Related Experiment Video
Updated: Feb 11, 2026
11:51
Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
Published on: March 2, 2011
15.7K
Clinical Excellence in Endocrinology
1Division of Diabetes, Endocrinology and Metabolism, Vanderbilt University Medical Center, Nashville TN.
Summary
Clinical excellence in endocrinology requires more than numerical data, encompassing teamwork, communication, system navigation, and scholarly approach for superior patient care. Defining and fostering these skills is crucial for endocrine fellowship training.
Area of Science:
- Endocrinology
- Medical Education
- Healthcare Quality
Background:
- Clinical endocrinology relies heavily on numerical data.
- Outstanding patient care requires
- clinical excellence,
- a broader skill set.
- No consensus definition or method for achieving excellence in endocrinology exists.
Purpose of the Study:
- To define clinical excellence in endocrinology.
- To identify key skills contributing to clinical excellence.
- To recommend strategies for fostering clinical excellence in endocrine practice and training.
Main Methods:
- Literature review of clinical outcomes guidelines.
- Review of professional society, clinical organization, and government agency websites.
- Synthesis of derived aspects of clinical excellence.
Main Results:
- Key aspects of clinical excellence include teamwork, communication, healthcare system navigation, and a strong knowledge base.
- These skills are essential for optimizing patient outcomes in endocrinology.
- Examples demonstrating the impact of these skills on patient care were discussed.
Conclusions:
- Clinical excellence is vital for optimizing endocrine patient care.
- Professional societies and institutions should adopt a definition of clinical excellence.
- Endocrine fellowship training and faculty development must emphasize skills for clinical excellence.
Related Concept Videos
01:18Microsoft Excel: Plotting Mean, SD, and SE
1.3K
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
1.3K
01:25Microsoft Excel: Student's t-Test
1.7K
Student's t-test in Microsoft Excel is a statistical method used to compare the means of two groups to determine if they are significantly different from each other. It's commonly used to evaluate hypotheses, such as testing whether a treatment has an effect compared to a control group. Excel provides built-in functions to perform t-tests, making it accessible for users needing to conduct basic statistical analysis.
To conduct a t-test in Excel, use the T.TEST function or the "Data...
To conduct a t-test in Excel, use the T.TEST function or the "Data...
1.7K
01:18Microsoft Excel: Pearson's Correlation
2.2K
Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
2.2K
01:18Microsoft Excel: Regression Analysis
1.6K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
To perform regression...
1.6K
01:13Overview of Microsoft Excel as a Data Analysis Tool
1.7K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.7K
01:24Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis
696
Central tendency refers to the central point or typical value of a dataset. It summarizes the data set with a single value that represents the center of its distribution. The three main measures of central tendency are:
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
696