Applied Healthcare Statistics (C784)

Correct Answer

B. Longitudinal Cohort Study

Explanation

A longitudinal cohort study involves following a group of individuals (the cohort) over a period of time to observe how exposure to certain factors (such as lifestyle, environment, or treatments) impacts the development of a particular disease or condition. This type of study design allows researchers to track changes over time and establish temporal relationships between exposures and outcomes, making it ideal for studying cause-and-effect relationships in healthcare research.

Why other options are wrong

A. Cross-sectional Study

A cross-sectional study collects data at a single point in time and does not follow participants over time. It provides a snapshot of the population's health status at a given moment but cannot establish causal relationships between exposure and disease development.

C. Case-Control Study

A case-control study compares individuals with a specific disease (cases) to those without it (controls) to assess the exposure to certain risk factors. While it is useful for studying rare diseases, it does not follow individuals over time, and thus, it cannot assess the temporal sequence of exposure and disease development.

D. Randomized Controlled Trial

A randomized controlled trial (RCT) involves randomly assigning participants to different groups (e.g., treatment vs. control) to assess the effects of an intervention. While it is a strong study design for evaluating treatment effectiveness, it does not primarily focus on following a cohort over time to observe disease development in relation to exposure, as seen in longitudinal cohort studies.

Correct Answer

B. To provide evidence of previous research findings

Explanation

The literature review serves as a foundational element in a research proposal, particularly for a healthcare grant, by summarizing and analyzing previous research findings relevant to the study topic. It helps demonstrate the existing body of knowledge, gaps in research, and the significance of the proposed study, supporting the need for the grant. The review establishes the theoretical basis and rationale for the research.

Why other options are wrong

A. To summarize the clinic's financial needs

A literature review is not focused on financial needs. Financial details may be discussed in other sections of the proposal, such as the budget, but not in the literature review.

C. To outline the clinic's operational procedures

Operational procedures are relevant to the methodology section of the research proposal but are not the primary focus of the literature review, which focuses on existing research and knowledge gaps.

D. To detail the anticipated costs of the program

Anticipated costs are typically included in the budget section of the grant proposal, not in the literature review. The literature review focuses on research findings, not financial aspects.

Correct Answer

B. Regression analysis; reimbursement rates and hospital debt

Explanation

Regression analysis is the most appropriate method for examining the relationship between two quantitative variables: Medicaid reimbursement rates (independent variable) and hospital debt (dependent variable). It allows the administrator to evaluate the strength and nature of this relationship across multiple facilities, providing actionable insights into financial outcomes based on reimbursement changes.

Why other options are wrong

A. ANOVA; patient satisfaction surveys

ANOVA is used to compare the means of multiple groups, not to assess relationships between continuous variables like reimbursement rates and debt. Additionally, patient satisfaction surveys are not the most relevant data for evaluating hospital financial outcomes.

C. Descriptive statistics; hospital staff feedback

Descriptive statistics summarize data but do not test hypotheses or reveal relationships between variables. Hospital staff feedback is qualitative or categorical and would not provide the necessary numerical data to assess the financial impact quantitatively.

D. Qualitative analysis; patient interviews

Qualitative analysis is useful for exploring experiences and perceptions but is not appropriate for analyzing the financial relationship between reimbursement rates and hospital debt. Patient interviews would not yield the numerical data needed for regression analysis.

Correct Answer

B. By offering insights into patient demographics and treatment outcomes

Explanation

A trauma registry is a database that collects detailed information about trauma patients, including demographics, the nature of injuries, treatment provided, and outcomes. This data allows healthcare facilities to analyze patterns, identify gaps in care, and evaluate the effectiveness of treatment protocols. By using this information, emergency services can implement evidence-based quality improvement initiatives to enhance patient care and safety.

Why other options are wrong

A. By providing financial data for budgeting

Trauma registries are not designed to focus on financial information. Budgeting data is typically handled by financial management systems, not clinical registries that aim to improve patient outcomes and care quality.

C. By tracking the number of outpatient visits

Outpatient visits are not within the scope of trauma registries, which are specifically focused on serious injuries requiring emergency or inpatient care. Other systems track outpatient data more effectively.

D. By managing hospital staffing levels

Staffing decisions are made using workforce management tools and hospital administrative data. While trauma registries may highlight workload trends, they are not designed to directly manage staffing levels.

Correct Answer

B. Regression analysis

Explanation

Regression analysis is the statistical method used to assess the relationship between variables. In healthcare research, it is commonly used to understand how different factors (such as treatments, demographics, and health conditions) impact outcomes. Regression models can be used to predict future outcomes and evaluate the strength and nature of these relationships between variables.

