Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.
Author(s): Catchpoole, Jesani, Nanda, Gaurav, Vallmuur, Kirsten, Nand, Goshad, Lehto, Mark
DOI: 10.1055/a-1863-7176
Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions.
Author(s): Chopannejad, Sara, Sadoughi, Farahnaz, Bagherzadeh, Rafat, Shekarchi, Sakineh
DOI: 10.1055/a-1863-1589
Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive [...]
Author(s): Chaparro, Juan D, Beus, Jonathan M, Dziorny, Adam C, Hagedorn, Philip A, Hernandez, Sean, Kandaswamy, Swaminathan, Kirkendall, Eric S, McCoy, Allison B, Muthu, Naveen, Orenstein, Evan W
DOI: 10.1055/s-0042-1748856
Chronic disease is the leading cause of mortality in the United States. Health information technology (HIT) tools show promise for improving disease management.
Author(s): Rajamani, Geetanjali, Lindemann, Elizabeth, Evans, Michael D, Pillai, Raghu, Badlani, Sameer, Melton, Genevieve B
DOI: 10.1055/s-0042-1751305
The aim of the study is to examine the relationship between electronic health record (EHR) use/functionality and hospital operating costs (divided into five subcategories), and to compare the results across rural and urban facilities.
Author(s): Rhoades, Claudia A, Whitacre, Brian E, Davis, Alison F
DOI: 10.1055/s-0042-1750415
We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.
Author(s): Major, Vincent J, Jones, Simon A, Razavian, Narges, Bagheri, Ashley, Mendoza, Felicia, Stadelman, Jay, Horwitz, Leora I, Austrian, Jonathan, Aphinyanaphongs, Yindalon
DOI: 10.1055/s-0042-1750416
Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.
Author(s): Yan, Mengying, Pencina, Michael J, Boulware, L Ebony, Goldstein, Benjamin A
DOI: 10.1093/jamia/ocac019
Scanned documents (SDs), while common in electronic health records and potentially rich in clinically relevant information, rarely fit well with clinician workflow. Here, we identify scanned imaging reports requiring follow-up with high recall and practically useful precision.
Author(s): Kumar, Akshat, Goodrum, Heath, Kim, Ashley, Stender, Carly, Roberts, Kirk, Bernstam, Elmer V
DOI: 10.1093/jamia/ocac007
This study aims to establish an informative dynamic prediction model of treatment outcomes using follow-up records of tuberculosis (TB) patients, which can timely detect cases when the current treatment plan may not be effective.
Author(s): Kheirandish, Maryam, Catanzaro, Donald, Crudu, Valeriu, Zhang, Shengfan
DOI: 10.1093/jamia/ocac003
To identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic.
Author(s): Amiri, Parham, Karahanna, Elena
DOI: 10.1093/jamia/ocac014