From Commercialization to Accountability: Responsible Health Data Collection, Use, and Disclosure for the 21st Century.
Author(s): McGraw, Deven, Petersen, Carolyn
DOI: 10.1055/s-0040-1710392
Author(s): McGraw, Deven, Petersen, Carolyn
DOI: 10.1055/s-0040-1710392
This study aimed to describe an alternative approach for accessing electronic medical records (EMRs) from clinical decision support (CDS) functions based on Arden Syntax Medical Logic Modules, which can be paraphrased as "map the entire record."
Author(s): Kraus, Stefan, Toddenroth, Dennis, Staudigel, Martin, Rödle, Wolfgang, Unberath, Philipp, Griebel, Lena, Prokosch, Hans-Ulrich, Mate, Sebastian
DOI: 10.1055/s-0040-1709708
The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR).
Author(s): Thyvalikakath, Thankam Paul, Duncan, William D, Siddiqui, Zasim, LaPradd, Michelle, Eckert, George, Schleyer, Titus, Rindal, Donald Brad, Jurkovich, Mark, Shea, Tracy, Gilbert, Gregg H, ,
DOI: 10.1055/s-0040-1709506
Making genomic data available at the point-of-care and for research is critical for the success of the Precision Medicine Initiative (PMI), a research initiative which seeks to change health care by "tak(ing) into account individual differences in people's genes, environments, and lifestyles." The Office of the National Coordinator for Health Information Technology (ONC) led Sync for Genes, a program to develop standards that make genomic data available when and where [...]
Author(s): Garcia, Stephanie J, Zayas-Cabán, Teresa, Freimuth, Robert R
DOI: 10.1055/s-0040-1708051
Failure to complete recommended diagnostic tests may increase the risk of diagnostic errors.
Author(s): Weingart, Saul N, Yaghi, Omar, Barnhart, Liz, Kher, Sucharita, Mazzullo, John, Roberts, Kari, Lominac, Eric, Gittelson, Nancy, Argyris, Philip, Harvey, William
DOI: 10.1055/s-0040-1708530
Physicians may spend a significant amount of time using the electronic health record (EHR), but this is understudied in the pediatric intensive care unit (PICU). The objective of this study is to quantify PICU attending physician EHR usage and determine its association with patient census and mortality scores.
Author(s): Krawiec, Conrad, Stetter, Christy, Kong, Lan, Haidet, Paul
DOI: 10.1055/s-0040-1705108
Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR [...]
Author(s): Ibrahim, Zina M, Wu, Honghan, Hamoud, Ahmed, Stappen, Lukas, Dobson, Richard J B, Agarossi, Andrea
DOI: 10.1093/jamia/ocz211
Memorial Sloan Kettering Cancer Center has more than a decade's experience creating online interfaces for obtaining data from patients as part of routine clinical care. We have developed a set of "golden rules" for design of these interfaces. Many relate to the knowledge imbalance between professional staff (whether medical or informatics) and patients, who are often old and sick and have limited knowledge of technology. Others relate to the clinical [...]
Author(s): Vickers, Andrew J, Chen, Ling Y, Stetson, Peter D
DOI: 10.1093/jamia/ocz215
To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the [...]
Author(s): Kuo, Tsung-Ting, Kim, Jihoon, Gabriel, Rodney A
DOI: 10.1093/jamia/ocz214
Author(s):
DOI: 10.1093/jamia/ocz219