1. Nashaat What is operations research? Operations research (OR) is an interdisciplinary field that employs advanced analytical methods to enhance decision-making processes within organizations.
1. Nashaat
What is operations research?
Operations research (OR) is an interdisciplinary field that employs advanced analytical methods to enhance decision-making processes within organizations. Originating from the military planners’ efforts during World War I can be defined as a discipline that deals with the application of advanced analytical methods to help make better decisions (Langabeer II & Helton, 2021). According to the Institute for Operations Research and the Management Sciences, it is characterized as “a discipline that deals with the application of advanced analytical methods to help make better decisions” (Langabeer II & Helton, 2021).
In the healthcare sector, operations research plays a pivotal role by systematically improving processes, resource allocation, and decision-making. It offers a structured and data-driven approach to problem-solving, enabling healthcare organizations to elevate the quality of their decisions. The overarching objective is to augment operational, financial, and strategic outcomes, making OR an indispensable asset for healthcare professionals seeking to optimize patient care from scheduling and resource utilization to logistical and system-wide efficiency (Langabeer II & Helton, 2021; Stoumpos et al., 2023).
Operations research is deeply rooted in historical military applications, initially emerging during World War I and gaining significant momentum during World War II. The application of OR methodologies in healthcare represents a strategic evolution, adapting military-inspired problem-solving to the complex challenges of the healthcare landscape. As the healthcare industry continues to evolve, embracing technological advancements and digital transformation, the role of operations research becomes increasingly critical in navigating the intricacies of modern healthcare delivery (Stoumpos et al., 2023).
What are the most common operations research methods used in health care today?
Within healthcare, three prevalent operations research methods stand out: Linear Programming, Simulation, and Forecasting. Linear Programming is widely embraced for its ability to optimize resource allocation decisions, factoring in constraints such as staffing levels and financial limitations (Langabeer II & Helton, 2021). Simulation involves crafting computer-based models that predict the performance of real-world processes, offering a dynamic platform to observe outcomes under diverse scenarios (Langabeer II & Helton, 2021). Forecasting is a cornerstone method for estimating patient volumes and comprehending fluctuations in activity levels over time, empowering healthcare organizations to adeptly align resources with fluctuating demand (Langabeer II & Helton, 2021).
The digital transformation in healthcare is also a crucial consideration, with technology acceptance and its applications playing a pivotal role (Stoumpos et al., 2023). The integration of digital technologies, such as Electronic Health Records (EHRs) and telehealth solutions, further expands the scope of operations research applications. These technologies provide rich datasets that can be analyzed using operations research methodologies to optimize processes, improve patient outcomes, and enhance overall healthcare delivery.
Based on your answer, choose one and describe a specific modern example.
Will delve into the realm of Simulation as a potent operations research method, elucidating its application with a contemporary healthcare example. Hospitals strategically leverage simulation to refine the operational efficiency of their Emergency Departments (EDs) (Langabeer II & Helton, 2021; Rowe & Knox, 2023). By constructing sophisticated computer models mirroring the intricacies of ED processes, encompassing patient arrivals, triage procedures, treatment protocols, and discharge mechanisms, healthcare administrators can meticulously assess the potential impact of various modifications on overall departmental performance (Sayah et al., 2014).
Consider a scenario where a hospital aims to optimize its ED function during peak hours. Through simulation, administrators can analyze the effects of adjusting staffing levels or implementing innovative patient flow protocols. The simulation facilitates a nuanced understanding of potential bottlenecks, guides optimal resource allocation, and predicts the resultant impact on patient wait times (Sayah et al., 2014). This foresighted and evidence-driven approach empowers healthcare organizations to make strategic decisions that proactively enhance overall care quality within the ED (Sayah et al., 2014; Rowe & Knox, 2023).
In understanding wait times, it is essential to consider that wait time is defined as “the time interval during which there is a temporary cessation of service” (Langabeer II & Helton, 2021). Recent improvements have been noted in wait times, with research indicating that the use of rapid assessment zones, advanced triage protocols, and tracking systems/whiteboards has contributed to the shortening of wait times and the enhancement of the quality of care within U.S. emergency departments (Sayah et al., 2014). Studies emphasize that wait times in healthcare are often linked to issues of capacity management. If capacity or demand is not well managed, significant variability over time in the demand for healthcare services can occur (Patrick & Puterman, 2008, p. 77).
Biblical integration
The Bible emphasizes responsible stewardship of resources. In the healthcare context, the effective use of resources, as advocated in operations research, aligns with biblical principles of stewardship. Efficient resource allocation, such as optimizing staff, facilities, and technology, reflects the biblical call to be good stewards of the resources entrusted to us (Luke 16:10).
References:
Langabeer, II, J. R., & Helton, J. (2021). Health care operations management (3rd ed.). Burlington, MA: Jones & Bartlett Learning.
Patrick, J., & Puterman, M. L. (2008). Reducing wait times through operations research: Optimizing the use of surge capacity. Healthcare Policy, 3(3), 75–88. https://doi.org/10.12927/hcpol.2008.19575Links to an external site..