Why other options are wrong

A. Descriptive statistics

Descriptive statistics is used to summarize and describe the features of a dataset, such as means, medians, and standard deviations, but it does not analyze relationships between variables. It focuses on presenting data rather than making predictions or assessing relationships.

C. Predictive modeling

While predictive modeling uses regression and other techniques to predict future outcomes, it is broader in scope. Predictive modeling often involves machine learning and other advanced methods, whereas regression analysis specifically looks at the relationship between variables.

D. ANOVA

ANOVA (Analysis of Variance) is used to compare means across three or more groups, not to assess relationships between variables. It evaluates the differences in means between groups rather than exploring how one variable impacts another.

Correct Answer

B. It identifies relationships between variables over time.

Explanation

Regression analysis is a statistical method used to determine the relationship between a dependent variable and one or more independent variables. A quality improvement manager can use it to analyze how enrollment in Medicaid and CHIP changed over time in response to the implementation of the Affordable Care Act. This method helps quantify the strength and direction of the relationship, providing insights into the policy's effectiveness.

Why other options are wrong

A. It summarizes data into categories.

Summarizing data into categories is typically a function of descriptive statistics or categorical data analysis, not regression. Regression focuses on continuous variables and their relationships rather than categorization.

C. It tests the differences between group means.

Testing differences between group means is a characteristic of methods such as t tests or ANOVA. Regression does not compare group averages directly; instead, it assesses how changes in one variable predict changes in another.

D. It provides a visual representation of data.

While regression results can be visualized in charts or graphs, the core function of regression is analytical, not purely visual. Graphing is a secondary feature used to support the understanding of relationships but is not the main purpose of regression analysis.

Correct Answer

C. Both are limited to a single table structure and manual data handling.

Explanation

Flat file databases and Excel spreadsheets are often compared because both are designed to store data in a simple, tabular format, typically in a single table. This limits their capacity for handling complex relationships between data points compared to relational databases. Additionally, both systems require manual handling of data entry and manipulation, which can be cumbersome and error-prone for large datasets.

Why other options are wrong

A. Both can handle large datasets efficiently.

While flat file databases and Excel spreadsheets can handle a certain amount of data, they are not particularly efficient for large datasets. For large, complex data, relational databases or more advanced data management systems are recommended.

B. Both require complex programming for data manipulation.

This is not necessarily true. Flat file databases and Excel spreadsheets do not require complex programming; they are generally designed for users who do not have programming expertise. However, more complex manipulation tasks can require advanced knowledge of scripting or formulas.

D. Both support real-time data analysis and visualization.

Excel can support basic data analysis and visualization, but flat file databases typically do not provide these capabilities. For real-time data analysis and visualization, more advanced database systems or software would be necessary.

Correct Answer

C. To analyze data to forecast future outcomes and effects

Explanation

The main purpose of a predictive study in healthcare research is to use data and statistical methods to forecast future outcomes, trends, or behaviors. Predictive studies typically analyze existing data to identify patterns and relationships that can help anticipate future health events, such as disease outbreaks, patient readmissions, or the effectiveness of treatments over time. This is crucial in healthcare for planning, resource allocation, and improving patient care.

Why other options are wrong

A. To describe the characteristics of a population

Descriptive studies focus on summarizing the characteristics of a population rather than predicting future outcomes. Predictive studies go beyond description and are designed to forecast future events based on data.

B. To explore new areas of research without specific hypotheses

Exploratory studies are aimed at identifying new areas of research without predefined hypotheses, whereas predictive studies are focused on forecasting specific outcomes based on data analysis.

D. To conduct experiments to determine cause-and-effect relationships

Experimental studies are typically used to determine cause-and-effect relationships through controlled experiments. Predictive studies do not focus on causality but instead on forecasting future events based on existing data.

Correct Answer

B. Paired t test

Explanation

A paired t test is used when comparing the means of two related samples, such as measurements taken from the same group at two different times. This test accounts for the fact that the data points are not independent but linked, making it ideal for before-and-after studies or matched subjects.

Why other options are wrong

A. Independent t test

The independent t test is used when comparing the means of two independent or unrelated groups. It is not appropriate for related samples, as it does not account for the paired nature of the data, which can lead to inaccurate conclusions if used incorrectly.

C. ANOVA

ANOVA (Analysis of Variance) is used when comparing the means of three or more groups. While it can be adapted for repeated measures, it is not specifically designed for just two related samples, making it less suitable than the paired t test for this purpose.

D. Chi-square test

The chi-square test is used to examine relationships between categorical variables, not means of numerical data. It does not compare averages and is not appropriate for analyzing the mean difference between two related numerical datasets.