HEALTHCARE FRAUD DETECTION USING AI AND MACHINE LEARNING

July 02-04, 2024, 08:00 AM - 04:00 PM HOTEL PARK ROTANA & PARK ARJAAN BY ROTANA ABU DHABI

Training Objectives

Completion of this course will provide attendees with a basic understanding of main AI / machine learning methods that could be used for detecting healthcare fraud ranging from developing visualization dashboards for faster and more efficient fraud investigation to creating more advanced predictive models for automated detection of various types of fraud

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Instructor(s) of this course

Aleksandar Lazarevic
Phd, Machine Learning
AI / Data Science / Data Analytics Executive | Keynote Speaker

PhD, Data Mining, Machine Learning, Predictive
Modelling, Temple University
Founder - AI&DA Insights
Consulting Partner - Sigmoid
MBA (ABD), Carnegie Melon University
MS in Computer Science & Engineering,
University of Belgrade

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Shauna Vistad
MBA, AHFI, CFE, CPC, CFI

Senior Vice President, Medical Investigation & Audit, National Health Insurance Company – Daman
MBA, University of North Dakota
B.Sc., Criminal Justice, North Dakota State University
Accredited Healthcare Fraud Investigator (AHFI), National Health Care Anti-Fraud Association
Certified Professional Coder (CPC), AAPC
Certified Forensic Interviewer, Center for Interviewer Standards and Assessment

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20.5 CPEs QUALIFY TO BE ACCEPTED TOWARDS ACFE's CPE CREDIT


This course will provide a basic overview of healthcare fraud and its detection. It will start with covering main fraud schemes exploited globally, follow with providing a comprehensive introduction to the basic components of healthcare data and finish with explaining how AI and Machine Learning can be used to identify potential fraud, waste and abuse in the public and private payer systems.

The software tools and different AI / machine learning methods will be reviewed as well as the newest fraud detection methods based on deep learning. Proactive algorithms used to identify real time large investigations will be reviewed with case studies examined.

Completion of this course will provide attendees with a basic understanding of main AI / machine learning methods that could be used for detecting healthcare fraud ranging from developing visualization dashboards for faster and more efficient fraud investigation to creating more advanced predictive models for automated detection of various types of fraud.

Departments

  • SIU/MIAD - Audit and investigations departments
  • Data Analytics and business intelligence
  • Medical cost containment
  • Fraud
  • Compliance
  • Medical claims
  • Reimbursement
  • Revenue Cycle Management
  • Strategy
  • Risk

Level

  • Directors
  • Managers
  • Auditors
  • Adjudicators
  • Supervisors
  • Senior Officers
  • Analysts
  • Attorneys
  • Investigators
  • Cost containment teams
  • Investigator to manager level
  • Group discussions focused on real-world case studies
  • Use of videos, images and brainstorming techniques for quality learning
  • Online quizzes
  • Daily feedback of previous day course activities
  • Live exercises for each attendee
  • Individual assignments
  • Course material
  • Certificate of Achievement

Course Fee $2200/Participant

Individual
15% Early Bird Discount $1,870 till May 7, 2024
10% Early Bird Discount $1,980 till Jun 3, 2024

Groups/Corporate
2+1 Offer  $4,400 for 3 Participants
3+2 Offer $6,600 for 5 Participants
OR
25%
Discount for Group/Corporate


HEALTHCARE FRAUD DETECTION USING AI AND MACHINE LEARNING - Course Schedule

Day 1 - Tuesday 02 July, 2024
Registration, Kickoff and Introduction
Session One

FWA, AI, Machine Learning, and Local Trends.

  • GCC v Western Health Care Systems
  • Most common fraud schemes in US and internationally
  • Examples of FWA experience in GCC
  • UAE Regulations for Medical Audit & Recovery
  • Usual Prevention & Detection and Investigation measures used in UAE and GCC region
  • How local regulations can impact off the shelf analytics
  • Ways Machine Learning will adapt your future analytics
  • Current local trends
  • How AI can provide insights to address shifting local trends
  • Group Case Study - based on a real-life experience
Coffee/Tea Break and Quiz
Session Two

Basic Machine Learning Applications in Health Care Fraud Detection – EDA, Reporting & Dashboards

  • The Evidence of Healthcare Fraud and How to Collect it / Discuss what data to analyze
  • What kind of data is available for the health care fraud detection?
  • What kind of EDA is useful in health care fraud detection?
  • What is Data Analytics?
    • Descriptive, Diagnostic, Predictive and Prescriptive
    • Importance of Descriptive & Diagnostic Analytics
    • When we need Predictive & Prescriptive Analytics
  • Fundamentals of Descriptive Analytics – Exploratory Data Analysis (EDA) / BI Reports / Visualization
Lunch Break & Quiz
Session Three

Descriptive / Diagnostic Analytics in Action to Combat Health Care Fraud

  • Visualization Dashboards
    • How to do efficient EDA?
    • How to develop effective and outcome driven dashboards?
    • What skills to build?
    • What tools / vendors to consider?
  • What KPIs to report?
  • How to convert your FWA expertise into business rules that could be run automatically every day / hour / week? – Case studies and Roundtable discussion
Coffee/Tea Break
Session Four

