Author Interviews, BMJ, Electronic Records / 07.03.2014

MedicalResearch.com Interview with: Stephanie Parks Taylor MD MS Associate Professor Director of Clinical Research Associate Division Director, Hospital Medicine USF Department of Internal Medicine MedicalResearch.com: What are the main findings of your study? Dr. Parks Taylor: The integration of electronic medical records has been proposed to have many benefits for the healthcare system. We investigated the effect of EMR implementation on communication between physicians and nurses in a hospital setting. The primary finding was that overall agreement about a patient's plan of care actually worsened after the implementation of EMR. This seemed to be related to a decrease in face-to-face communication between physicians and nurses. (more…)
Author Interviews, Electronic Records, Rheumatology / 05.02.2014

Gabriela Schmajuk M.D. M.S. Department of Medicine (Rheumatology) University of California, San Francisco San Francisco VA Medical Center San Francisco, CA 94121MedicalResearch.com Interview with: Gabriela Schmajuk M.D. M.S. Department of Medicine (Rheumatology) University of California, San Francisco San Francisco VA Medical Center San Francisco, CA 94121 MedicalResearch.com: What are the main findings of the study? Dr. Schmajuk: Our main findings were that moderate LFT abnormalities were uncommon in the first 7 months of methotrexate use among new users, and more likely to occur in patients with obesity, untreated high cholesterol, pre-methotrexate LFT elevations, biologic agent use, and lack of folic acid supplementation. (more…)
Author Interviews, Electronic Records / 29.12.2013

MedicalResearch.com Interview with: Leo Anthony Celi, MD, MS, MPH Massachusetts Institute of Technology Cambridge, MA 02139 Leo Anthony Celi, MD, MS, MPH Massachusetts Institute of Technology Cambridge, MA 02139 MedicalResearch.com: What are the main findings of the study? Dr. Celi: The main take home point from the paper is that we know little about how drug perform in the real world. Which patients truly benefit? Which patients are harmed? How do drugs interact with different acute (such as critical illness) and chronic conditions? These questions are almost never answered during pre-marketing research due to cost. We need a better system of following the life cycle of drugs post-marketing. Clinical databases provide us with this opportunity. (more…)
AHRQ, Author Interviews, Electronic Records, Hospital Readmissions, University of Pennsylvania / 28.11.2013

Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania Philadelphia, PA 19104MedicalResearch.com Interview with: Craig A Umscheid, MD, MSCE, FACP Assistant Professor of Medicine and Epidemiology Director, Center for Evidence-based Practice Medical Director, Clinical Decision Support Chair, Department of Medicine Quality Committee Senior Associate Director, ECRI-Penn AHRQ Evidence-based Practice Center University of Pennsylvania Philadelphia, PA 19104 MedicalResearch.com: What are the main findings of the study? Dr. Umscheid: We developed and successfully deployed into the electronic health record of the University of Pennsylvania Health System an automated prediction tool which identifies newly admitted patients who are at risk for readmission within 30 days of discharge.  Using local data, we found that having been admitted to the hospital two or more times in the 12 months prior to admission was the best way to predict which patients are at risk for being readmitted in the 30 days after discharge. Using this finding, our automated tool identifies patients who are “high risk” for readmission and creates a “flag” in their electronic health record (EHR). The flag appears next to the patient’s name in a column titled “readmission risk.” The flag can be double-clicked to display detailed information relevant to discharge planning.  In a one year prospective validation of the tool, we found that patients who triggered the readmission alert were subsequently readmitted 31 percent of the time. When an alert was not triggered, patients were readmitted only 11 percent of the time.  There was no evidence for an effect of the intervention on 30-day all-cause readmission rates in the 12-month period after implementation. (more…)