Note on Friedman's 'what informatics is and isn't'.
Author(s): Maojo, Victor, Kulikowski, Casimir A
DOI: 10.1136/amiajnl-2013-001807
Author(s): Maojo, Victor, Kulikowski, Casimir A
DOI: 10.1136/amiajnl-2013-001807
Author(s): Friedman, Charles P
DOI: 10.1136/amiajnl-2013-002120
Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated [...]
Author(s): Richesson, Rachel L, Hammond, W Ed, Nahm, Meredith, Wixted, Douglas, Simon, Gregory E, Robinson, Jennifer G, Bauck, Alan E, Cifelli, Denise, Smerek, Michelle M, Dickerson, John, Laws, Reesa L, Madigan, Rosemary A, Rusincovitch, Shelley A, Kluchar, Cynthia, Califf, Robert M
DOI: 10.1136/amiajnl-2013-001926
Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats.
Author(s): Newton, Katherine M, Peissig, Peggy L, Kho, Abel Ngo, Bielinski, Suzette J, Berg, Richard L, Choudhary, Vidhu, Basford, Melissa, Chute, Christopher G, Kullo, Iftikhar J, Li, Rongling, Pacheco, Jennifer A, Rasmussen, Luke V, Spangler, Leslie, Denny, Joshua C
DOI: 10.1136/amiajnl-2012-000896
To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia.
Author(s): Haug, Peter J, Ferraro, Jeffrey P, Holmen, John, Wu, Xinzi, Mynam, Kumar, Ebert, Matthew, Dean, Nathan, Jones, Jason
DOI: 10.1136/amiajnl-2012-001376
To be eligible for incentives through the Electronic Health Record (EHR) Incentive Program, many providers using older or locally developed EHRs will be transitioning to new, commercial EHRs. We previously evaluated prescribing errors made by providers in the first year following transition from a locally developed EHR with minimal prescribing clinical decision support (CDS) to a commercial EHR with robust CDS. Following system refinements, we conducted this study to assess [...]
Author(s): Abramson, Erika L, Malhotra, Sameer, Osorio, S Nena, Edwards, Alison, Cheriff, Adam, Cole, Curtis, Kaushal, Rainu
DOI: 10.1136/amiajnl-2012-001328
Incorporating accurate life expectancy predictions into clinical decision making could improve quality and decrease costs, but few providers do this. We sought to use predictive data mining and high dimensional analytics of electronic health record (EHR) data to develop a highly accurate and clinically actionable 5 year life expectancy index.
Author(s): Mathias, Jason Scott, Agrawal, Ankit, Feinglass, Joe, Cooper, Andrew J, Baker, David William, Choudhary, Alok
DOI: 10.1136/amiajnl-2012-001360
Author(s): Daniel, Jodi G, Reider, Jacob M, Posnack, Steven L
DOI: 10.1136/amiajnl-2013-001669
The implementation of health information technology interventions is at the forefront of most policy agendas internationally. However, such undertakings are often far from straightforward as they require complex strategic planning accompanying the systemic organizational changes associated with such programs. Building on our experiences of designing and evaluating the implementation of large-scale health information technology interventions in the USA and the UK, we highlight key lessons learned in the hope of [...]
Author(s): Cresswell, Kathrin M, Bates, David W, Sheikh, Aziz
DOI: 10.1136/amiajnl-2013-001684
In 2005, the authors published a paper, 'Will the wave finally break? A brief view of the adoption of electronic medical records in the United States', which predicted that rapid adoption of electronic health records (EHR) would occur in the next 5 years given appropriate incentives. The wave has finally broken with the stimulus of the health information technology for economic and clinical health legislation in 2009, and there have [...]
Author(s): Simborg, Donald W, Detmer, Don Eugene, Berner, Eta S
DOI: 10.1136/amiajnl-2012-001508