MedicalResearch.com Interview with: Robert S. Rudin, Ph.D.
Associate Policy Researcher
RAND Corporation
Boston, MA 02116
MedicalResearch.com: What are the main findings of the study?Dr. Rudin:We found that most published health IT implementation studies report positive effects on quality, safety, and efficiency. Most evaluations focus on clinical decision support and computerized provider order entry. However, not all studies report equally positive results, and differences in context and implementation are one likely reason for these varying results, yet details of context and implementation are rarely reported in these studies.
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MedicalResearch.com Interview with:
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.
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MedicalResearch.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.
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MedicalResearch.com Interview with:Saul Blecker, MD, MHS
Assistant Professor
Department of Population Health
NYU School of Medicine
227 East 30th St., #648
New York, NY 10016
MedicalResearch.com: What are the main findings of the study?Dr. Blecker:We tracked utilization of the inpatient electronic health record (EHR) as a proxy for hospital intensity of care. EHR utilization was found to have variations over time, particularly when comparing days to nights and weekdays to weekends.
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