Assessing Data Quality in Manual Entry of Ventilator Settings
- David K Vawdrey,
- Reed M Gardner,
- RScott Evans,
- James F Orme Jr,
- Terry P Clemmer,
- Loren Greenway,
- Frank A Drews
- Affiliations of the authors: Department of Biomedical Informatics, (DKV, RMG, RSE, TPC), University of Utah School of Medicine, Department of Medical Informatics, (RSE), LDS Hospital, Intermountain Healthcare, Department of Medicine, (JFO, TPC, LG), Intermountain Healthcare, Department of Psychology, (FAD), University of Utah, Salt Lake City, Utah
- Correspondence and reprints: David K. Vawdrey, MS, Department of Biomedical Informatics, University of Utah School of Medicine, 26 South 2000 East, Suite 5700 HSEB, Salt Lake City, UT 84112-5750; e-mail: <david.vawdrey{at}hsc.utah.edu>
- Received 20 July 2006
- Accepted 29 January 2007
Abstract
Objective To evaluate the data quality of ventilator settings recorded by respiratory therapists using a computer charting application and assess the impact of incorrect data on computerized ventilator management protocols.
Design An analysis of 29,054 charting events gathered over 12 months from 678 ventilated patients (1,736 ventilator days) in four intensive care units at a tertiary care hospital.
Measurements Ten ventilator settings were examined, including fraction of inspired oxygen (Fio2), positive end-expiratory pressure (PEEP), tidal volume, respiratory rate, peak inspiratory flow, and pressure support. Respiratory therapists entered values for each setting approximately every two hours using a computer charting application. Manually entered values were compared with data acquired automatically from ventilators using an implementation of the ISO/IEEE 11073 Medical Information Bus (MIB). Data quality was assessed by measuring the percentage of time that the two sources matched. Charting delay, defined as the interval between data observation and data entry, also was measured.
Results The percentage of time that settings matched ranged from 99.0% (PEEP) to 75.9% (low tidal volume alarm setting). The average charting delay for each charting event was 6.1 minutes, including an average of 1.8 minutes spent entering data in the charting application. In 559 (3.9%) of 14,263 suggestions generated by computerized ventilator management protocols, one or more manually charted setting values did not match the MIB data.
Conclusion Even at institutions where manual charting of ventilator settings is performed well, automatic data collection can eliminate delays, improve charting efficiency, and reduce errors caused by incorrect data.
Footnotes
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Dr. Vawdrey is funded through a training grant in medical informatics from the National Library of Medicine, LM007124. Additional support for this research was provided by Intermountain Healthcare.
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The authors thank Kyle Johnson, Bill Hawley, and Tupper Kinder of the Department of Medical Informatics at LDS Hospital for technical assistance throughout the data collection process. Lori Carpenter and Vrena Flint from the Department of Respiratory Care provided helpful suggestions about the study design and the data analysis.
