PROF LEX ONLY

discussion board due by 9:59 Thursday 150-200 words cite all references into discussion board answer all questions.

Discussion: Interdisciplinary Teams

This week, you’ve examined the rationale, form, and function of an interdisciplinary team in providing health care. As you’ve seen, members of an interdisciplinary team may come from a variety of professional backgrounds, depending on the needs of the patient, the setting in which care is provided, and the resources available.

To prepare:

For this Discussion, review the case study on pages 2–4 of your course text.

Post a comprehensive response to the following:

• Name two additional types of health professionals that could have been involved in the case.
• Provide a rationale as to why they should have been included.
• In your own words, explain why that professional is important to an interdisciplinary team and what you believe he or she would contribute.

______________________________________________________________________

resources

__________________________________________________

Freshman, B., Rubino, L., & Reid Chassiakos, Y. (Eds.). (2010). Collaboration across disciplines in health care. Sudbury, MA: Jones and Bartlett Learning.

• Chapter 1, “The Healthcare Team Members: Who Are They and What Do They Do?”

This chapter introduces the concept of an interdisciplinary health care team, the professions that contribute, and the roles and functions of each. Additionally, a case study of a successful interdisciplinary approach is presented.

• Chapter 6, “Interprofessional Collaboration: A Nursing Perspective”

The important difference between interdisciplinary collaboration and interprofessional collaboration is examined in this chapter. There is also a discussion on how different professions view collaboration differently. Critical principles for interprofessional collaboration are offered.

Clements, D., Dault, M., & Priest, A. (2007). Effective teamwork in healthcare: Research and reality. HealthcarePapers, 7 (Sp), 26-34. Retrieved from http://www.longwoods.com/content/18669

Download the PDF and read pages 26-30.

This article analyzes the results of research in Canada on the effectiveness of health care teamwork and the conditions in which this approach can succeed. Interprofessional collaboration is identified as both a process that promotes teamwork and an end in itself.

Colonna, J. (2005). Why teams matter in healthcare: 7 characteristics define successful teams. Healthcare Purchasing News, 29(7), 70-71.

Note: Retrieved from the Walden Library databases.

Although the composition of interdisciplinary teams often changes, there are key characteristics most successful teams all share. This article identifies and discusses those key characteristics, including a clear sense of goals, clear roles and responsibilities, open communication, productive disagreement, and others.

Ellingson, L. (2002). I. Introduction to the field of health communication. Communication Research Trends, 21(3), 3-4.

Note: Retrieved from the Walden Library databases.

Ellingson, L. (2002). II. Theoretical approaches. Communication Research Trends, 21(3), 4.

Note: Retrieved from the Walden Library databases.

Ellingson, L. (2002). III. Collaboration in health care. Communication Research Trends, 21(3), 5-7.

Note: Retrieved from the Walden Library databases.

Ellingson, L. (2002). IV. Health care teams. Communication Research Trends, 21(3), 7-11.

Note: Retrieved from the Walden Library databases.

The emerging field of health communication research is introduced and its theoretical approaches are discussed in this series of four brief articles. The importance of health communication is examined, particularly as it relates to collaboration between and among various practitioners in an age of increasing specialization.

Ithaca College Gerontology Institute. (2003). Interdisciplinary health care: Interdisciplinary, multidisciplinary-What’s the difference? Retrieved from http://www.ithaca.edu/ahgitt/rochester/Interdisciplinary.htm

This article describes the important differences between the terms interdisciplinary and multidisciplinary, particularly as these terms are used in relation to health care providers.

Buchbinder, S. B., & Thompson, J. M. (2007). Teamwork. In S. H. Buchbinder and N. H. Shanks (Eds.), Introduction to health care management (pp. 303-322). Sudbury, MA: Jones and Bartlett Publishers.

This chapter analyzes the nature of teamwork in health care organizations. The authors describe the distinguishing characteristics of a team and discuss challenges, trends, costs, and benefits of teams and teamwork in health care settings.

Katzenbach, J., & Smith, D. (2004). The discipline of teams. In Harvard Business Review on teams that succeed (pp. 1-25). Boston, MA: Harvard Business School Press.

Effective multidisciplinary health care teams are composed of members trained in a number of disciplines working collaboratively for the benefit of the patient. In this chapter, members of health care teams are considered by professional background, role, and function.

Meads, G., & Ashcroft, J. (2005). Collaboration. In The case for interprofessional collaboration in health and social care (pp. 15-35). Oxford, England: Blackwell Publishing.

This chapter explores the nature of collaboration, including its components and expressions. There is also a discussion on how to analyze collaboration, particularly in health or social care settings. The benefits of collaboration are enumerated as are its implications for key relationships.

University of Iowa Health Care. (2010). Patient and guest services: Your health care team. Retrieved from http://www.uihealthcare.org/PatientandGuestServices/

This website page identifies and describes various members of a typical health care team at an academic medical center in Iowa.

How to Survive Virtual Group Work. (2010) [Online] eLearners.com. Retrieved from: http://www.elearners.com/online-education-resources/online-learning/how-to-survive-virtual-group-work/ (Accessed 23 November 2010)

Laureate Education, Inc. (Executive Producer). (2010). Interdisciplinary healthcare team. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/HLTH/3105/01/mm/hct/hlth3105_hct.html

This interactive animation sequence identifies key professionals in the interdisciplinary health model, their roles and responsibilities, and how these professionals interact with the patient.

NURS 6231: HEALTHCARE SYSTEMS AND QUALITY OUTCOMES – Discussion 3 (Grading Rubic and Media Attached)

Discussion: Comparing Frameworks for Analyzing Organizations

Avedis Donabedian’s work generated a pivotal means of assessing organizational performance relative to structure, process, and outcomes. However, it is clearly not sufficient to view health care quality merely in terms of outcomes—the structures and processes that facilitate these outcomes are equally as important.

In this Discussion, you consider multiple frameworks that can be used to analyze an organization. As you proceed, consider how these frameworks allow you to examine the interplay of interdependent and related parts and processes that comprise the systems within an organization, as well as the arrangements or structures that connect these parts.

To prepare:

Investigate and reflect on the systems and structures of an organization with which you are familiar. Consider the following:

What is the reporting structure?

Who holds formal and informal authority?

How many layers of management are there between the frontline and the highest office-holders of the organization?

How are interdisciplinary teams organized?

How is communication facilitated?

How well integrated is decision making among clinical personnel and administrative professionals?

How are particular service lines organized?

Which departments, groups, and/or individuals within the organization are responsible for monitoring matters related to performance, such as quality and finances?

Select two of the following frameworks:

Learning organizations, presented in the Elkin, Haina, and Cone article

Complex adaptive systems (CAS), presented in the Nesse, Kutcher, Wood, and Rummans article

Clinical microsystems, presented in the Sabino, Friel, Deitrick, and Sales-Lopez article

Good to great, presented in the Geller article

The 5 Ps, presented in the ASHP Foundation article

Review the Learning Resources for each of the frameworks that you selected. Also conduct additional research to strengthen your understanding of how to use each framework to assess an organization.

Compare the two frameworks. How could each framework be used to identify opportunities to improve performance? In particular, how would you use each of these frameworks to analyze the organization that you have selected?

