Correction to: Measuring interpersonal firearm violence: natural language processing methods to address limitations in criminal charge data.
Author(s):
DOI: 10.1093/jamia/ocae268
Author(s):
DOI: 10.1093/jamia/ocae268
The Supplemental Nutrition Assistance Program (SNAP) is one of the most successful national programs to reduce poverty and improve health outcomes, but millions of Americans who qualify still do not have access to SNAP, and limited data are available to determine how referrals to the program can be completed successfully.
Author(s): Oliveira, Eliel, Hautala, Matti, Henry, JaWanna, Lakshminarayanan, Vidya, Abrol, Vishal, Granado, Linda, Shah, Shashank, Khurshid, Anjum
DOI: 10.1055/a-2441-5941
To understand barriers to obtaining and using interoperable information at US hospitals.
Author(s): Everson, Jordan, Richwine, Chelsea
DOI: 10.1093/jamia/ocae263
Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.
Author(s): Kim, Mary S, Park, Beomseok, Sippel, Genevieve J, Mun, Aaron H, Yang, Wanzhao, McCarthy, Kathleen H, Fernandez, Emely, Linguraru, Marius George, Sarcevic, Aleksandra, Marsic, Ivan, Burd, Randall S
DOI: 10.1093/jamia/ocae262
Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care.
Author(s): Gonzalez, Xiomara T, Steger-May, Karen, Abraham, Joanna
DOI: 10.1093/jamia/ocae257
The inability of large language models (LLMs) to communicate uncertainty is a significant barrier to their use in medicine. Before LLMs can be integrated into patient care, the field must assess methods to estimate uncertainty in ways that are useful to physician-users.
Author(s): Savage, Thomas, Wang, John, Gallo, Robert, Boukil, Abdessalem, Patel, Vishwesh, Safavi-Naini, Seyed Amir Ahmad, Soroush, Ali, Chen, Jonathan H
DOI: 10.1093/jamia/ocae254
The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities in biomedical and health informatics, aligning with AMIA's mission to transform healthcare. AMIA's DEI initiatives were spurred by member voices responding to police brutality and COVID-19's impact on Black/African American communities.
Author(s): Bright, Tiffani J, Bear Don't Walk Iv, Oliver J, Johnson, Carl Erwin, Petersen, Carolyn, Dykes, Patricia C, Martin, Krista G, Johnson, Kevin B, Walters-Threat, Lois, Craven, Catherine K, Lucero, Robert J, Jackson, Gretchen P, Rizvi, Rubina F
DOI: 10.1093/jamia/ocae258
Missed and delayed cancer diagnoses are common, harmful, and often preventable. We previously validated a digital quality measure (dQM) of emergency presentation (EP) of lung cancer in 2 US health systems. This study aimed to apply the dQM to a new national electronic health record (EHR) database and examine demographic associations.
Author(s): Zimolzak, Andrew J, Khan, Sundas P, Singh, Hardeep, Davila, Jessica A
DOI: 10.1093/jamia/ocae253
This study aimed to describe the current landscape of electronic health record (EHR) training and optimization programs (ETOPs) and their impact on health care workers' (HCWs) experience with the EHR.
Author(s): McEntee, Rachel K, Hitt, Juvena R, Sieja, Amber
DOI: 10.1055/a-2437-0185
Health professions trainees (trainees) are unique as they learn a chosen field while working within electronic health records (EHRs). Efforts to mitigate EHR burden have been described for the experienced health professional (HP), but less is understood for trainees. EHR or documentation burden (EHR burden) affects trainees, although not all trainees use EHRs, and use may differ for experienced HPs.
Author(s): Levy, Deborah R, Rossetti, Sarah C, Brandt, Cynthia A, Melnick, Edward R, Hamilton, Andrew, Rinne, Seppo T, Womack, Dana, Mohan, Vishnu
DOI: 10.1055/a-2434-5177