The PSNet Collection: All Content

The AHRQ PSNet Collection comprises an extensive selection of resources relevant to the patient safety community. These resources come in a variety of formats, including literature, research, tools, and Web sites. Resources are identified using the National Library of Medicine’s Medline database, various news and content aggregators, and the expertise of the AHRQ PSNet editorial and technical teams.

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Displaying 1 - 20 of 273 Results

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

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Patient Safety Innovation May 31, 2023

Seeking a sustainable process to enhance their hospitals’ response to sepsis, a multidisciplinary team at WellSpan Health oversaw the development and implementation of a system that uses customized electronic health record (EHR) alert settings and a team of remote nurses to help frontline staff identify and respond to patients showing signs of sepsis. When the remote nurses, or Central Alerts Team (CAT), receive an alert, they assess the patient’s information and collaborate with the clinical care team to recommend a response.

Surveillance Monitoring to Improve Patient Safety in Acute Hospital Care Units

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Perspective on Safety April 26, 2023

This piece discusses surveillance monitoring of patients in low-acuity units of the hospital to prevent failure to rescue events, its difference from high-acuity continuous monitoring, and its potential applications in other settings.

In Conversation with Susan McGrath, PhD and George Blike, MD about Surveillance Monitoring

Drs. Susan McGrath and George Blike discuss surveillance monitoring and its challenges and opportunities.

Effect of a Veterans Health Administration mandate to case review patients with opioid prescriptions on mortality among patients with opioid use disorder: a secondary analysis of the STORM randomized control trial.

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Auty SG, Barr KD, Frakt AB, et al. Addiction. 2023;118:870-879.

To combat serious adverse events (SAE) and suicide among veterans with opioid use disorder (OUD), the Veterans Health Administration (VHA) implemented the Stratification Tool for Opioid Risk Mitigation (STORM) in all VHA facilities. Patients identified as high-risk for SAE by STORM received a mandatory case review. This study focuses on high-risk patients with a new OUD diagnosis. Mandatory case review increased the odds of all-cause mortality, but not SAE. Patients whose opioids were discontinued after case review showed even higher odds of mortality.

Technology as a Tool for Improving Patient Safety

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Perspective on Safety March 29, 2023

In the past several decades, technological advances have opened new possibilities for improving patient safety. Using technology to digitize healthcare processes has the potential to increase standardization and efficiency of clinical workflows and to reduce errors and cost across all healthcare settings.¹ However, if technological approaches are designed or implemented poorly, the burden on clinicians can increase. For example, overburdened clinicians can experience alert fatigue and fail to respond to notifications. This can lead to more medical errors.

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.

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Yasrebi-de Kom IAR, Dongelmans DA, de Keizer NF, et al. J Am Med Inform Assoc. 2023;30:978-988.

Prediction models are increasingly used in healthcare to identify potential patient safety events. This systematic review including 25 articles identified several challenges related to electronic health record (EHR)-based prediction models for adverse drug event diagnosis or prognosis, including adherence to reporting standards, use of best practices to develop and validate prediction models, and absence of causal prediction modeling.

Reducing hospital harm: establishing a command centre to foster situational awareness.

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Patient Safety Innovation March 29, 2023

Situational awareness during complex care events, such as clinical deterioration, medication administration, or the admission process, is an important component of teamwork and high reliability.

The accuracy of the Global Trigger Tool is higher for the identification of adverse events of greater harm: a diagnostic test study.

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Moraes SM, Ferrari TCA, Beleigoli A. Int J Qual Health Care. 2023;34:mzad005.

The IHI Global Trigger Tool (GTT) is used to detect adverse events (AE) in hospitalized patients, but studies have shown variability in the types and rates of errors detected. In this study, researchers aimed to determine the accuracy of the GTT through a diagnostic test study. The GTT showed satisfactory sensitivity, specificity, and global accuracy for AE detection, but performed better when minor harm AEs were excluded.

Automated capture of intraoperative adverse events using artificial intelligence: a systematic review and meta-analysis.

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Eppler MB, Sayegh AS, Maas M, et al. J Clin Med. 2023;12:1687.

Real-time use of artificial intelligence in the operating room allows surgeons to avoid or immediately address intraoperative adverse events. This review summarizes 13 articles published since 2010 that report on the use of artificial intelligence to predict intraoperative adverse events. Most studies used video and more than half were intended to detect bleeding.

Near-miss events detected using the emergency department trigger tool.

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Griffey RT, Schneider RM, Todorov AA. J Patient Saf. 2023;19:59-66.

Near-miss incidents present useful learning opportunities but frequently go unreported. This study used a computerized trigger tool to identify near-miss incidents in the emergency department (ED). Results show approximately 23% of ED visits during the 13-month study period included a near-miss incident. This analysis suggests computerized trigger tools can be useful to identify near misses that otherwise go unreported.

Using automated methods to detect safety problems with health information technology: a scoping review.

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Surian D, Wang Y, Coiera E, et al. J Am Med Inform Assoc. 2022;30:382-392.

Health information technology (HIT), such as electronic health records (EHRs) or computerized provider order entry (CPOE) systems, are important approaches to improving safety. This scoping review of 45 articles found that machine learning and statistical modeling are the most commonly used automated, HIT-based methods for early detection of safety threats. Machine learning was often used to detect errors occurring in laboratory test results, prescriptions, and patient records. Statistical modeling was used to detect issues with clinical decision support systems.

