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Patient exposure to allergens healthcare settings, such as latex or certain medications, can lead to adverse outcomes. Based on data from an incident reporting system, researchers in this study developed a system for classifying allergy-related safety events. Classification categories include: (1) incomplete or inaccurate EHR documentation, (2) human factors, such as overridden allergy alerts, (3) alert limitation or malfunction, (4) data exchange and interoperability failures, and (5) issues with EHR system default options. This classification system can be used to support improvements at the individual, team, and systems levels. 

Diagnostic errors can be identified by measuring concordance of initial and final diagnosis, for instance admission and discharge diagnoses. In this study, researchers developed and tested an automated trigger system to determine concordance between pairs of diagnoses. In comparison to the reference standard (concordance determined by experts) the automated system performance was excellent.

Trigger tools and incident reporting systems are two commonly used methods for detecting adverse events.  This retrospective study compared the performance of an electronic trigger tool plus manual screening versus existing incident reporting systems for identifying probable diagnostic errors among children with unplanned admissions following a prior emergency department (ED) visit. Of the diagnostic errors identified by the trigger tool and substantiated by manual review, less than 10% were identified through existing incident reporting systems.

Computerized decision support alerts for drug-drug interactions are commonly overridden by clinicians. This study examined fifteen well-known drug-drug interactions and identified risk factors that could reduce risk in the majority of interactions (e.g., medication order timing, medication dose, and patient factors).

Trigger tools are one method of retrospectively detecting adverse events. In this study, researchers used data from 31 Spanish hospitals to validate a trigger tool in general and gastrointestinal surgery departments. Of 40 triggers, 12 were identified for optimizing predictive power of the trigger tool, including broad spectrum antibiotherapy, unscheduled postoperative radiology, and reintervention.

The near ubiquitous use of electronic health records has increased interest in using artificial intelligence (AI) to detect errors, including preventable adverse drug events (ADEs). This scoping review identified 78 studies of AI conducted since 1998, and identified promising areas where AI could be used to predict (e.g., therapeutic response) or detect (e.g., medication prescribing errors) patient harm. 

The Harvard Medical Practice Study (HMPS) and the Institute for Healthcare Improvement Global Trigger Tool (GTT) are two of the most widely used trigger tools to identify adverse events and prompt medical record review. Fifty studies using either trigger tool to prompt a medical record review (MRR) for potentially preventable adverse events were included in this literature review. MRR reveals more adverse events than other methods; however, research is still lacking or is of moderate quality. 

Many medications and medical devices can result in preventable harm in pediatric patients. This article describes one hospital’s efforts to implement explicit, structured processes and huddles to increase situational awareness regarding high-risk therapies among the care team and family members. After implementation, the percentage of electronic health record (EHR) alerts correctly describing high-risk therapies increased from 11% to 96%.

Based on the Safer Dx and SPADE frameworks, researchers applied a symptom-disease pair-based electronic trigger (e-trigger) to identify patients hospitalized for stroke who had been previously discharged from the emergency department with a diagnosis of headache or dizziness in the preceding 30 days. Analyses show that the e-trigger identified missed diagnoses of stroke with a modest positive predictive value.

This single-site retrospective cohort found that vulnerable populations (defined by race/ethnicity, insurance status, and limited English proficiency) were generally not at increased risk of patient safety events. However, stratified analyses comparing events identified via automated versus voluntary incident reporting systems found voluntary systems may undercount events in some racial/ethnic populations.

This article describes the implementation of a new system for identifying, communicating, and resolving safety reports pertaining to COVID-19 in one academic tertiary care center through the use of electronic safety reporting systems and daily huddles.

This study assessed the performance of an automated emergency department (ED) trigger tool designed to identify a more efficient sample of adverse event cases for chart review. Beginning with a set of 97 candidate triggers, researchers identified those triggers associated with adverse events and arrived at a narrowed set of 30 triggers, eliminating almost half of the population of records eligible for manual review. This computerized query may eliminate the need for manual screening for triggers.  

This systematic review explored how artificial intelligence (AI) based on machine learning algorithms and natural language processing is used to address and report patient safety outcomes. The review suggests that AI-enabled decision support systems can improve error detection, patient stratification, and drug management, but that additional evidence is needed to understand how well AI can predict safety outcomes.  

Efforts to track hospital quality and safety result in data and incentive complexities that detract from effective leadership decision making to improve safety. This article examines the juxtaposition of three emerging technologies to capture safety metrics and the pressures they bring to bear on effective management of adverse events and patient compensation schemes. The author suggests roles for leadership and Medicare to drive improvements.   

Use of data can improve the response of clinicians to patient concerns and deterioration. This article discusses how data surveillance can provide insights at the point of care to inform action and improve safety.   

Building upon prior research developing trigger tools for identifying preventable errors in dentistry, this study reviewed 1,885 electronic health records (EHR) across four dental practices and found that 16% contained an adverse event. The most common events were pain (27.5%), hard tissue (14.8%) or soft tissue injuries (14.8%) and nerve injuries (13.3%). An EHR-based trigger tool can be an effective approach to identifying safety incidents and measuring the quality of care.

Underreporting of adverse drug reactions (ADRs) is an international patient safety problem. This systematic review of studies assessed how various strategies designed to improve ADR reporting impacted ADR rates. While all strategies increased ADR reporting, particularly those using electronic reporting tools, the quality of the studies was generally low. The authors expressed the need for higher quality studies to focus on how electronic methods might improve ADR reporting.