In the discussion, it is important to compare the US health care system with health care systems in other advanced industrialized countries. Canada and Germany involve a single payer system rather than a multiple payer system like that of the US. Their health care systems provide nearly universal access to medical care services and involve a greater financing and regulatory role for the federal government and less reliance on competition in health care matters. The available data suggests that the US spends more on medical care as a fraction of GDP than to the other two countries. In fact, as a fraction of GDP, the US spends slightly over 35% more than Germany, the next biggest spender. Comparatively high health care expenditures coupled with low medical utilization rates have led some to believe that medical prices must be significantly higher in the US than in the other two countries. The quality of medical services may be higher in the US and account for the alleged higher medical prices. Evidence suggests that waiting times are shorter for most medical services in the United States. In addition, the government in the US is responsible for financing about 44% of all health care spending. The comparable figure for other countries is well over 90% (Anderson, 1997).
Accessing primary health care is therefore far more complex than simply locating a service within or close to Indigenous communities . Nevertheless, measures of access at a population level are often confined to spatial factors including location and distance, using primarily quantitative data. In Australia, for example, a rural index of access combines system measures such as the number of health services within a given area and the population-provider ratio, with measures including the type and degree of identified health needs, distance to the nearest service and a mobility score . Others have used less complex scores focusing on distance , travel time  and supply and demand ratios . Quantitative measures of socioeconomic status with indicators of disadvantage have also been included [14, 15]. Clearly these quantitative perspectives ignore many of the access issues relevant to Indigenous peoples such as the ability of the service to accommodate the social and cultural needs of Indigenous peoples, the provision of health care by Indigenous staff in an Indigenous friendly space and considering the important role that communities and families often play within the care process .
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Archimedes (Archimedes, Inc., San Francisco, CA), for example, is a mathematical model that represents the anatomy, physiology, and pathology pertinent to diabetes and its complications . This model has been extensively validated and has already found multiple applications in healthcare policy and research [43, 44]. Recently, chronic obstructive pulmonary disease (COPD) models have been developed to offer multilevel approaches to investigate COPD complexity . A new model  that was recently developed for ALI detection uses three information sources: electronic medical record (EMR) data, published epidemiologic studies, and mechanistic understanding of domain experts (Figure3). Three modeling techniques were combined: rule-based fuzzy inference, state flow diagrams, and data-based correlation using Bayesian Networks. Clinical ALI development knowledge from ICU physicians were articulated verbally and subsequently written mathematically in terms of linguistic variables and rules. This method leads to a crisp value ALI detection score.
The process model can be used for several purposes, such as describing the flow of patients admitted to an ICU or predicting the future state of a patient conditional upon the complex interactions of several activities and decisions made in the prior state. Each phase requires an elaborate and in-depth multiscale study of key ICU processes. For example, using a process-modeling approach, a prototype of a DES model of septic shock resuscitation has been developed recently (Figure5) . This model is based on data collected through field observations, electronic medical records, and healthcare provider estimations. Options for improvements in system performance and workflow redesign were then implemented and tested by using a computer simulation model specific to sepsis resuscitation. Several key interventions could be investigated without patient harm: reduction of central line procedure time, modification of laboratory sample draws, etc. The model aims to refine, verify, and validate a comprehensive resuscitation process. This approach also is applicable to other critical care emergencies, including massive bleeding (hemorrhagic shock) or increased intracranial pressure.
Next-generation-sequencing (NGS) has revolutionized the field of genome assembly because of its much higher data throughput and much lower cost compared with traditional Sanger sequencing. However, NGS poses new computational challenges to de novo genome assembly. Among the challenges, GC bias in NGS data is known to aggravate genome assembly. However, it is not clear to what extent GC bias affects genome assembly in general. In this work, we conduct a systematic analysis on the effects of GC bias on genome assembly. Our analyses reveal that GC bias only lowers assembly completeness when the degree of GC bias is above a threshold. At a strong GC bias, the assembly fragmentation due to GC bias can be explained by the low coverage of reads in the GC-poor or GC-rich regions of a genome. This effect is observed for all the assemblers under study. Increasing the total amount of NGS data thus rescues the assembly fragmentation because of GC bias. However, the amount of data needed for a full rescue depends on the distribution of GC contents. Both low and high coverage depths due to GC bias lower the accuracy of assembly. These pieces of information provide guidance toward a better de novo genome assembly in the presence of GC bias.
(Deoxyribonucleic acid) DNA was demonstrated as the genetic material by Oswald Theodore Avery in 1944. Its double helical strand structure composed of four bases was determined by James D. Watson and Francis Crick in 1953, leading to the central dogma of molecular biology. In most cases, genomic DNA defined the species and individuals, which makes the DNA sequence fundamental to the research on the structures and functions of cells and the decoding of life mysteries . DNA sequencing technologies could help biologists and health care providers in a broad range of applications such as molecular cloning, breeding, finding pathogenic genes, and comparative and evolution studies. DNA sequencing technologies ideally should be fast, accurate, easy-to-operate, and cheap. In the past thirty years, DNA sequencing technologies and applications have undergone tremendous development and act as the engine of the genome era which is characterized by vast amount of genome data and subsequently broad range of research areas and multiple applications. It is necessary to look back on the history of sequencing technology development to review the NGS systems (454, GA/HiSeq, and SOLiD), to compare their advantages and disadvantages, to discuss the various applications, and to evaluate the recently introduced PGM (personal genome machines) and third-generation sequencing technologies and applications. All of these aspects will be described in this paper. Most data and conclusions are from independent users who have extensive first-hand experience in these typical NGS systems in BGI (Beijing Genomics Institute). 153554b96e