UMGC HMGT400 2021 April Complete Course Latest (Full)

HMGT400 Research and Data Analysis in Healthcare

Assignment 1 & 2 Topic Selection

Instructions

Here are a few topics for MedicareNationalDataCSV dataset.

Make sure to decide on a topic for assignment 1 and a different topic for assignment 2. Clearly identify what topic for what assignment you chose.

Between Black and White Population

- Comparing annual percent of Medicare enrollees having at least one ambulatory visit between B and W

- Comparing average annual percent of diabetic Medicare enrollees age 65-75 having hemoglobin A1c between B and W

- Comparing average annual percent of diabetic Medicare enrollees age 65-75 having eye examination between B and W

- Comparing average annual percent of diabetic Medicare enrollees age 65-75 having blood lipids between B and W

- Comparing average percent of female Medicare enrollees age 67-69 having at least one mammogram over a two-year period between B and W

- Comparing percent of Medicare beneficiaries Part A eligible between B and W

- Comparing discharges for ambulatory care sensitive conditions per 1,000 Medicare enrollees between B and W

 Between Two States

- Comparing annual percent of Medicare enrollees having at least one ambulatory visit between AL and AR (or any two States but the observations for each state MUST be more than 30 obs.)

- Comparing average annual percent of diabetic Medicare enrollees age 65-75 having haemoglobin A1c between State1 and State2 (e.g. MD & VA or any other states)

- Comparing average annual percent of diabetic Medicare enrollees age 65-75 having eye examination between State1 and State2 (e.g. CA & MI or any other states)

- Comparing average annual percent of diabetic Medicare enrollees age 65-75 having blood lipids between State1 and State2 (you must select states)

- Comparing average percent of female Medicare enrollees age 67-69 having at least one mammogram over a two-year period between State1 and State2 (you must select states)

- Comparing percent of Medicare beneficiaries Part A eligible between State1 and State2

- Comparing discharges for ambulatory care sensitive conditions per 1,000 Medicare enrollees between State1 and State2 (you must select states)

Here are a few topics for MNHCCISImagingProcedures2013

Comparing the number of MRI Procedures and PET/CT Procedures (all states)

Comparing the number of MRI Procedures between Clinics and Hospitals (all states)

Comparing the number of MRI Procedures and PET/CT Procedures in State1 & State2

The same for PET/CT Procedures, CT Procedures and SPECT Procedures.

Here are a few topics for MN Admission

Comparing the number of admission between Med/Surg and Cardiac in MN (or any two procedures)

Comparing Average Length of Stay between Acute Care and Not Acute Care Hospitals

 DO:

The students can choose a topic and any other datasets provided for the class, or the students can choose their own topic

BUT post your topic first and DO NOT start working before receiving the faculty's approval.

DO NOT use the same topics you used for the other individual assignment or the group project.

Also, this is fine if two students work with similar topics, but make sure to submit your topics and receive the faculty's feedback first.

 

 

 

 

 

HMGT400 Research and Data Analysis in Healthcare

Assignment 1

Quantitative Analysis

For this assignment, students should choose data from the quantitative analysis below and are  asked to analyze it using Excel, RStuido (BONUS points)

Data set:

Minnesota Healthcare Database.xlsx

Medicare National Data by County

MN Hospital Report Data by Care Unit FY2013

MN HCCIS Imaging Procedures 2013

MEPS Dental Files

MEPS Inpatient Stay Database

Students will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.

Here are the main steps for this assignment.

Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.

Step 2: Develop the research question and

Step 3:  Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.

The Report Structure:

Start with the

1.Cover page (1 page, including running head).

Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umgc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.

In the title page include:

Title, this is the approved topic by your instructor.

Student name

Class name

Instructor name

Date

2.Introduction

Introduce the problem or topic being investigated. Include relevant background information, for example; 

Indicates why this is an issue or topic worth researching;

Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and

Specify how others have operationalized this concept and measured these phenomena

Note: Introduction should not be more than one or two paragraphs.

Literature Review

There is no need for a literature review in this assignment

3.Research Question or Research Hypothesis

What is the Research Question or Research Hypothesis?

***Just in time information: Here are a few points for Research Question or Research Hypothesis

There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.

Examples of non-testable questions are:

How do managers feel about the reorganization?

What do residents feel are the most important problems facing the community?

Respondents' answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.

In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:

Is there a significant difference between ...?

Is there a significant relationship between ...?

For example:

Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?

