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
Smartphone devices capable of monitoring users' health, physiology, activity, and environment revolutionize care delivery, medical research, and remote patient monitoring. Such devices, laden with clinical-grade sensors and cloud connectivity, allow clinicians, researchers, and patients to monitor health longitudinally, passively, and persistently, shifting the paradigm of care and research from low-resolution, intermittent, and discrete to one of persistent, continuous, and high resolution. The collection, transmission, and storage of sensitive health data [...]
Author(s): Aalami, Oliver, Hittle, Mike, Ravi, Vishnu, Griffin, Ashley, Schmiedmayer, Paul, Shenoy, Varun, Gutierrez, Santiago, Venook, Ross
DOI: 10.1093/jamiaopen/ooad044
Synthea is a synthetic patient generator that creates synthetic medical records, including medication profiles. Prior to our work, Synthea produced unrealistic medication data that did not accurately reflect prescribing patterns. This project aimed to create an open-source synthetic medication database that could integrate with Synthea to create realistic patient medication profiles.
Author(s): Hodges, Robert, Tokunaga, Kristen, LeGrand, Joseph
DOI: 10.1093/jamiaopen/ooad052
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 determine the methods and metrics used to evaluate the usability of mobile application Clinical Decision Support Systems (CDSSs) used in healthcare emergencies. Secondary aims were to describe the characteristics and usability of evaluated CDSSs.
Author(s): Wohlgemut, Jared M, Pisirir, Erhan, Kyrimi, Evangelia, Stoner, Rebecca S, Marsh, William, Perkins, Zane B, Tai, Nigel R M
DOI: 10.1093/jamiaopen/ooad051
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
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
To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR).
Author(s): Wang, Lijing, Zipursky, Amy R, Geva, Alon, McMurry, Andrew J, Mandl, Kenneth D, Miller, Timothy A
DOI: 10.1093/jamiaopen/ooad047
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