The Harvard medical practice study trigger system performance in deceased patients

Abstract

BackgroundTo detect possible threats to quality and safety, multiple systems have been developed. One of them is retrospective chart review. A team of experts scrutinizes medical records, selected by trigger systems, to detect possible adverse events (AEs). The most important AEs and more hints for possible improvement of care appear in deceased patients. Using triggers in a sample of these patients might increase the performance and lower the burden of scrutinizing records without possible preventable AEs. The aim of this study was therefore to determine the performance of the trigger system in a sample of deceased patients and to calculate the specificity and the sensitivity of this trigger system for predicting AEs.MethodsWe performed a study in which the records of deceased patients were screened for triggers by a team of trained nurses. A sample of 100 medical records was randomly selected out of records which had been screened between 2012 and 2015 for the first time, prior to the study in 2016. For the determination of significant differences between the first and second screening, McNemar’s test of symmetry was used. Also, observed agreement, Cohen’s Kappa and prevalence-adjusted and-bias-adjusted-kappa (PABAK) statistics were calculated. This was done for the two trigger rounds on both any trigger present and for every trigger separately.ResultsThe observed agreement for any given trigger was 75% with a Kappa and PABAK of 0.5. For the individual triggers, the observed agreement was on average 90%. The corresponding Kappa was on average 0.42 (range: − 0.03-0.78) and the average PABAK was 0.8 (range: 0.44–0.92). Two adverse events were found in cases without triggers previously. The recalculated specificity and sensitivity for the original population were 58 and 92% respectively.ConclusionsFor the reproducibility of triggers it seems that some perform better than others, but on average this is to our opinion suboptimal. The low specificity implies that many records are selected without AEs. This leads to a high false-positive rate making this labour-intensive record review process costly. Therefore, research for better and more expedient systems is required.

Evaluation of a prospective adverse event reporting system in interventional radiology

“…Automated methods for encoding and classifying electronic clinical narratives in an electronic health record (EHR) and digital capture of progress notes and other clinical results will provide data for trigger tools such as the Harvard Medical Practice Study trigger system to identify AEs from the patient record for review. 13 Discharge coding of patient medical records can also be used to identify AEs. Among patients undergoing inpatient surgical procedures at Department of Veterans Affairs (DVA) medical centres, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.…”