Post an analysis of the systems and structures of the organization you selected, sharing specific examples. Explain insights that you gained by comparing the two frameworks, and how each can be used to assess an organization, identify a need for improvement, and, ultimately, enhance the performance of an organization.

Read a selection of your colleagues’ responses.

Respond to at least two of your colleagues on two different days using one or more of the following approaches:

Compare the organizational structure of your colleague’s selected organization to your own.

Ask a clarifying question.

Select an attribute of the identified organizational structure and ask your colleague to elaborate on how this attribute is evidenced in their organization.

Required Readings

Hickey, J. V., & Brosnan, C. A. (2017). Evaluation  of health care quality in for DNPs (2nd  ed.). New York, NY: Springer Publishing Company.

Chapter 1, “Evaluation and DNPs: The Mandate for Evaluation” (pp. 3-36)

Chapter 3, “Conceptual Models for Evaluation in Advanced Nursing Practice” (pp. 61-86)

Chapter 6, “Evaluating Organizations and Systems” (pp. 127-142)

Chapter 1 defines microsystem, mesosystem, and macrosystem and notes that evaluation can focus on one of these levels or all three. Chapter 5 examines the evaluation of organizations and systems.

Sadeghi, S., Barzi, A., Mikhail, O., & Shabot, M. M. (2013). Integrating quality and strategy in health care organizations, Burlington, MA: Jones & Bartlett Publishers.

Chapter 2, “Understanding the Healthcare Organization” (pp. 31–43)

Although this chapter focuses on hospitals, the authors provide information about strategic planning and organizational structure that is applicable in many health care settings. The authors examine financial and quality issues as key aspects of performance measurement.

Elkin, G., Zhang, H., & Cone, M. (2011). The acceptance of Senge’s learning organisation model among managers in China: An interview study. International Journal of Management, 28(4), 354–364.

Retrieved from the Walden Library databases.

This article outlines the five disciplines that Senge argued could be found in a learning organization. The authors also discuss the worldview that is inherent in business organizations in China and explain how this relates to Senge’s theory.

Geller, E. S. (2006). From good to great in safety: What does it take to be world class? Professional Safety, 51(6), 35–40.

Retrieved from the Walden Library databases.

Geller reviews and applies Collin’s foundational Good to Great theory from its focus on financial success to safety.

Nesse, R. E., Kutcher, G., Wood, D., & Rummans, T. (2010). Framing change for high-value healthcare systems. Journal for Healthcare Quality, 32(1), 23–28.

Retrieved from the Walden Library databases.

This article explores how to implement change in complex adaptive systems (CAS) such as health care. The authors purport that an understanding of the principles of change management in CAS is critical for success.

Sabino, J. N., Friel, T., Deitrick, L. M., & Salas-Lopez, D. (2009). Striving for cultural competence in an HIV program: The transformative impact of a microsystem in a larger health network. Health & Social Work, 34(4), 309–313.

Retrieved from the Walden Library databases.

The authors discuss cultural competence as part of a patient-centered perspective on health care delivery. They examine an approach to creating innovation that originates at the unit (microsystem) level and can be diffused to the larger health care environment (macrosystem).

 ASHP Foundation. (n.d.). Assessing your microsystem with the 5 Ps. Retrieved February 5, 2012, from http://www.ashpfoundation.org/lean/CMS9.html

This article discusses 5 Ps—purpose, patients, professionals, processes, and patterns—that you can analyze to deepen your understanding of a microsystem.

 Document: Course Project Overview (PDF)

Required Media

Laureate Education (Producer). (2013f). Organizational structures. Retrieved from https://class.waldenu.edu 

Note:  The approximate length of this media piece is 3 minutes.

Dr. Carol Huston discusses the influence of organizational structure on the delivery of quality care.

Please see below and contact

Exercise 14

Understanding Simple Linear Regression

Statistical Technique in Review

In nursing practice, the ability to predict future events or outcomes is crucial, and researchers calculate and report linear regression results as a basis for making these predictions. Linear regression provides a means to estimate or predict the value of a dependent variable based on the value of one or more independent variables. The regression equation is a mathematical expression of a causal proposition emerging from a theoretical framework. The linkage between the theoretical statement and the equation is made prior to data collection and analysis. Linear regression is a statistical method of estimating the expected value of one variable, y, given the value of another variable, x. The focus of this exercise is simple linear regression, which involves the use of one independent variable, x, to predict one dependent variable, y.

The regression line developed from simple linear regression is usually plotted on a graph, with the horizontal axis representing x (the independent or predictor variable) and the vertical axis representing the y (the dependent or predicted variable; see Figure 14-1). The value represented by the letter a is referred to as the y intercept, or the point where the regression line crosses or intercepts the y-axis. At this point on the regression line, x = 0. The value represented by the letter b is referred to as the slope, or the coefficient of x. The slope determines the direction and angle of the regression line within the graph. The slope expresses the extent to which y changes for every one-unit change in x. The score on variable y (dependent variable) is predicted from the subject’s known score on variable x (independent variable). The predicted score or estimate is referred to as Ŷ (expressed as y-hat) (Cohen, 1988; Grove, Burns, & Gray, 2013; Zar, 2010).

FIGURE 14-1  GRAPH OF A SIMPLE LINEAR REGRESSION LINE

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Simple linear regression is an effort to explain the dynamics within a scatterplot (see Exercise 11) by drawing a straight line through the plotted scores. No single regression line can be used to predict, with complete accuracy, every y value from every x value. However, the purpose of the regression equation is to develop the line to allow the highest degree of prediction possible, the line of best fit. The procedure for developing the line of best fit is the method of least squares. If the data were perfectly correlated, all data points would fall along the straight line or line of best fit. However, not all data points fall on the line of best fit in studies, but the line of best fit provides the best equation for the values of y to be predicted by locating the intersection of points on the line for any given value of x.

The algebraic equation for the regression line of best fit is y = bx + a, where:

y=dependentvariable(outcome) 

x=independentvariable(predictor) 

b=slopeoftheline(beta,orwhattheincreaseinvalueisalongthex-axisforeveryunitofincreaseintheyvalue),alsocalledtheregressioncoefficient.  

a=y−intercept(thepointwheretheregressionlineintersectsthe y-axis),alsocalledtheregressionconstant(Zar,2010).  

In Figure 14-2, the x-axis represents Gestational Age in weeks and the y-axis represents Birth Weight in grams. As gestational age increases from 20 weeks to 34 weeks, birth weight also increases. In other words, the slope of the line is positive. This line of best fit can be used to predict the birth weight (dependent variable) for an infant based on his or her gestational age in weeks (independent variable). Figure 14-2 is an example of a line of best fit that was not developed from research data. In addition, the x-axis was started at 22 weeks rather than 0, which is the usual start in a regression figure. Using the formula y = bx + a, the birth weight of a baby born at 28 weeks of gestation is calculated below.