A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words "Is there" with the words "There is," and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:

There is a significant relationship between the age of managers and their attitudes towards the reorganization.

 It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words "no" or "not" to the statement. For example, the null hypotheses for the two examples would be:

There is no significant relationship between the age of managers and their attitudes towards the reorganization.

There is no significant difference between white and minority residents with respect to what they feel are the most important problems facing the community.

All statistical testing is done on the null hypothesis...never the hypothesis. The result of a statistical test will enable you to either:

1) reject the null hypothesis, or

2) fail to reject the null hypothesis. Never use the words "accept the null hypothesis."

*Source: StatPac for Windows Tutorial. (2017). User's Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019 from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm

What does significance really mean?

“Significance is a statistical term that tells how sure you are that a difference or relationship exists.  To say that a significant difference or relationship exists only tells half the story.  We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship?  After finding a significant relationship, it is important to evaluate its strength.  Significant relationships can be strong or weak.  Significant differences can be large or small.  It just depends on your sample size.

To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:

P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.

Example of Welch Two Sample T-test from Exercise 1

The p-value from above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).

Note: This is an example from the week1 exercise.

An example from Exercise 1

The p-value from above example, 0.0001, indicates that we’d expect to see a meaningless (random) ‘number of the employees on payer’ difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).

CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):

Confidence Interval Example

CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):

Confidence Interval Example

The boundaries of this confidence interval around the difference also provide a way to see what the upper [40.44] and lower bounds [-40.82].

As a summary:

“Statistically significant means a result is unlikely due to chance.

The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.

Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.

The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aide in determining if a difference really is noteworthy.

With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”("Measuring U", 2019).

*Resource

Measuring U. (2019). Statistically significant. Retrieved May 17, 2019 from: https://measuringu.com/statistically-significant/

Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.

4.Research Method

Discuss the Research Methodology (in general). Describe the variable or variables that are being analyzed. Identify the statistical test you will select to analyze these data and explain why you chose this test.  Summarize your statistical alternative hypothesis. This section includes the following sub-sections:

a)Describe the Dataset

Example:  The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (citation). This dataset provides information on hospital characteristics, such as: Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).

Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”

b)Describe Variables

Next, review the database you selected and select a variable or variables that are a “best-fit.”  That is, choose a variable that quantitatively measures the concept or concepts articulated in your research question or hypothesis.

Return to your previously stated Research Question or Hypothesis and evaluate it considering the variables you have selected. (See the sample Table 1).

Table 1. List of variables used for the analysis

Variable	Definition	Description of code	Source	Year
Total Hospital Beds	Total facility beds set up and staffed at the end of the reporting period	Numeric	MN Data	2013
….
…..

 

To cite a dataset, you can go with two approaches:

First, look at the note in the dataset for example;

Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A

Second, use the online citation, for example:

Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at: http://campus.umgc.edu

See two examples describing the variables from Minnesota Data:

Table 2. Definition of variables used in the analysis

Variable	Definition	Description of code	Source	Year
hospital_beds	Total facility beds set up and staffed at the end of the reporting period	Numeric	MN data	2013
year	FY	Categorical	MN data	2013

c)Describe the Research Method for Analysis

First, describe the research method as a general (e.g., this is a quantitative method and then explain about this method in about one paragraph. If you have this part in the introduction, you do not need to add here).

Then, explain the statistical method you plan to use for your analysis (Refer to content in week 3 on Biostatistics for information on various statistical methods you can choose from).

Example:

Hypothesis:  AZ hospitals are more likely to have lower readmission rates for PN compared to CA.

Research Method:  To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).

d)Describe statistical package

Add one paragraph for the statistical package, e.g., Excel or RStudio.

5. Results

Discuss your findings considering the following tips:

? Why you needed to see the distribution of data before any analysis (e.g., check for outliers, finding the best fit test; for example, if the data had not a normal distribution, you can’t use the parametric test, etc., so just add 1 or 2 sentences).

? Did you eliminate outliers? (Please write 1 or 2 sentences, if applicable).

? How many observations do you have in your database and how many for selected variables, report % of missing.

? When you are finished with this, go for the next steps:

Present the results of your statistical analysis; include any relevant statistical information (summary tables, including N, mean, std. dev.). Make sure to completely and correctly name all your columns and rows, tables and variables. For this part you could have at least 1-2 tables and 1-2 figures (depending on your variables bar-chart, pi-chart, or scatter-plot), you can use a table like this:

Table 3. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013

Variable	Obs.	Mean	SD	P-value
Per of Lipid in MD	24	83.20	2.32	0.4064
Per of Lipid in VA	124	82.69	4.41

When you have tables and plots ready, think about your finding and state the statistical conclusion.  That is, do the results present evidence in favor or the null hypothesis or evidence that contradicts the null hypothesis?

