuries, and adverse drug events in older hospitalized patients using routinely collected administrative hospitalization data. The information is relatively inexpensive and easy to access with no impact on clinical staff.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".