Formula:y=bx+a 

Inthisexample,a=500,b=20,andx=28weeks 

y=20(28)+500=560+500=1,060grams 

FIGURE 14-2  EXAMPLE LINE OF BEST FIT FOR GESTATIONAL AGE AND BIRTH WEIGHT

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The regression line represents y for any given value of x. As you can see, some data points fall above the line, and some fall below the line. If we substitute any x value in the regression equation and solve for y, we will obtain a ŷ that will be somewhat different from the actual values. The distance between the ŷ and the actual value of y is called residual, and this represents the degree of error in the regression line. The regression line or the line of best fit for the data points is the unique line that will minimize error and yield the smallest residual (Zar, 2010). The step-by-step process for calculating simple linear regression in a study is presented in Exercise 29.

Research Article

Source

Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), 927–931.

Introduction

Medications and other therapies often necessitate knowing a patient’s weight. However, a child may be admitted to a pediatric intensive care unit (PICU) without a known weight, and instability and on-going resuscitation may prevent obtaining this needed weight. Clinicians would benefit from a tool that could accurately estimate a patient’s weight when such information is unavailable. Thus Flannigan et al. (2014) conducted a retrospective observational study for the purpose of determining “if the revised APLS UK [Advanced Paediatric Life Support United Kingdom] formulae for estimating weight are appropriate for use in the paediatric care population in the United Kingdom” (Flannigan et al., 2014, p. 927). The sample included 10,081 children (5,622 males and 4,459 females), who ranged from term-corrected age to 15 years of age, admitted to the PICU during a 5-year period. Because this was a retrospective study, no geographic location, race, and ethnicity data were collected for the sample. A paired samples t-test was used to compare mean sample weights with the APLS UK formula weight. The “APLS UK formula ‘weight = (0.05 × age in months) + 4’ significantly overestimates the mean weight of children under 1 year admitted to PICU by between 10% [and] 25.4%” (Flannigan et al., 2014, p. 928). Therefore, the researchers concluded that the APLS UK formulas were not appropriate for estimating the weight of children admitted to the PICU.

Relevant Study Results

“Simple linear regression was used to produce novel formulae for the prediction of the mean weight specifically for the PICU population” (Flannigan et al., 2014, p. 927). The three novel formulas are presented in Figures 1, 2, and 3, respectively. The new formulas calculations are more complex than the APLS UK formulas. “Although a good estimate of mean weight can be obtained by our newly derived formula, reliance on mean weight alone will still result in significant error as the weights of children admitted to PICU in each age and sex [gender] group have a large standard deviation . . . Therefore as soon as possible after admission a weight should be obtained, e.g., using a weight bed” (Flannigan et al., 2014, p. 929).

FIGURE 1  Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (0.5 × age in months) + 4” and novel formula “Weight in kg = (0.502 × age in months) + 3.161” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 928.

FIGURE 2  Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (2 × age in years) + 8” and novel formula “Weight in kg = (0.176 × age in months) + 7.241” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 928.

FIGURE 3  Comparison of actual weight with weight calculated using APLS formula “Weight in kg = (3 × age in years) + 7” and novel formula “Weight in kg = (0.331 × age in months) − 6.868” Flannigan, C., Bourke, T. W., Sproule, A., Stevenson, M., & Terris, M. (2014). Are APLS formulae for estimating weight appropriate for use in children admitted to PICU? Resuscitation, 85(7), p. 929.

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Study Questions

1. What are the variables on the x- and y-axes in Figure 1 from the Flannigan et al. (2014) study?

2. What is the name of the type of variable represented by x and y in Figure 1? Is x or y the score to be predicted?

3. What is the purpose of simple linear regression analysis and the regression equation?

4. What is the point where the regression line meets the y-axis called? Is there more than one term for this point and what is the value of x at that point?

5. In the formula y = bx + a, is a or b the slope? What does the slope represent in regression analysis?

6. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms for a child at 5 months of age? Show your calculations.

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7. What are the variables on the x-axis and the y-axis in Figures 2 and 3? Describe these variables and how they might be entered into the regression novel formulas identified in Figures 2 and 3.

8. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child at 4 years of age? Show your calculations.

9. Does Figure 1 have a positive or negative slope? Provide a rationale for your answer. Discuss the meaning of the slope of Figure 1.

10. According to the study narrative, why are estimated child weights important in a pediatric intensive care (PICU) setting? What are the implications of these findings for practice?

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Answers to Study Questions

1. The x variable is age in months, and the y variable is weight in kilograms in Figure 1.

2. x is the independent or predictor variable. y is the dependent variable or the variable that is to be predicted by the independent variable, x.

3. Simple linear regression is conducted to estimate or predict the values of one dependent variable based on the values of one independent variable. Regression analysis is used to calculate a line of best fit based on the relationship between the independent variable x and the dependent variable y. The formula developed with regression analysis can be used to predict the dependent variable (y) values based on values of the independent variable x.

4. The point where the regression line meets the y-axis is called the y intercept and is also represented by a (see Figure 14-1). a is also called the regression constant. At the y intercept, x = 0.

5. b is the slope of the line of best fit (see Figure 14-1). The slope of the line indicates the amount of change in y for each one unit of change in x. b is also called the regression coefficient.

6. Use the following formula to calculate your answer: y = bx + ay = 0.502 (5) + 3.161 = 2.51 + 3.161 = 5.671 kilogramsNote: Flannigan et al. (2014) expressed the novel formula of weight in kilograms = (0.502 × age in months) + 3.161 in the title of Figure 1.

7. Age in years is displayed on the x-axis and is used for the APLS UK formulas in Figures 2 and 3. Figure 2 includes children 1 to 5 years of age, and Figure 3 includes children 6 to 12 years of age. However, the novel formulas developed by simple linear regression are calculated with age in months. Therefore, the age in years must be converted to age in months before calculating the y values with the novel formulas provided for Figures 2 and 3. For example, a child who is 2 years old would be converted to 24 months (2 × 12 mos./year = 24 mos.). Then the formulas in Figures 2 and 3 could be used to predict y (weight in kilograms) for the different aged children. The y-axis on both Figures 2 and 3 is weight in kilograms (kg).

8. First calculate the child’s age in months, which is 4 × 12 months/year = 48 months.y = bx + a = 0.176 (48) + 7.241 = 8.448 + 7.241 = 15.689 kilogramsNote the x value needs to be in age in months and Flannigan et al. (2014) expressed the novel formula of weight in kilograms = (0.176 × age in months) + 7.241.

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9. Figure 1 has a positive slope since the line extends from the lower left corner to the upper right corner and shows a positive relationship. This line shows that the increase in x (independent variable) is associated with an increase in y (dependent variable). In the Flannigan et al. (2014) study, the independent variable age in months is used to predict the dependent variable of weight in kilograms. As the age in months increases, the weight in kilograms also increases, which is the positive relationship illustrated in Figure 1.

