Shining a little light and a little heat on the issue of EHRs and fraud.
Author(s): Simborg, Donald W
DOI: 10.1136/amiajnl-2012-001369
Author(s): Simborg, Donald W
DOI: 10.1136/amiajnl-2012-001369
Clinically oriented interface terminologies support interactions between humans and computer programs that accept structured entry of healthcare information. This manuscript describes efforts over the past decade to introduce an interface terminology called CHISL (Categorical Health Information Structured Lexicon) into clinical practice as part of a computer-based documentation application at Vanderbilt University Medical Center. Vanderbilt supports a spectrum of electronic documentation modalities, ranging from transcribed dictation, to a partial template of [...]
Author(s): Rosenbloom, Samuel Trent, Miller, Randolph A, Adams, Perry, Madani, Sina, Khan, Naqi, Shultz, Edward K
DOI: 10.1136/amiajnl-2012-001384
To determine factors that physicians find encouraging and discouraging about e-prescribing and to compare these factors based on physicians' adoption status, a cross-sectional study was conducted using an internet-based survey administered to a national convenience sample of primary care physicians. A scale was developed to measure factors related to the adoption of e-prescribing. Analysis procedures included exploratory factor analysis, multivariate analysis of variance, and Tukey's post-hoc tests. 443 surveys were [...]
Author(s): Jariwala, Krutika S, Holmes, Erin R, Banahan, Benjamin F, McCaffrey, David J
DOI: 10.1136/amiajnl-2012-001214
In 2008 we developed a shared health research information network (SHRINE), which for the first time enabled research queries across the full patient populations of four Boston hospitals. It uses a federated architecture, where each hospital returns only the aggregate count of the number of patients who match a query. This allows hospitals to retain control over their local databases and comply with federal and state privacy laws. However, because [...]
Author(s): Weber, Griffin M
DOI: 10.1136/amiajnl-2012-001299
To evaluate the complex dynamics involved in implementing electronic health information exchange (HIE) for public health reporting at a state health department, and to identify policy implications to inform similar implementations.
Author(s): Merrill, Jacqueline A, Deegan, Michael, Wilson, Rosalind V, Kaushal, Rainu, Fredericks, Kimberly
DOI: 10.1136/amiajnl-2012-001289
For a health information exchange (HIE) organization to succeed in any given region, it is important to understand the optimal catchment area for the patient population it is serving. The objective of this analysis was to understand the geographical distribution of the patients being served by one HIE organization in New York City (NYC).
Author(s): Onyile, Arit, Vaidya, Sandip R, Kuperman, Gilad, Shapiro, Jason S
DOI: 10.1136/amiajnl-2012-001217
To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies.
Author(s): Fung, Kin Wah, Jao, Chiang S, Demner-Fushman, Dina
DOI: 10.1136/amiajnl-2012-001291
With increasing use electronic health records (EHR) in the USA, we looked at the predictive values of the International Classification of Diseases, 9th revision (ICD-9) coding system for surveillance of chronic hepatitis B virus (HBV) infection.
Author(s): Mahajan, Reena, Moorman, Anne C, Liu, Stephen J, Rupp, Loralee, Klevens, R Monina, ,
DOI: 10.1136/amiajnl-2012-001558
Self-monitoring of physical activity (PA) and diet are key components of behavioral weight loss programs. The purpose of this study was to assess the relationship between diet (mobile app, website, or paper journal) and PA (mobile app vs no mobile app) self-monitoring and dietary and PA behaviors.
Author(s): Turner-McGrievy, Gabrielle M, Beets, Michael W, Moore, Justin B, Kaczynski, Andrew T, Barr-Anderson, Daheia J, Tate, Deborah F
DOI: 10.1136/amiajnl-2012-001510
A sizable fraction of patients experiences adverse drug events or lack of drug efficacy. A part of this variability in drug response can be explained by genetic differences between patients. However, pharmacogenomic data as well as computational clinical decision support systems for interpreting such data are still unavailable in most healthcare settings. We address this problem by introducing the medicine safety code (MSC), which captures compressed pharmacogenomic data in a [...]
Author(s): Samwald, Matthias, Adlassnig, Klaus-Peter
DOI: 10.1136/amiajnl-2012-001275