Patients and consumers (and the data they generate): an underutilized resource.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocab040
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocab040
Health and biomedical informatics graduate-level degree programs have proliferated across the United States in the last 10 years. To help inform programs on practices in teaching and learning, a survey of master's programs in health and biomedical informatics in the United States was conducted to determine the national landscape of culminating experiences including capstone projects, research theses, internships, and practicums. Almost all respondents reported that their programs required a culminating [...]
Author(s): Cox, Suzanne Morrison, Johnson, Stephen B, Shiu, Eva, Boren, Sue
DOI: 10.1093/jamia/ocaa348
The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic changes in policies globally. We describe a platform, Watson Assistant (WA), which has been used to develop conversational agents to deliver COVID-19 related information. We characterized the diverse use cases and implementations during the early pandemic and measured adoption through a number of users, messages sent, and conversational turns (ie, pairs of [...]
Author(s): McKillop, Mollie, South, Brett R, Preininger, Anita, Mason, Mitch, Jackson, Gretchen Purcell
DOI: 10.1093/jamia/ocaa316
IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice.
Author(s): Suwanvecho, Suthida, Suwanrusme, Harit, Jirakulaporn, Tanawat, Issarachai, Surasit, Taechakraichana, Nimit, Lungchukiet, Palita, Decha, Wimolrat, Boonpakdee, Wisanu, Thanakarn, Nittaya, Wongrattananon, Pattanawadee, Preininger, Anita M, Solomon, Metasebya, Wang, Suwei, Hekmat, Rezzan, Dankwa-Mullan, Irene, Shortliffe, Edward, Patel, Vimla L, Arriaga, Yull, Jackson, Gretchen Purcell, Kiatikajornthada, Narongsak
DOI: 10.1093/jamia/ocaa334
We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.
Author(s): Gao, Junyi, Sharma, Rakshith, Qian, Cheng, Glass, Lucas M, Spaeder, Jeffrey, Romberg, Justin, Sun, Jimeng, Xiao, Cao
DOI: 10.1093/jamia/ocaa322
The study sought to learn if it were possible to develop an ontology that would allow the Food and Drug Administration approved indications to be expressed in a manner computable and comparable to what is expressed in an electronic health record.
Author(s): Nelson, Stuart J, Flynn, Allen, Tuttle, Mark S
DOI: 10.1093/jamia/ocaa331
Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs) with antimicrobial lock therapy is an alternative to removal and replacement of the CVC. However, the success of CVC salvage is uncertain, and when it fails the catheter has to be removed and replaced. We describe a machine learning approach [...]
Author(s): Walker, Lorne W, Nowalk, Andrew J, Visweswaran, Shyam
DOI: 10.1093/jamia/ocaa328
Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical [...]
Author(s): Song, Wenyu, Kang, Min-Jeoung, Zhang, Linying, Jung, Wonkyung, Song, Jiyoun, Bates, David W, Dykes, Patricia C
DOI: 10.1093/jamia/ocaa336
There is little debate about the importance of ethics in health care, and clearly defined rules, regulations, and oaths help ensure patients' trust in the care they receive. However, standards are not as well established for the data professions within health care, even though the responsibility to treat patients in an ethical way extends to the data collected about them. Increasingly, data scientists, analysts, and engineers are becoming fiduciarily responsible [...]
Author(s): Montague, Elizabeth, Day, T Eugene, Barry, Dwight, Brumm, Maria, McAdie, Aaron, Cooper, Andrew B, Wignall, Julia, Erdman, Steve, Núñez, Diahnna, Diekema, Douglas, Danks, David
DOI: 10.1093/jamia/ocaa307