10. According to Flannigan et al. (2014, p. 927), “The gold standard for prescribing therapies to children admitted to Paediatric Intensive Care Units (PICU) requires accurate measurement of the patient’s weight. . . . An accurate weight may not be obtainable immediately because of instability and on-going resuscitation. An accurate tool to aid the critical care team estimate the weight of these children would be a valuable clinical tool.” Accurate patient weights are an important factor in preventing medication errors particularly in pediatric populations. The American Academy of Pediatrics (AAP)’s policy on Prevention of Medication Errors in the Pediatric Inpatient Setting can be obtained from the following website: https://www.aap.org/en-us/advocacy-and-policy/federal-advocacy/Pages/Federal-Advocacy.aspx#SafeandEffectiveDrugsandDevicesforChildren. The Centers for Medicare & Medicaid Services, Partnership for Patients provides multiple links to Adverse Drug Event (ADE) information including some resources specific to pediatrics at http://partnershipforpatients.cms.gov/p4p_resources/tsp-adversedrugevents/tooladversedrugeventsade.html.

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EXERCISE 14 Questions to Be Graded

Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/Statistics/ under “Questions to Be Graded.”

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. According to the study narrative and Figure 1 in the Flannigan et al. (2014) study, does the APLS UK formula under- or overestimate the weight of children younger than 1 year of age? Provide a rationale for your answer.

2. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms (kg) for a child at 9 months of age? Show your calculations.

3. Using the values a = 3.161 and b = 0.502 with the novel formula in Figure 1, what is the predicted weight in kilograms for a child at 2 months of age? Show your calculations.

4. In Figure 2, the formula for calculating y (weight in kg) is Weight in kg = (0.176 × Age in months) + 7.241. Identify the y intercept and the slope in this formula.

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5. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child 3 years of age? Show your calculations.

6. Using the values a = 7.241 and b = 0.176 with the novel formula in Figure 2, what is the predicted weight in kilograms for a child 5 years of age? Show your calculations.

7. In Figure 3, some of the actual mean weights represented by blue line with squares are above the dotted straight line for the novel formula, but others are below the straight line. Is this an expected finding? Provide a rationale for your answer.

8. In Figure 3, the novel formula is (weight in kilograms = (0.331 × Age in months) − 6.868. What is the predicted weight in kilograms for a child 10 years old? Show your calculations.

9. Was the sample size of this study adequate for conducting simple linear regression? Provide a rationale for your answer.

10. Describe one potential clinical advantage and one potential clinical problem with using the three novel formulas presented in Figures 1, 2, and 3 in a PICU setting.

(Grove 139-150)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Exercise 19

Understanding Pearson Chi-Square

Statistical Technique in Review

The Pearson Chi-square (χ2) is an inferential statistical test calculated to examine differences among groups with variables measured at the nominal level. There are different types of χ2 tests and the Pearson chi-square is commonly reported in nursing studies. The Pearson χ2 test compares the frequencies that are observed with the frequencies that were expected. The assumptions for the χ2 test are as follows:

1. The data are nominal-level or frequency data.

2. The sample size is adequate.

3. The measures are independent of each other or that a subject’s data only fit into one category (Plichta & Kelvin, 2013).

The χ2 values calculated are compared with the critical values in the χ2 table (see Appendix D Critical Values of the χ2 Distribution at the back of this text). If the result is greater than or equal to the value in the table, significant differences exist. If the values are statistically significant, the null hypothesis is rejected (Grove, Burns, & Gray, 2013). These results indicate that the differences are probably an actual reflection of reality and not just due to random sampling error or chance.

In addition to the χ2 value, researchers often report the degrees of freedom (df). This mathematically complex statistical concept is important for calculating and determining levels of significance. The standard formula for df is sample size (N) minus 1, or df = N − 1; however, this formula is adjusted based on the analysis technique performed (Plichta & Kelvin, 2013). The df formula for the χ2 test varies based on the number of categories examined in the analysis. The formula for df for the two-way χ2 test is df = (R − 1) (C − 1), where R is number of rows and C is the number of columns in a χ2 table. For example, in a 2 × 2 χ2 table, df = (2 − 1) (2 − 1) = 1. Therefore, the df is equal to 1. Table 19-1 includes a 2 × 2 chi-square contingency table based on the findings of An et al. (2014) study. In Table 19-1, the rows represent the two nominal categories of alcohol 192use and alcohol nonuse and the two columns represent the two nominal categories of smokers and nonsmokers. The df = (2 − 1) (2 − 1) = (1) (1) = 1, and the study results were as follows: χ2 (1, N = 799) = 63.1; p < 0.0001. It is important to note that the df can also be reported without the sample size, as in χ2(1) = 63.1, p < 0.0001.

TABLE 19-1

CONTINGENCY TABLE BASED ON THE RESULTS OF AN ET AL. (2014) STUDY Nonsmokers n = 742Smokers n = 57*No alcohol use55114Alcohol use†19143

*Smokers defined as “smoking at least 1 cigarette daily during the past month.”

†Alcohol use “defined as at least 1 alcoholic beverage per month during the past year.”

An, F. R., Xiang, Y. T., Yu., L., Ding, Y. M., Ungvari, G. S., Chan, S. W. C., et al. (2014). Prevalence of nurses’ smoking habits in psychiatric and general hospitals in China. Archives of Psychiatric Nursing, 28(2), 120.

If more than two groups are being examined, χ2 does not determine where the differences lie; it only determines that a statistically significant difference exists. A post hoc analysis will determine the location of the difference. χ2 is one of the weaker statistical tests used, and results are usually only reported if statistically significant values are found. The step-by-step process for calculating the Pearson chi-square test is presented in Exercise 35.

Research Article

Source

Darling-Fisher, C. S., Salerno, J., Dahlem, C. H. Y., & Martyn, K. K. (2014). The Rapid Assessment for Adolescent Preventive Services (RAAPS): Providers’ assessment of its usefulness in their clinical practice settings. Journal of Pediatric Health Care, 28(3), 217–226.

Introduction

Darling-Fisher and colleagues (2014, p. 219) conducted a mixed-methods descriptive study to evaluate the clinical usefulness of the Rapid Assessment for Adolescent Preventative Services (RAAPS) screening tool “by surveying healthcare providers from a wide variety of clinical settings and geographic locations.” The study participants were recruited from the RAAPS website to complete an online survey. The RAAPS risk-screening tool “was developed to identify the risk behaviors contributing most to adolescent morbidity, mortality, and social problems, and to provide a more streamlined assessment to help providers address key adolescent risk behaviors in a time-efficient and user-friendly format” (Darling-Fisher et al., 2014, p. 218). The RAAPS is an established 21-item questionnaire with evidence of reliability and validity that can be completed by adolescents in 5–7 minutes.

“Quantitative and qualitative analyses indicated the RAAPS facilitated identification of risk behaviors and risk discussions and provided efficient and consistent assessments; 86% of providers believed that the RAAPS positively influenced their practice” (Darling-Fisher et al., 2014, p. 217). The researchers concluded the use of RAAPS by healthcare providers could improve the assessment and identification of adolescents at risk and lead to the delivery of more effective adolescent preventive services.

Relevant Study Results

In the Darling-Fisher et al. (2014, p. 220) mixed-methods study, the participants (N = 201) were “providers from 26 U.S. states and three foreign countries (Canada, Korea, and Ireland).” More than half of the participants (n = 111; 55%) reported they were using the RAAPS in their clinical practices. “When asked if they would recommend the RAAPS to other providers, 86 responded, and 98% (n = 84) stated they would recommend RAAPS. The two most common reasons cited for their recommendation were for screening (n = 76, 92%) and identification of risk behaviors (n = 75, 90%). Improved communication (n = 52, 63%) and improved documentation (n = 46, 55%) and increased patient understanding of their risk behaviors (n = 48, 58%) were also cited by respondents as reasons to recommend the RAAPS” (Darling-Fisher et al., 2014, p. 222).

