Assessing Data Quality in Manual Entry of Ventilator Settings

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 endexpiratory 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. J Am Med Inform Assoc. 2007;14:295–303. DOI 10.1197/jamia.M2219. by gest on Jauary 9, 2016 rns.org/ For patients treated with continuous positive-pressure ventilation in the intensive care unit (ICU), numerous ventilator settings are adjusted to provide appropriate oxygenation and ventilation and to facilitate weaning. Nurses or respiratory therapists periodically observe settings on the ventilator that control oxygen, flow, volume, and pressures, and chart these values either on paper or into electronic patient records. Variation exists in how and when such values are observed 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. 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. 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. 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: [email protected] . Received for review: 7/20/2006; accepted for publication: 1/29/ 2007. and documented. For ICU staff, access to timely and accurate ventilator settings is essential for making treatment decisions and for maintaining situation awareness. Computerized decision support tools, such as ventilator management protocols, require accurate and timely data to generate instructions for caregivers and to effect changes in patient care. We hypothesized that the ventilator settings entered manually into a computer charting application would not always match the settings automatically acquired from ventilators at 5-second intervals. To test this hypothesis, we measured the percentage of time that the manually charted settings matched 5-second ventilator values. To assess the possible impact of incorrect data on computerized ventilator management protocols, we measured the number of suggestions generated by computerized ventilator management protocols where one or more manually charted setting values did not match the automatically acquired data.