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Advanced Medical Statistics

About the instructor:Dr. Mohamed Elsherif, an Egyptian physician specialized in medical statistics, public health, epidemiology, and healthcare management. He has over 7 years of experience in performing statistical analysis for medical and non-medical research. He did the statistical analysis for more than 250 different researches. Experienced in statistical analysis using SPSS, Stata, and R.

Advanced Medical Statistics

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CoNNect International Academy founded in 2011, ever since we strive for excellence in medical education & clinical practice. Reaching more than 900 doctors yearly to prepare them for the international medical level.

In 2018, the U.S. spent 16.9 percent of gross domestic product (GDP) on health care, nearly twice as much as the average OECD country. The second-highest ranking country, Switzerland, spent 12.2 percent. At the other end of the spectrum, New Zealand and Australia devote only 9.3 percent, approximately half as much as the U.S. does. The share of the economy spent on health care has been steadily increasing since the 1980s for all countries because health spending growth has outpaced economic growth,2 in part because of advances in medical technologies, rising prices in the health sector, and increased demand for services.3

First, greater attention should be placed on reducing health care costs. The U.S. could look to approaches taken by other industrialized nations to contain costs,12 including budgeting practices and using value-based pricing of new medical technologies. Approaches that aim to lower health care prices are likely to have the greatest impact, since previous research has indicated that higher prices are the primary reason why the U.S. spends more on health care than any other country.13

Students are prepared to sit for the Nuclear Medicine Technology Certification Board (NMTCB) or American Registry of Radiological Technologists (ARRT) certification exam. Graduates go on to work in in hospitals, physician offices, and medical and diagnostic laboratories. Jobs include nuclear medicine technologist, MRI technologist, and CT technologist.

A medical device can be any instrument, apparatus, implement, machine, appliance, implant, reagent for in vitro use, software, material or other similar or related article, intended by the manufacturer to be used, alone or in combination for a medical purpose.

Member States recognized in World Health Assembly (WHA) resolutions WHA60.29 (2007) and WHA 67.20 (2014) that medical devices are indispensable for health-caredelivery but that their selection, regulation and use present enormous challenges,especially for low- and middle-income countries (LMIC).

The WHO Global Fora onMedical Devices serve as opportunities to share WHO initiatives to support countryneeds towards Universal Health Coverage (UHC) and the achievement of theSustainable Development Goals (SDGs).The Fora also serve as occasions to listen to regional and country activities on medicaldevices issues. The Fora present the WHO resources available to Member States in a range of topics concerning medical devices:

The programmes of the WHO Global Fora have include presentations on the a huge range of topics on medical devices and also help present WHO projects, initiatives, tools, resources and work in progress.

PURPOSE: This program is designed to provide the technical knowledge and skills needed for employment as an advanced medical coder. The coursework for the advanced level will equip students to work in medical records and offer an opportunity for existing coders to further their management, supervisory, auditing, or alternative coding skills.

Similar to EHR, an electronic medical record (EMR) stores the standard medical and clinical data gathered from the patients. EHRs, EMRs, personal health record (PHR), medical practice management software (MPM), and many other healthcare data components collectively have the potential to improve the quality, service efficiency, and costs of healthcare along with the reduction of medical errors. The big data in healthcare includes the healthcare payer-provider data (such as EMRs, pharmacy prescription, and insurance records) along with the genomics-driven experiments (such as genotyping, gene expression data) and other data acquired from the smart web of internet of things (IoT) (Fig. 1). The adoption of EHRs was slow at the beginning of the 21st century however it has grown substantially after 2009 [7, 8]. The management and usage of such healthcare data has been increasingly dependent on information technology. The development and usage of wellness monitoring devices and related software that can generate alerts and share the health related data of a patient with the respective health care providers has gained momentum, especially in establishing a real-time biomedical and health monitoring system. These devices are generating a huge amount of data that can be analyzed to provide real-time clinical or medical care [9]. The use of big data from healthcare shows promise for improving health outcomes and controlling costs.

The analysis of data from IoT would require an updated operating software because of its specific nature along with advanced hardware and software applications. We would need to manage data inflow from IoT instruments in real-time and analyze it by the minute. Associates in the healthcare system are trying to trim down the cost and ameliorate the quality of care by applying advanced analytics to both internally and externally generated data.

Big data is the huge amounts of a variety of data generated at a rapid rate. The data gathered from various sources is mostly required for optimizing consumer services rather than consumer consumption. This is also true for big data from the biomedical research and healthcare. The major challenge with big data is how to handle this large volume of information. To make it available for scientific community, the data is required to be stored in a file format that is easily accessible and readable for an efficient analysis. In the context of healthcare data, another major challenge is the implementation of high-end computing tools, protocols and high-end hardware in the clinical setting. Experts from diverse backgrounds including biology, information technology, statistics, and mathematics are required to work together to achieve this goal. The data collected using the sensors can be made available on a storage cloud with pre-installed software tools developed by analytic tool developers. These tools would have data mining and ML functions developed by AI experts to convert the information stored as data into knowledge. Upon implementation, it would enhance the efficiency of acquiring, storing, analyzing, and visualization of big data from healthcare. The main task is to annotate, integrate, and present this complex data in an appropriate manner for a better understanding. In absence of such relevant information, the (healthcare) data remains quite cloudy and may not lead the biomedical researchers any further. Finally, visualization tools developed by computer graphics designers can efficiently display this newly gained knowledge.

Heterogeneity of data is another challenge in big data analysis. The huge size and highly heterogeneous nature of big data in healthcare renders it relatively less informative using the conventional technologies. The most common platforms for operating the software framework that assists big data analysis are high power computing clusters accessed via grid computing infrastructures. Cloud computing is such a system that has virtualized storage technologies and provides reliable services. It offers high reliability, scalability and autonomy along with ubiquitous access, dynamic resource discovery and composability. Such platforms can act as a receiver of data from the ubiquitous sensors, as a computer to analyze and interpret the data, as well as providing the user with easy to understand web-based visualization. In IoT, the big data processing and analytics can be performed closer to data source using the services of mobile edge computing cloudlets and fog computing. Advanced algorithms are required to implement ML and AI approaches for big data analysis on computing clusters. A programming language suitable for working on big data (e.g. Python, R or other languages) could be used to write such algorithms or software. Therefore, a good knowledge of biology and IT is required to handle the big data from biomedical research. Such a combination of both the trades usually fits for bioinformaticians. The most common among various platforms used for working with big data include Hadoop and Apache Spark. We briefly introduce these platforms below.

In healthcare, patient data contains recorded signals for instance, electrocardiogram (ECG), images, and videos. Healthcare providers have barely managed to convert such healthcare data into EHRs. Efforts are underway to digitize patient-histories from pre-EHR era notes and supplement the standardization process by turning static images into machine-readable text. For example, optical character recognition (OCR) software is one such approach that can recognize handwriting as well as computer fonts and push digitization. Such unstructured and structured healthcare datasets have untapped wealth of information that can be harnessed using advanced AI programs to draw critical actionable insights in the context of patient care. In fact, AI has emerged as the method of choice for big data applications in medicine. This smart system has quickly found its niche in decision making process for the diagnosis of diseases. Healthcare professionals analyze such data for targeted abnormalities using appropriate ML approaches. ML can filter out structured information from such raw data.

AI has also been used to provide predictive capabilities to healthcare big data. For example, ML algorithms can convert the diagnostic system of medical images into automated decision-making. Though it is apparent that healthcare professionals may not be replaced by machines in the near future, yet AI can definitely assist physicians to make better clinical decisions or even replace human judgment in certain functional areas of healthcare. 041b061a72


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