AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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We present and test the intuition that letters to the editor in journals carry early signals of adverse drug events (ADEs). Surprisingly these letters have not yet been exploited for automatic ADE detection unlike for example, clinical records and PubMed. Part of the challenge is that it is not easy to access the full-text of letters (for the most part these do not appear in PubMed). ⋯ We also involve natural language processing for feature definitions. Overall we achieve high accuracy in our experiments and our method also works well on a second new test set. Our results encourage us to further pursue this line of research.
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AMIA Annu Symp Proc · Jan 2012
Optimizing perioperative decision making: improved information for clinical workflow planning.
Perioperative care is complex and involves multiple interconnected subsystems. Delayed starts, prolonged cases and overtime are common. Surgical procedures account for 40-70% of hospital revenues and 30-40% of total costs. ⋯ Perioperative leaders desire a broad range of tools for planning and assessing alternate solutions. Our modeled solutions generated feasible solutions that varied as expected, based on resource and policy assumptions and found better utilization of scarce resources. Combinatorial optimization modeling can effectively evaluate alternatives to support key decisions for planning clinical workflow and improving care efficiency and satisfaction.
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AMIA Annu Symp Proc · Jan 2012
A clinical decision tool for predicting patient care characteristics: patients returning within 72 hours in the emergency department.
The primary purpose of this study was to develop a clinical tool capable of identifying discriminatory characteristics that can predict patients who will return within 72 hours to the Pediatric emergency department (PED). We studied 66,861 patients who were discharged from the EDs during the period from May 1 2009 to December 31 2009. We used a classification model to predict return visits based on factors extracted from patient demographic information, chief complaint, diagnosis, treatment, and hospital real-time ED statistics census. ⋯ The resulting tool could enable ED staff and administrators to use patient specific values for each of a small number of discriminatory factors, and in return receive a prediction as to whether the patient will return to the ED within 72 hours. Our prediction accuracy can be as high as over 85%. This provides an opportunity for improving care and offering additional care or guidance to reduce ED readmission.
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AMIA Annu Symp Proc · Jan 2012
Harnessing a health information exchange to identify surgical device adverse events for urogynecologic mesh.
We sought to create an automated means to conduct surveillance of complications related to urogynecologic mesh because current postmarket surveillance fails to detect the true incidence of device-related adverse events. Using health information exchange data, we developed a search algorithm to identify urogynecologic surgeries with mesh implantation and associated inpatient adverse events. We validated the algorithm search results against those obtained from a manual case review of mesh surgical records. ⋯ Complications were identified in 380 of the 2874 mesh cases. This is the first known report of an automated process for identifying urogynecologic surgical mesh implantation cases from a health information exchange. Automated surveillance of health information exchange data may contribute to tracking of device-related adverse events.