Derivation and Validation of a Record Linkage Algorithm between EMS and the Emergency Department

Abstract

Conflicts of interest: SH receives grant funding by Philips Healthcare in the areas of heart failure risk stratification, imaging analysis, and big data. The submitted manuscript has no relationship to the grants. LAN has stock in Forerun Systems, an emergency department information system. The submitted manuscript did not use this system. SH and LAN conceived and designed the study. CR and AT collected the data. AT, YH, DAS, and SH performed the analysis. CR, AT, and SH drafted the manuscript, and all authors contributed substantially to its revision. SH takes responsibility for the paper as a whole. AbstractBackground: Linking EMS electronic patient care reports (ePCRs) to ED records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR -ED record linkage have had limited success.Objective: To derive and validate an automated record linkage algorithm between EMS ePCR's and ED records using supervised machine learning.Methods: All consecutive ePCR's from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCR's to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number (SSN), and date of birth (DOB) were extracted. Data was randomly split into 80%/20% training and test data sets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5k fold cross-validation, using label kfold, L2 regularization, and class re-weighting.Results: A total of 14,032 ePCRs were included in the study. Inter-rater reliability between the primary and secondary reviewer had a Kappa of 0.9. The algorithm had a sensitivity of 99.4%, a PPV of 99.9% and AUC of 0.99 in both the training and test sets. DOB match had the highest odd ratio of 16.9, followed by last name match (10.6). SSN match had an odds ratio of 3.8.