||The objective of this study was to develop an automated charting algorithm (for artifact rejection and data reduction) for parameters from ventilators used for routine respiratory care (RC) charting. Six artifact rejections (moving average, moving medium, moving exponential weighted using two different weighting factors, Lowell and moving LOWESS) and a data reduction method using two different critical time were tried. These six artifact rejection and two data reduction methods mad a combination of 12 automated charting algorithms. Real time data collected by Medical Information Bus (MIB) from 10 different patients (total 617.1 hours) were processed by these 12 automated charging algorithms and the results were compared to the respiratory therapistsâ€™ manual charted data. These ventilator data could be categorized into groups: measured ventilator data and machine settings. Only measured ventilator data were processed by artifact rejection algorithm, machine setting were not processed by artifact rejection algorithms since machine settings data had almost no artifact. The differences between manual and automated charting of data for machine settings were not statistically significant except respiratory rate setting, sensitivity setting and plateau pause time setting, but these difference were caused by therapistsâ€™ manual entry errors and in any event were not found to be clinically important. Automated chartings could easily detect setting changes automatically and accurately. The differences between manual and automated charting data for measured ventilator data were statistically significant for machine respiratory rate, spontaneous respiratory rate, corrected tidal volume and spontaneous tidal volume. In most cases the difference were less than 10% of the normal range of each parameter and were not considered clinically important. It improved that automated charting could also easily detect measured ventilator changes automatically and accurately. The algorithms developed eliminated not only artifact from the ventilatory data by selecting representative dta, but also reported only clinically important events to the host computer to minimize the amount of data stored in patientâ€™s record. All the 12 automated charting algorithms could also record clinically important data into patientâ€™s record more timely than manual charting, eliminate manual data entry errors, save time for medical practitionerâ€™s on paperwork. Among the 12 automated charging algorithms tired in this study, the moving median with critical time of 3 minutes provided the bet results for artifact rejection and data reduction. Two user questionnaires survey were completed. The first determined RC therapistsâ€™ general opinions abut automated ventilator charting using computers. The results showed that therapists were neutral about automated charting system. However, some of the therapists agreed that computer system would be helpful, and some worried about the â€œownershipâ€ of the data and the inconvenience of using computerized system. The second questionnaire was designed to determine how medical practitioners selected data in their routine charting. Seven medical practitioners participated in the second survey. The results showed that they each had different opinions about what were â€œimportant eventsâ€ to chart. Automated charting using computers can be used to provide a more efficient health care. It is essential for the health care. It is essential for health providers to establish a set of rules for automated charting that emphasize how often and what kind of data should be charted.