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“Respondents who were not using the RAAPS (n = 90; 45%), had a variety of reasons for not using it. Most reasons were related to constraints of their health system or practice site; other reasons were satisfaction with their current method of assessment . . . and that they were interested in the RAAPS for academic or research purposes rather than clinical use” (Darling-Fisher et al., 2014, p. 220).

Chi-square analysis was calculated to determine if any statistically significant differences existed between the characteristics of the RAAPS users and nonusers. Darling-Fisher et al. (2014) did not provide a level of significance or α for their study, but the standard for nursing studies is α = 0.05. “Statistically significant differences were noted between RAAPS users and nonusers with respect to provider types, practice setting, percent of adolescent patients, years in practice, and practice region. No statistically significant demographic differences were found between RAAPS users and nonusers with respect to race, age” (Darling-Fisher et al., 2014, p. 221). The χ2 results are presented in Table 2.

TABLE 2

DEMOGRAPHIC COMPARISONS BETWEEN RAPID ASSESSMENT FOR ADOLESCENT PREVENTIVE SERVICE USERS AND NONUSERSCurrent userYes (%)No (%)χ2pProvider type (n = 161)  12.7652, df = 2< .00 Health care provider64 (75.3)55 (72.4) Mental health provider13 (15.3)2 (2.6) Other8 (9.4)19 (25.0)Practice setting (n = 152)  12.7652, df = 1< .00 Outpatient health clinic20 (24.1)36 (52.2) School-based health clinic63 (75.9)33 (47.8)% Adolescent patients (n = 154)  7.3780, df = 1.01 ≤50%26 (30.6)36 (52.2) >50%59 (69.4)33 (47.8)Years in practice (n = 157)  6.2597, df = 1.01 ≤5 years44 (51.8)23 (31.9) >5 years41 (48.2)49 (68.1)U.S. practice region (n = 151)  29.68, df = 3< .00 Northeastern United States13 (15.3)15 (22.7) Southern United States11 (12.9)22 (33.3) Midwestern United States57 (67.1)16 (24.2) Western United States4 (4.7)13 (19.7)Race (n = 201)  1.2865, df = 2.53 Black/African American11 (9.9)5 (5.6) White/Caucasian66 (59.5)56 (62.2) Other34 (30.6)29 (32.2)Provider age in years (n = 145)  4.00, df = 2.14 20–39 years21 (25.6)8 (12.7) 40–49 years24 (29.3)19 (30.2) 50+ years37 (45.1)36 (57.1)

χ2, Chi-square statistic.

df, degrees of freedom.

Darling-Fisher, C. S., Salerno, J., Dahlem, C. H. Y., & Martyn, K. K. (2014). The Rapid Assessment for Adolescent Preventive Services (RAAPS): Providers’ assessment of its usefulness in their clinical practice settings. Journal of Pediatric Health Care, 28(3), p. 221.

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Study Questions

1. What is the sample size for the Darling-Fisher et al. (2014) study? How many study participants (percentage) are RAAPS users and how many are RAAPS nonusers?

2. What is the chi-square (χ2) value and degrees of freedom (df) for provider type?

3. What is the p value for provider type? Is the χ2 value for provider type statistically significant? Provide a rationale for your answer.

4. Does a statistically significant χ2 value provide evidence of causation between the variables? Provide a rationale for your answer.

5. What is the χ2 value for race? Is the χ2 value statistically significant? Provide a rationale for your answer.

6. Is there a statistically significant difference between RAAPS users and RAAPS nonusers with regard to percentage adolescent patients? In your own opinion is this an expected finding? Document your answer.

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7. What is the df for U.S. practice region? Complete the df formula for U.S. practice region to visualize how Darling-Fisher et al. (2014) determined the appropriate df for that region.

8. State the null hypothesis for the years in practice variable for RAAPS users and RAAPS nonusers.

9. Should the null hypothesis for years in practice developed for Question 8 be accepted or rejected? Provide a rationale for your answer.

10. How many null hypotheses were accepted by Darling-Fisher et al. (2014) in Table 2? Provide a rationale for your answer.

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Answers to Study Questions

1. The sample size is N = 201 with n = 111 (55%) RAAPS users and n = 90 (45%) RAAPS nonusers as indicated in the narrative results.

2. The χ2 = 12.7652 and df = 2 for provider type as presented in Table 2.

3. The p = < .00 for the provider type. Yes, the χ2 = 12.7652 for provider type is statistically significant as indicated by the p value presented in Table 2. The specific χ2 value obtained could be compared against the critical value in a χ2 table (see Appendix D Critical Values of the χ2 Distribution at the back of this text) to determine the significance for the specific degrees of freedom (df), but readers of research reports usually rely on the p value provided by the researcher(s) to determine significance. Most nurse researchers set the level of significance or alpha (α) = 0.05. Since the p value is less than alpha, the result is statistically significant. You need to note that p values never equal zero as they appear in this study. The p values would not be zero if carried out more decimal places.

4. No, a statistically significant χ2 value does not provide evidence of causation. A statistically significant χ2 value indicates a significant difference between groups exists but does not provide a causal link (Grove et al., 2013; Plichta & Kelvin, 2013).

5. The χ2 = 1.2865 for race. Since p = .53 for race, the χ2 value is not statistically significant. The level of significance is set at α = 0.05 and the p value is larger than alpha, so the result is nonsignificant.

6. Yes, there is a statistically significant difference between RAAPS users and RAAPS nonusers with regard to percent of adolescent patients. The chi-square value = 7.3780 with a p = .01.You might expect that nurses caring for more adolescents might have higher RAAPS use as indicated in Table 2. However, nurses need to be knowledgeable of assessment and care needs of populations and subpopulations in their practice even if not frequently encountered. Two valuable sources for adolescent care include the Centers for Disease Control and Prevention (CDC) Adolescent and School Health at http://www.cdc.gov/HealthyYouth/idex.htm and the World Health Organization (WHO) adolescent health at http://www.who.int/topics/adolescent_health/en/.

7. The df = 3 for U.S. practice region is provided in Table 2. The df formula, df = (R − 1) (C − 1) is used. There are four “R” rows, Northeastern United States, Southern United States, Midwestern United States, and Western United States. There are two “C” columns, RAAPS users and RAAPS nonusers. df = (4 − 1)(2 − 1) = (3)(1) = 3.

8. The null hypothesis: There is no difference between RAAPS users and RAAPS nonusers for providers with ≤5 years of practice and those with >5 years of practice.

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9. The null hypothesis for years in practice stated in Questions 8 should be rejected. The χ2 = 6.2597 for years in practice is statistically significant, p = .01. A statistically significant χ2 indicates a significant difference exists between the users and nonusers of RAAPS for years in practice; therefore, the null hypothesis should be rejected.

10. Two null hypotheses were accepted since two χ2 values (race and provider age) were not statistically significant (p > 0.05), as indicated in Table 2. Nonsignificant results indicate that the null hypotheses are supported or accepted as an accurate reflection of the results of the study.

