Data Science for Healthcare Claims Data
- Description
- Curriculum
- FAQ
- Reviews
The most commonly available and widely used type of data in healthcare is claims data. Claims data is sometimes also called billing data, insurance data or administrative data. The reason why claims data is the most large scale, reliable and complete type of big data in healthcare is rather straightforward. It has to do with reimbursement, that is, the payment of health care goods and services depends on claims data. Healthcare providers may not always find the time to fill in all required paperwork in healthcare, but they will always do that part of their administration on which their income depends. Thus, in many cases, analyzing healthcare claims data is a much more pragmatic alternative for extracting valuable insights.
Claims data allows for the analysis of many non-biological elements pertaining to the organization of health care, such as patient referral patterns, patient registration, waiting times, therapy adherence, health care financing, patient pathways, fraud detection and budget monitoring. Claims data also allows for some inferences about biological facts, but these are limited when compared to medical records.
By following this course, students will gain a solid theoretical understanding of the purpose of healthcare claims data. Moreover, a significant portion of this course is dedicated to the application of data science and health information technology (Healthcare IT) to obtain meaningful insights from raw healthcare claims data.
This course is for professionals that (want to) work in health care organizations (providers and payers) that need to generate actionable insights out of the large volume of claims data generated by these organizations. In other words, people that need to apply data science and data mining techniques to healthcare claims data.
Examples of such people are: financial controllers and planners, quality of care managers, medical coding specialists, medical billing specialists, healthcare or public health researchers, certified electronic health records specialist, health information technology or health informatics personnel, medical personnel tasked with policy, personnel at procurement departments and fraud investigators. Finally, this course will also be very useful for data scientists and consultants that lack domain knowledge about the organization of healthcare, but somehow got pulled into a healthcare claims data project.
The instructor of this course is Dennis Arrindell, MSc., MBA. Dennis has a bachelor’s degree in Public Health, a master’s degree in Health Economics and a Master’s degree in Business Administration.
Upon completion of this course, students will be able to contribute significantly towards making healthcare organizations (providers and payers) more data driven.
What this course is NOT about:
– Although we will be applying some important statistics and machine learning concepts, this course is NOT about statistics or machine learning as a topic on itself.
– Although we will be using multiple software tools and programming languages for the practical parts of this course, this course is NOT about any of these tools (Excel, SQL, Python, Celonis for process mining) as topics on themselves.
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23Introduction to higher level categorization
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24Consult the data dictionary
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25Consult the dimension tables
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26(Re)Discover the underlying logic of codes
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27Use existing hierarchies of (inter)national coding systems
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28Ask a domain expert
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29Summary of higher level categorization
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30Higher level categorization quiz
Test your knowledge about this section by taking this quiz
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32Getting started with the practice dataset
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33Basic filtering of data in Excel
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34Introduction to pivot tables
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35Working with a pivot table in Excel
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36Selecting aggregations in a pivot table
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37Grouping by date in a pivot table
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38Using a pivot table to create and control a chart
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39Introduction to vertical lookup
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40Vertical look-up part 1: Exploring the look-up table in Excel
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41Vertical look-up part 2: Applying the function
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42Vertical look-up part 3: Filling down the results
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43A note on filling down in Excel
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44Vertical look-up part 4: Finalizing the dataset
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45Benefit of introducing categories in claims data
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46Background information about the practice data warehouse
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47Relational data schema
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48A note about the new Big Query Interface
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49Getting started with Google Big Query
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50Access the Medicare dataset in the new Big Query interface
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51Introduction to SQL in Google Big Query interface
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52Writing a simple SQL script to extract healthcare claims data
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53Merging data using SQL
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54Visualizing the data in Big Query
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55Calculating the age of the patient at the time of knee replacement
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56Confirming the correct code using the where clause and a regular expression
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57Inspecting the compatibility between the tables
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58Concatenate and cast data to allow compatibility
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59Create a subquery
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60Date difference function to calculate age
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66Introduction to process mining
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67Benefits of process mining with healthcare claims data
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68Process mining tools
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69Warning! Please read this word of caution before using Celonis
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70Getting started with Celonis Free Plan
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71Configure the dataset for process mining
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72August 2023 update: New interface after file upload
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73Introduction to process mining with Celonis part 1
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74Introduction to process mining with Celonis part 2
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75Discover patient pathways using process mining (part 1)
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76Discover patient pathways using process mining (part 2)
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77Isolate a sub process by focussing on the sub process spider activity
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78Introduction to specifying a sequence order
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79Theory of sequence order when dealing with identical timestamps
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80A note about specifying a sequence order
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81Manipulating the raw data to specify a sequence order (part 1)
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82Manipulating the raw data to specify a sequence order (part 2)
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83A note about concatenation
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84Confirm the correct sequence in a new process map
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85Detect anomalies by comparing the processes of different providers
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86Moving from process mining to statistics and machine learning
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87Process mining quiz
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88Process mining assignment