Literature DB >> 11187690

Comparison of manual and automated documentation of adverse events with an Anesthesia Information Management System (AIMS).

M Benson1, A Junger, A Michel, G Sciuk, L Quinzio, K Marquardt, G Hempelmann.   

Abstract

In this study, an Anesthesia Information Management System (AIMS) is used for the comparison of manually recorded adverse events with automatically detected events from anesthesiological procedures. In 1998, data from all anesthesia procedures, including the data set for quality assurance defined by the German Society of Anesthesiology and Intensive Care Medicine (DGAI), were recorded online with the documentation software NarkoData 4 (IMESO GmbH, Hüttenberg, Germany) followed by storage into a relational database (Oracle Corporation). The occurrence of manually recorded adverse events, as defined by the DGAI, is compared with automatically detected events. Automated detection was done with SQL-statements. The following adverse events were selected: hypotension, hypertension, bradycardia, tachycardia and hypovolemia. Data obtained from 16,019 electronic anesthesia records show that in 911 patients (5.7%), one of the selected adverse events was documented manually whereas in 2,996 patients (18.7%) a adverse event was detected automatically. The incidence of automatically detected events is obviously higher compared to manually recorded events. With the help of an AIMS, automatic detection proved significant deficiencies in the manual documentation of adverse events.

Entities:  

Mesh:

Year:  2000        PMID: 11187690

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  7 in total

Review 1.  Detecting adverse events using information technology.

Authors:  David W Bates; R Scott Evans; Harvey Murff; Peter D Stetson; Lisa Pizziferri; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2003 Mar-Apr       Impact factor: 4.497

2.  Automated detection of adverse events using natural language processing of discharge summaries.

Authors:  Genevieve B Melton; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2005-03-31       Impact factor: 4.497

Review 3.  The anesthesia information management system for electronic documentation: what are we waiting for?

Authors:  Eric L Bloomfield; Neil G Feinglass
Journal:  J Anesth       Date:  2008-11-15       Impact factor: 2.078

4.  Electronic medical records as a replacement for prospective research data collection in postoperative pain and opioid response studies.

Authors:  Todd Lingren; Senthilkumar Sadhasivam; Xue Zhang; Keith Marsolo
Journal:  Int J Med Inform       Date:  2017-12-17       Impact factor: 4.046

5.  Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia.

Authors:  Jorge A Gálvez; Ali Jalali; Luis Ahumada; Allan F Simpao; Mohamed A Rehman
Journal:  J Med Syst       Date:  2017-08-23       Impact factor: 4.460

6.  Development and usage of an anesthesia data warehouse: lessons learnt from a 10-year project.

Authors:  Antoine Lamer; Mouhamed Djahoum Moussa; Romaric Marcilly; Régis Logier; Benoit Vallet; Benoît Tavernier
Journal:  J Clin Monit Comput       Date:  2022-08-06       Impact factor: 1.977

7.  Perioperative anesthetic documentation: Adherence to current Australian guidelines.

Authors:  Islam Elhalawani; Simon Jenkins; Nicole Newman
Journal:  J Anaesthesiol Clin Pharmacol       Date:  2013-04
  7 in total

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