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Stephen G. Pauker
Researcher at Tufts Medical Center
Publications - 269
Citations - 24203
Stephen G. Pauker is an academic researcher from Tufts Medical Center. The author has contributed to research in topics: Decision analysis & Cost effectiveness. The author has an hindex of 70, co-authored 269 publications receiving 23206 citations. Previous affiliations of Stephen G. Pauker include University of Illinois at Chicago & Brigham and Women's Hospital.
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Journal ArticleDOI
Discontinuing medications: A novel approach for revising the prescribing stage of the medication-use process
Kevin T. Bain,Holly M. Holmes,Mark H. Beers,Mark H. Beers,Vittorio Maio,Steven M. Handler,Stephen G. Pauker +6 more
TL;DR: A conceptual framework for revising the prescribing stage of the medication‐use process to include discontinuing medications is proposed, which has substantial practice and research implications, especially for the clinical care of older persons, who are particularly susceptible to the adverse effects of medications.
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Voluntary electronic reporting of medical errors and adverse events. An analysis of 92,547 reports from 26 acute care hospitals.
TL;DR: Hospital-based electronic ERS (e-ERS) may facilitate voluntary reporting of all types of medical errors and adverse events through ease of use and accessibility, and may allow real-time review, oversight, and intervention.
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Screening for HIV: can we afford the false positive rate?
TL;DR: A mobile phone app may be a viable option to diagnose and treat acquired immunodeficiency syndrome (AIDS) in patients without resorting to traditional methods, such as chemotherapy and radiation therapy.
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Voluntary Electronic Reporting of Medical Errors and Adverse Events
TL;DR: An e-ERS provides an accessible venue for reporting medical errors, adverse events, and near misses, and the wide variation in reporting rates among hospitals, and very low reporting rates by physicians, requires investigation.
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The epidemiology of delays in a teaching hospital. The development and use of a tool that detects unnecessary hospital days.
TL;DR: A tool that detects, quantifies, and assigns causes for medically unnecessary hospital delays and uses it to describe the epidemiology of delays at a teaching tertiary care hospital to help address inefficiencies in health care delivery is developed.