M L S Driessen1, E W van Zwet2, L M Sturms3, M A C de Jongh4, L P H Leenen5. 1. Dutch Network for Emergency Care (LNAZ), Newtonlaan 115, 3584 BH, Utrecht, The Netherlands. mls.driessen@lnaz.nl. 2. Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands. 3. Dutch Network for Emergency Care (LNAZ), Newtonlaan 115, 3584 BH, Utrecht, The Netherlands. 4. Brabant Trauma Registry, Network Emergency Care Brabant, Tilburg, The Netherlands. 5. Department of Surgery, University Medical Centre Utrecht, Utrecht, The Netherlands.
Abstract
BACKGROUND: Using patient outcomes to monitor medical centre performance has become an essential part of modern health care. However, classic league tables generally inflict stigmatization on centres rated as "poor performers", which has a negative effect on public trust and professional morale. In the present study, we aim to illustrate that funnel plots, including trends over time, can be used as a method to control the quality of data and to monitor and assure the quality of trauma care. Moreover, we aimed to present a set of regulations on how to interpret and act on underperformance or overperformance trends presented in funnel plots. METHODS: A retrospective observational cohort study was performed using the Dutch National Trauma Registry (DNTR). Two separate datasets were created to assess the effects of healthy and multiple imputations to cope with missing values. Funnel plots displaying the performance of all trauma-receiving hospitals in 2020 were generated, and in-hospital mortality was used as the main indicator of centre performance. Indirect standardization was used to correct for differences in the types of cases. Comet plots were generated displaying the performance trends of two level-I trauma centres since 2017 and 2018. RESULTS: Funnel plots based on data using healthy imputation for missing values can highlight centres lacking good data quality. A comet plot illustrates the performance trend over multiple years, which is more indicative of a centre's performance compared to a single measurement. Trends analysis offers the opportunity to closely monitor an individual centres' performance and direct evaluation of initiated improvement strategies. CONCLUSION: This study describes the use of funnel and comet plots as a method to monitor and assure high-quality data and to evaluate trauma centre performance over multiple years. Moreover, this is the first study to provide a regulatory blueprint on how to interpret and act on the under- or overperformance of trauma centres. Further evaluations are needed to assess its functionality. LEVEL OF EVIDENCE: Retrospective study, level III.
BACKGROUND: Using patient outcomes to monitor medical centre performance has become an essential part of modern health care. However, classic league tables generally inflict stigmatization on centres rated as "poor performers", which has a negative effect on public trust and professional morale. In the present study, we aim to illustrate that funnel plots, including trends over time, can be used as a method to control the quality of data and to monitor and assure the quality of trauma care. Moreover, we aimed to present a set of regulations on how to interpret and act on underperformance or overperformance trends presented in funnel plots. METHODS: A retrospective observational cohort study was performed using the Dutch National Trauma Registry (DNTR). Two separate datasets were created to assess the effects of healthy and multiple imputations to cope with missing values. Funnel plots displaying the performance of all trauma-receiving hospitals in 2020 were generated, and in-hospital mortality was used as the main indicator of centre performance. Indirect standardization was used to correct for differences in the types of cases. Comet plots were generated displaying the performance trends of two level-I trauma centres since 2017 and 2018. RESULTS: Funnel plots based on data using healthy imputation for missing values can highlight centres lacking good data quality. A comet plot illustrates the performance trend over multiple years, which is more indicative of a centre's performance compared to a single measurement. Trends analysis offers the opportunity to closely monitor an individual centres' performance and direct evaluation of initiated improvement strategies. CONCLUSION: This study describes the use of funnel and comet plots as a method to monitor and assure high-quality data and to evaluate trauma centre performance over multiple years. Moreover, this is the first study to provide a regulatory blueprint on how to interpret and act on the under- or overperformance of trauma centres. Further evaluations are needed to assess its functionality. LEVEL OF EVIDENCE: Retrospective study, level III.
Authors: Mitchell L S Driessen; Mariska A C de Jongh; Leontien M Sturms; Frank W Bloemers; Henk Jan Ten Duis; Michael J R Edwards; Dennis den Hartog; Peter A Leenhouts; Martijn Poeze; Inger B Schipper; Richard W Spanjersberg; Klaus W Wendt; Ralph J de Wit; Stefan W A M van Zutphen; Luke P H Leenen Journal: Eur J Trauma Emerg Surg Date: 2022-04-21 Impact factor: 2.374