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3025 Individual Anesthesia Provider Performance Assessment
OBJECTIVES/SPECIFIC AIMS: We developed a multilevel hierarchical statistical model which describes the association of prophylactic interventions to patient PONV risk, and provides an intuitive summary for anesthesiologists to understand how well they are adhering to PONV guidelines. METHODS/STUDY PO...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6799210/ http://dx.doi.org/10.1017/cts.2019.334 |
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author | Maman, Stephan Andreae, Michael |
author_facet | Maman, Stephan Andreae, Michael |
author_sort | Maman, Stephan |
collection | PubMed |
description | OBJECTIVES/SPECIFIC AIMS: We developed a multilevel hierarchical statistical model which describes the association of prophylactic interventions to patient PONV risk, and provides an intuitive summary for anesthesiologists to understand how well they are adhering to PONV guidelines. METHODS/STUDY POPULATION: Accepted PONV risk factors as well as preventative interventions to reduce the PONV risk, (e.g. total intravenous anesthesia or pharmacological prophylaxis) are retrieved from the electronic medical record (EMR). Risk is regressed against interventions. Fig 1, Panel A visualizes adherence for an individual provider by plotting anesthesia cases, with PONV risk in the x-axis and the number of interventions in the y-axis. Fig 1, Panel B shows a “Jitterplot”, jittering individual cases, which would otherwise plot onto the same coordinates (Panel A). The distribution of the number of interventions in each risk category is better summarized in Fig 1 Panel C by overlaying a violin plot onto the “Jitterplot”. Finally, a fitted regression line provides a summary measure for the individual provider’s risk-adjusted utilization of PONV prophylaxis in Fig 1, Panel D. The model can control for confounders and interactions, such as patient or procedure characteristics, such as supervision by attending physicians, institutional culture, and surgical procedure. RESULTS/ANTICIPATED RESULTS: Fig. 2, Panel A demonstrates good adherence. The provider responded to increased risk with additional interventions leading to a steep regression line. Less discriminate administration of prophylaxis is shown in Fig 2, Panel B. The graphical representation of our proposed measure of individual provider performance is intuitive, allowing us to compare adherence of two distinct groups of providers (light lines) and institutional averages (dark lines) as shown in Fig 2, Panel C. Controlling for known risk factors and potential confounders renders the assessment irrepudiable. The rigorous statistical approach allows for multi-level modeling and comparative effectiveness research, realistically evaluating process changes and interventions like CDS in the hierarchical structure of contemporary healthcare delivery. DISCUSSION/SIGNIFICANCE OF IMPACT: The strength of our novel measure of individual provider performance is its generalizability to other care settings, as well as the intuitive graphical representation of risk-adjusted individual performance. However, accuracy, precision and validity, sensitivity to system perturbations (like the implementation of CDS), and acceptance among providers remain to be evaluated. Fig 1. Risk-Adjusted Utilization of Antiemetic Prophylaxis Fig 2. Comparing Performance between Provider Groups |
format | Online Article Text |
id | pubmed-6799210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-67992102019-10-28 3025 Individual Anesthesia Provider Performance Assessment Maman, Stephan Andreae, Michael J Clin Transl Sci Translational Science, Policy, & Health Outcomes Science OBJECTIVES/SPECIFIC AIMS: We developed a multilevel hierarchical statistical model which describes the association of prophylactic interventions to patient PONV risk, and provides an intuitive summary for anesthesiologists to understand how well they are adhering to PONV guidelines. METHODS/STUDY POPULATION: Accepted PONV risk factors as well as preventative interventions to reduce the PONV risk, (e.g. total intravenous anesthesia or pharmacological prophylaxis) are retrieved from the electronic medical record (EMR). Risk is regressed against interventions. Fig 1, Panel A visualizes adherence for an individual provider by plotting anesthesia cases, with PONV risk in the x-axis and the number of interventions in the y-axis. Fig 1, Panel B shows a “Jitterplot”, jittering individual cases, which would otherwise plot onto the same coordinates (Panel A). The distribution of the number of interventions in each risk category is better summarized in Fig 1 Panel C by overlaying a violin plot onto the “Jitterplot”. Finally, a fitted regression line provides a summary measure for the individual provider’s risk-adjusted utilization of PONV prophylaxis in Fig 1, Panel D. The model can control for confounders and interactions, such as patient or procedure characteristics, such as supervision by attending physicians, institutional culture, and surgical procedure. RESULTS/ANTICIPATED RESULTS: Fig. 2, Panel A demonstrates good adherence. The provider responded to increased risk with additional interventions leading to a steep regression line. Less discriminate administration of prophylaxis is shown in Fig 2, Panel B. The graphical representation of our proposed measure of individual provider performance is intuitive, allowing us to compare adherence of two distinct groups of providers (light lines) and institutional averages (dark lines) as shown in Fig 2, Panel C. Controlling for known risk factors and potential confounders renders the assessment irrepudiable. The rigorous statistical approach allows for multi-level modeling and comparative effectiveness research, realistically evaluating process changes and interventions like CDS in the hierarchical structure of contemporary healthcare delivery. DISCUSSION/SIGNIFICANCE OF IMPACT: The strength of our novel measure of individual provider performance is its generalizability to other care settings, as well as the intuitive graphical representation of risk-adjusted individual performance. However, accuracy, precision and validity, sensitivity to system perturbations (like the implementation of CDS), and acceptance among providers remain to be evaluated. Fig 1. Risk-Adjusted Utilization of Antiemetic Prophylaxis Fig 2. Comparing Performance between Provider Groups Cambridge University Press 2019-03-27 /pmc/articles/PMC6799210/ http://dx.doi.org/10.1017/cts.2019.334 Text en © The Association for Clinical and Translational Science 2019 http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work. |
spellingShingle | Translational Science, Policy, & Health Outcomes Science Maman, Stephan Andreae, Michael 3025 Individual Anesthesia Provider Performance Assessment |
title | 3025 Individual Anesthesia Provider Performance Assessment |
title_full | 3025 Individual Anesthesia Provider Performance Assessment |
title_fullStr | 3025 Individual Anesthesia Provider Performance Assessment |
title_full_unstemmed | 3025 Individual Anesthesia Provider Performance Assessment |
title_short | 3025 Individual Anesthesia Provider Performance Assessment |
title_sort | 3025 individual anesthesia provider performance assessment |
topic | Translational Science, Policy, & Health Outcomes Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6799210/ http://dx.doi.org/10.1017/cts.2019.334 |
work_keys_str_mv | AT mamanstephan 3025individualanesthesiaproviderperformanceassessment AT andreaemichael 3025individualanesthesiaproviderperformanceassessment |