J Med Syst
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Mobile Personal Health Records (PHRs) have achieved a particularly strong market share since the appearance of more powerful mobile devices and popular worldwide mobile application markets such as Apple's App Store and Android's Google Play. However, Android and Apple have a set of recommendations on design and usability targeted towards developers who wish to publish apps in their stores: Android Design Guidelines and iOS Human Interface Guidelines. ⋯ The results show that the 24 mobile PHR applications studied are not suitably structured. 46 % of these applications do not use any of the recommended patterns, using instead lists or springboards, which are deprecated patterns for top-level menus. 70 % of the PHRs require a registration to be able to test the application when these interactions should be delayed. Our study will help both PHR users to select user-friendly mobile PHRs and PHR providers and developers to identify the good usability practices implemented by the applications with the highest scores.
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A key purpose of electronic medical records (EMR) introduced in medical institutions is to improve work efficiency. The average length of stay (LOS) is just an important indicator to evaluate work efficiency of medical care in hospitals. Recently, there have been reports about effects of EMR application on LOS in medical institutions, but they have been mostly based on the overall analysis of a region or a hospital and not of specific clinical departments and diseases or based on longer time periods. ⋯ The trend changes in LOS reversed from increasing to decreasing in the orthopedics department (coefficient: 0.016 to -0.079), the cardiovascular surgery department (coefficient: 0.007 to -0.126) and all departments overall (coefficient: 0.004 to -0.070), as well as for the intervertebral disc disorders (coefficient: 0.026 to -0.068). Furthermore, the decreasing trend gained a larger slope in the cardiology department (coefficient: -0.017 to -0.023), the neurology department (coefficient: -0.012 to -0.043) and for the coronary heart disease (coefficient: -0.010 to -0.018), the ventricular septal defect (coefficient: -0.024 to -0.059), and the cerebral infarction (coefficient: -0.031 to -0.040). Together, these findings indicate that EMR application coincided with a decrease in LOS and may have a contribution to the decrease.
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Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. "Big Data"). Health care systems have leveraged Big Data for quality and performance improvements using analytics-the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. ⋯ Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.
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Multicenter Study
Variability of subspecialty-specific anesthesia-controlled times at two academic institutions.
Realistic scheduling of operating room cases decreases costs, optimizes utilization and improves staff and patient satisfaction. Currently limited data exists to establish anesthesia-controlled time benchmarks based on specific subspecialty service. In this multicenter retrospective analysis of cases performed during a 53 month period at two large multispecialty academic institutions, data were retrieved from the perioperative information systems at each center. ⋯ Subspecialties with higher total anesthesia controlled times in both centers included cardiac surgery, neurosurgery, transplant and vascular. Cardiac surgery had the highest total time of 60 min and 50 min at Hospital A and B respectively. Individual specialty-specific anesthesia controlled times should be used for case scheduling and to benchmark anesthesia performance.
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Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. ⋯ After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.