Correction to: Predicting risk of metastases and recurrence in soft-tissue sarcomas via Radiomics and Formal Methods.
[This corrects the article DOI: 10.1093/jamiaopen/ooad025.].
Author(s):
DOI: 10.1093/jamiaopen/ooad064
[This corrects the article DOI: 10.1093/jamiaopen/ooad025.].
Author(s):
DOI: 10.1093/jamiaopen/ooad064
To test the association between the initial red blood cell distribution width (RDW) value in the emergency department (ED) and hospital admission and, among those admitted, in-hospital mortality.
Author(s): Hong, Woo Suk, Rudas, Akos, Bell, Elijah J, Chiang, Jeffrey N
DOI: 10.1093/jamiaopen/ooad053
The aim of this study was to understand the usability and acceptability of virtual reality (VR) among a racially and ethnically diverse group of patients who experience chronic pain.
Author(s): Dy, Marika, Olazo, Kristan, Lyles, Courtney R, Lisker, Sarah, Weinberg, Jessica, Lee, Christine, Tarver, Michelle E, Saha, Anindita, Kontson, Kimberly, Araojo, Richardae, Brown, Ellenor, Sarkar, Urmimala
DOI: 10.1093/jamiaopen/ooad050
The aim of this study was to understand the influence of clinician encouragement and sociodemographic factors on whether patients access online electronic medical records (EMR).
Author(s): Sisk, Bryan A, Lin, Sunny, Balls-Berry, Joyce Joy E, Servin, Argentina E, Mack, Jennifer W
DOI: 10.1093/jamiaopen/ooad049
Clinical notes are a veritable treasure trove of information on a patient's disease progression, medical history, and treatment plans, yet are locked in secured databases accessible for research only after extensive ethics review. Removing personally identifying and protected health information (PII/PHI) from the records can reduce the need for additional Institutional Review Boards (IRB) reviews. In this project, our goals were to: (1) develop a robust and scalable clinical text [...]
Author(s): Radhakrishnan, Lakshmi, Schenk, Gundolf, Muenzen, Kathleen, Oskotsky, Boris, Ashouri Choshali, Habibeh, Plunkett, Thomas, Israni, Sharat, Butte, Atul J
DOI: 10.1093/jamiaopen/ooad045
Geocoding, the process of converting addresses into precise geographic coordinates, allows researchers and health systems to obtain neighborhood-level estimates of social determinants of health. This information supports opportunities to personalize care and interventions for individual patients based on the environments where they live. We developed an integrated offline geocoding pipeline to streamline the process of obtaining address-based variables, which can be integrated into existing data processing pipelines.
Author(s): Guo, Kevin, McCoy, Allison B, Reese, Thomas J, Wright, Adam, Rosenbloom, Samuel Trent, Liu, Siru, Russo, Elise M, Steitz, Bryan D
DOI: 10.1055/a-2148-6414
Standard ontologies are critical for interoperability and multisite analyses of health data. Nevertheless, mapping concepts to ontologies is often done with generic tools and is labor-intensive. Contextualizing candidate concepts within source data is also done in an ad hoc manner.
Author(s): Xu, Justin, Mazwi, Mjaye, Johnson, Alistair E W
DOI: 10.1093/jamiaopen/ooad046
This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians [...]
Author(s): Reading Turchioe, Meghan, Harkins, Sarah, Desai, Pooja, Kumar, Shiveen, Kim, Jessica, Hermann, Alison, Joly, Rochelle, Zhang, Yiye, Pathak, Jyotishman, Benda, Natalie C
DOI: 10.1093/jamiaopen/ooad048
Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods.
Author(s): Ward, Ralph, Obeid, Jihad S, Jennings, Lindsey, Szwast, Elizabeth, Hayes, William Garrett, Pipaliya, Royal, Bailey, Cameron, Faul, Skylar, Polyak, Brianna, Baker, George Hamilton, McCauley, Jenna L, Lenert, Leslie A
DOI: 10.1093/jamiaopen/ooad081
Clinical Informatics (CI) fellowship programs utilize the Electronic Residency Application Service (ERAS) to gather applications but until recently used an American Medical Informatics Association (AMIA) member-developed, simultaneous offer-acceptance process to match fellowship applicants to programs. In 2021, program directors collaborated with the AMIA to develop a new match to improve the process.
Author(s): Hron, Jonathan D, Lehmann, Christoph U, Long, S Wesley, Pageler, Natalie M, Kannry, Joseph, Levy, Bruce, Leu, Michael G
DOI: 10.1055/s-0043-1777000