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.
Donovan AL, Aaronson EL, Black L, et al. Jt Comm J Qual Patient Saf. 2021;47:23-30.
Patient suicide, attempted suicide, or self-harm are considered ‘never events.’ This article describes the development and implementation of a safety protocol for emergency department (ED) patients at risk for self-harm, including the creation of safe bathrooms and increasing the number of trained observers in the ED. Implementation of the protocol was correlated with lower rates of self-harm.
This review expands upon previous work evaluating implementation strategies for high-reliability organizations. Review findings indicate that health care system adoption of high-reliability principles is associated with improved outcomes, but the level of evidence is low. Future research should include concurrent control groups to minimize bias and focus on whether certain high-reliability frameworks, metrics, or intervention components lead to greater improvements.
Choudhury A, Asan O. JMIR Med Inform. 2020;8:e18599.
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.
Härkänen M, Turunen H, Vehviläinen-Julkunen K. J Patient Saf. 2020;16.
This study compared medication errors detected using incident reports, the Global Trigger Tool method, and direct observations of patient records. Incident reports and the Global Trigger Tool more commonly identified medication errors likely to cause harm. Omission errors were commonly identified by all three methods, but identification of other errors varied. For example, incident reports most commonly identified wrong dose and wrong time errors. The contributing factors also varied by method, but in general, communication issues and human factors were the most common contributors.
Fortman E, Hettinger AZ, Howe JL, et al. J Am Med Info Assoc. 2020;27:924-928.
Physicians from different health systems using two computerized provider order entry (CPOE) systems participated in simulated patient scenarios using eye movement recordings to determine whether the physician looked at patient-identifying information when placing orders. The rate of patient identification overall was 62%, but the rate varied by CPOE system. An expert panel identified three potential reasons for this variation – visual clutter and information density, the number of charts open at any given time, and the importance placed on patient identification verification by institutions.
This commentary explores two scientific cultures in modern medicine. A ‘traditional culture’ leaves error control up to individuals and groups of healthcare practitioners; the author describes how this culture leads to an overconfidence among practitioners about personal abilities to reduce errors. In contrast, a ‘modern scientific culture’ considers errors as inevitable and pervasive throughout medicine and beyond individuals or groups to control. The author describes the competing priorities of these cultures, and suggests that error control efforts in medicine will be more successful if there is a paradigm shift towards a more ‘modern’ attitude.
Russo E, Sittig DF, Murphy DR, et al. Healthc (Amst). 2016;4:285-290.
Using a case study on missed and delayed follow-up of test results, this commentary explores challenges and opportunities that data from electronic health records present for patient safety research. Key barriers to utilizing electronic health record data to inform improvement work include restricted access to data, difficulty interpreting data, and workforce issues.
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