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Braun EJ, Singh S, Penlesky AC, et al. BMJ Qual Saf. 2022;Epub Apr 15.
Early warning systems (EWS) use patient data from the electronic health record to alert clinicians to potential patient deterioration. Twelve months after a new EWS was implemented in one hospital, nurses were interviewed to gather their perspectives on the program experience, utility, and implementation. Six themes emerged: timeliness, lack of accuracy, workflow interruptions, actionability of alerts, underappreciation of core nursing skills, and opportunity cost.
Bennion J, Mansell SK. Br J Hosp Med (Lond). 2021;82:1-8.
Many strategies have been developed to improve recognition of, and response, to clinically deteriorating patients. This review found that simulation-based educational strategies was the most effective educational method for training staff to recognize unwell patients. However, the quality of evidence was low and additional research into simulation-based education is needed.
Bates DW, Levine DM, Syrowatka A, et al. NPJ Digit Med. 2021;4:54.
Artificial Intelligence (AI) is used across healthcare settings to address a variety of patient safety targets. This scoping review evaluated the potential of AI to improve patient safety across eight domains including adverse drug events, decompensation, and diagnostic errors. Both traditional (e.g. EHR) and novel (e.g. wearables) data sources can be used to develop models and interventions to improve patient safety.
Dykes PC, Lowenthal G, Faris A, et al. J Patient Saf. 2021;17:56-62.
Failure to rescue – the lack of adequate response to patient deterioration – has been associated with adverse patient outcomes, particularly in acute care settings. This article describes two health systems’ efforts to implement in-hospital Clinical Monitoring System Technology (CMST) which positively impacted failure-to-rescue events. The authors identified barriers and facilitators to CMST use, which informed the development of an implementation toolkit addressing readiness, implementation, patient/family introduction, champions, and troubleshooting. 
Lin DM, Peden CJ, Langness SM, et al. Anesth Analg. 2020;131:e155-1159.
The anesthesia community has been a leader in patient safety innovation for over four decades. This conference summary highlights presented content related to the conference theme of “preventing, detecting, and mitigating clinical deterioration in the perioperative period.” The results of a human-centered design analysis exploring tactics to reduce failure to rescue were summarized.
Cho K-J, Kwon O, Kwon J-myoung, et al. Crit Care Med. 2020;48:e285-e289.
This study compared an artificial intelligence (AI)-based early warning system using machine learning with conventional trigger methods for predicting deterioration among hospitalized patients, defined as in-hospital cardiac arrest resulting in ICU admissions. The AI system accurately predicted deterioration and was more accurate than conventional methods, demonstrating its potential effectiveness in EHR-based rapid response systems.
Ross C. STAT. May 13, 2019.
Nuisance alarms, interruptions, and insufficient staff availability can hinder effective monitoring and response to acute patient deterioration. This news article reports on how hospital logistics centers are working toward utilizing artificial intelligence to improve clinician response to alarms by proactively identifying hospitalized patients at the highest risk for heart failure to trigger emergency response teams when their condition rapidly declines.
Dasani SS, Simmons KD, Wirtalla CJ, et al. J Surg Educ. 2019;76:1319-1328.
Surgical proficiency gained from performing a higher volume of certain procedures is associated with fewer errors. This study used data from the National Surgical Quality Improvement Program to examine uncommon procedures and their surgical complication rates, with and without trainee participation. As expected, uncommon operations entailed significant rates of morbidity and mortality. Resident involvement was associated with higher likelihood that a patient in distress would be successfully resuscitated but was also associated with a longer operative time. The authors suggest that simulation training for uncommon procedures for residents may improve outcomes. A PSNet perspective reflected on patient safety in surgery.
Bach TA, Berglund L-M, Turk E. BMJ Open Qual. 2018;7:e000202.
Alarm fatigue limits the utility of physiologic monitoring devices intended to keep hospitalized patients safe. The authors conducted a literature review and interviewed experts to identify best practices to optimize device alarms. They present a step-by-step guide to alarm improvement that incorporates a human factors engineering approach.

Newcastle Upon Tyne, UK: Care Quality Commission; December 2016. CQC-356-122016.

Patients and families can contribute to improvement when they are treated with respect and openness. This report explored the extent to which those characteristics are present in National Health Service (NHS) investigations regarding patient deaths and found them to be lacking, particularly in cases involving patients with mental health conditions or learning disabilities. The authors recommend a framework to guide behaviors consistently across the NHS to improve the timeliness and quality of investigations and ensure system-level learning.
Sammon JD, Pucheril D, Abdollah F, et al. BJU Int. 2015;115:666-674.
This analysis of national hospital data found that while odds of overall mortality from urological surgery decreased, failure to rescue increased over time, with lower-income, older aged, and ethnic minority patients as predictors for higher risk. This work emphasizes the need to examine disparities in patient safety outcomes.