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    Data Science for Healthcare Claims Data

    BY
    Udemy

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    Lavel

    Beginner

    Mode

    Online

    Fees

    ₹ 499 3299

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 499  ₹3,299
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Welcome to the course
    • Claims Data Defined
    • Why analyze healthcare claims data 
    • Who this course is for

    Theory of Healthcare systems

    • The four functions of any healthcare system
    • The three key actors in claims data
    • Vertical integration of healthcare system functions
    • Healthcare systems quiz

    Healthcare provider payment systems

    • Introduction to healthcare provider payment systems
    • Fee-for-service
    • Capitation
    • Bundled payments
    • Global budgets
    • Summary of healthcare provider payment systems
    • Healthcare provider payment systems

    Theory of claims data

    • The two core challenges for healthcare payers
    • Fact tables and dimension tables
    • Authorisation signals
    • Theory of claims data quiz

    Merging healthcare claims data

    • Introduction to merging data
    • Merging data from a data warehouse
    • Merging an episode of care

    Higher level categorization

    • Introduction to Higher level categorization
    • Consult the data dictionary
    • Consult the Dimensions Tables 
    • (Re)Discover the underlying logic of codes
    • Use existing hierarchies of (inter)national coding systems
    • Ask a domain expert
    • Summary of higher level categorization
    • Higher level categorization quiz

    Relevant resources for this course

    • Get all relevant resources here

    Basic exploration of Healthcare claims data

    • Getting started with the practice dataset
    • Basic filtering of data in Excel
    • Introduction to pivot tables
    • Working with a pivot table in Excel
    • Selecting aggregations in a pivot table
    • Grouping by date in a pivot table
    • Using a pivot table to create and control a chart
    • Introduction to vertical lookup
    • Vertical look-up part 1: Exploring the look-up table in Excel
    • Vertical look-up part 2: Applying the function
    • Vertical look-up part 3: Filling down the results
    • A note on filling down in Excel
    • Vertical look-up part 4: Finalizing the dataset
    • Benefit of introducing categories in claims data

    Extract Transform and Load (ETL) from the Data warehouse using SQL

    • Background information about the practice data warehouse
    • Relational data schema
    • Getting started with Google Big Query
    • Alternative method to get to the Google Big Query Medicare dataset
    • Introduction to SQL in Google Big Query interface
    • Writing a simple SQL script to extract healthcare claims data
    • Merging data using SQL
    • Visualizing the data in Big Query
    • Calculating the age of the patient at the time of knee replacement
    • Confirming the correct code using the where clause and a regular expression
    • Inspecting the compatibility between the tables
    • Concatenate and cast data to allow compatibility
    • Create a subquery
    • Date difference function to calculate age

    Absolute and relative comparisons

    • Absolute and relative comparisons
    • Using a 100% Stacked column chart for relative comparisons
    • Using percentages for relative numbers
    • Per capita calculations using distinct count
    • Using distinct count for relative comparisons in Excel

    Process mining with healthcare claims data

    • Benefits of process mining with healthcare claims data
    • Process mining tools
    • Warning! Please read this word of caution before using Celonis Snap
    • Getting started with Celonis Snap
    • Configure the dataset for process mining
    • Introduction to process mining with Celonis Snap part 1
    • Introduction to process mining with Celonis Snap part 2
    • Discover patient pathways using process mining (part 1)
    • Discover patient pathways using process mining (part 2)
    • Isolate a sub process by focussing on the sub process spider activity
    • Introduction to specifying a sequence order
    • Theory of sequence order when dealing with identical timestamps
    • A note about specifying a sequence order
    • Manipulating the raw data to specify a sequence order (part 1)
    • Manipulating the raw data to specify a sequence order (part 2)
    • A note about concatenation
    • Confirm the correct sequence in a new process map
    • Detect anomalies by comparing the processes of different providers
    • Moving from process mining to statistics and machine learning
    • Process mining quiz
    • Process mining assignment

    Proxy diagnosis and cohort analysis

    • A note about proxy diagnosis and cohort analysis
    • Proxy diagnosis
    • Method for obtaining a proxy diagnosis
    • Why use a subquery for proxy diagnosis
    • Querying healthcare consumption of diabetics using a proxy diagnosis
    • Identify insuline users (diabetics)
    • Use identified diabetics to capture their full episode of care
    • Capture the episode of care for patients undergoing a total knee replacement

    Tidying healthcare claims data

    • Introduction to tidy data
    • Tidying healthcare claims data with Excel
    • Converting the target variable to a binary field with Excel
    • Getting started with Google Colab
    • Tidying healthcare claims data with Python
    • Converting the target variable to a binary field with Python
    • A note about metric selection

    Predicting Consumption Events

    • Introduction to this section
    • Preparing the data for logistic regression with Python
    • Performing logistic regression
    • Evaluating the performance with a confusion matrix
    • Using a different categorization logic as input for logistic regression (part 1)
    • Using a different categorization logic as input for logistic regression (part 2)
    • Using a different categorization logic as input for logistic regression (part 3)
    • Considerations for advanced machine learning practitioners
    • Applying logistic regression with different metrics

    Detecting Irregularities and Possible Fraud

    • Introduction to this section
    • Preparing the data for unsupervised machine learning
    • Applying Principal Component Analysis
    • A note about the Python code
    • Applying K-means Clustering
    • Calculating the distance from the nearest cluster
    • Combining the machine learning outputs with the original claims data
    • Exporting the machine learning output to a csv file
    • Prepare for case by case analysis guided by the machine learning output
    • Explore the different clusters (part 1)
    • Interpret and rename the different clusters
    • Compare the healthcare providers by patient clusters (part 1)
    • Compare the healthcare providers by patient clusters (part 2)
    • Example of absolute versus relative data
    • Analyzing the distance from nearest cluster (part 1)
    • Analyzing the distance from nearest cluster (part 2)
    • Analyzing the distance from nearest cluster (part 3)
    • Identifying red flags (part 1)
    • Identifying red flags (part 2)
    • Inspecting the red flag indivual patients on a case-by-case basis (part 1)
    • Inspecting the red flag indivual patients on a case-by-case basis (part 2)

    Performance Tracking (Compare defined targets with actual performance)

    • Introduction to performance tracking with healthcare claims data
    • Method part 1: Harmonizing the actual data with the targets
    • Method part 2: Merging the two tables
    • Method part 3: Feed the data into a business intelligence dashboard
    • Trivia: Using compound/composite keys rather than multiple join keys
    • Practice performance tracking with claims data
    • Exploration of the business intelligence dashboard
    • Visually inspecting the targets table
    • Uploading the target table in the data warehouse
    • Preparing the raw claims data for aggregation using SQL (part 1)
    • Preparing the raw claims data for aggregation using SQL (part 2)
    • Aggregating the raw claims data using SQL
    • Creating a subquery containing the actual performance
    • Joining the subquery with the uploaded table
    • Calculating the percentage realized
    • Saving the output as a new table in the data warehouse
    • Feeding the output into the business intelligence dashboard
    • Creating the business intelligence dashboard

    Value based healthcare and health outcome indicators

    • Introduction to value-based healthcare and health outcomes
    • Introduction to types of health outcome indicators
    • Patient reported health outcomes
    • Biological health outcome indicators
    • Adverse health episodes as outcome indicators
    • Aftercare signals as a health outcome indicator
    • Mortality as a health outcome indicator
    • Merging different types of health outcome indicators
    • Challenges with value-based healthcare and health outcomes

    Conclusion

    • Final Words

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