6.Conclusion and Discussion

Review your research questions or hypothesis.

How has your analysis informed this question or hypothesis?  Present your conclusion(s) from the results (presented above) and discuss the meaning of this conclusion(s) considering the research question or hypothesis presented in your introduction.  

At the end of this section, add one or two sentences and discuss the limitations (including biases) associated with this analysis and any other statements you think are important in understanding the results of this analysis.

References

Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

 

 

HMGT400 Research and Data Analysis in Healthcare

Assignment 2

Quantitative Analysis

Assignment # 2: Quantitative Analysis

For this assignment, students will be given data from quantitative analysis and will be asked to analyze it using Excel, RStuido (BONUS points)

Data set:

Minnesota Healthcare Database.xlsx

Medicare National Data by County

MN Hospital Report Data by Care Unit FY2013

MN HCCIS Imaging Procedures 2013

MEPS Dental Files

MEPS Inpatient Stay Database

Students will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.

Here are the main steps for this assignment.

Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.

Step 2: Develop the research question and

Step 3:  Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.

The Report Structure:

Start with the

1.Cover page (1 page, including running head).

Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.

In the title page include:

Title, this is the approved topic by your instructor.

Student name

Class name

Instructor name

Date

2.Introduction

Introduce the problem or topic being investigated. Include relevant background information, for example; 

Indicates why this is an issue or topic worth researching;

Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and

Specify how others have operationalized this concept and measured these phenomena

Note: Introduction should not be more than one or two paragraphs.

Literature Review

There is no need for a literature review in this assignment

3.Research Question or Research Hypothesis

What is the Research Question or Research Hypothesis?

***Just in time information: Here are a few points for Research Question or Research Hypothesis

There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.

Examples of non-testable questions are:

How do managers feel about the reorganization?

What do residents feel are the most important problems facing the community?

Respondents' answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.

In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:

Is there a significant difference between ...?

Is there a significant relationship between ...?

For example:

Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?

A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words "Is there" with the words "There is," and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:

There is a significant relationship between the age of managers and their attitudes towards the reorganization.

 It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words "no" or "not" to the statement. For example, the null hypotheses for the two examples would be:

There is no significant relationship between the age of managers and their attitudes towards the reorganization.

There is no significant difference between white and minority residents with respect to what they feel are the most important problems facing the community.

All statistical testing is done on the null hypothesis...never the hypothesis. The result of a statistical test will enable you to either:

1) reject the null hypothesis, or

2) fail to reject the null hypothesis. Never use the words "accept the null hypothesis."

What does significance really mean?

“Significance is a statistical term that tells how sure you are that a difference or relationship exists.  To say that a significant difference or relationship exists only tells half the story.  We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship?  After finding a significant relationship, it is important to evaluate its strength.  Significant relationships can be strong or weak.  Significant differences can be large or small.  It just depends on your sample size.

To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:

P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.

Example of Welch Two Sample T-test from Exercise 1

The p-value from above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).

Note: This is an example from the week1 exercise.

An example from Exercise 1

The p-value from above example, 0.0001, indicates that we’d expect to see a meaningless (random) ‘number of the employees on payer’ difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).

CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):

Confidence Interval Example

CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):

Confidence Interval Example

The boundaries of this confidence interval around the difference also provide a way to see what the upper [40.44] and lower bounds [-40.82].

As a summary:

“Statistically significant means a result is unlikely due to chance.

The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.

Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.

The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aide in determining if a difference really is noteworthy.

With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”("Measuring U", 2019).

*Resource

Measuring U. (2019). Statistically significant. Retrieved May 17, 2019 from: https://measuringu.com/statistically-significant/

Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.

4.Research Method

Discuss the Research Methodology (in general). Describe the variable or variables that are being analyzed. Identify the statistical test you will select to analyze these data and explain why you chose this test.  Summarize your statistical alternative hypothesis. This section includes the following sub-sections:

a)Describe the Dataset

Example:  The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as: Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).

Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”

b)Describe Variables

Next, review the database you selected and select a variable or variables that are a “best-fit.”  That is, choose a variable that quantitatively measures the concept or concepts articulated in your research question or hypothesis.

