Setting the agenda: an informatics-led policy framework for adaptive CDS.
Author(s): Smith, Jeffery
DOI: 10.1093/jamia/ocaa239
Author(s): Smith, Jeffery
DOI: 10.1093/jamia/ocaa239
The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices [...]
Author(s): Ferryman, Kadija
DOI: 10.1093/jamia/ocaa133
Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports.
Author(s): Marshall, Iain J, Nye, Benjamin, Kuiper, Joël, Noel-Storr, Anna, Marshall, Rachel, Maclean, Rory, Soboczenski, Frank, Nenkova, Ani, Thomas, James, Wallace, Byron C
DOI: 10.1093/jamia/ocaa163
The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility.
Author(s): Bozkurt, Selen, Cahan, Eli M, Seneviratne, Martin G, Sun, Ran, Lossio-Ventura, Juan A, Ioannidis, John P A, Hernandez-Boussard, Tina
DOI: 10.1093/jamia/ocaa164
Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.
Author(s): Aliabadi, Ali, Sheikhtaheri, Abbas, Ansari, Hossein
DOI: 10.1093/jamia/ocaa186
To explore whether racial/ethnic differences in telehealth use existed during the peak pandemic period among NYC patients seeking care for COVID-19 related symptoms.
Author(s): Weber, Ellerie, Miller, Sarah J, Astha, Varuna, Janevic, Teresa, Benn, Emma
DOI: 10.1093/jamia/ocaa216
This case report describes the innovative design and build of an algorithm that integrates available data from separate hospital-based informatics systems, which perform different daily functions to augment the contact-tracing process of COVID-19 patients by identifying exposed neighboring patients and healthcare workers and assessing their risk. Prior to the establishment of the algorithm, contact-tracing teams comprising 6 members would spend up to 10 hours each to complete contact tracing for [...]
Author(s): Venkataraman, Narayan, Poon, Beng Hoong, Siau, Chuin
DOI: 10.1093/jamia/ocaa184
India reported its first coronavirus disease 2019 (COVID-19) case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, these unstructured data limit upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets.
Author(s): Ulahannan, Jijo Pulickiyil, Narayanan, Nikhil, Thalhath, Nishad, Prabhakaran, Prem, Chaliyeduth, Sreekanth, Suresh, Sooraj P, Mohammed, Musfir, Rajeevan, E, Joseph, Sindhu, Balakrishnan, Akhil, Uthaman, Jeevan, Karingamadathil, Manoj, Thomas, Sunil Thonikkuzhiyil, Sureshkumar, Unnikrishnan, Balan, Shabeesh, Vellichirammal, Neetha Nanoth, ,
DOI: 10.1093/jamia/ocaa203
Large health systems responding to the coronavirus disease 2019 (COVID-19) pandemic face a broad range of challenges; we describe 14 examples of innovative and effective informatics interventions.
Author(s): Lin, Chen-Tan, Bookman, Kelly, Sieja, Amber, Markley, Katie, Altman, Richard L, Sippel, Jeffrey, Perica, Katharine, Reece, Lori, Davis, Christopher, Horowitz, Edward, Pisney, Larissa, Sottile, Peter D, Kao, David, Adrian, Bonnie, Szkil, Melissa, Griffin, Julie, Youngwerth, Jeanie, Drew, Brendan, Pell, Jonathan
DOI: 10.1093/jamia/ocaa171
We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care.
Author(s): Ford, Dee, Harvey, Jillian B, McElligott, James, King, Kathryn, Simpson, Kit N, Valenta, Shawn, Warr, Emily H, Walsh, Tasia, Debenham, Ellen, Teasdale, Carla, Meystre, Stephane, Obeid, Jihad S, Metts, Christopher, Lenert, Leslie A
DOI: 10.1093/jamia/ocaa157