TEST 1 Flashcards
Anything that is dates, numbers, images, symbols, letters, words, anything that represents basic facts and observations that go into the EHR about the people, processes, measurements, and conditions
Data
What is the data that is collected?
- Number of Diagnostic Tests
- Services Provided – by admissions
- Costs and Payment
- Diagnosis and Procedure codes
- Number of procedures preformed
Task of transforming, summarizing, or modeling data to allow the end user to make meaningful conclusions – uses data to make decisions
Data Analysis
The use of data for its primary purpose
EX: Billing and claims data’s primary use it to determine services rendered and payment to from a patient or third party payer
Performing an analysis of typical payment received from a payer for emergency visits
information collected for the specific purpose at hand
Example: checking glucose level to see if patient has diabetes
Primary Data
The use of data beyond its primary purpose
EX: ICD-9 diagnosis codes are assigned to a patient to record diseases present or discovered during an encounter
Using a profile of the most common ICD-9 diagnosis categories for the purposes of determining the patient load by service line
data previously collected for any purpose other than the one at hand
example: checking glucose post op
Secondary Data
Summarize or describes a given data set, which can be either a representation of the entire population or a sample of a population. Broken down into measures of central tendency and measures of variability (spread).
Uses mean, median, and mode – measures of central tendency
o Characterizes the distribution of the data
o Estimates the center or ‘typical’ value
o Measures the spread or variation in the data
EX: Graphs and Charts
Descriptive Statistics
very high error rate
o Using sample data to make conclusions or decisions regarding a population
o Not practical to observe the entire population
o Often accompanied with a probability of making an incorrect decision based on the sample
allows you to make predictions (“inferences”) from that data.
you take data from samples and make generalizations about a population.
EX: Hypothesis Tests
Inferential Statistics
Elements that are stored in fields specifically designed for that data element. Concrete data that uses a drop box or you can key in to complete the entire field.
EX: Gender or date of admissions, ETC
Typically have a set of valid values or a range of acceptable values to ensure data integrity.
Structured Data
Free-form text data that is captured in a narrative form and Not designed for database querying based on the field values.
EX: Progress notes, radiology reports, all narrative reports, etc
Queries can be complied to search for key words but will not be nearly as reliable as querying a structured data element
Unstructured Data
- Not naturally numeric
- Evaluates Observations
- Assesses the quality of the object or encounter
- Commonly unstructured
- Can recategorize it and put it in the number form
Qualitative Analysis
What are the 2 Ways Qualitative Data can be categorized?
Nominal and Ordinal
Categories without a natural order
EXAMPLES
• Diagnosis codes
• Clinical units
• Colors
Nominal
Categories with a natural order
EXAMPLES
• Patient satisfaction surveys
• Patient severity scores
• Evaluation and management code levels
Ordinal
Naturally numeric. You can use this to confirm qualitative data
Quantitative Analysis
What are the 2 Forms of Quantitative Data analysis?
Interval and Ratio
Numeric values where the distance between two values has meaning, but there is no true zero and the interpretation is not preserved when multiplying/dividing
EX: Temperature and Dates
Interval
Numeric values where zero has meaning and multiplying/dividing values has meaning
EXAMPLE
- Length of stay
- Age
- Weight
Ratio
__________ data will be either Discrete –OR – Continuous
Quantitative
Includes a finite number of values.
Example: One admission…you cannot have a half of an admission.
Discrete data
Can take on fractional and whole number values.
Example: Charges can be in fractions of a dollar amount (50 cents)
Continuous data
Includes evaluating the quality and adequacy of the documentation of a medical record.
Qualitative analysis
Examines the accuracy and completeness of the contents of the medical record.
Quantitative analysis
_______ = average
Mean
______ = Midpoint or middle number
Median
______ = The most frequently occurring score(s) in a distribution
Mode
A range calculated based on a sample that is likely to include the true population value of a parameter.
EX: A patient satisfaction survey result of 90 percent plus or minus two percentage points. (88 percent to 92)
Confidence Interval
Used to uncover patterns in data
Typically a secondary use of data
Primarily graphical analysis (plots, trends, etc.)
Exploratory Data Analysis (EDA)
Also looking for patterns in data
Adds in descriptive statistics and more formal statistical techniques
May be used for benchmarking and determining high/lower performers
May also be used to improve financial performance
Used to identify the potentially best predictors
Data Mining
Historical data is used to build models to determine most likely outcome in future
Examples
Used by CMS for pre-payment reviews to fight fraud
Used by credit card companies to prevent fraud
Used by providers to identify missed charges
Historical payment data can be used to identify potentially fraudulent claims such as a facility submitting a claim for an appendectomy when the patient has already had an appendectomy claim paid.
Predictive Modeling
Used to formulate models that determine the most likely outcome in predictive modeling
Statistical models
Random sample of records.
Sample size is based on the desired precision and confidence of the estimate and the sampling technique.
Auditing
What are the 2 audit sample techniques?
Internal and external audits
Compliance Charge Audits
Internal Audits
Example: Recovery Audit Contractors (RAC)
External Audits
_________= the difference between the highest and lowest scores in a distribution
RANGE
“typical value” descriptive statistic example:
Mean, Median, Mode
descriptive statistic variance example:
range and standard deviation
