Describing adverse events in medical inpatients using the Global Trigger Tool

Abstract

AIMS:The purpose of the study was to describe the type, prevalence, severity and preventability of adverse events (AEs) that affected hospitalised medical patients. We used the previously developed and validated Global Trigger Tool from the Institute for Healthcare Improvement. METHODS: Using an adapted version of the Global Trigger Tool, we conducted a retrospective chart review of adult patients hospitalised in five medical wards at a university hospital in Switzerland. We reviewed a random sample of 20 patients' charts for a total study period of 12 months (September 2016 to August 2017). Two trained nurses searched independently for triggers and possible AEs. All AEs were further validated by a senior physician. The number of triggers and AEs detected, as well as the severity and preventability of each, was assessed and analysed using descriptive statistics.RESULTS: From a sample of 240 patient charts, we identified 1371 triggers and 336 AEs in 144 (60%) inpatients. This translates to an AE rate of 95.7 AEs per 1000 patient days. Most AEs (86.1%) caused temporary harm to the patient and required an intervention and/or prolonged hospitalisation. The estimated preventability of the in-hospital AEs was 29%. Healthcare-associated infections (25.8%) and neurological reactions (22.9%) were the most frequent AE types. CONCLUSION:We found that about two thirds of patients suffered from AEs with harm during hospitalisation. It is common knowledge that AEs occur in hospitals and that they have potentially harmful consequences for patients, as well as a strong economic impact. However, to adequately prioritise patient safety interventions, it is essential to explore the nature, prevalence, severity and preventability of AEs. This is not only beneficial for the patients, but also cost effective in terms of shorter hospital stays.

Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study

“…This retrospective diagnostic accuracy study consisted of 2 parts: the first, for algorithm development and the second, for validation. For the development of the electronic health record fall detection algorithm, we used falls identified through the Global Trigger Tool in a previous study [ 22 ] along with incident reports for comparison. To validate the algorithm, we collected additional data to compare the algorithm against falls identified through the manual chart review of electronic health records (“Global Trigger Tool for falls only”) and patient-reported falls based on the LPZ/ANQ measure.…”

“…The algorithm was developed by one of the first authors (BS), using data from a previous Global Trigger Tool study [ 22 ]. That study’s [ 22 ] sample consisted of patients admitted to the Internal Medicine department between September 1, 2016 and August 31, 2017. Further inclusion criteria were (1) adult patients (aged ≥18 years), (2) closed and completed patient record, and (3) inpatients with a length of stay of at least 24 hours.…”

“…Because this was a diagnostic accuracy study, no formal power analysis for sample size was conducted. However, for the Global Trigger Tool study [ 22 ] and an expected overall adverse event rate of 12.3% as detected by Soop et al [ 23 ], a sample size of 240 gives a 95% confidence interval of 8.9%-16.7%. Electronic patient records (n=30) from hospitalized patients were randomly selected each month and checked for eligibility, including their general consent, by one reviewer.…”

Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study (Preprint)

“…The algorithm was developed by one of the first authors (BS), using data from a previous Global Trigger Tool study [22]. That study's [22] sample consisted of patients admitted to the Internal Medicine department between September 1, 2016 and August 31, 2017.…”

Occurrence of No-Harm Incidents and Adverse Events in Hospitalized Patients with Ischemic Stroke or TIA: A Cohort Study Using Trigger Tool Methodology

“…The GTT consists of six trigger modules (e.g., care module, medication module). In some studies, all of these modules are used [ 16 , 17 ], while most researchers use only those modules relevant to their clinical setting [ 18 , 19 ]. However, other users of the GTT methodology design additional modules (e.g., oncology module [ 20 , 21 ]) or create modified versions, tailored specifically to their clinical context [ 8 , 15 , 22 ].…”