Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership’s Common Data Model: Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Falls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication. OBJECTIVE: The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods. METHODS: As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest). RESULTS: In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models. CONCLUSIONS: To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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".