| Title |
The use of a clinical database in an anesthesia unit: focus on its limits
|
|---|---|
| Published in |
Journal of Clinical Monitoring and Computing, May 2014
|
| DOI | 10.1007/s10877-014-9581-7 |
| Pubmed ID | |
| Authors |
Grégoire Weil, Cyrus Motamed, Alexandre Eghiaian, Marie Laurence Guye, Jean Louis Bourgain |
| Abstract |
Anesthesia information management system (AIMS) can be used a part of quality assurance program to improve patient care, however erroneous or missing data entries may lead to misinterpretation. This study assesses the accuracy of information extracted for six consecutive years from a database linked to an automatic anesthesia record-keeping system. An observational study was conducted on a database linked AIMS system. The database was filled in real time during surgical/anesthesia procedure and in the post-anesthesia care unit. The following items: name of the anesthetist, duration of anesthesia, duration of monitoring, ventilatory status upon arrival in postoperative care unit, pain scores, nausea and vomiting scores, pain medication (morphine) and anti nausea and vomiting drug consumption (ondansetron) were extracted and analysed in order to determine exhaustivity (percentage of missing data) and accuracy of the database. The analysis covered 55,946 anaesthetic procedures. The rate of missing data was initially high upon installation but decreased over time. It was limited to 5 % after 3 years for items such as start of anesthesia or name of the anesthetist. However exhaustivity/completeness of some other variable, such as nausea and vomiting started as low as 50 % to reach 20 % at 2008. After cross analysing pain and post-operative nausea and vomiting scores with related medication consumption, (morphine and ondansetron) we conclude that missing data was due to omission of a zero score rather than human error. The follow-up of quality assurance program may use data from AIMS provided that missing or erroneous values be mentioned and their impact on calculations accurately analysed. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 19% |
| Other | 3 | 14% |
| Student > Master | 3 | 14% |
| Student > Bachelor | 3 | 14% |
| Lecturer | 2 | 10% |
| Other | 3 | 14% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 43% |
| Engineering | 4 | 19% |
| Computer Science | 1 | 5% |
| Materials Science | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 5 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 November 2015.
All research outputs
#20,251,039
of 22,780,165 outputs
Outputs from Journal of Clinical Monitoring and Computing
#560
of 666 outputs
Outputs of similar age
#192,947
of 227,445 outputs
Outputs of similar age from Journal of Clinical Monitoring and Computing
#4
of 7 outputs
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