Bmc Med Inform Decis
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Bmc Med Inform Decis · Jan 2013
Against conventional wisdom: when the public, the media, and medical practice collide.
In 2009, the U.S. Preventive Services Task Force released new mammography screening guidelines that sparked a torrent of criticism. The subsequent conflict was significant and pitted the Task Force against other health organizations, advocacy groups, the media, and the public at large. We argue that this controversy was driven by the systematic removal of uncertainty from science communication. To increase comprehension and adherence, health information communicators remove caveats, limitations, and hedging so science appears simple and more certain. This streamlining process is, in many instances, initiated by researchers as they engage in dissemination of their findings, and it is facilitated by public relations professionals, journalists, public health practitioners, and others whose tasks involve using the results from research for specific purposes. ⋯ Science is routinely simplified as it is prepared for public consumption. In line with the model of information overload, this practice may increase short-term adherence to recommendations at the expense of long-term message consistency and trust in science.
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Bmc Med Inform Decis · Dec 2012
Development of a decision aid to inform patients' and families' renal replacement therapy selection decisions.
Few educational resources have been developed to inform patients' renal replacement therapy (RRT) selection decisions. Patients progressing toward end stage renal disease (ESRD) must decide among multiple treatment options with varying characteristics. Complex information about treatments must be adequately conveyed to patients with different educational backgrounds and informational needs. Decisions about treatment options also require family input, as families often participate in patients' treatment and support patients' decisions. We describe the development, design, and preliminary evaluation of an informational, evidence-based, and patient-and family-centered decision aid for patients with ESRD and varying levels of health literacy, health numeracy, and cognitive function. ⋯ This rigorously designed decision aid may help patients and families make informed decisions about their treatment options for RRT that are well aligned with their values.
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Bmc Med Inform Decis · Nov 2012
Comparative StudyDecision tree-based learning to predict patient controlled analgesia consumption and readjustment.
Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. ⋯ This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.
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Bmc Med Inform Decis · Nov 2012
The Computer-based Health Evaluation Software (CHES): a software for electronic patient-reported outcome monitoring.
Patient-reported Outcomes (PROs) capturing e.g., quality of life, fatigue, depression, medication side-effects or disease symptoms, have become important outcome parameters in medical research and daily clinical practice. Electronic PRO data capture (ePRO) with software packages to administer questionnaires, storing data, and presenting results has facilitated PRO assessment in hospital settings. Compared to conventional paper-pencil versions of PRO instruments, ePRO is more economical with regard to staff resources and time, and allows immediate presentation of results to the medical staff.The objective of our project was to develop software (CHES - Computer-based Health Evaluation System) for ePRO in hospital settings and at home with a special focus on the presentation of individual patient's results. ⋯ During the last decade several software packages for ePRO have emerged for different purposes. Whereas commercial products are available primarily for ePRO in clinical trials, academic projects have focused on data collection and presentation in daily clinical practice and on extending cancer registries with PRO data. CHES includes several features facilitating the use of PRO data for individualized medical decision making. With its web-interface it allows ePRO also when patients are home. Thus, it provides complete monitoring of patients'physical and psychosocial symptom burden.
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Bmc Med Inform Decis · Oct 2012
Comparative StudyNon-linear dynamical signal characterization for prediction of defibrillation success through machine learning.
Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique. ⋯ We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.