Healthcare Fraud

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ISBN: 9780470127100 | 0470127104
Cover: Hardcover
Copyright: 12/1/2007

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According to private and public estimates, approximately $24 million is lost per hour to healthcare waste, fraud, and abuse. A must-have reference for auditors, fraud investigators, and healthcare managers, Healthcare Fraud: Auditing and Detection Guide provides tips and techniques to help you spot-and prevent-the "red flags" of fraudulent activity within your organization. Eminently readable, it is your "go-to" resource, equipping you with the necessary skills to look for and deal with potential fraudulent situations.

Preface	p. xiii
Acknowledgments	p. xvii
Introduction to Healthcare Fraud	p. 1
What Is Healthcare Fraud?	p. 2
What Does Healthcare Fraud Look Like?	p. 4
Healthcare Fraud in the United States	p. 8
Healthcare Fraud in International Markets	p. 9
Who Commits Healthcare Fraud?	p. 10
What Is Healthcare Fraud Examination?	p. 11
The Healthcare Continuum: An Overview	p. 13
Healthcare Fraud Overview: Implications for Prevention, Detection, and Investigation	p. 14
Defining Market Players within the Healthcare Continuum	p. 17
The Patient	p. 18
Who Is the Patient?	p. 18
What Are Some Examples of Patient Fraud?	p. 22
How Does the Patient Role Relate to Other Healthcare Continuum Players?	p. 23
The Provider	p. 23
Who Is the Provider?	p. 23
What Are Some Examples of Provider Fraud?	p. 33
How Does the Provider Role Relate to Other Healthcare Continuum Players?	p. 35
The Payer	p. 35
Who Is the Payer?	p. 35
What Are Some Examples of Payer Fraud?	p. 38
How Does the Payer Role Relate to Other Healthcare Continuum Players?	p. 41
The Employer/Plan Sponsor	p. 42
Who Is the Employer/Plan Sponsor?	p. 42
What Are Some Examples of Employer/Plan Sponsor Fraud?	p. 43
How Does the Employer/Plan Sponsor Role Relate to Other Healthcare Continuum Players?	p. 43
The Vendor and the Supplier	p. 44
Who Are the Vendor and the Supplier?	p. 44
What Are Some Examples of Vendor and Supplier Fraud?	p. 44
How Do the Vendor and Supplier Roles Relate to Other Healthcare Continuum Players?	p. 44
The Government	p. 45
Who Is the Government?	p. 45
What Are Some Examples of Government Fraud?	p. 45
How Does the Government Role Relate to Other Healthcare Continuum Players?	p. 46
Organized Crime	p. 46
Who Is Organized Crime?	p. 47
How Does the Organized Crime Role Relate to Other Healthcare Continuum Players?	p. 48
Market Players Overview: Implications for Prevention, Detection, and Investigation	p. 48
Protected Health Information	p. 51
Health Insurance Portability and Accountability Act of 1996	p. 51
Audit Guidelines in Using PHI	p. 52
Protected Health Information Overview: Implications for Prevention, Detection, and Investigation	p. 54
Health Information Pipelines	p. 57
The Auditor's Checklist	p. 57
What Are the Channels of Communication in a Health Information Pipeline?	p. 58
The Patient	p. 58
The Provider	p. 60
The Payer	p. 61
The Employer/Plan Sponsor	p. 63
The Vendor/Supplier	p. 65
The Government Plan Sponsor	p. 66
Unauthorized Parties	p. 67
HIP Overview: Implications for Prevention, Detection, and Investigation	p. 69
Accounts Receivable Pipelines	p. 71
Overview of Healthcare Reimbursement	p. 72
Types of Reimbursement Models	p. 74
Fee-for-Service Model	p. 74
Prospective Model	p. 74
Capitation-Structured Model	p. 77
Data Contained in Accounts Receivable Pipelines	p. 77
Accounts Receivable Pipelines by HCC Player	p. 79
The Patient	p. 80
The Provider	p. 84
The Payer	p. 87
The Employer/Plan Sponsor	p. 92
Others	p. 95
ARP Overview: Implications for Prevention, Detection, and Investigation	p. 99
Operational Flow Activity	p. 101
Operational Flow Activity Assessment	p. 101
The Patient	p. 102
The Provider	p. 103
The Payer	p. 103
The Employer	p. 105
