Use of a falls incident reporting system to improve care process documentation in nursing homes
Bibliographic record
Abstract
BACKGROUND: Falls are the most frequently reported adverse event among frail nursing home residents and are an important resident safety issue. Incident reporting systems have been successfully used to improve quality and safety in healthcare. The purpose of this study was to test the effect of a systematically guided menu-driven incident reporting system (MDIRS) on documentation of post-fall evaluation processes in nursing homes. METHODS: Six for-profit nursing homes in southeastern USA participated in the study. Over a 4-month period, MDIRS was used in three nursing homes matched with another three nursing homes which continued using their existing narrative incident report to document falls. Trained geriatric nurse practitioner auditors used a data collection audit tool to collect medical record documentation of the processes of care for residents who fell. Multivariate analysis of covariance was used to compare the post-fall nursing care processes documented in the medical records. RESULTS: 207 medical records of resident who fell were examined. Over 75% of the sample triggered at high risk for falls by the minimum data set. An adequate neurological assessment was documented for only 18.4% of residents who had experienced a fall. Although two-thirds of the sample had a diagnosis of incontinence, less than 20% of the records had incontinence-related interventions in the nursing care plan. Overall, there was more complete documentation of the post-fall evaluation process in the medical records in nursing homes using the MDIRS than in nursing homes using standard narrative incident reports (p<0.001). CONCLUSION: Further improvements are necessary in reporting mechanisms to improve the post-fall assessment in nursing home residents.
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How this classification was reachedexpand
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 | 0.049 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".