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EXERCISE 19 Questions to Be Graded

Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/Statistics/ under “Questions to Be Graded.”

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

1. According to the relevant study results section of the Darling-Fisher et al. (2014) study, what categories are reported to be statistically significant?

2. What level of measurement is appropriate for calculating the χ2 statistic? Give two examples from Table 2 of demographic variables measured at the level appropriate for χ2.

3. What is the χ2 for U.S. practice region? Is the χ2 value statistically significant? Provide a rationale for your answer.

4. What is the df for provider type? Provide a rationale for why the df for provider type presented in Table 2 is correct.

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5. Is there a statistically significant difference for practice setting between the Rapid Assessment for Adolescent Preventive Services (RAAPS) users and nonusers? Provide a rationale for your answer.

6. State the null hypothesis for provider age in years for RAAPS users and RAAPS nonusers.

7. Should the null hypothesis for provider age in years developed for Question 6 be accepted or rejected? Provide a rationale for your answer.

8. Describe at least one clinical advantage and one clinical challenge of using RAAPS as described by Darling-Fisher et al. (2014).

9. How many null hypotheses are rejected in the Darling-Fisher et al. (2014) study for the results presented in Table 2? Provide a rationale for your answer.

10. A statistically significant difference is present between RAAPS users and RAAPS nonusers for U.S. practice region, χ2 = 29.68. Does the χ2 result provide the location of the difference? Provide a rationale for your answer

(Grove 191-200)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Exercise 29

Calculating Simple Linear Regression

Simple linear regression is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (Cohen & Cohen, 1983). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in Exercise 14, Figure 14-1 illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.

A regression equation can be generated with a data set containing subjects’ x and y values. Once this equation is generated, it can be used to predict future subjects’ y values, given only their x values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable y (dependent variable, or outcome) is predicted from the same subject’s known score on variable x (independent variable, or predictor).

Research Designs Appropriate for Simple Linear Regression

Research designs that may utilize simple linear regression include any associational design (Gliner et al., 2009). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.

Statistical Formula and Assumptions

Use of simple linear regression involves the following assumptions (Zar, 2010):

1. Normal distribution of the dependent (y) variable

2. Linear relationship between x and y

3. Independent observations

4. No (or little) multicollinearity

5. Homoscedasticity

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Data that are homoscedastic are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of x, the distribution of y values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (Cohen & Cohen, 1983; Zar, 2010). Visual examples of homoscedasticity and heteroscedasticity are presented in Exercise 30.

In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters a and b are computed to obtain a regression equation. To understand the mathematical process, recall the algebraic equation for a straight line:

y=bx+a 

where

y=the dependent variable(outcome) 

x=the independent variable(predictor) 

b=the slope of the line 

a=y-intercept(the point where the regression line intersects the y-axis) 

No single regression line can be used to predict with complete accuracy every y value from every x value. In fact, you could draw an infinite number of lines through the scattered paired values (Zar, 2010). However, the purpose of the regression equa­tion is to develop the line to allow the highest degree of prediction possible—the line of best fit. The procedure for developing the line of best fit is the method of least squares. The formulas for the beta (β) and slope (α) of the regression equation are computed as follows. Note that once the β is calculated, that value is inserted into the formula for α.

β=n∑xy−∑x∑yn∑x 2 −(∑x) 2   

α=∑y−b∑xn  

Hand Calculations

This example uses data collected from a study of students enrolled in a registered nurse to bachelor of science in nursing (RN to BSN) program (Mancini, Ashwill, & Cipher, 2014). The predictor in this example is number of academic degrees obtained by the student prior to enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is “Number of degrees does not predict the number of months until completion of an RN to BSN program.”

The data are presented in Table 29-1. A simulated subset of 20 students was selected for this example so that the computations would be small and manageable. In actuality, studies involving linear regression need to be adequately powered (Aberson, 2010; Cohen, 1988). Observe that the data in Table 29-1 are arranged in columns that correspond to 321the elements of the formula. The summed values in the last row of Table 29-1 are inserted into the appropriate place in the formula for b.

TABLE 29-1

ENROLLMENT GPA AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAMStudent IDxyx2xy(Number of Degrees)(Months to Completion)1117117229418301700419195016006111111701500801200911511510112112111141141211011013117117140200015294181621242417114114182104201911711720211422sum Σ2026730238

The computations for the b and α are as follows:

Step 1: Calculate b.From the values in Table 29-1, we know that n = 20, Σx = 20, Σy = 267, Σx2 = 30, and Σxy = 238. These values are inserted into the formula for b, as follows:

b=20(238)−(20)(267)20(30)−20 2   

b=−2.9 

Step 2: Calculate α.From Step 1, we now know that b = −2.9, and we plug this value into the formula for α.

α=267−(−2.9)(20)20  

α=16.25 

Step 3: Write the new regression equation:

y=−2.9x+16.25 

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Step 4: Calculate R.The multiple R is defined as the correlation between the actual y values and the predicted y values using the new regression equation. The predicted y value using the new equation is represented by the symbol ŷ to differentiate from y, which represents the actual y values in the data set. We can use our new regression equation from Step 3 to compute predicted program completion time in months for each student, using their number of academic degrees prior to enrollment in the RN to BSN Program. For example, Student #1 had earned 1 academic degree prior to enrollment, and the predicted months to completion for Student 1 is calculated as:

y ̂ =−2.9(1)+16.25 

y ̂ =13.35 

Thus, the predicted ŷ is 13.35 months. This procedure would be continued for the rest of the students, and the Pearson correlation between the actual months to completion (y) and the predicted months to completion (ŷ) would yield the multiple R value. In this example, the R = 0.638. The higher the R, the more likely that the new regression equation accurately predicts y, because the higher the correlation, the closer the actual y values are to the predicted ŷ values. Figure 29-1 displays the regression line where the x axis represents possible numbers of degrees, and the y axis represents the predicted months to program completion (ŷ values).

FIGURE 29-1  REGRESSION LINE REPRESENTED BY NEW REGRESSION EQUATION.

Step 5: Determine whether the predictor significantly predicts y.

t=Rn−21−R 2   ‾ ‾ ‾ ‾  √  

To know whether the predictor significantly predicts y, the beta must be tested against zero. In simple regression, this is most easily accomplished by using the R value from Step 4:

t=.638200−21−.407  ‾ ‾ ‾ ‾ ‾  √  

t=3.52 

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The t value is then compared to the t probability distribution table (see Appendix A). The df for this t statistic is n − 2. The critical t value at alpha (α) = 0.05, df = 18 is 2.10 for a two-tailed test. Our obtained t was 3.52, which exceeds the critical value in the table, thereby indicating a significant association between the predictor (x) and outcome (y).