Return to your previously stated Research Question or Hypothesis and evaluate it considering the variables you have selected. (See the sample Table 1).

Table 1. List of variables used for the analysis

Variable	Definition	Description of code	Source	Year
Total Hospital Beds	Total facility beds set up and staffed at the end of the reporting period	Numeric	MN Data	2013
….
…..

Source: UMUC, 2019

 ***Just in time information:

To cite a dataset, you can go with two approaches:

First, look at the note in the dataset for example;

Medicare National Data by County. (2012). Dartmouth Atlas of Health Care, A

Second, use the online citation, for example:

Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at: http://campus.umuc.edu

See two examples describing the variables from Minnesota Data:

Table 2. Definition of variables used in the analysis

Variable	Definition	Description of code	Source	Year
hospital_beds	Total facility beds set up and staffed at the end of the reporting period	Numeric	MN data	2013
year	FY	Categorical	MN data	2013

 

Source: UMUC, 2019

c)Describe the Research Method for Analysis

First, describe the research method as a general (e.g., this is a quantitative method and then explain about this method in about one paragraph. If you have this part in the introduction, you do not need to add here).

Then, explain the statistical method you plan to use for your analysis (Refer to content in week 3 on Biostatistics for information on various statistical methods you can choose from).

Example:

Hypothesis:  AZ hospitals are more likely to have lower readmission rates for PN compared to CA.

Research Method:  To determine whether Arizona hospitals are more likely to have lower readmission rate than California, we will use a t-test, to determine whether differences across hospital types are statistically significant (You can change the test depends on your analysis).

d)Describe statistical package

Add one paragraph for the statistical package, e.g., Excel or RStudio.

5. Results

Discuss your findings considering the following tips:

? Why you needed to see the distribution of data before any analysis (e.g., check for outliers, finding the best fit test; for example, if the data had not a normal distribution, you can’t use the parametric test, etc., so just add 1 or 2 sentences).

? Did you eliminate outliers? (Please write 1 or 2 sentences, if applicable).

? How many observations do you have in your database and how many for selected variables, report % of missing.

? When you are finished with this, go for the next steps:

Present the results of your statistical analysis; include any relevant statistical information (summary tables, including N, mean, std. dev.). Make sure to completely and correctly name all your columns and rows, tables and variables. For this part you could have at least 1-2 tables and 1-2 figures (depending on your variables bar-chart, pi-chart, or scatter-plot), you can use a table like this:

Table 3. Descriptive analysis to compare % of BL in Medicare beneficiary, MD vs. VA- 2013

Variable	Obs.	Mean	SD	P-value
Per of Lipid in MD	24	83.20	2.32	0.4064
Per of Lipid in VA	124	82.69	4.41

Source: UMUC, 2019

When you have tables and plots ready, think about your finding and state the statistical conclusion.  That is, do the results present evidence in favor or the null hypothesis or evidence that contradicts the null hypothesis?

6.Conclusion and Discussion

Review your research questions or hypothesis.

How has your analysis informed this question or hypothesis?  Present your conclusion(s) from the results (presented above) and discuss the meaning of this conclusion(s) considering the research question or hypothesis presented in your introduction.  

At the end of this section, add one or two sentences and discuss the limitations (including biases) associated with this analysis and any other statements you think are important in understanding the results of this analysis.

References

Include a reference page listing the bibliographic information for all sources cited in this report. This information should be consistent with the requirements specified in the American Psychological Association (APA) format and style guide.

 

HMGT400 Research and Data Analysis in Healthcare

Assignment 3

Group Project

This is not more than 20-page submission. In this assignment, students will go through the steps to set up a quantitative research study. The Instructor will divide the class into groups to complete the assignment in week 1.  Each group should submit 1 topic selection in week 1, and 1 final report. This report should include qualitative and quantitative sections. Here are the main steps to perform this team project:

Step #1: Topic selection

The instructor will provide a list of topics;  the team also should submit the team agreement plan with the selected topic in week 1 for the faculty feedback and approval. 

Step #2: Performing Qualitative analysis

2.1. This section of the assignment is aimed at giving students an opportunity to select and analyze at least 5 articles using the ‘Review Manager 5’ tools to analyze the risk of bias for 5 selected articles following these steps:

- The methodological quality and risk of bias evaluation of the selected studies should be  conducted independently by two team members, following the Cochrane Handbook for Systematic Reviews (Higgins, 2011), a domain-based evaluation for each study should be done across five domains:                    

- Selection bias,

- Performance bias,

- Detection bias,

- Attrition bias and,

- Reporting bias.

