The Reduction in Medical Errors on Implementing an Intensive Care Information System in a Setting Where a Hospital Electronic Medical Record System is Already in Use: Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Although the various advantages of clinical information systems in intensive care units (ICUs), such as intensive care information systems (ICISs), have been reported, their role in preventing medical errors remains unclear. OBJECTIVE: This study aimed to investigate the changes in the incidence and type of errors in the ICU before and after ICIS implementation in a setting where a hospital electronic medical record system is already in use. METHODS: An ICIS was introduced to the general ICU of a university hospital. After a step-by-step implementation lasting 3 months, the ICIS was used for all patients starting from April 2019. We performed a retrospective analysis of the errors in the ICU during the 6-month period before and after ICIS implementation by using data from an incident reporting system, and the number, incidence rate, type, and patient outcome level of errors were determined. RESULTS: From April 2018 to September 2018, 755 patients were admitted to the ICU, and 719 patients were admitted from April 2019 to September 2019. The number of errors was 153 in the 2018 study period and 71 in the 2019 study period. The error incidence rates in 2018 and 2019 were 54.1 (95% CI 45.9-63.4) and 27.3 (95% CI 21.3-34.4) events per 1000 patient-days, respectively (P<.001). During both periods, there were no significant changes in the composition of the types of errors (P=.16), and the most common type of error was medication error. CONCLUSIONS: ICIS implementation was temporally associated with a 50% reduction in the number and incidence rate of errors in the ICU. Although the most common type of error was medication error in both study periods, ICIS implementation significantly reduced the number and incidence rate of medication errors. TRIAL REGISTRATION: University Hospital Medical Information Network Clinical Trials Registry UMIN000041471; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047345.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 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".