Jun 03, 2020
HIT 450 - Health Statistics Credits: 2
Lecture Hours: 2
Lab Hours: 0
Practicum Hours: 0
Work Experience: 0
Course Type: Voc/Tech
This course covers the collection, analysis, verification and display of health statistics. Students will learn uses for health statistics, basic statistical principles, commonly computed rates, vital health statistics, uniform reporting requirements, effective data display and background on data analytic concepts.
- Explain health statistics, data analytics and data and how they apply in health care.
- Explain the importance of statistics and data analytics in healthcare.
- Identify the users and uses of health care data.
- Describe the difference between descriptive and inferential statistics.
- Classify the levels of data measurement.
- Differentiate between populations and samples.
- Distinguish common health care data sets and databases.
- Solve basic math concepts, central tendency and dispersion.
- Solve calculations with fractions, decimals and percentages.
- Demonstrate the function of rates, ratios, and proportions in health care statistics.
- Explain and calculate frequencies and frequency distribution.
- Identify and calculate the most useful measure of central tendencies for a given data set.
- Compute and define dispersion using, range, variance and standard deviation from a frequency distribution.
- Evaluate data presentation using tables, charts and graphs used to communicate findings.
- Construct a variety of tables.
- Compare samples of tables to determine missing or faulty elements.
- Construct and interpret tables, pie charts, line graphs, bar charts, pictograms, scatter diagrams, and histograms.
- Compare how each of the graphic displays is best used.
- Critique samples of charts and graphs to determine missing or faulty elements.
- Explain presentation tools and their uses.
- Examine different types of health statistics calculated by healthcare organizations and public health.
- Differentiate between inpatient and outpatient and other types of health care settings.
- List the types of beds in an inpatient facility and describe bed count.
- Describe inpatient census, inpatient service days, how they are calculated and complete the calculations.
- Compute occupancy rates in a facility, length of stay, average length of stay, leave of absence days and bed turnover rate.
- Explain and compute morbidity rates in health care settings and rates for infections, general complications and various patient populations.
- Explain and compute consultation rates and mortality rates for patient populations.
- Define, discuss trends and calculate obstetric and autopsy rates.
- Define key terms and list sources of public health data.
- Compare among crude, specific and adjusted rates.
- Explain formulas for public health statistics.
- Calculate sample public health data using various morbidity and mortality rates.
- Analyze measuring HIM productivity, reimbursement and compliance statistics utilized in healthcare.
- Apply productivity standard to various functions within HIM.
- Examine inpatient prospective payment systems (DRG) and case mix index, and how the calculations are performed.
- Examine case mix index data and standards to identify trends.
- Solve and define calculations used for compliance and accreditation purposes.
- Examine budgets and other calculations used financially in a department
- Distinguish between a capital and an operational budget.
- Determine return on investment (ROI) and the payback period for a capital expenditure.
- Explain and determine monthly budgets and compute budget variances.
- Evaluate what constitutes quality data.
- Explain the importance of data quality and define the characteristics of quality data.
- Define data scrubbing terminology.
- Analyze a data set for data cleansing issues.
- Define data mapping terminology.
- Evaluate a mapped data set for errors.
- Differentiate between descriptive and inferential statistics used in healthcare.
- Differentiate between descriptive and inferential statistics.
- Explain the importance and types of random sampling in inferential statistics.
- Define confidence intervals, levels, significance, hypothesis testing and probability.
- Calculate and define standard error.
- Define and identify type I and type II errors.
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