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The fundamental concern of this research study is to learn the quality and efficiency of US healthcare services. It seeks to examine the impact of quality and efficiency on various stakeholders to achieve the best value for each dollar spent for healthcare. The study aims to offer insights on quality reformation efforts, contemporary healthcare policy and a forthcoming change shaped by the Federal healthcare fiscal policy and to recommend the improvement objective by comparing the US healthcare system with those of other developed nations.

The US healthcare system is examined utilizing various data on recent trends in: spending, budgetary implications, economic indicators, i.e. GDP, inflation, wage and population growth. Process maps, cause and effect diagrams and descriptive data statistics are utilized to understand the various drivers that influence the rising healthcare cost. A proposed cause and effect diagram is presented to offer potential solutions, for significant improvement in US healthcare.

At present, the US healthcare system is of vital interest to the nation's economy and government policy (spending). The US healthcare system is characterized as the world's most expensive yet least effective compared with other nations. Growing healthcare costs have made millions of citizens vulnerable. Major drivers of the healthcare costs are institutionalized medical practices and reimbursement policies, technology‐induced costs and consumer behavior.

Reviewing many articles, congressional reports, internet websites and related material, a simplified process map of the US healthcare system is presented. The financial process map is also created to further understand the overall process that connects the stakeholders in the healthcare system. Factors impacting healthcare are presented by a cause and effect diagram to further simplify the complexities of healthcare. This tool can also be used as a guide to improve efficiency by removing the “waste” from the system. Trend analyses are presented that display the crucial relationship between economic growth and healthcare spending.

There are many articles and reports published on the US healthcare system. However, very few articles have explored, in a comprehensive manner, the links between the economic indicators and measures of the healthcare system and how to reform this system. As a result of the US healthcare system's complex structure, process map and cause‐effect diagrams are utilized to simplify, address and understand. This study linked top‐level factors, i.e. the societal, government policies, healthcare system comparison, potential reformation solutions and the enormity of the recent trends by presenting serious issues associated with US healthcare.

Hospital costs in the USA are a large part of the national GDP. Medical billing and supplies processes are significant and growing contributors to hospital operations costs in the USA. This article aims to identify cost drivers associated with these processes and to suggest improvements to reduce hospital costs.

A Monte Carlo simulation model that uses @Risk software facilitates cost analysis and captures variability associated with the medical billing process (administrative) and medical supplies process (variable). The model produces estimated savings for implementing new processes.

Significant waste exists across the entire medical supply process that needs to be eliminated. Annual savings, by implementing the improved process, have the potential to save several billion dollars annually in US hospitals. The other analysis in this study is related to hospital billing processes. Increased spending on hospital billing processes is not entirely due to hospital inefficiency.

The study lacks concrete data for accurately measuring cost savings, but there is obviously room for improvement in the two US healthcare processes. This article only looks at two specific costs associated with medical supply and medical billing processes, respectively.

This study facilitates awareness of escalating US hospital expenditures. Cost categories, namely, fixed, variable and administrative, are presented to identify the greatest areas for improvement.

The study will be valuable to US Congress policy makers and US healthcare industry decision makers. Medical billing process, part of a hospital's administrative costs, and hospital supplies management processes are part of variable costs. These are the two major cost drivers of US hospitals' expenditures that were examined and analyzed.

Urinary incontinence (UI) is a common chronic health condition, a problem specifically among elderly women that impacts quality of life negatively. However, UI is usually viewed as likely result of old age, and as such is generally not evaluated or even managed appropriately. Many treatments are available to manage incontinence, such as bladder training and numerous surgical procedures such as Burch colposuspension and Sling for UI which have high success rates. The purpose of this paper is to analyze which of these popular surgical procedures for UI is effective.

This research employs randomized, prospective studies to obtain robust cost and utility data used in the Markov chain decision model for examining which of these surgical interventions is more effective in treating women with stress UI based on two measures: number of quality adjusted life years (QALY) and cost per QALY. Treeage Pro Healthcare software was employed in Markov decision analysis.

Results showed the Sling procedure is a more effective surgical intervention than the Burch. However, if a utility greater than certain utility value, for which both procedures are equally effective, is assigned to persistent incontinence, the Burch procedure is more effective than the Sling procedure.

This paper demonstrates the efficacy of a Markov chain decision modeling approach to study the comparative effectiveness analysis of available treatments for patients with UI, an important public health issue, widely prevalent among elderly women in developed and developing countries. This research also improves upon other analyses using a Markov chain decision modeling process to analyze various strategies for treating UI.