Step 6: Calculate R2.After establishing the statistical significance of the R value, it must subsequently be examined for clinical importance. This is accomplished by obtaining the coefficient of determination for regression—which simply involves squaring the R value. The R2 represents the percentage of variance explained in y by the predictor. Cohen describes R2 values of 0.02 as small, 0.15 as moderate, and 0.26 or higher as large effect sizes (Cohen, 1988). In our example, the R was 0.638, and, therefore, the R2 was 0.407. Multiplying 0.407 × 100% indicates that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at admission (Cohen & Cohen, 1983).The R2 can be very helpful in testing more than one predictor in a regression model. Unlike R, the R2 for one regression model can be compared with another regression model that contains additional predictors (Cohen & Cohen, 1983). The R2 is discussed further in Exercise 30.The standardized beta (β) is another statistic that represents the magnitude of the association between x and y. β has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. This value can be calculated by hand but is best computed with statistical software. The standardized beta (β) is calculated by converting the x and y values to z scores and then correlating the x and y value using the Pearson r formula. The standardized beta (β) is often reported in literature instead of the unstandardized b, because b does not have lower or upper limits and therefore the magnitude of b cannot be judged. β, on the other hand, is interpreted as a Pearson r and the descriptions of the magnitude of β can be applied, as recommended by Cohen (1988). In this example, the standardized beta (β) is −0.638. Thus, the magnitude of the association between x and y in this example is considered a large predictive association (Cohen, 1988).

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SPSS Computations

This is how our data set looks in SPSS.

Step 1: From the “Analyze” menu, choose “Regression” and “Linear.”

Step 2: Move the predictor, Number of Degrees, to the space labeled “Independent(s).” Move the dependent variable, Number of Months to Completion, to the space labeled “Dependent.” Click “OK.”

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Interpretation of SPSS Output

The following tables are generated from SPSS. The first table contains the multiple R and the R2 values. The multiple R is 0.638, indicating that the correlation between the actual y values and the predicted y values using the new regression equation is 0.638. The R2 is 0.407, indicating that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at enrollment.

Regression

The second table contains the ANOVA table. As presented in Exercises 18 and 33, the ANOVA is usually performed to test for differences between group means. However, ANOVA can also be performed for regression, where the null hypothesis is that “knowing the value of x explains no information about y”. This table indicates that knowing the value of x explains a significant amount of variance in y. The contents of the ANOVA table are rarely reported in published manuscripts, because the significance of each predictor is presented in the last SPSS table titled “Coefficients” (see below).

The third table contains the b and a values, standardized beta (β), t, and exact p value. The a is listed in the first row, next to the label “Constant.” The β is listed in the second row, next to the name of the predictor. The remaining information that is important to extract when interpreting regression results can be found in the second row. The standardized beta (β) is −0.638. This value has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. The t value is −3.516, and the exact p value is 0.002.

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Final Interpretation in American Psychological Association (APA) Format

The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). Simple linear regression was performed with number of earned academic degrees as the predictor and months to program completion as the dependent variable. The student’s number of degrees significantly predicted months to completion among students in an RN to BSN program, β = −0.638, p = 0.002, and R2 = 40.7%. Higher numbers of earned academic degrees significantly predicted shor

quiz

Question 1

1. When a provider receives a fixed amount to provide only the care that an individual needs from the provider, this is known as a _____________ payment. capitation fixed premium sub-capitation

4 points  

Question 2

1. The healthcare industry is heavily regulated by ____ and ____ legislation. city; local state; city county; state federal; state

4 points  

Question 3

1. When a patient signs a release of medical information at a physician’s office, that release is generally considered to be valid for six months for a single visit to the physician for one year from the date entered on the form until the patient changes insurance companies

4 points  

Question 4

1. When the provider is required  to receive as payment in full whatever amount the insurance reimburses for services, the provider is agreeing to accept assignment assignment of benefits authorize services coordination of benefits

4 points  

Question 5

1. Which document is used to guarantee the patient’s financial and medical record? encounter form patient insurance form patient ledger patient registration form

4 points  

Question 6

1. The person responsible for paying the charges for services rendered by the provider is the beneficiary guarantor guardian subscriber

4 points  

Question 7

1. Which federal legislation was enacted in1995 to restrict the referral of patients to organizations in which providers have a financial interest? Federal Anti-Kickback Law Hill-Burton Act HIPAA Stark II laws

4 points  

Question 8

1. The recognized difference between fraud and abuse is the cost intent payer timing

4 points  

Question 9

1. The specified amount of annual out-of-pocket expenses for covered health care services that the insured must pay annually for health care is called the coinsurance copayment deductible premium

4 points  

Question 10

1. Which three components constitute the RBRVS payment system? fee schedule, practice expense, and malpractice expense physician work, practice expense, and geographical location physician work, practice expense, and malpractice insurance espense practice expense, malpractice insurance expense, and liability insurance expense

4 points  

Question 11

1. Mandates are directives laws regulations standards

4 points  

Question 12

1. Which type of HMO offers subscribers health care services by physicians who remain in their individual office setting? closed panel independent practice association network model staff model

4 points  

Question 13

1. HIPAA requires payers to implement rules called electronic __________, which result in a uniform language for electronic data interchange.     data interchanges   health records    medical records     transaction standards

4 points  

Question 14

1. The ambulatory payment classification prospective payment system is used to reimburse claims for what services? inpatient nursing facility outpatient rehabilitation

4 points  

Question 15

1. Breach of confidentiality can result from discussing patient health care information with unauthorized sources discussing the patient’s case in the business office sending medical information to non-health care entities with the patient’s consent sending patient health care information to the patient’s insurance company

4 points  

Question 16

1. When a patient elects to receive care from a non-PAR, the patient will accrue _____. higher copays higher out-of-pocket expenses lower premiums lower copays

4 points  

Question 17

1. When a number of people are grouped for insurance purposes, this is known as a(n) adverse selection insurance pool member group risk pool

4 points  

Question 18

1. Because the diagnosis and procedure codes reported affect the DRG selected (and resultant payment), some hospitals engaged in a practice called __________, which is the assignment of an ICD-10-CM diagnosis code that does not match patient record documentation, for the purpose of illegally increasing reimbursement.     downcoding   jamming    unbundling     upcoding

4 points  

Question 19

1. The problem-oriented record (POR) is a systematic method of documentation that consists of a database. progress notes. an initial plan. all of the above.

4 points  

Question 20

1. Which of the following is an example of fraud?     billing noncovered services as covered services     falsifying certificates of medical necessity plans of treatment     reporting duplicative charges on an insurance claim     submitting claims for services not medically necessary

4 points  

Question 21

1. Care rendered to a patient that was not properly approved (e.g., preapproved) by the insurance company is known as medical necessity noncovered benefits unapproved services unauthorized services

4 points  

Question 22

1. A risk contract is defined as an arrangement among health care providers stating that the HMO can provide services to Medicare beneficiaries only that allows higher payments to the HMO if they treat Medicare beneficiaries to make available capitated health care services to Medicare beneficiaries to offer fee-for-service health care services to Medicare beneficiaries

4 points  

Question 23

1. Which of the following is an example of abuse?     billing noncovered services/procedures as covered services/procedures     falsifying health care certificates of medical necessity plans of treatment     misrepresenting ICD-10-CM and CPT/HCPCS codes to justify payment     submitting claims for services and procedures knowingly not provided

4 points  

Question 24

1. Preventive services may result in the early detection of health problems. are required by most insurance companies. allow treatment options that are less dramatic and less expensive. both a and c.

