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Simulation as a New Tool to Establish Benchmark Outcome Measures in Obstetrics
Please always quote using this URN: urn:nbn:de:bvb:20-opus-151646
- Background There are not enough clinical data from rare critical events to calculate statistics to decide if the management of actual events might be below what could reasonably be expected (i.e. was an outlier). Objectives In this project we used simulation to describe the distribution of management times as an approach to decide if the management of a simulated obstetrical crisis scenario could be considered an outlier. Design Twelve obstetrical teams managed 4 scenarios that were previously developed. Relevant outcome variablesBackground There are not enough clinical data from rare critical events to calculate statistics to decide if the management of actual events might be below what could reasonably be expected (i.e. was an outlier). Objectives In this project we used simulation to describe the distribution of management times as an approach to decide if the management of a simulated obstetrical crisis scenario could be considered an outlier. Design Twelve obstetrical teams managed 4 scenarios that were previously developed. Relevant outcome variables were defined by expert consensus. The distribution of the response times from the teams who performed the respective intervention was graphically displayed and median and quartiles calculated using rank order statistics. Results Only 7 of the 12 teams performed chest compressions during the arrest following the 'cannot intubate/cannot ventilate' scenario. All other outcome measures were performed by at least 11 of the 12 teams. Calculation of medians and quartiles with 95% CI was possible for all outcomes. Confidence intervals, given the small sample size, were large. Conclusion We demonstrated the use of simulation to calculate quantiles for management times of critical event. This approach could assist in deciding if a given performance could be considered normal and also point to aspects of care that seem to pose particular challenges as evidenced by a large number of teams not performing the expected maneuver. However sufficiently large sample sizes (i.e. from a national data base) will be required to calculate acceptable confidence intervals and to establish actual tolerance limits.…
Author: | Matt M. Kurrek, Pamela Morgan, Steven Howard, Peter Kranke, Aaron Calhoun, Joshua Hui, Alex Kiss |
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URN: | urn:nbn:de:bvb:20-opus-151646 |
Document Type: | Journal article |
Faculties: | Medizinische Fakultät / Klinik und Poliklinik für Anästhesiologie (ab 2004) |
Language: | English |
Parent Title (English): | PLoS ONE |
Year of Completion: | 2015 |
Volume: | 10 |
Issue: | 6 |
Pagenumber: | e0131064 |
Source: | PLoS ONE 10(6): e0131064 (2015). DOI: 10.1371/journal.pone.0131064 |
DOI: | https://doi.org/10.1371/journal.pone.0131064 |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 617 Chirurgie und verwandte medizinische Fachrichtungen |
Tag: | anesthesiologists; performance |
Release Date: | 2017/10/27 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |