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Healthcare organizations in the United States had significant financial difficulties following the COVID-19 outbreak. These were made worse by the healthcare industry's fast-evolving workforce, which was influenced by years of physical and psychological stress on personnel and led to retirements, resignations, and relocations. The cost of operating for healthcare organizations increased as a result of the need for more adaptable working conditions, greater wages, and better benefits. In the beginning, the government's pandemic funding was frequently used to subsidize expensive temporary or contract labor as a response to the healthcare personnel shortage. Healthcare organizations struggled with increasing labor costs and declining reimbursement rates when these funds ran out and inflationary pressures remained. This article describes a complete 6-week program held from July 6 to August 11, 2023, within a community-based healthcare system, to educate and empower clinical and operational teams in problem-solving and financial responsibility. Five guiding concepts (Pillars) were used by the Sustainable Management Above Reimbursement Transformation (SMART) initiative to direct cost-cutting activities: Practice Efficiency, Access Improvement, Working Differently, Expense Reductions, and Revenue Generation. The goal of an advisory panel supported by sub-teams was to find areas for improvement without sacrificing patient care while evaluating costsaving targets and suggested measures for various service areas. With an average predicted cost savings of 13% across service lines, the outcomes exceeded expectations. This successful initiative represents a significant stride toward financial sustainability in healthcare institutions, demonstrating the resilience and adaptability of healthcare providers during challenging economic times and offering valuable lessons for the future. [ABSTRACT FROM AUTHOR]

The utilization of carbon fiber 3D printed specimens is progressively expanding across various applications that need exceptional levels of strength and stiffness. Nevertheless, the impact strength of these specimens may be influenced by several parameters, including carbon fiber content, infill pattern, density, printing speed, and layer thickness. This research study uses the design of experiments (DOE) methodology to examine the parameters that influence the impact strength of carbon fiber 3D printed specimens. A statistical model has been established to forecast the impact strength of carbon fiber 3D printed specimens, taking into consideration the elements. The study's findings indicate that the impact strength of carbon fiber 3D printed specimens is mostly influenced by the infill density in comparison to other parameters. The impact strength is significantly influenced by the interaction effects of layer thickness, infill pattern, and infill density. The statistical model created in this research may be used to ascertain the most suitable amalgamation of variables to get the intended level of impact strength for a certain application. This phenomenon has the potential to enhance the overall performance, longevity, and safety attributes of many items. [ABSTRACT FROM AUTHOR]

This study is aimed at using various statistical analyses within the Design of Experiments (DOE) technique to investigate and analyze the effect of welding process parameters on the tensile strength of A36-grade mild steel. Three process parameters and two corresponding levels were considered and the MIG welding process was performed. The ideal conditions to achieve the highest strength were determined and the results were obtained by running the design on a statistical software Minitab. This experimental study found that the only factor having a significant contribution to the tensile stress of the plates in the selected scenario is the weld speed. In addition, this study provides valuable insights for enhancing welding procedures and techniques. The paper concludes with a reasonable interpretation of the test results and justifies the research questions that were proposed. [ABSTRACT FROM AUTHOR]

One of the biggest challenges facing small and medium manufacturers in today’s economic environment is the recruitment and retention of talent across all functions in an organization. It is extremely challenging to find and keep shop floor associates as welders, painters, machinists, and assemblers. Even if an organization can hire skilled personnel, training them on specialized equipment and work practices takes a lot of effort and time. Work instructions (WI) or standard operating procedures (SOP) are a set of instructions that enumerate all the steps and activities required to complete a job. Traditional WI or SOP suffer from many disadvantages: they are hard to control, difficult to understand in many situations, time consuming to create and maintain, and do not capture the tacit knowledge of trained employees effectively. To overcome the challenges posed by regular SOP, a new way of creating them has gained traction in the last few years called Video Work Instructions (VWI). In this approach, a video of the manufacturing process is taken while the operation is being carried out. The video is later edited to include the work instruction in the frames during the actual operation. In this project, VWI was created for a medium manufacturer in Southern MN for welding and assembly processes. The VWI was later used in employee training and cross training. It was found that there was a reduction of the new employee training period by approximately 50% and a productivity increase by approximately 12%. This paper will talk about the advantages and challenges associated with such an approach. [ABSTRACT FROM AUTHOR]

