Bmc Med Inform Decis
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Bmc Med Inform Decis · Aug 2010
Comparative StudySpeech recognition software and electronic psychiatric progress notes: physicians' ratings and preferences.
The context of the current study was mandatory adoption of electronic clinical documentation within a large mental health care organization. Psychiatric electronic documentation has unique needs by the nature of dense narrative content. Our goal was to determine if speech recognition (SR) would ease the creation of electronic progress note (ePN) documents by physicians at our institution. ⋯ Although our findings are mixed, SR may be a technology with some promise for mental health documentation. Future investigations of this nature should use more participants, a broader range of document types, and compare front- and back-end SR methods.
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Bmc Med Inform Decis · Jan 2010
Implementation and evaluation of a nurse-centered computerized potassium regulation protocol in the intensive care unit--a before and after analysis.
Potassium disorders can cause major complications and must be avoided in critically ill patients. Regulation of potassium in the intensive care unit (ICU) requires potassium administration with frequent blood potassium measurements and subsequent adjustments of the amount of potassium administrated. The use of a potassium replacement protocol can improve potassium regulation. For safety and efficiency, computerized protocols appear to be superior over paper protocols. The aim of this study was to evaluate if a computerized potassium regulation protocol in the ICU improved potassium regulation. ⋯ Computerized potassium control, integrated with the nurse-centered GRIP program for glucose regulation, is effective and reduces the prevalence of hypo- and hyperkalemia in the ICU compared with physician-driven potassium regulation.
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Bmc Med Inform Decis · Jan 2010
Comparative StudyA novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks.
Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echo-state approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks. ⋯ This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echo-state network was more easily configured than other time series modeling technologies.
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Bmc Med Inform Decis · Jan 2010
ReviewA knowledge-based taxonomy of critical factors for adopting electronic health record systems by physicians: a systematic literature review.
The health care sector is an area of social and economic interest in several countries; therefore, there have been lots of efforts in the use of electronic health records. Nevertheless, there is evidence suggesting that these systems have not been adopted as it was expected, and although there are some proposals to support their adoption, the proposed support is not by means of information and communication technology which can provide automatic tools of support. The aim of this study is to identify the critical adoption factors for electronic health records by physicians and to use them as a guide to support their adoption process automatically. ⋯ The critical adoption factors established here provide a sound theoretical basis for research to understand, support, and facilitate the adoption of electronic health records to physicians in benefit of patients.
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Bmc Med Inform Decis · Jan 2010
Comparative StudyA bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system.
Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. ⋯ Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context.