The Informatics Paper Club of the Air presented by the AMIA CIS-WG is a regularly scheduled Paper Club series that will address the gap in knowledge and performance by an ongoing review of literature and by exposing our clinical informaticists to evidence-based approaches and strategies with discussions centered on incorporating those strategies into their practices.
Wrong-patient orders (placing the right order on the wrong patient) will be our theme for the next episodes. The following three papers will be discussed during this webinar:
Chassin, Mark R., and Elise C. Becher. "The wrong patient." Annals of Internal Medicine 136.11 (2002): 826-833.
Henneman, Philip L., et al. "Patient identification errors are common in a simulated setting." Annals of emergency medicine 55.6 (2010): 503-509.
Henneman, Philip L., et al. "Providers do not verify patient identity during computer order entry." Academic emergency medicine 15.7 (2008): 641-648.
After participating in this activity, the learner should be better able to:
- Identify some of the most important recently published clinical research findings
- Reduce uncertainties for clinical information systems informaticians that may accompany complex problems and procedures faced in the workplace
- Incorporate new knowledge into the evidence base
Joseph Kannry, MD
Lead Technical Informaticist
Epic EMR Clinical Transformation Group
Mount Sinai Medical Center
Professor of Medicine
Icahn School of Medicine at Mount Sinai
Past Chair, Clinical Information Systems Working Group, AMIA
Tomasz Adamusiak, MD PhD
Senior Data Scientist
Joseph Kannry, MD has dual appointments in IT and Medicine at Mount Sinai Medical Center. He is Lead Technical Informaticist, EMR Clinical Transformation Group, Mount Sinai Health System. Dr. Kannry is a professor of Medicine, and a practicing board certified Internist at Mount Sinai’s IMA (Internal Medicine Associates). Dr. Kannry is a graduate of the Yale Center for Medical Informatics, a National Library of Medicine training program in Informatics. In 2009 he was elected Chair of Clinical Information System Working Group (CIS-WG) of AMIA (American Medical Informatics Association) and re-elected chair with term ending in 2014. Dr. Kannry was Co-PI for a 1.5 million grant award by AHRQ to study the integration of Clinical Prediction Rules into a Commercial EMR and an investigator and Epic Lead on the eMerge2 grant which seeks to integrate genomic information at the point of care. In his capacity as Epic Lead he was a member of the eMERGE EHR Integration Workgroup. In 2004, Dr. Kannry successfully led the Ambulatory EMR Selection process for Mount Sinai Medical Center and since 2005 he has been the Lead Technical Informaticist for the EMR Clinical Transformation Group. In his latest work as Lead Technical Informaticist, he oversees the Personal Health Record implementation, Enterprise Clinical Decision Support, mobile solutions for EHR access, EHR Clinical Research Integration and assists with the Ambulatory EHR implementation for both the Hospital Based Practices and Faculty Practice Associates which encompasses over 800,000 visits, as well as the Inpatient Implementation which includes a 1,130 bed hospital with approximately 56,000 discharges and the EMR rollout to Voluntary Physicians as well as working to support Mount Sinai's ACO. In 2013 Mount Sinai was recipient of the prestigious 2013 Davies Award for Enterprise EHR. The Davies award recognizes “outstanding achievement in the implementation and value” from EHRs.
Dr. Tomasz Adamusiak is Senior Data Scientist at Thomson Reuters utilizing Big Data and Linked Data technologies to drive new scientific and strategic insights in target finding, drug repurposing, and precision medicine. He trained in clinical informatics at the U.S. National Library of Medicine, and bioinformatics at the European Bioinformatics Institute in Cambridge, UK. Dr. Adamusiak is a Chair of the AMIA Knowledge Representation and Semantics Working Group and has extensive experience in secondary use of EHR data, clinical terminologies, and predictive analytics.