Cyrus Motamed1, Jean Louis Bourgain2. 1. Department of Anaesthesia, Gustave-Roussy Cancer Institute, 114, rue Édouard-Vaillant, 94805 Villejuif cedex, France. Electronic address: motamed@igr.fr. 2. Department of Anaesthesia, Gustave-Roussy Cancer Institute, 114, rue Édouard-Vaillant, 94805 Villejuif cedex, France.
Abstract
INTRODUCTION: Anaesthesia Information Management Systems (AIMS) generate large amounts of data, which might be useful for quality assurance programs. This study was designed to highlight the multiple contributions of our AIMS system in extracting quality indicators over a period of 10years. METHODS: The study was conducted from 2002 to 2011. Two methods were used to extract anaesthesia indicators: the manual extraction of individual files for monitoring neuromuscular relaxation and structured query language (SQL) extraction for other indicators which were postoperative nausea and vomiting (PONV), pain, sedation scores, pain-related medications, scores and postoperative hypothermia. For each indicator, a program of information/meetings and adaptation/suggestions for operating room and PACU personnel was initiated to improve quality assurance, while data were extracted each year. RESULTS: The study included 77,573 patients. The mean overall completeness of data for the initial years ranged from 55 to 85% and was indicator-dependent, which then improved to 95% completeness for the last 5years. The incidence of neuromuscular monitoring was initially 67% and then increased to 95% (P<0.05). The rate of pharmacological reversal remained around 53% throughout the study. Regarding SQL data, an improvement of severe postoperative pain and PONV scores was observed throughout the study, while mild postoperative hypothermia remained a challenge, despite efforts for improvement. DISCUSSION: The AIMS system permitted the follow-up of certain indicators through manual sampling and many more via SQL extraction in a sustained and non-time-consuming way across years. However, it requires competent and especially dedicated resources to handle the database.
INTRODUCTION: Anaesthesia Information Management Systems (AIMS) generate large amounts of data, which might be useful for quality assurance programs. This study was designed to highlight the multiple contributions of our AIMS system in extracting quality indicators over a period of 10years. METHODS: The study was conducted from 2002 to 2011. Two methods were used to extract anaesthesia indicators: the manual extraction of individual files for monitoring neuromuscular relaxation and structured query language (SQL) extraction for other indicators which were postoperative nausea and vomiting (PONV), pain, sedation scores, pain-related medications, scores and postoperative hypothermia. For each indicator, a program of information/meetings and adaptation/suggestions for operating room and PACU personnel was initiated to improve quality assurance, while data were extracted each year. RESULTS: The study included 77,573 patients. The mean overall completeness of data for the initial years ranged from 55 to 85% and was indicator-dependent, which then improved to 95% completeness for the last 5years. The incidence of neuromuscular monitoring was initially 67% and then increased to 95% (P<0.05). The rate of pharmacological reversal remained around 53% throughout the study. Regarding SQL data, an improvement of severe postoperative pain and PONV scores was observed throughout the study, while mild postoperative hypothermia remained a challenge, despite efforts for improvement. DISCUSSION: The AIMS system permitted the follow-up of certain indicators through manual sampling and many more via SQL extraction in a sustained and non-time-consuming way across years. However, it requires competent and especially dedicated resources to handle the database.