Method Inform Med
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The International Medical Informatics Association (IMIA) agreed on international recommendations in health informatics/medical informatics education. These should help to establish courses, course tracks or even complete programs in this field, to further develop existing educational activities in the various nations and to support international initiatives concerning education in health and medical informatics (HMI), particularly international activities in educating HMI specialists and the sharing of courseware. The IMIA recommendations centre on educational needs for healthcare professionals to acquire knowledge and skills in information processing and information and communication technology. ⋯ Learning outcomes are defined in terms of knowledge and practical skills for healthcare professionals in their role (a) as IT user and (b) as HMI specialist. Recommendations are given for courses/course tracks in HMI as part of educational programs in medicine, nursing, healthcare management, dentistry, pharmacy, public health, health record administration, and informatics/computer science as well as for dedicated programs in HMI (with bachelor, master or doctor degree). To support education in HMI, IMIA offers to award a certificate for high quality HMI education and supports information exchange on programs and courses in HMI through a WWW server of its Working Group on Health and Medical Informatics Education (http:www.imia.org/wg1).
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The recognition of clinically significant trends in monitored signals plays an important role in many medical diagnostic applications. A template-based system technique to identify characteristic patterns in time-series data is described, based on fuzzy logic. ⋯ The resulting fuzzy template system can accommodate multiple time signals, relative or absolute trends, and automatically generates a normalised "goodness of fit" score. The template approach was originally developed for monitoring during anaesthesia but has the potential to be useful in other domains that require temporal pattern recognition.
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Comparative Study
Distinction between planned and unplanned readmissions following discharge from a Department of Internal Medicine.
Readmission rate is often used as an indicator for the quality of care. However, only unplanned readmissions may have a link with substandard quality of care. We compared two databases of the Geneva University Hospitals to determine which information is needed to distinguish planned from unplanned readmissions. ⋯ Encoded reports allowed the classification of 64% of the readmissions, whereas full-text reports could classify 97% of the readmissions (p < 0.001). The concordance between encoded reports and full-text reports was fair (kappa = 0.40). We conclude that encoded reports alone are not sufficient to distinguish planned from unplanned readmissions and that the automation of detailed clinical databases seems promising.
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Achieving and monitoring adequate depth of anaesthesia is a challenge to the anaesthetist. With the introduction of muscle relaxing agents, the traditional signs of awareness are often obscured or difficult to interpret. These signs include blood pressure, heart rate, pupil size, etc. ⋯ The EEG was collected from the left hemisphere and analysed by FFT to 1 sec epochs and the spectral edge frequency was calculated. Both the changes in ARX extracted AEP and the spectral edge frequency of the EEG correlated well with the time interval between propofol induction and onset of anaesthesia measured by clinical signs (i.e., cessation of eye-lash reflex). The MTA extracted AEP was significantly slower in tracing the transition from consciousness to unconsciousness.
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Clinical Trial Controlled Clinical Trial
Autoregressive modeling with exogenous input of middle-latency auditory-evoked potentials to measure rapid changes in depth of anesthesia.
Obtaining an adequate depth of anesthesia is a continuous challenge to the anesthetist. With the introduction of muscle-relaxing agents the traditional signs of awareness are often obscured, or difficult to interpret. These signs include blood pressure, heart rate, pupil size, etc. ⋯ The method was clinically evaluated in 10 patients anesthetized with alfentanil and propofol. The time interval between propofol induction and the time when the Na-Pa amplitude was decreased to 25% of the initial amplitude was measured. These measurements showed that ARX-estimated compared to MTA-estimated AEP was significantly faster in tracing transition from consciousness to unconsciousness during propofol induction (p < 0.05).