Chi-square,ANOVA, ANOM,and Regression in Healthcare Quality Management

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Chi-square, ANOVA, ANOM, and Regression in Healthcare Quality Management
Evidence-based healthcare is a crucial concept of modern medicine. It involves relying on evidence from data analysis, scientific experiments, observations, and studies to support and inform decision-making. Analysis of data is fundamental in making sense of healthcare data to support planning and decision-making. As a result, applying statistical analysis methods and concepts is essential in managing quality in healthcare. Examples of these concepts include Chi-square, ANOM, ANOV, and Regression. While these concepts serve a similar goal, they have underlying structure, purpose, and application differences.
Similarities, Differences, and Application
Chi-square
The concept is used to establish whether a statistical relationship exists between two categorical variables (Rana and Singhal). It is used in the identification of problems, their causative factors, and the efficacy of interventions. Within a categorical variable, there are one or more possible values that use labels instead of numbers. A typical example is yes and no. Categorical variables can exist in the form of structure (e.g., whether a nurse is certified), process (whether the medication was done correctly), and outcome (whether the patient improved). In each of these categories, the chi-square will provide answers in the form of yes or no. Yes is when there is a significant statistical relationship, and No when such a relation is not statistically significant. In the given examples of categorical variables, chi-square can be used to determine the impact of nurse certification on medicine administration and the influence on medical outcomes. In other words, the concept can reveal whether nursing certification has any significant impact on the quality of service provided.
ANOM
Also called analysis of means, ANOM is used to determine how a section of the population is significantly different from the average population based on the number of events such as a cholera outbreak. For instance, if x people die of cholera in a specific community, ANOM can help determine how the mortality rate defers from the overall population at risk of cholera. It involves comparing the mean calculated for a group of people against the mean of the overall population (Jayalath and Turner). Like chi-square, ANOM can also reveal problems and their causes. An application of ANOM would be the comparison of rates of Covid-19 infections among states. The average rate of each state can be compared to either the national or other states’ averages which will show existing gaps. These gaps can be exploited to determine why some states have higher rates of average infections than others.
ANOVA
ANOVA is also called the analysis of variance. The critical difference between ANOM and ANOV is that while the former focuses on means (averages), the latter focuses on variances. ANOVA is used to establish a statistically significant relationship betw...