Visualization Dashboards / Tools

  • Building business rules in practice – real world example with realistic data
  • Simple examples of visualization dashboards / tools to help fraud investigation
  • Quick exercise in building a dashboard
  • Possible demo of some of these visualization tools / dashboards

Wrap up discussion

Day 2 - Wednesday 03 July, 2024
Review of previous day learning sessions and data analytics discussed
Session One

1 - generative Al

  • Introduction to Generative Al in Healthcare Fraud Detection
  • Understanding the Role and Potential of Generative Al
  • Real-World Applications and Case Studies of Generative Al in Detecting Fraud
  • Discussion on the Ethical Considerations and Limitations of Generative A
Coffee/Tea Break and Quiz
Session Two

Basic Machine Learning Applications in Health Care Fraud Detection – EDA, Reporting & Dashboards

  • The Evidence of Healthcare Fraud and How to Collect it / Discuss what data to analyze
  • What kind of data is available for the health care fraud detection?
  • What kind of EDA is useful in health care fraud detection?
  • What is Data Analytics?
    • Descriptive, Diagnostic, Predictive and Prescriptive
    • Importance of Descriptive & Diagnostic Analytics
    • When we need Predictive & Prescriptive Analytics
  • Fundamentals of Descriptive Analytics – Exploratory Data Analysis (EDA) / BI Reports / Visualization
Lunch Break & Quiz
Session Three

Descriptive / Diagnostic Analytics in Action to Combat Health Care Fraud

  • Visualization Dashboards
    • How to do efficient EDA?
    • How to develop effective and outcome driven dashboards?
    • What skills to build?
    • What tools / vendors to consider?
  • What KPIs to report?
  • How to convert your FWA expertise into business rules that could be run automatically every day
    / hour / week? – Case studies and Roundtable discussion
Coffee/Tea Break
Session Four

Visualization Dashboards / Tools

  • Building business rules in practice – real world example with realistic data
  • Simple examples of visualization dashboards / tools to help fraud investigation
  • Quick exercise in building a dashboard
  • Possible demo of some of these visualization tools / dashboards
Day 3 - Thursday 04 July, 2024
Review of previous day learning sessions and data analytics discussed
Session One

Deep Dive on Machine Learning in Health Care Fraud Detection - Case Studies and Round Table Discussions

  • Fundamentals of Machine Learning - Predictive & Prescriptive Analytics Major types of Machine Learning (e.g., classification, regression, association rules, clustering, text analytics, anomaly / outlier detection, etc) and relevant examples
    • Common use cases where Machine Learning is used but you may not know it
    • Common health care use cases where e AI / machine learnings used today
  • Bringing data to your environment (data ingestion) and data preparation (data wrangling / preprocessing)
  • Effective feature construction for successful fraud detection using ML / AI
  • How basic AI and machine learning could be used to fight health care FWA?
    • Business rules – quick review from previous day
    • Classification models – detecting variations of know health care fraud
  • Common challenges and risks using classification predictive models
Coffee/Tea Break and Quiz
Session Two

Predictive / Prescriptive Analytics in Action to Combat Health Care Fraud

  • How advanced unsupervised AI and machine learning could be used to fight health care FWA?
    • Feature generation for unsupervised learning
    • Clustering approaches for fraud detection
    • Anomaly / Outlier Detection – detecting novel types of health care fraud
    • Link Analysis – detecting fraud rings and nation-wide organized fraud groups
  • Doing your homework – assets, marital and financial status, bankruptcies/divorces/substance abuse data
  • Common challenges and risks using unsupervised ML models for fraud detection
  • AI and machine learning for Post-pay vs. Pre-Pay Fraud Detection
  • Reviewing actual use cases of successful investigations from AI and machine learning
  • Round table discussion on possible fraud use cases and how AI and machine learning could help
Lunch Break & Quiz
Session Three

Reviewing Use Cases of Investigations and Round Table Discussion on Possible Use Cases

  • What’s next after you build you data analytics solution? Deploying the analytics solutions
  • What it takes to successfully adopt your analytics solution?
  • Reviewing actual use cases of successful investigations from AI and machine learning
  • Round table discussion on possible fraud use cases and how AI and machine learning could help
Coffee/Tea Break
Session Four

Open Group Discussions / Analyzing How to Address and Apply ML & AI

  • Open discussion of the course
  • Each participant will come prepared with their own fraud use case and we will work together to analyze how to address it and how to apply machine learning and AI
  • Quiz and discussion of responses

Closing, Certificate Distribution, and End of Course!

Course Program
Time Topic
Day 1
07:45 to 08:00Registration & Introduction
Day 1-3
08:00 to 10:00Session One
10:00 to 10:20Coffee/Tea Break and Quiz
10:20 to 11:50Session Two
11:50 to 12:50Lunch Break & Quiz
12:50 to 14:20Session Three
14:20 to 14:40Coffee/Tea Break
14:40 to 16:00Session Four