AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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The Tele Intensive Care Unit (tele-ICU) supports a high volume, high acuity population of patients. There is a high-volume of incoming and outgoing calls, especially during the evening and night hours, through the tele-ICU hubs. The tele-ICU clinicians must be able to communicate effectively to team members in order to support the care of complex and critically ill patients while supporting and maintaining a standard to improve time to intervention. ⋯ The software provides a multi-relational database of message instances to mine information for evaluation and quality improvement for all entities that touch the tele-ICU. The software design incorporates years of critical care and software design experience combined with new skills acquired in an applied Health Informatics program. This software tool will function in the tele-ICU environment and perform as a front-end application that gathers, routes, and displays internal communication messages for intervention by priority and provider.
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AMIA Annu Symp Proc · Jan 2013
A modified real AdaBoost algorithm to discover intensive care unit subgroups with a poor outcome.
The Intensive Care Unit (ICU) population is heterogeneous. At individual ICUs, the quality of care may vary within subgroups. We investigate whether poor outcomes of an ICU can be traced back to excess deaths in specific patient subgroups, by discovering candidate subgroups, with a modified adaptive decision tree boosting algorithm applied to 80 Dutch ICUs. ⋯ Variables Glasgow Coma Scale and age were used most. There were 29 ICUs with overall poor outcomes, and for 22 our algorithm found all excess deaths. A new method based on adaptive decision tree boosting discovered many subgroups of ICU patients for which there is potentially room for outcomes improvement.
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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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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
Comparative StudyEnsuring patient safety in care transitions: an empirical evaluation of a Handoff Intervention Tool.
Successful handoffs ensure smooth, efficient and safe patient care transitions. Tools and systems designed for standardization of clinician handoffs often focuses on ensuring the communication activity during transitions, with limited support for preparatory activities such as information seeking and organization. ⋯ We found that the use of HAND-IT led to fewer transition breakdowns, greater tool resilience, and likely led to better learning outcomes for less-experienced clinicians when compared to the current tool. We discuss the implications of our results for improving patient safety with a continuity of care-based approach.