In the World Trade Organization (WTO) agreements, United States has continued to enforce trade agreements with many countries through the World Trade Organization (WTO), with 14 of the 20 countries being active participants in these agreements. Free trade agreements (FTAs) have grown in importance as essential trade agreements that facilitate trade negotiations among member countries. These FTAs could have a wide range of positive effects, including the ability to strengthen a country's overall economy and open new economic opportunities. Whether they support or oppose trade agreements, economists can agree that they are good for the economy. Trade agreements, in a brief, are the "rules of the road" that guide American companies on how to do international business while simultaneously defending American interests and utilizing international trade regulations. According to recent data, the US has continued to enter trade deals with several other countries, including the UK, Mexico, and Canada. One of the most important trade agreements for the US in recent years is the United States-Mexico-Canada Agreement (USMCA), which replaced the North American Free Trade Agreement (NAFTA) provides a crucial framework for commercial relations with these neighboring nations. The Trans-Pacific Partnership (TPP) and the Transatlantic Trade and Investment Partnership (TTIP), two other recent trade deals, intended to improve trade ties between countries in the Pacific Rim and the European Union, respectively. Businesses in the United States can sign a variety of trade agreements, including Free Trade Agreements, Agreements with the World Trade Organization, Suspension Agreements, Bilateral Investment Treaties, Agreements on Intellectual Property Rights, Trade Agreement Guides, and the Florence Agreement Program. The purpose of this paper is to provide valuable insights into the benefits and drawbacks of these trade agreements and their long-term effects on the US economy and future generations. [ABSTRACT FROM AUTHOR]

The paper discusses the problems of cyber-attacks on supply chain management systems in different sectors and industries. It causes high severity and economic losses. Along with various technical terms, the countermeasure technique, both theoretically and in application, are discussed. If developed countermeasures are applied in every sector, it will reduce the cyberattack problem on supply chain management systems. It will create more job opportunities, more secure information, more awareness among people, and more economic generation for any country. [ABSTRACT FROM AUTHOR]

The United States manufacturing sector has managed to remain an impressive and productive sector in the country and globally owing to the U.S. government's support in matters of manufacturing. Due to the increasing use of sophisticated technological advancements that streamline operations and standardize product quality and quantity, the manufacturing sector in the U.S. produces exceptional output. Technological advances in the U.S. manufacturing sector enable manufacturers to remain competitive in an aggressive business environment and profitable amid stiff competition. Moreover, technological advances streamline communication processes along supply chains, enabling manufacturers to better serve their customers. The enormous potential of these technologies to rescue economies from recession and other economic obstacles makes it almost inevitable that the future of manufacturing in the U.S. will be promising. However, automation and innovative technologies have not been easy on the manufacturing workforce for the obvious reason that they tend to replace human labor. As a result, people who have been integrated into the manufacturing sector are either laid off or completely frustrated, and those who are still integrated receive low wages. Because of this, the manufacturing industry's employment prospects appear dim because machines receive more attention than human resources. The Covid-19 pandemic had crippling effects on U.S. manufacturing by dramatically hiking prices of raw materials and finished goods, altering delivery schedules and demand patterns. Manufacturers resorted to low inventory to avoid losses. The sudden demand spike caused by the pandemic caught manufacturers off guard, disrupting supply chains and weakening the sector. That said, it is critical that the government of the U.S. increase its efforts to enhance the manufacturing sector and protect it from unforeseen occurrences that could jeopardize the sector's progress to date, both locally and internationally. [ABSTRACT FROM AUTHOR]