A customized triggers program: a children's hospital's experience in improving trigger usability.

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Reinhart RM, Safari-Ferra P, Badh R, et al. Pediatrics. 2023;151:e2022056452.

Trigger tools are widely used for detecting potential adverse events among adult and pediatric inpatients. This article describes the development of a pediatric triggers program that can identify potential adverse events in near real-time to facilitate appropriate preventative measures. The tool includes criteria from the IHI Global Trigger Tool as well as novel triggers (such as pain reassessment time, hospital readmissions, and suspected sepsis). The trigger team created a process for linking triggers to the organizational incident reporting system based on specific criteria (to reduce false-positive reports). The trigger team is continuously developing and refining triggers based on stakeholder input.

A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts.

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Malik MA, Motta-Calderon D, Piniella N, et al. Diagnosis (Berl). 2022;9:446-457.

Structured tools are increasingly used to identify diagnostic errors and related harms using electronic health record data. In this study, researchers compared the performance of two validated tools (Safer Dx and the DEER taxonomy) to identify diagnostic errors among patients with preventable or non-preventable deaths. Findings indicate that diagnostic errors and diagnostic process failures contributing to death were higher in preventable deaths (56%) but were also present in non-preventable deaths (17%).

Curated Libraries

Diagnostic Safety

October 10, 2022

Selected PSNet materials for a general safety audience focusing on improvements in the diagnostic process and the strategies that support them to prevent diagnostic errors from harming patients.

Variation in detected adverse events using trigger tools: a systematic review and meta-analysis.

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Eggenschwiler LC, Rutjes AWS, Musy SN, et al. PLoS ONE. 2022;17:e0273800.

Trigger tools alert patient safety personnel to potential adverse events (AE) which can then be followed up with retrospective chart review. This review sought to understand the variability in adverse event detection in acute care and study characteristics that may explain the variation. Fifty-four studies were included with a wide range of AEs detected per 100 admissions. The authors suggest developing guidelines for studies reporting on AEs identified using trigger tools to decrease study heterogeneity.

Using the Global Trigger Tool in surgical and neurosurgical patients: a feasibility study.

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Brösterhaus M, Hammer A, Gruber R, et al. PLoS ONE. 2022;17:e0272853.

Healthcare organizations use trigger tools to identify potential errors or adverse events in the electronic health record (EHR), measure the frequency of errors, and track safety improvements. Three hospitals in Germany conducted a feasibility study of implementing the Institute for Healthcare Improvement (IHI) Global Trigger Tool (GTT) in two general surgery units and one neurosurgery unit. Twenty-two feasibility criteria were developed (low-, moderate-, problematic-level of challenge) which may help guide successful implementation of the GTT.

Emergency department adverse events detected using the emergency department trigger tool.

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Griffey RT, Schneider RM, Todorov AA. Ann Emerg Med. 2022;80:528-538.

Trigger tools are a novel method of detecting adverse events. This article describes the location, severity, omission/commission, and type of adverse events retrospectively detected using the computerized Emergency Department Trigger Tool (EDTT). Understanding the characteristics of prior adverse events can guide future quality and safety improvement efforts.

The impact of drug error reduction software on preventing harmful adverse drug events in England: a retrospective database study.

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Sutherland A, Gerrard WS, Patel A, et al. BMJ Open Qual. 2022;11:e001708.

Smart pump software can improve medication safety but can also introduce patient safety hazards, such as alert fatigue. In this study, dose error reduction software (DERS) was implemented across two large UK National Health Service (NHS) institutes for one year. Findings indicate that compliance with DERS was 45%, but across one year of implementation, severe harm or death was avoided in up to 110 patients.

Comparison of a voluntary safety reporting system to a global trigger tool for identifying adverse events in an oncology population.

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Samal L, Khasnabish S, Foskett C, et al. J Patient Saf. 2022;18:611-616.

Adverse events can be identified through multiple methods, including trigger tools and voluntary reporting systems. In this comparison study, the Global Trigger Tool identified 79 AE in 88 oncology patients, compared to 21 in the voluntary reporting system; only two AE were identified by both. Results indicate multiple sources should be used to detect AE.

Disparities in adverse event reporting for hospitalized children.

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Halvorson EE, Thurtle DP, Easter A, et al. J Patient Saf. 2022;18:e928-e933.

Voluntary event reporting (VER) systems are required in most hospitals, but their effectiveness is limited if adverse events (AE) go unreported. In this study, researchers compared rates of AE submitted to the VER against those identified using the Global Assessment of Pediatric Patient Safety (GAPPS) trigger tool to identify disparities based on patient characteristics (i.e., weight, race, English proficiency). The GAPPS tool identified 37 AE in patients with limited English proficiency; none of these were reported to the VER system, suggesting a systematic underreporting of AE in this population.

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes.

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Blythe R, Parsons R, White NM, et al. BMJ Qual Saf. 2022;31:725-734.

Early recognition of clinical deterioration in patients is often difficult to detect and often results in poor patient outcomes. This scoping review focused on the delivery and response to deterioration alerts and their impact on patient outcomes. Only four out of 18 studies included in the review reported statistically significant improvements in at least two patient outcomes, Authors suggest that workflow and integration of the early warning system model’s features into the decision-making process may be helpful.

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