The judgment of studies for potential bias should be indicated by assigning ‘low risk’, ‘high risk, or ‘unclear risk’, for each respective source of bias.

Reference

Cochrane Handbook for Systematic Reviews of Interventions. (n.d.). Retrieved July 12, 2019, from https://training.cochrane.org/handbook

2.2. Report your finding using the Bias-Tables and Bias-Plots.

                                             Table 1: Literature Review Analysis

Authors, Year of Publication	Intervention/ Policy evaluated	Study design/ Time Period	Data/ Study Population	Relevant Findings/ Recommendations
Author1, YYYY
Author2, YYYY
Author3, YYYY
Author4, YYYY
Author5, YYYY

 

Table 2. Literature Review, Table of Biases

Selection Bias	Performance Bias	Detection Bias	Attrition Bias	Reporting Bias
Authors	Systematic differences between baseline characteristics of the groups that are compared.	Systematic differences between groups in the care that is provided, or in exposure to factors other than the interventions of interest.	Systematic differences between groups in how outcomes are determined.	Systematic differences between groups in withdrawals from a study.	Systematic differences between reported and unreported findings.
Author1, 2019	Y	Y	Y	Y	Y
Author2, 2019
Author3, 2019
Author4, 2019
Author5, 2019

 

Note:

Y: Low risk

N: High risk

U: Unclear

Step #3: Performing Quantitative data analysis design, this is very similar to the Individual Assignments 1 and 2 (please look at the instruction for more details).

For this section:

3.1.  Select one of the class data sets

3.2. Identify relevant variables

3.3. Choose the statistical method you plan to use for your analysis

3.4. Identify statistical software your team will use to run the statistical analysis focusing on EXCEL or RStudio (BONUS points) as the main software (you can use any other software such as SAS, STATA or SPSS, but the master-code will be available only for RStudio)

3.5. Analyze the data, state your conclusions and support them, this section should be included:

3.5.1. Hypothesis or research questions with a short paragraph to discuss the issue.

3.5.2.  Research Method for this section first report the table of variables, then define the variables using the example provided in step-by-step instruction.

3.5.3.  Report the software and type of analysis you performed in this section included descriptive table and plots

3.5.4. Discuss your findings

3.5.5. Discussion: for this section compare your finding with the LR (literature review) you performed in the first section

3.5.6. Recommendations: suggest a few policy recommendations based on your finding and LR.

Note: Make sure to submit your RStudio codes for your instructor review (for BONUS points).

 

HMGT400 Research and Data Analysis in Healthcare

Assignment 4

Final Exam

The main idea of the final exam is to evaluate students analytical skills.  For the final exam, students will need to use RStudio (2 % BONUS), Excel or any other statistical packages (approved by the faculty).

Question #1 (15 credits): [RStudio Users and Excel Users]

The FINAL EXAM dataset, provides some information about hospitals in 2011 and 2012, download the FINAL EXAM data and then complete the descriptive table. Please answer the following questions.

A) In term of hospital characters what are the significant difference between 2011 and 2012?

B) In term of socio-economic variables what are the significant difference between 2011 and 2012?

To report the “Per Capita Hospital Beds to Population”, you need to divide “total_hospital_beds/ tot_population)

C) Based on your findings in which years hospitals had better performance? How hospital performance related to hospital characteristics and socio-economic characteristics? Please write at least three main different movements between 2011 and 2012.

Table 1. Descriptive statistics between hospitals in 2011 & 2012

2011 2012 p-value

N Mean St. Dev N Mean St. Dev

Hospital Characteristics

1. Hospital beds 100 150 0.004

2. Number of paid Employee

3. Number of non-paid Employee

4. Internes and Residents

5. System Membership

6. Total hospital cost

7. Total hospital revenues

8. Hospital net benefit

9. Available Medicare days

10. Available Medicaid days

11. Total Hospital Discharge

12. Medicare discharge

13. Medicaid discharge

Socio-Economic Variables

14. Per Capita Hospital Beds to Population

15. Percent of population under poverty

16. Percent of Female population under poverty

17.  Percent of Male population under poverty

18. Median Household Income

Question #2 (15 credits): [RStudio Users and Excel Users]

Use the final exam dataset and then answer the following questions:

1) Compare the following information between for-profit and non-for-profit hospitals.

2) What are the main significant differences between for-profit and non-for-profit hospitals? Which test is the best fit test? Why?

