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The decision tree is a hierarchical approach to select one among many alternatives and then observing the outcome. The outcome has a different value and a different probability of occurrence. What is a value? A value is a priority. The value can be negative (in dollars) in the case of loss or positive in the case of gain/profit. What is probability? Probability is a numerical speculation of an occurrence in a closed interval [0, 1]. When the probability is zero, the uncertain outcome is unlikely. When the probability is one, the outcome is almost certain.
The expected value is the weighted average of the possible values. Each value is weighted according to its probability of occurrence. The expected value is a guide. Because of the realities of professional and personal life, decision-making is not a one-time action but is repeated.
Six Sigma methods and lean management principles yield high-quality healthcare services to patients in hospitals, clinical, and health agencies. Hence, it is worthwhile to learn, apply, and appreciate them. Six Sigma comprises a rigorous, well-focused, and scientific approach to improve the quality of healthcare operations. Healthcare professionals and patients are equally fond of the Six Sigma methods and lean management principles. The basic Six Sigma concept provides effective incorporation of statistical properties. The approach originated from the so-called Gaussian (normal) frequency pattern. Analysts who desire to apply the Six Sigma methods or lean management principles ought to confirm whether the collected data have Gaussian frequency patterns under their original scale or in a transformed scale.
The variables emerge from a quantitatively measurable attribute of the quality of the sample units. The sample units in this context are patients who receive treatment for an illness or medical professionals who deliver healthcare services to patients. The Six Sigma methods are based on the statistical properties of Gaussian data. Recall from your statistics courses that 99.974% of the standardized Z-values of a measurable attribute/variable fall between −3.0 and 3.0 in their frequency pattern.
Economic value and cost-effectiveness are integral parts of a sound healthcare operation. Cost-effectiveness focuses on quality-adjusted life years (QALY). A cost-effectiveness analysis (CEA) aims to attain maximum health benefits with limited consumption of resources. The cost is, of course, a value gained from the use of resources elsewhere.
The benefits that exist in one program if money is shifted to it from another program are the opportunity cost. The cost of drugs, medicines, staff salaries, equipment, and patients’ out-of-pocket expense are direct costs. Productivity costs include losses due to time. Indirect costs include pain, suffering, adverse outcomes, and so forth.
This chapter examines three software programs that assist users to compute the necessary quantities, make summaries, construct graphical displays – including charts – from data, and create two- and three-dimensional pictures from expressions or numbers.
Microsoft Excel is a module of the Microsoft Office suite of software programs (www.microsoft.com/en-us/download). Excel is composed of several subroutines, Excel commands, through which users can perform calculations. JASP, now in the public domain (https://jasp-stats.org), was created and is supported by the University of Amsterdam in the Netherlands. Input data for analysis by JASP need to be entered in an Excel spreadsheet, saved as a comma-delimited (.CSV) file, and fed to JASP software. Microsoft Math Solver, developed and maintained by the Microsoft Corporation, is free to download. It allows users to solve math and science problems. It was developed and maintained by the Microsoft Corporation.
First, let us understand the need for making a group decision in the healthcare setting. A single person making healthcare decisions is likely to offer more liabilities than strengths. Group decision-making can reduce this risk. One advantage of group decision-making is that it brings in different perspectives. Because group members have different orientations and interests, the group decision-making process is chaotic with conflicts and contradictions. Strategies can be employed to rectify these difficulties.
The Delphi approach increases the chance for reaching a consensus by utilizing qualitative techniques as well as descriptive and inferential concepts. The Delphi procedure is appropriate when several criteria are to be met and hence gets the name multi-criteria decision analysis (MCDA) (Aires and Ferreira, 2018). The MCDA functions based on five components: goal, criteria, alternatives, decision makers, and outcomes (Kumar et al., 2017). The MCDA builds consensus using the analytic hierarchy process (AHP).
A model is a description of the system that generates data. Box commented that some models are useful, but many are wrong. A reason exists for such a belief (Box, 2013). Nature functions in accordance with mysterious principles. Do such principles precisely fit the mathematical system humans have created? An example of such a mathematical system is the calendar. Over millennia, astronomers, scientists, and philosophers have struggled to redefine and refine the calendar. Different Tamil, Greek, Egyptian, and Sumerian calendars exist, but scholars realized an adjustment is necessary every four years to the number of days in the month of February. The current Gregorian calendar was introduced as an improvement on the Julian calendar in 1582. In a similar way to developing the calendar, modeling the chance-oriented healthcare system is a formidable task even for experts.
What is a model value? In general, values are depictions of a decision maker’s priorities. Model values are not unique to the healthcare field. Model values originated in finance. Swiss mathematician and physicist Daniel Bernoulli (1738) states money’s value decreases over time.
Risk is a measure of vulnerability and is defined, assessed, and rectified in several ways. For example, national security describes the protection of a nation and its assets, resources, or people. Risk analysis and the consequent preventive actions in a hospital or clinic aim to protect safety, to keep unauthorized persons from providing healthcare and authorized healthcare providers from abusing their privileges, to preserve confidentiality, and to safeguard advanced medical technologies, among others. Stored records need to be secured and a website, processed information, or data collected in a computer system are vulnerable to insecurity.
We investigate structural features and processes associated with the onset of systemic conflict using an approach which integrates complex systems theory with network modeling and analysis. We present a signed network model of cooperation and conflict dynamics in the context of international relations between states. The model evolves ties between nodes under the influence of a structural balance force and a dyad-specific force. Model simulations exhibit a sharp bifurcation from peace to systemic war as structural balance pressures increase, a bistable regime in which both peace and war stable equilibria exist, and a hysteretic reverse bifurcation from war to peace. We show how the analytical expression we derive for the peace-to-war bifurcation condition implies that polarized network structure increases susceptibility to systemic war. We develop a framework for identifying patterns of relationship perturbations that are most destabilizing and apply it to the network of European great powers before World War I. We also show that the model exhibits critical slowing down, in which perturbations to the peace equilibrium take longer to decay as the system draws closer to the bifurcation. We discuss how our results relate to international relations theories on the causes and catalysts of systemic war.
We show how convergence to the Gumbel distribution in an extreme value setting can be understood in an information-theoretic sense. We introduce a new type of score function which behaves well under the maximum operation, and which implies simple expressions for entropy and relative entropy. We show that, assuming certain properties of the von Mises representation, convergence to the Gumbel distribution can be proved in the strong sense of relative entropy.
Test anxiety refers to maladaptive cognitive and physiological reactions that interfere with optimal performance. Self-regulatory models suggest test anxiety occurs when there is a perceived discrepancy between current functioning and mental representations of desired academic goals. Interestingly, prior investigations have demonstrated those with greater interhemispheric communication are better able to detect discrepancies between current functioning and preexisting mental representations. Thus, the current study was designed to investigate the relationship between test anxiety and handedness—a commonly used proxy variable for interhemispheric communication. Undergraduate and graduate students (N = 277, 85.20% female, 68.19% Caucasian, $ \overline{\chi} $age = 29.88) (SD = 9.53) completed the FRIEDBEN Test Anxiety Scale and Edinburgh Handedness Inventory – Short Form. A series of Mann–Whitney U tests were used to test for differences in the cognitive, physiological, and social components of test anxiety between mixed- and consistent-handers. The results indicated that mixed-handers had significantly higher levels of cognitive test anxiety than consistent-handers. We believe this information has important implications for our understanding of the role of discrepancy detection and interhemispheric communication in eliciting and maintaining test-anxious responses.