The "Other"	p. 107
OFA Overview: Implications for Prevention, Detection, and Investigation	p. 108
Product, Service, and Consumer Market Activity	p. 109
Product Market Activity	p. 110
Service Market Activity	p. 111
Consumer Market Activity	p. 112
PMA, SMA, and CMA Overview: Implications for Prevention, Detection, and Investigation	p. 120
Data Management	p. 123
Data Management	p. 124
Market Example: Setting Up a Claims RDBMS	p. 128
Data Management Overview: Implications for Prevention, Detection, and Investigation	p. 129
References	p. 129
Normal Infrastructure	p. 131
Normal Profile of a Fraudster	p. 132
What Types of People or Entities Commit Fraud?	p. 132
What Is the Key Element of a Fraudster?	p. 133
Anomalies and Abnormal Patterns	p. 134
Normal Infrastructure Overview: Implications for Prevention, Detection, and Investigation	p. 135
Normal Infrastructure and Anomaly Tracking Systems	p. 137
The Patient	p. 137
Sample Patient Fraud Scenarios	p. 138
Data Management Considerations	p. 139
The Untold Story	p. 139
The Provider	p. 139
Sample Provider Fraud Scenarios	p. 140
Data Management Considerations	p. 142
The Untold Story	p. 142
The Payer	p. 143
Sample Payer Fraud Scenarios	p. 144
Data Management Considerations	p. 145
The Untold Story	p. 145
The Vendor/Other Parties	p. 146
Sample Vendor/Other Fraud Scenarios	p. 147
Data Management Considerations	p. 148
The Untold Story	p. 149
Organized Crime	p. 150
Sample Organized Crime Fraud Scenarios	p. 150
Data Management Considerations	p. 151
The Untold Story	p. 151
Normal Infrastructure and Anomaly Tracking Systems Overview: Implications for Prevention, Detection, and Investigation	p. 152
Components of the Data Mapping Process	p. 153
What Is Data Mapping?	p. 153
Data Mapping Overview: Implications for Prevention, Detection, and Investigation	p. 158
Components of the Data Mining Process	p. 159
What Is Data Mining?	p. 159
Data Mining in Healthcare	p. 160
Components of the Data Mining Process within the HCC	p. 161
Data Mining Overview: Implications for Prevention, Detection, and Investigation	p. 162
Components of the Data Mapping and Data Mining Process	p. 165
Forensic Application of Data Mapping and Data Mining	p. 167
Data Mapping and Data Mining Overview: Implications for Prevention, Detection, and Investigation	p. 170
Data Analysis Models	p. 173
Detection Model	p. 173
Pipeline Application	p. 175
Detection Model Application	p. 176
Investigation Model	p. 176
Mitigation Model	p. 181
Prevention Model	p. 182
Response Model	p. 189
Recovery Model	p. 194
Data Analysis Model Overview: Implications for Prevention, Detection, and Investigation	p. 204
Clinical Content Data Analysis	p. 207
What Is SOAP?	p. 208
The SOAP Methodology	p. 209
Electronic Records	p. 225
Analysis Considerations with Electronic Records	p. 226
Narrative Discourse Analysis	p. 229
Clinical Content Analysis Overview: Implications for Prevention, Detection, and Investigation	p. 237
Profilers	p. 239
Fraud and Profilers	p. 239
Medical Errors and Profilers	p. 244
Financial Errors and Profilers	p. 249
Internal Audit and Profilers	p. 253
Recovery and Profilers	p. 256
Anomaly and Profilers	p. 257
Fraud Awareness and Profilers	p. 259
Profiler Overview: Implications for Prevention, Detection, and Investigation	p. 260
Market Implications	p. 261
The Myth	p. 261
"Persistent"	p. 264
"Persuasive"	p. 265
"Unrealistic"	p. 266
Market Overview: Implications for Prevention, Detection, and Investigation	p. 268
Conclusions	p. 271
Micromanagement Perspective	p. 271
Macromanagement Perspective	p. 278
Overview of Prevention, Detection, and Investigation	p. 279
Index	p. 283
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Healthcare Fraud

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Healthcare Fraud

Summary

Author Biography

Table of Contents

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