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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 - 5 of 5 Results
Hailu EM, Maddali SR, Snowden JM, et al. Health Place. 2022;78:102923.
Racial and ethnic health disparities are receiving increased attention, and yet structural racism continues to negatively impact communities of color. This review identified only six papers studying the impact of structural racism on severe maternal morbidity (SMM). Despite heterogeneity in measures and outcomes, the studies all demonstrated a link between structural racism and SMM; additional research is required.
Curated Libraries
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.
Atallah F, Hamm RF, Davidson CM, et al. Am J Obstet Gynecol. 2022;227:b2-b10.
The reduction of cognitive bias is generating increased interest as a diagnostic error reduction strategy. This statement introduces the concept of cognitive bias and discusses methods to manage the presence of bias in obstetrics such as debiasing training and teamwork.
Navathe AS, Liao JM, Yan XS, et al. Health Aff (Millwood). 2022;41:424-433.
Opioid overdose and misuse continues to be a major public health concern with numerous policy- and organization-level approaches to encourage appropriate clinician prescribing. A northern California health system studied the effects of three interventions (individual audit feedback, peer comparison, both combined) as compared to usual care at several emergency department and urgent care sites. Peer comparison and the combined interventions resulted in a significant decrease in pills per prescription.
Park Y, Hu J, Singh M, et al. JAMA Netw Open. 2021;4:e213909.
Machine learning uses data and statistical methods to enhance risk prediction models and it has been promoted as a tool to improve healthcare safety. Using Medicaid claims data for a large cohort of White and Black pregnant females, this study evaluated approaches to reduce bias in clinical prediction algorithms for postpartum depression and mental health service utilization. The researchers found that a reweighing method in machine learning models was associated with a greater reduction in bias than excluding race from the prediction models. The authors suggest further examination of potentially biased data informing clinical prediction models and consideration of other methods to mitigate bias.