This paper aims to analyze and review the most recent scientific literature regarding the implementation of Machine Learning (ML) in Supply Chain Management (SCM). Moreover, an examination was conducted to find out what areas and industries were addressed in the use cases, and how ML may impact SCM. A Systematic Literature Review (SLR) was conducted for this purpose. It was found that the applied examples mainly used ML's prediction capabilities, and some industries such as health care and logistics were the main focus areas. Then, discussions were developed based on the results of the systematic literature analysis, which can be used as motivation for future research. [ABSTRACT FROM AUTHOR]

Allied Health staffing has been a multi-decade challenge for many healthcare organizations and has become a crisis for nearly all healthcare organizations throughout the COVID-19 global pandemic. Mayo Clinic Health System in the Southwest Minnesota Region was not immune to these challenges. Recognition that staffing issues needed to be clearly defined, articulated, and planned served as a catalyst for developing and implementing a novel systematic approach coupled with process optimization using the staffing to workload tool. This revelation led to the present study, which explored establishing a centralized prospective staffing model for ambulatory clinics in the health system region. The study leveraged agile methods, widely utilized as a project management technique in the software development life cycle. Agile methodology has also been referred to as a mindset and principle where continuous feedback is incorporated on the product or service line being developed, along with the progress made, which was also an essential element of this study. Apart from establishing a staffing model, achievements made within this study included benchmarking well-established staffing related key metrics to national benchmarks, setting up a structure to manage staffing guidelines and standards centrally, an overview of key performance indicators and metrics as a region apart from siloed managing of resources by clinical sections and departments, and setting up processes to manage staffing and metrics of success on an ongoing basis. The work resulted in effective staffing to workload tool, allowing frontline leadership to identify and articulate staffing needs based upon outpatient visits and predictive capabilities in a practice or provider template change. Findings were well received by the key stakeholders, leadership constituencies, frontline leadership, and staff. [ABSTRACT FROM AUTHOR]

Quality improvement has been widely used in healthcare in the last few decades. The US healthcare sector has made significant strides in quality improvement, technological advancement, and innovations in the last century, yet it is often referenced as a complex, expensive and an industry with many inefficient processes and systems. One of the widely used key performance metrics in US hospitals is the 30-day emergency department visit after a surgical procedure. ED visits within 30 days of a surgical procedure are considered one of the key quality outcome measures, adding millions of dollars each year as a cost burden to US healthcare. This study aimed to identify key predictors that are known prior to the patient's surgery date contributing to undesirable ED visits within 30 days of a bariatric surgical procedure. The study was conducted in three phases. The first phase of the study engaged a panel of experts to narrow down important preoperative factors for patients undergoing bariatric surgery in the form of a Delphi study. The second phase of the study included quantitative data analysis, which utilized the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program Participant Use Data File from 2019 to identify statistically significant preoperative factors that can contribute to the likelihood of patients returning to the emergency department within 30 days of bariatric surgery. There were N = 193,774 cases with complete information from 868 MBSAQIPaccredited bariatric surgery centers across the United States in the Data File among which 15,533 (8% of the total cases) visited an ED without needing admission as inpatients. Examining the feasibility of developing a predictive model with only statistically significant factors and checking if the model has an acceptable fit was also part of the analysis. The third phase of the study reengaged the same panel of experts from the first phase to validate the findings from the second phase and to document the subject matter experts' perception regarding the model developed and the overall findings. Out of 33 preoperative variables, only 9 variables were selected in the first phase of the study with the help of a panel of experts. Out of the 9 selected variables, 8 variables i.e., Pre-Op GERD requiring medication, Number of Hypertensive Medications, Pre-Op BMI closest to bariatric surgery, Highest Recorded Pre-Op BMI, Pre-Op vein thrombosis requiring therapy, Pre-Op diabetes mellitus, Pre-Op history of COPD, and Pre-Op Steroid/Immunosuppressant Use for Chronic Condition significantly contributed to the likelihood of patients coming back to ED within 30 days of a bariatric procedure. The second phase of the study also yielded a predictive model using only the statistically significant and weighted variables, and each predictor exhibited statistical significance. Although Omnibus Tests of Model Coefficients showed that the Chi-square value was highly significant (X^2=505.052, df=8, p<0.001) suggesting the revised model after addition of exploratory variables being statistically significant, Hosmer & Lemeshow test suggested that the revised model was not a good fit to the data (X^2=22.152, df=8, p< 0.05). In the third phase of the study, a panel of experts weighted in mostly with positive feedback deeming the study to be clinically and operationally valuable for the bariatric patient population. Practical implication of this study is that the model can be used by the MBSAQIP Centers to determine the probability of patient's likelihood of coming back to ED after a bariatric surgical procedure. Based on the set criteria, if the patient has higher chance of coming back to ED, care team can take interventions during and in the first few days or weeks of the discharge to prevent potential ED visit. Limitations of this study include but is not limited to the timeframe of the data was limited to only one year i.e., 2019 and size of the panel of experts was limited to 5. For future research studies, subject matter experts recommended taking the study to the next level by applying the findings to prevent future ED visits that are avoidable. Researchers can also take the findings from this study and expand the research by increasing the timeframe of the dataset and explore other variables that could also be important from day-to-day operations perspective which were not part of this study. [ABSTRACT FROM AUTHOR]

