RT Journal Article
SR Electronic
T1 Evaluating the influence of data collector training for predictive risk of death models: an observational study
JF BMJ Quality & Safety
JO BMJ Qual Saf
FD BMJ Publishing Group Ltd
SP bmjqs-2020-010965
DO 10.1136/bmjqs-2020-010965
A1 Arvind Rajamani
A1 Stephen Huang
A1 Ashwin Subramaniam
A1 Michele Thomson
A1 Jinghang Luo
A1 Andrew Simpson
A1 Anthony McLean
A1 Anders Aneman
A1 Thodur Vinodh Madapusi
A1 Ramanathan Lakshmanan
A1 Gordon Flynn
A1 Latesh Poojara
A1 Jonathan Gatward
A1 Raju Pusapati
A1 Adam Howard
A1 Debbie Odlum
YR 2020
UL http://qualitysafety.bmj.com/content/early/2020/04/15/bmjqs-2020-010965.abstract
AB Background Severity-of-illness scoring systems are widely used for quality assurance and research. Although validated by trained data collectors, there is little data on the accuracy of real-world data collection practices.Objective To evaluate the influence of formal data collection training on the accuracy of scoring system data in intensive care units (ICUs).Study design and methods Quality assurance audit conducted using survey methodology principles. Between June and December 2018, an electronic document with details of three fictitious ICU patients was emailed to staff from 19 Australian ICUs who voluntarily submitted data on a web-based data entry form. Their entries were used to generate severity-of-illness scores and risks of death (RoDs) for four scoring systems. The primary outcome was the variation of severity-of-illness scores and RoDs from a reference standard.Results 50/83 staff (60.3%) submitted data. Using Bayesian multilevel analysis, severity-of-illness scores and RoDs were found to be significantly higher for untrained staff. The mean (95% high-density interval) overestimation in RoD due to training effect for patients 1, 2 and 3, respectively, were 0.24 (0.16, 0.31), 0.19 (0.09, 0.29) and 0.24 (0.1, 0.38) respectively (Bayesian factor >300, decisive evidence). Both groups (trained and untrained) had wide coefficients of variation up to 38.1%, indicating wide variability. Untrained staff made more errors in interpreting scoring system definitions.Interpretation In a fictitious patient dataset, data collection staff without formal training significantly overestimated the severity-of-illness scores and RoDs compared with trained staff. Both groups exhibited wide variability. Strategies to improve practice may include providing adequate training for all data collection staff, refresher training for previously trained staff and auditing the raw data submitted by individual ICUs. The results of this simulated study need revalidation on real patients.