What Big Data means to me.
Author(s): Bourne, Philip E
DOI: 10.1136/amiajnl-2014-002651
Author(s): Bourne, Philip E
DOI: 10.1136/amiajnl-2014-002651
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2014-002666
To compare the agreement of electronic health record (EHR) data versus Medicaid claims data in documenting adult preventive care. Insurance claims are commonly used to measure care quality. EHR data could serve this purpose, but little information exists about how this source compares in service documentation. For 13 101 Medicaid-insured adult patients attending 43 Oregon community health centers, we compared documentation of 11 preventive services, based on EHR versus Medicaid claims [...]
Author(s): Heintzman, John, Bailey, Steffani R, Hoopes, Megan J, Le, Thuy, Gold, Rachel, O'Malley, Jean P, Cowburn, Stuart, Marino, Miguel, Krist, Alex, DeVoe, Jennifer E
DOI: 10.1136/amiajnl-2013-002333
To examine how patient portals contribute to health service delivery and patient outcomes. The specific aims were to examine how outcomes are produced, and how variations in outcomes can be explained.
Author(s): Otte-Trojel, Terese, de Bont, Antoinette, Rundall, Thomas G, van de Klundert, Joris
DOI: 10.1136/amiajnl-2013-002501
To design, build, and evaluate a storage model able to manage heterogeneous digital imaging and communications in medicine (DICOM) images. The model must be simple, but flexible enough to accommodate variable content without structural modifications; must be effective on answering query/retrieval operations according to the DICOM standard; and must provide performance gains on querying/retrieving content to justify its adoption by image-related projects.
Author(s): Savaris, Alexandre, Härder, Theo, von Wangenheim, Aldo
DOI: 10.1136/amiajnl-2013-002337
Data-driven risk stratification models built using data from a single hospital often have a paucity of training data. However, leveraging data from other hospitals can be challenging owing to institutional differences with patients and with data coding and capture.
Author(s): Wiens, Jenna, Guttag, John, Horvitz, Eric
DOI: 10.1136/amiajnl-2013-002162
As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it.
Author(s): Heath, Allison P, Greenway, Matthew, Powell, Raymond, Spring, Jonathan, Suarez, Rafael, Hanley, David, Bandlamudi, Chai, McNerney, Megan E, White, Kevin P, Grossman, Robert L
DOI: 10.1136/amiajnl-2013-002155
The aim of this study was to assess the accuracy of clinician-entered data in imaging clinical decision support (CDS). We used CDS-guided CT angiography (CTA) for pulmonary embolus (PE) in the emergency department as a case example because it required clinician entry of d-dimer results which could be unambiguously compared with actual laboratory values. Of 1296 patients with CTA orders for suspected PE during 2011, 1175 (90.7%) had accurate d-dimer [...]
Author(s): Gupta, Anurag, Raja, Ali S, Khorasani, Ramin
DOI: 10.1136/amiajnl-2013-001617
The Office of the National Coordinator will be defining the architecture of the Nationwide Health Information Network (NWHIN) together with the proposed HealtheWay public/private partnership as a development and funding strategy. There are a number of open questions--for example, what is the best way to realize the benefits of health information exchange? How valuable are regional health information organizations in comparison with a more direct approach? What is the role [...]
Author(s): Gaynor, Mark, Lenert, Leslie, Wilson, Kristin D, Bradner, Scott
DOI: 10.1136/amiajnl-2013-001719
Using electronic health records (EHR) to automate publicly reported quality measures is receiving increasing attention and is one of the promises of EHR implementation. Kaiser Permanente has fully or partly automated six of 13 the joint commission measure sets. We describe our experience with automation and the resulting time savings: a reduction by approximately 50% of abstractor time required for one measure set alone (surgical care improvement project). However, our [...]
Author(s): Garrido, Terhilda, Kumar, Sudheen, Lekas, John, Lindberg, Mark, Kadiyala, Dhanyaja, Whippy, Alan, Crawford, Barbara, Weissberg, Jed
DOI: 10.1136/amiajnl-2013-001789