Since the COVID-19 global pandemic surfaced and soon impacted many lives and industries across the globe, many organizations did not even have time to think about how to respond to such a crisis. This paper explores how the use of one such quality improvement methodology: Lean Six Sigma, helped the healthcare sector to be better prepared during the time of a global pandemic. Define, measure, analyze, improve, and control (DMAIC) is a Lean Six Sigma tool for continuous improvement. This paper discusses how DMAIC was instrumental in developing a COVID-19 Surge Planning Strategy for hospitals in the Midwestern US. A dedicated multidisciplinary team can look at the number of patients, trends, and available resources to plan effectively. Being one of the first such studies, the findings are useful for researchers and practitioners. [ABSTRACT FROM AUTHOR]

US healthcare has made huge progress in technological advancements and regulatory standards over the last century. Despite this, healthcare as an industry has many challenges which are experienced by many patients on a daily basis and has also become a hot topic for politicians to debate in recent years. This study aims to identify technological advancements in healthcare and their socioeconomic impacts. In order to precisely pin down the opportunities related to technology in healthcare, researchers seek to find answers to questions related to technological advancements in healthcare over the past century to present day, make comparisons of advances in healthcare with the manufacturing industry, and observe technology's relationship with socioeconomic status of today's society and healthcare. The research was conducted through an extensive literature review using online resources available through the university and public databases. Findings of this research study suggest that healthcare is nimble in its overall advancement but in regards to technology, it is still catching up compared with other industries like manufacturing. Technology is definitely a boon to society from a healthcare perspective but it can also be a disadvantage at times due to its poor management and system wide applications. [ABSTRACT FROM AUTHOR]

This study analyzes the reliability modelling and quality analysis of SQL servers in millennium database reporting, predicts failures on Microsoft SQL Server version 2012 (pre-) and version 2014 (current or post-), and compares failure data on both the servers. The SQL reporting server runs as an application server that provides capabilities to process and manage reporting services. The SQL server helps in processing reports that are placed on the server either by scheduling the reports to run at a certain time or they could be run on demand. The data required for this project is collected from Millennium database servers of a US organization. Each of these server versions is analyzed based on report failure rate for two years and compared using the paired t-test. This data is used to determine the mean time to failure (MTTF) and is fit to four statistical distributions: Weibull, lognormal, exponential, and normal. Weibull is found to be the best fit. Goodness of fit test validates the Weibull model. For the Weibull distribution, parameters are estimated which can be used to determine reliability, hazard rate, etc. of the database management server. Quality analysis was done using quality tools like the fishbone diagram to perform root cause analysis of the failures and a Pareto chart to know the vital causes from trivial many. [ABSTRACT FROM AUTHOR]