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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 - 4 of 4 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.
Rosen IEW, Shiekh RM, Mchome B, et al. Acta Obstet Gynecol Scand. 2021;100:704-714.
Improving maternal safety is an ongoing patient safety priority. This systematic review concluded that maternal near miss events are negatively associated with various aspects of quality of life. Women exposed to maternal near miss events were more likely to have overall lower quality of life, poorer mental and social health, and suffer negative economic consequences.
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.