Author response for "Anti‐LGI1, anti‐GABABR, and Anti‐CASPR2 encephalitides in Asia: A systematic review"
Prinska GhimireUjjwal Prakash KhanalBikram Prasad GajurelRagesh KarnReema RajbhandariSunanda PaudelNiraj GautamRajeev Ojha
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Evidence based medicine (EBM) refers to the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. This article presents systematic review of the use of information technology (IT) to support EBM with a particular emphasis on status and opportunities. Out of 2,490 papers initially scanned, 585 articles were included at the title level review. This was followed by an abstract review, which resulted in 196 articles. On full text scanning of the 196 articles, 69 articles met the inclusion criteria and were included in the final analyses. The key issues and potential for IT support for the practice of EBM are insufficient techniques to produce evidence in a computer interpretable format, insufficient research to combine the evidence from the multiple sources, inadequate techniques that automatically rate the literature and practice-based evidence, and integration of evidence at the clinician’s workflow.
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Reasons for errors in medication administration are complex, and healthcare facilities are on constant vigil to find ways to reduce medication errors and subsequent harm to patients. Hepler and Segal (2003) discuss the idea of medication errors as result of system failure. The authors describe system as a set of interdependent elements interacting to achieve common aim (Hepler & Segal, 2003, p. 49), and agree that elements of systems may include individuals, equipment, and techniques. Nurses, pharmacists, physicians, other healthcare providers, and the patients themselves are all involved in this system. The mnemonic device described in this column can help to improve the safety of nurses' practice by assisting them to remember the many facets of safe techniques for IV medication preparation, administration, and use of the various IV pumps.
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Medical errors are often associated with inadequate cognitive processing that is based upon impaired access to information.1 Understanding the information needs of nurses and physicians' when using a clinical information system (CIS) is difficult largely because there are few systematic attempts made to do so. We collected 15.5 hours of data of nurses and physicians' CIS interactions in three clinical environments (cardiac ICU, a general medical/surgical nursing, and an ambulatory clinic). We accomplished this through observational, cognitive-based methods (e.g., thinking aloud during interaction) and the video capturing of events with a portable usability laboratory.2 We developed a systematic approach to identify and define in-context clinical information needs while using a CIS, and determine how to categorize and code such events. 3
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Ratings systems and awards for medical Web sites have proliferated, but the validity and utility of the systems has not been well established. This study examined the effect of awards on the perceived credibility and retention of health information on a Web page. We recruited study participants from Internet newsgroups and presented them with information on the claimed health benefits of shark cartilage. Participants were randomized to receive health information with and without a medical award present on the page. We subsequently asked them to evaluate the credibility of the Web page and posed multiple-choice questions regarding the content of the pages. 137 completed responses were included for analysis. Our results show that the presentation of awards has no significant effect on the credibility or retention of health information on a Web page. Significantly, the highly educated participants in our study found inaccurate and misleading information on shark cartilage to be slightly believable.
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Epilepsy is a chronic brain disorder that is characterized by abrupt discharge of neurons. Epilepsy has two main classes: generalized and focal epilepsy. In focal epilepsy source of the seizure within the brain is localized but in generalized epilepsy, it is distributed. About 1% of world populations suffer from epilepsy and one third of them have intractable seizure by medicine. Epileptics tolerate many difficulties due to seizure. Most of them also live in social seclusion. In addition, because of the medicine side effects and treatments, they may have troubles such as: double vision, fatigue, sleepiness, unsteadiness, as well as stomach upset. An effective treatment for epileptics in some rare cases with focal epilepsy (usually in median-temporal lobe) is by operation to separate a huge part of the brain tissue which has no essential function. Consequently, most of these patients need permanent care and treatment and 25% of them have to receive high dose of drugs and laboratory treatments. Therefore, diagnostic and warning algorithms for epilepsy infinite recognition, controlling seizure (to prepare for seizure e.g., pull over if driving) and organizing medicine schedule (to reduce unwanted side effects of not on time medication) will be useful. Such algorithms use brain electrical activity signals called electro encephalography (EEG) and have 2 methods of detection: visual (by specialist inspection) and automatic (by using signal processing knowledge). There are some problems faced by a neurologist in the inspection of long term EEG such as; being too time consuming, analytical precision requirement, similarity of epileptic spikes with artifacts like eye blinking, and too slight epileptic spikes nature to be detected in time domain. Proposing an automatic system to reduce time for epilepsy detection has been interesting field in recent decades. Most epilepsy types, even in inter-ictal (between two seizure) period, have transient signs in EEG called as spike and sharp waves (SSWs) that represent epilepsy disorder and its category. Most important signs are spikes. In this thesis an automated system has been developed to detect spikes from EEG to increase diagnosis speed, inspection precision and accuracy by applying some preprocessing such as filtering and artifact removing. Wavelet is applied as a feature extraction method and adaptive neuro-fuzzy inference system (ANFIS) is used for classification. Total accuracy of 97.5% has been obtained.
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Epidemiological investigations on nutrition and chronic diseases require high reliability, accuracy, low cost and feasibility of the system through which dietary information is collected. Food frequency questionnaire are commonly used in large epidemiological investigations. However, repeated 24 hour recall should be considered to integrate this information. Multiple 24 h recalls in random samples of large cohorts appear to be convenient choice to improve data accuracy. This procedure also allow calibration of dietary information in case of pooling of data from different cohorts. Multiple 24 h recalls can be easily carried out through telephone interviews instead of multiple face-to-face. This reliable procedure, tested in a study reported in this paper, may improve compliance.
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