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Assessing data quality in manual entry of ventilator settings
David K Vawdrey1, Reed M Gardner, R Scott Evans
1Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112-5750, USA. david.vawdrey@hsc.utah.edu
Summary
Automatic data collection for ventilator settings significantly improves accuracy and efficiency compared to manual charting by respiratory therapists. This technology reduces charting delays and errors, enhancing patient care protocols.
Area of Science:
- Medical Informatics
- Respiratory Care
- Clinical Engineering
Background:
- Manual charting of ventilator settings by respiratory therapists can introduce data quality issues.
- Incorrect data may negatively impact automated ventilator management protocols.
- Evaluating data accuracy is crucial for optimizing patient care in intensive settings.
Purpose of the Study:
- To assess the data quality of manually charted ventilator settings.
- To determine the impact of data inaccuracies on computerized ventilator management.
- To compare manual charting with automatic data acquisition.
Main Methods:
- Analysis of 29,054 charting events from 678 ventilated patients over 12 months.
- Comparison of manually entered ventilator settings with automatically acquired data via Medical Information Bus (MIB).
- Measurement of data matching percentage and charting delay.
Main Results:
- Data matching rates varied from 99.0% for PEEP to 75.9% for low tidal volume alarms.
- Average charting delay was 6.1 minutes per event.
- 3.9% of protocol suggestions were based on mismatched data.
Conclusions:
- Automatic data collection enhances charting efficiency and reduces errors.
- Eliminating manual data entry improves data accuracy for ventilator management.
- Even with good manual practices, automation offers significant benefits.
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