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The field of operational research entails the use of advanced analytical methods by practitioners, educators, researchers, other professionals and students to facilitate better decision-making. Analytical techniques include the mathematical modeling and optimization of situations during the design of systems within an entity such as a medical facility. Operation Research (OR) professional employ analytical techniques to model data and analyze risks and outcomes associated with a particular challenge. In this regard, it is easy to obtain options of significant benefits. Operational research influences organizations in diverse disciplines and thus opportunities for operational researchers are numerous. Some of the major employers of operational researchers are airlines, financial institutions, insurance agencies, health care and telecommunication companies. Within health care, operation research serves important functions in quality assurance, medical informatics, the scheduling of emergency rooms and patient diagnosis among other roles. Emergency response services such as air ambulances cannot effectively execute their roles without the input of operational researchers. Revenue and resource management departments within hospitals rely on operational research to cut down costs and improve utilization of resources.

The article, “A POMDP Approach to Personalize Mammography Screening Decisions”, analyzes approaches applicable in the diagnosis and treatment of breast cancer. It evaluates the risks, outcomes and benefits associated with mammography which is one of the most commonly methods of breast cancer diagnosis. This method is popular due to its wide availability, time-effectiveness and acceptable levels of accuracy. Research shows that despite its potential to reduce breast cancer mortality by an estimated 25 percent, mammography exposes patients to risks such as radiation exposure and over-diagnosis (Pinker, 2012). The design and implementation of a mammography-screening schedule employs various analytical techniques to ensure that the selected schedule maintains a balance between various considerations pertaining to the task in question. This screening strategy combines various static and dynamic risk factors ignored in the traditional mammography approach.

Using information about screening histories and personal risks of a sample population of women it is possible to implement screening program based on challenge-specific guidelines rather than existing guidelines. This approach employs stimulation and optimization techniques that considerably enhance the evaluation of benefits of an option (Benton, 1996). The decision to deviate from the use of information on previous research and observing existing guidelines emanates from the need to try out approaches that provide improvement benefits. The analysis of a variety of possible screening scenarios provides the necessary data in this regard.

Minimizing the options in designing the mammography-screening schedule is a crucial aspect of operation research that ensures only the most viable options influences the project of concern. The process employs an analytical model known as POMDP, which facilitates easier collection and analysis of data crucial to the designing of the screening schedule. This POMDP model differs from the conventional ones as it provides scenarios applicable in numerous aspects of medical decision-making.

Analysis of the data obtained from the POMDP model employs probability and statistics to ensure optimal results. Probability and statistics provide an effective tool for measuring risks, data extraction and analysis to establish connections, and make conclusions and dependable predictions. The mammography-screening schedule employs data from diverse sources to enhance accuracy of results. The results of the POMDP approach illustrate the effectiveness of operation research in improving mammography screening. The approach for developing screening schedule presents effective data analysis compared to the results obtained by using existing guidelines. Significant disparities concern the expected quality-adjusted life years, number of mammograms and false-positives. Operation research concepts have helped to minimize lifetime risks associated with the development of invasive cancer due to exposure to screening. Data modeling in this case incorporates various assumptions such as self-detection and disease factors dependent on age.

Despite approaches such as the POMDP in breast cancer diagnosis, the application of operations research in cancer therapy has been below average. Research shows positive prospects for the optimization model for cancer diagnosis, which helps to determine biopsy protocols. Such a model is important in addressing challenges concerning radiation therapy for cancer patients. It helps to optimize the results of cancer detection.  Operation research plays a significant role in the planning of treatment for cancer using various optimization tools. Mathematical modeling illustrates that a combination of systems such as Markov chain model can help to reduce the total time spent in cancer radiotherapy considerably (Lee, 2008). Another prospective approach to cancer treatment is Brachytherapy whose use together with the Mixed Integer Program facilitates the optimization of therapy.

The identification of the intensity of radiation beams is crucial in order to optimize tumor control measures. In addition, it ensures that the irradiation of vital organs remains at minimal levels throughout treatment.  Statistics show that major cancer treatment techniques such as radiation therapy can improve more with the adoption of appropriate strategies. These include the 3D-CRT method, which treats affected tissues while minimizing the risks posed to normal tissues and organs. Another approach is the MLC method, which facilitates flexibility in the application of radiation therapy by means of leaves (Rais & Viana, 2010). Since the position of leaves is adjustable, appropriate dose gradients are easy to achieve and thus optimize treatment.

The need to enhance medical and clinical decision-making has promoted the incorporation of various operations research models within the health care sector.  The role of these models is most significant in diagnosis and therapy planning. Stimulation, optimization, probability and statistics, and other forms of data modeling play a central role in the realization of effective and responsive medical strategies that fit various scenarios within the health care sector.

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