International journal of medical informatics
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Comparative Study
Concordance of information in parallel electronic and paper based patient records.
to evaluate the results of parallel use of both paper based and electronic patient records with respect to concordance of corresponding information in two continuously updated versions of the same records. ⋯ parallel use of electronic and paper based patient records has resulted in inconsistencies between the record systems in our setting. Documentation is missing in both the electronic and paperbased records. When implementing electronic record systems intended to operate in parallel with paperbased systems, focus should be on securing the validity of all versions of the record.
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We developed and evaluated the Emergency Department Expert Charting System (EDECS) to provide real-time guidance regarding the care of low back pain in adults, fever in children, and occupational exposure to blood and body fluids in health care workers, by embedding clinical guidelines within an electronic medical record. ⋯ These data illuminate both the potentials of computer-assisted decision making and the need for context-specific approaches when attempting to implement guidelines.
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Small changes that occur in a patient's physiology over long periods of time are difficult to detect, yet they can lead to catastrophic outcomes. Detecting such changes is even more difficult in intensive care unit (ICU) environments where clinicians are bombarded by a barrage of complex monitoring signals from various devices. Early detection accompanied by appropriate intervention can lead to improvement in patient care. ⋯ The TDNN perform remarkably not only in identifying all hemodynamic conditions, but also in quickly detecting their changes. On average, the networks were able to detect the hemodynamic changes in less than 1 s after the onset. Based on the results of this pilot investigation, the use of this form of TDNN to successfully predict hemodynamic conditions appears to be promising.
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Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in medicine for tasks such as the interpretation of sets of medical findings, syndrome differentiation in Eastern medicine, diagnosis of diseases in Western medicine, mixed diagnosis of integrated Western and Eastern medicine, the optimal selection of medical treatments integrating Western and Eastern medicine, and for real-time monitoring of patient data. This was verified by trials with the following systems that were developed by our group in Vietnam: a fuzzy Expert System for Syndromes Differentiation in Oriental Traditional Medicine, an Expert System for Lung Diseases using fuzzy logic, Case Based Reasoning for Medical Diagnosis using fuzzy set theory, a diagnostic system combining disease diagnosis of Western Medicine with syndrome differentiation of Oriental Traditional Medicine, a fuzzy system for classification of Western and Eastern medicaments and finally, a fuzzy system for diagnosis and treatment of integrated Western and Eastern Medicine.
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Emergency ambulances traditionally inform receiving hospitals of impending arrival of patients only in instances of load and go situations, which on average constitute less than 5% of ambulance runs. Information transmitted is usually sparse. For all other runs, information is made available only on arrival at the emergency department (ED). ⋯ Transmission was wireless via the public mobile data network. A 3-month analysis of this pilot run revealed the following. (1) It was possible to capture a complete ambulance case record electronically at a mean time of 94 s vs 7 min 7 s for the traditional written record. (2) Air transmission time for data was approximately 4 s unless ECG wave forms were also transmitted resulting in transmission times frequently exceeding 60 s. (3) At least 68% of data was transmissible in 75% of Hospital & Emergency Ambulance Link (HEAL) ambulances as opposed to only 25% in less than 5% of non-HEAL ambulances. (4) Paramedics' time in the ED decreased from 15 to 8 min as a result of HEAL. (5) The waiting time for critical care patients to be seen at the ED decreased from 35 to 17 min if brought by HEAL ambulances.(6) The HEAL system was able to effectively prompt paramedics in carrying out critical aspects of treatment in close to 100% of instances. (7) The pilot HEAL system was able to demonstrate a limited amount of automated audit of specific aspects of ambulance runs. Having demonstrated the feasibility of the HEAL system, it is a matter of time before enhanced features such as electronic data collection at patient site, voice activated data entry, transmission of data from site, automated ambulance audits and an enhanced level of professional care in the ambulances become common-place reality.