Data Science for Healthcare Claims Data

BY
Udemy

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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