Publications by authors named "Anne Belsito"

Background: Observational data are increasingly being used for pharmacoepidemiological, health services and clinical effectiveness research. Since pharmacies first introduced low-cost prescription programs (LCPP), researchers have worried that data about the medications provided through these programs might not be available in observational data derived from administrative sources, such as payer claims or pharmacy benefit management (PBM) company transactions.

Method: We used data from the Indiana Network for Patient Care to estimate the proportion of patients with type 2 diabetes to whom an oral hypoglycemic agent was dispensed.

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Introduction: The goal of the Enhanced Medication History (EMH) project is to provide medication histories to ambulatory primary care practices in the Indiana Network for Patient Care.

Methods: Medications were aggregated from three different sources of pharmacy data (Medicaid, SureScripts, and the county health system of Indianapolis). Dispensing events were assembled into the Continuity of Care Document (CCD), and presented to clinicians as RxNorm Clinical Drugs.

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We describe our early experience with use in emergency department settings of a standards-based medication history service integrated into a health information exchange (HIE). The service sends queries from one Exchange's emergency department interface both to a local ambulatory care system and to the medication hub services provided by a second HIE. This second HIE in turn sends requests to SureScripts and returns histories for incorporation into the first Exchange's clinical interface.

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Poor medication management practices can lead to serious erosion of health care quality and safety. The DHHS Medication Management Use Case outlines methods for the exchange of electronic health information to improve medication management practices. In this case report, the authors describe initial development of Nationwide Health Information Network (NHIN) services to support the Medication Management Use Case.

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Medication histories improve health care quality and safety; formularies serve to control costs. We describe the implementation of the Regenstrief Medication Hub: a system to provide both histories and formularies to the Gopher ePrescribing application. Currently the Medication Hub aggregates data from two sources: the RxHub consortium of pharmacy benefit managers, and Wishard Health Services.

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The Regenstrief Medication Hub system collects pharmacy data from two different sources: Wishard Health Services, and dispensing claims provided by RxHub. These lists are indexed, aggregated, and filtered, to create a single Medication History for each patient. This history is then provided to the Gopher computerized prescribing system.

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Purpose: To improve contact isolation rates among patients admitted to the hospital with a known history of infection with Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin-resistant Enterococci (VRE).

Methods: A before and after interventional study implementing computerized reminders for contact isolation between February 25, 2005 and February 28, 2006. We measured rates of appropriate contact isolation, and time to isolation for the 4 month pre-intervention period, and the 12 month intervention period.

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Clinicians at Wishard Hospital in Indianapolis print and carry clinical reports called "Pocket Rounds". This paper describes a new process we developed to improve these clinical reports. The heart of our new process is a World Wide Web Consortium standard: Extensible Stylesheet Language Formatting Objects (XSL-FO).

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Objective: To assess the effect of a computerized decision support system (CDSS) on the accuracy of patient temperature recording at the bed side.

Design: This is a randomized, controlled trial comparing nurses assigned to an intervention group that received CDSS whenever they attempted to store a low temperature ( View Article and Find Full Text PDF

Objectives: To (1) discover the types of errors most commonly found in clinical notes that are generated either using automatic speech recognition (ASR) or via human transcription and (2) to develop efficient rules for classifying these errors based on the categories found in (1). The purpose of classifying errors into categories is to understand the underlying processes that generate these errors, so that measures can be taken to improve these processes.

Methods: We integrated the Dragon NaturallySpeaking v4.

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