4 points  

Question 25

1. Drew Baker is referred to a health care provider by an employer for treatment of a fracture that occurred during a fall at work. The physician billed Medicare and did not indicate on the claim that the injury was work related. Medicare benefits were paid to the provider for services rendered. This resulted in Medicare contacting the provider, who is liable for the __________ because of the provider’s failure to disclose that the injury was work-related.    adjudication   mediation     overpayment     unbundling

Signature AssignmentCase Presentations of PsoriasisSummary:  Each Student will prepare and present 2 pathophysiology case presentations.  The following week students will work in small groups and di

Signature Assignment

Case Presentations of Psoriasis

Summary:  Each Student will prepare and present 2 pathophysiology case presentations.  The following week students will work in small groups and discuss the presentations posted by the members in their small group.  Presentations are due in week 3 and 6, while the discussions will be held during weeks 4 and 7.  

Students will be assigned a diagnostic topic and patient scenario to present as a case presentation. Presentations will be completed as a narrated Power Point Presentation and posted in the Discussion Board for the corresponding week. Please see the presentation content tab for instructions on how to post in DB as well as a table of student topic assignments.  

Directions:  

1. Give an overview of the patient with the presumptive diagnosis*.   You can “create” a patient case based on your knowledge of the diagnosis, based on a patient you have seen in your work or personal experience, or use a case study from the following recommended text: 

Harold J. Bruyere (2009) 100 Case Studies in Pathophysiology. Login Bros. ISBN 978-0-7817-6145-1

1. Give an overview of the diagnosis including risk factors and how they cause or contribute to adaptation or pathogenesis.  
2. Give an in-depth discussion of the pathophysiology of your diagnosis as it occurs over time; include signs, symptoms, diagnostic studies and the underlying pathophysiologic process causing these signs and symptoms.   Support this pathophysiology with high level evidence. 
3. Summarize treatments and outcomes in terms of pathophysiologic alterations.  
4. Once you have had an in-depth discussion of your diagnosis overall,  be sure to connect your case discussion back to the patient you have presented.  Example, If your patient has HTN and DM, be sure to include a discussion of the symptoms that your patient is exhibiting and discuss treatments specifically aimed for your patient. 
5. Support your case discussion with current (in the past 5 years) high level evidence.  Patient information portals, disease association websites  and other resources such as Uptodate, Epocrates, Medscape, Mayo Clinic are not considered high levels of evidence and should be avoided for your presentations.  While use of the course textbooks is permitted, a minimum of 3 high level citations outside of the course texts is required.  If you are unclear about what is considered high level evidence, please review content from your Evidence Based Nursing Practice Course
6. Include some graphics to support your presentation and to make it more visually engaging
7. Be sure that you are using your slides to support the verbal presentation.  Do not simply read the slides to your audience.  Presentations that are read to the audience without expansion of the content will be graded as if no narration were included. 
8. Presentation Length: The Patient Presentation component is typically 5 min or less, and a maximum of 3 slides.  Be sure to include just the pertinent positive and pertinent negatives.  The discussion of the case progression and pathophysiology is typically 10 -15 slides, and 15-20 minutes in length.  The entire presentation is a maximum  of 25 minutes and maximum of 18 slides which includes your title and reference slides.  
9. APA formatting for your presentation is required. 

Power Point Post Requirements:  

1. You will post 2 files.    
   1. The first is a copy of your presentation with a transcript of your planned narration in the notes section of the ppt file.  This file should not have the narration included. 
   2. The second file is of your narrated presentation.   
2. It is acceptable to post a link to a youtube video of your ppt presentation**.  If you do so, you must also post the non-narrated ppt with transcription of your planned narration.    
3. Please do not post your presentation as any other type of document or movie file.  This include MPV4 files etc. 

**View these instructions for recording your presentation using Screencast-o-matic https://drive.google.com/open?id=10IgEjiJCp5s2WIxposCGNLJNqO3WxAnJaeqAMiVxvjs 

*Patient Presentation Guidelines

Summary:

Advance Practice Registered Nurses frequently need to present a patient to another health care provider in a concise manner. Your case presentation is an excellent opportunity to practice patient presentations to a colleague.

Overview: 

• Patient presentations are concise, and brief. 
• Presentation should be completed in 2- 3 minutes unless it is a new patient which may extend up to 5-6 minutes.
• Delivery should be smooth and succinct. Avoid the inclusion any superfluous information.
• Format should be made so that the collaborating provider can anticipate your assessment and plan: each piece of information should provide the listener clues regarding your thinking process and differential diagnosis.
• Notes should be made regarding some pertinent information which may be difficult to remember. The presenter should refer to these notes if necessary, but should not read these notes to the listener.

TYPES OF PATIENT PRESENTATIONS

New Patients: 

• Includes: Pt name, age, then proceed to Chief complaint. Give brief and pertinent HPI, important and related PMH, PSH, and FH. (you will not include an entire PMH, PSH, and FH as the other provider can read about this at another time)
• ROS is not included as anything important would have been included in the HPI.
• PE positive and pertinent negatives,
• Assessment and plan if you know it, including what diagnostic tests done, and tx that you have previously implemented.
• Finally, State what your question, or indicate what you are asking for help with. (this should match the chief complaint)

Follow Up 

• The presentation on a follow up may be updating the collaborating provider regarding results of tx plan previously implemented, or results of diagnostic tests ordered previously ordered. 
• You will need to give some information to remind the collaborating provider of what tests or tx were implemented, why these were ordered, and what the results were or response was to the treatment.

Problem Focused:

• Includes: Pt name, age, chief complaint or problem. Give brief HPI for this problem, with related PMH and medications. (will not be as detailed as for new patients).
• Next give PE including pertinent positive and negatives. Include any pertinent diagnostic information here
• Assessment and Plan: presentation is concluded with the assessment and plan, or with the request for tx recommendations from the collaborating provider.

PREPARATION

• Information Search: have information prepared regarding pathophysiology of the problem and usual tx of the problem
• Know what questions you have about this particular patient.

PROCESS:

• Be prepared for this presentation, have information ready, including medications, lab and procedure results.
• Be concise and complete: expect to have the attention of the listener for less than 3 minutes.
• Know your question: usually the presentation is formatted so that the listener can anticipate the possible diagnoses. You are guiding them through the case presentation and essentially telling a story. This may be very different from a case presentation done with your preceptor, where you are anticipating your preceptor to have questions about the patient and to discuss/determine the diagnosis and treatment plan with you.
• Anticipate and Expect questions from the listener. Often it will appear as if the listener is trying to trick you and is only out to identify that you missed some bit of information. In reality it is that this information was missing in the first place or was not completely clear, or that the listener has an idea of the diagnosis and treatment plan and needs more information to confirm this.

TIPS

• Include only the most essential facts: but be ready to answer ANY questions about all aspects of your patient.
• Keep your presentation lively.
• Do not read the presentation
• Expect your listeners to ask questions
• Follow the order of the written case report
• Keep in mind the limitation of your listeners.
• Beware of jumping back and forth between descriptions of separate problems
• Use the presentation to build your case.
• Your reasoning process should help the listener consider a differential diagnosis