Rowe, A., & Knox, M. (2023). The Impact of the Healthcare Environment on Patient Experience in the Emergency Department: A Systematic Review to Understand the Implications for Patient-Centered Design. HERD, 16(2), 310–329. https://doi.org/10.1177/19375867221137097Links to an external site..
Sayah, A., Rogers, L., Devarajan, K., Kingsley-Rocker, L., & Lobon, L. F. (2014). Minimizing ED waiting times and improving patient flow and experience of care. Emergency Medicine International, 2014, 1–8. https://doi.org/10.1155/2014/981472Links to an external site..
Stoumpos, A. I., Kitsios, F., & Talias, M. A. (2023). Digital Transformation in Healthcare: Technology Acceptance and Its Applications. International Journal of Environmental Research and Public Health, 20(4), 3407. https://doi.org/10.3390/ijerph20043407Links to an external site..
2. Ann
Operations research is the use of quantitative tools and techniques that helps to incorporate a data-driven approach to making decisions. Operations research is useful in the health care industry because it has the ability to improve the quality of the projections and operational decisions. There are multiple operations research methods that are used in health care today with the main ones being a data-driven decision approach, quantitative tools, debottlenecking, and forecasting patient demand and volumes (Langabeer & Helton, 2021).
The data-driven decision approach addresses efficiency and quality in operations management by applying analytical methods and models to generate better decisions. This method allows for data to be explored in new ways such as by using technology or building models that can help examine the effect of decisions so that operations managers and other decision makers can improve the quality of their decisions. The use of quantitative tools in operations research aids in solving problems that involve variability, uncertainty, and risks by the modeling of patient volumes and flow through health care systems. This involves complete understanding of the important processes, engagements, and transactions that a patient must go through from their initial time of entry to when they exit the system. Linear programming is a quantitative tool used to minimize labor costs by making decisions that optimize trade-offs necessary for resource allocation. Stimulation is another quantitative tool used to aid in labor staffing problems by using a computer model to predict behaviors and performances of processes or how it may perform in real world situations. De-bottlenecking is a tool to help when a health care organization’s demand is greater than their capacity, which is called a bottleneck. This occurs when the capacity is unable to meet the demand of patients due to physical or logistical constraints. By de-bottlenecking, this increases throughput or capacity by removing bottlenecks. This is done by thoroughly analyzing both demand and capacity in order to determine where there is a bottleneck. To conceptualize bottlenecking, Al-Refaie et al. (2018) says that a hospital’s main goal is to provide quality services at the lowest costs and since operating rooms generate highest costs, they “are prone to bottlenecking in numerous hospitals because they require a high number of expensive available resources” and “therefore, the scheduling and planning of surgeries is considered the most important means of operation room maintenance” (p. 246) to prevent bottlenecking. Lastly, forecasting patient demand and volumes is the first step to be able to completely understand changes in activity levels over time by tracking patient logistic flow volumes in order to project volumes for individual departments and services throughout the day and week. Understanding volume and demand allows for resources to be more accurately aligned. These decision tools can help improve key operational decisions for the health care organization (Langabeer & Helton, 2021).
Applying the data-driven decision method to the modern example of health care providers making the decision of which type of medications, services, and overall care to give to patients of the same diagnosis related group can be done many ways. For example, data can be collected by comparing data from the past and using charts in order to determine the best route to take for similar patients. Fu et al. (2018) says that using the data-driven decision-making approach, “in the field of medical treatment, applications include scheduling nurses in emergency departments, exploring the influence of historical data collected in different periods on future clinical decisions, and screening lung cancer by using the dataset from the National Lung Screening Trial” (p. 2). By collecting this data, appropriate decisions regarding care for each type of patient can be made in the future.
Proverbs 11:14 says “Without guidance, a people will fall, but with many counselors there is a deliverance” (NIV, 2011). This verse explains how having guidance and making the right decisions can be beneficial. The same goes for making decisions in health care. There can be increased efficiency, effectiveness, and productivity if the correct decisions are made.
References
Al-Refaie, A., Judeh, M., & Chen, T. (2018). Optimal multiple-period scheduling and sequencing
of operating room and Intensive Care Unit. Operational Research, 18(3), 645–670. https://doi.org/10.1007/s12351-016-0287-0Links to an external site.
Fu, C., Liu, W., & Chang, W. (2018). Data-driven multiple criteria decision making for diagnosis
of thyroid cancer. Annals of Operations Research, 293(2), 833–862. https://doi.org/10.1007/s10479-018-3093-7Links to an external site.
Langabeer, II, J. R. and Helton, J. (2021). Health care operations management (3rd ed.).
Burlington, MA: Jones & Bartlett Learning. ISBN: 978-1-284-19414-2.
New International Version Bible (2011). Bible Gateway Online. https://www.biblegateway.com
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Thread must include a biblical integration and 2 peer-reviewed source citations, in addition to the course textbook, in current APA format (4 references total)
Course textbook: Langabeer, J. R., & Helton, J. (2021). Health Care Operations Management: A systems perspective. Jones & Bartlett Learning.
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