3) Use a box-plot and compare Hospital net benefit between for-profit and non-for-profit hospitals.

4) Show another scatter plot and compare hospital cost (x-axes) and revenue (y-axes) and discuss your findings?

5) Comparing hospital net-benefit which hospitals has better performance? To answer this question first compute the hospital net benefits with subtracting hospital costs and revenues and then use ttest to compare the significant differences between FP and NFP hospitals.

6) Overall, what are the main significant differences between for-profit and non-for-profit hospitals?

Table 2. Descriptive statistics between FP & NFP

For Profit Non-For-Profit p-value

N Mean St. Dev N Mean St. Dev

Hospital Characteristics

1. Hospital beds

2. Number of paid Employee

3. Number of non-paid Employee

4. Internes and Residents

5. System Membership

6. Total hospital cost

7. Total hospital revenues

8. Hospital net benefit

9. Available Medicare days

10. Available Medicaid days

11. Total Hospital Discharge

12. Medicare discharge

13. Medicaid discharge

Socio-Economic Variables

14. Per Capita Hospital Beds to Population

15. Percent of population under poverty

16. Percent of Female population under poverty

17.  Percent of Male population under poverty

18. Median Household Income

Question #3 (15 credits): [RStudio Users and Excel Users]

The dataset provides Herfindahl–Hirschman Index for health insurance market, please use the herf_ins variable and answer the following questions:

For this exercise you do not need to compute the HHI, but if you have any questions, please do not hesitate to ask me, but try to learn more about this you will need that to report your findings.

Please remember for the class exercise you used the herf_cat as a hospital Herfindahl index. For this question make sure to use herf_ins as Herfindahl index for insurance market.

Use the final exam dataset and then answer the following questions:

1) In a short paragraph explain the Herfindahl index you can use the reference provided in the class exercise or any other citation.

2) Compare the following information between hospitals located in high, moderate and low competitive health insurance markets?

o 2.1. What are the main significant differences between hospitals in different markets? (use Anova test)

o 2.2. What is the impact of being in high-competitive health insurance market on hospital revenues and cost?

o 2.3. Do you think being in high-competitive market has positive impact on net hospital benefits? 

o 2.4. What about the number of Medicare and Medicaid discharge? Do you think hospitals in higher completive market more likely to accept more Medicare and Medicaid patients?

o 2.5. What is the impact of other variables?

(Note: to answer to the last question, please compute the ratio-Medicare-discharge and ratio-Medicaid-discharge first and then run 2 ttest) high vs. moderate and high vs. low competitive market), please support your findings with box-plot).

Table 3. Comparing hospital characteristics and market

High Competitive Market Moderate Competitive Market Low Competitive

 Market ANOVA/Chi-Sq (results)

Hospital Characteristics N Mean STD N Mean STD N Mean STD

1. Hospital beds

2. Number of paid Employee

3. Number of non-paid Employee

4. Internes and Residents

5. System Membership

6. Total hospital cost

7. Total hospital revenues

8. Hospital net benefit

9. Available Medicare days

10. Available Medicaid days

11. Total Hospital Discharge

12. Medicare discharge-ratio

13. Medicaid discharge-ratio

Socio-Economic Variables

14. Per Capita Hospital Beds to Population

15. Median Household Income

Question #4 (Credits 20)- [RStudio Users]

Linear Regression Model

If you have chosen to work with RStudio, please run the following model and complete the following tables.

1st Model:

Run a linear model and predict the difference between hospital beds (use the bed-tot) and hospital’s ownership on hospital net-benefit? Discuss your finding, do you think having higher beds has positive impact on the hospital net benefit? What about the ownership?

Benefit=F(b0+B1bed+b2Own2+B3ow2+b4own3+e)

Model 1a

Hospital Characteristics Coef. St. Err p-value

Hospital beds

Ownership

For Profit

Non-for profit Ref. Ref. Ref.

Other

N Df+K+1

R-Squared

2nd Model:

Now, estimate the impact of being a member of a system on hospital net benefit? And discuss your finding (not more than 2 lines)? Is it significant?

 

HMGT400 Research and Data Analysis in Healthcare

Exercise 1

 

HMGT400 Research and Data Analysis in Healthcare

Exercise 2

 

HMGT400 Research and Data Analysis in Healthcare

Exercise 3

 

HMGT400 Research and Data Analysis in Healthcare

Exercise 4

 

HMGT400 Research and Data Analysis in Healthcare

Exercise 5