Big Data: Implications of Data Mining for Employed Physician Compliance Management

March 31, 2016
GA HFMA WEBINAR
Big Data: Implications of Data Mining
for Employed Physician Compliance
Management

Prepared for GA HFMA Webinar Page 1
Big Data
“Big-data initiatives have the potential to transform
healthcare, as they have revolutionized other industries. In
addition to reducing costs, they could save millions of lives
and improve patient outcomes. Healthcare stakeholders
that take the lead in investing in innovative data
capabilities and promoting data transparency will not only
gain a competitive advantage, but will lead the industry to
a new era.” (McKinsey)

Agenda
 Public relations and litigation risk from the public
dissemination of data being harvested and aggregated by
the government (e.g., physician payment data, Sunshine
Act regulations, discharge data)
 Internal use of Broad Spectrum Analytics in Employed
Physician Compliance Management
 Determination of Risk Tolerance and Customizing
Analytics that are “Outside the Box”
 Benchmarking, Monitoring, and Defining
Physician/Focused Risk Area Reviews

Physician and Other
Supplier Public Use File
 Physician and Other Supplier Public Use File released for
the first time in April 2014
 Contains 100% of final-action physician/supplier Part B
non-institutional line items for the Medicare fee-for-
service population for CY2012-2013

Physician and Other
Supplier Public Use File (cont.)
 Contains information on services and procedures
provided to Medicare beneficiaries by physicians and
other healthcare professionals, including:
 Utilization
 Submitted charges
 Payment (allowed amount and Medicare payment)
See http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-
Reports/Medicare-Provider-Charge-Data/Physician-and-Other-Supplier.html

Broad Disclosure of Physician
Payment Info under Sunshine Act
 Manufacturers of drugs, devices, biologicals, and medical
supplies, and some group purchasing organizations
(GPOs), must report payments and other transfers of
value to “covered recipients” which are defined as:
 Teaching hospitals
 Physicians (except physicians who are employees of the
applicable manufacturer)
 CMS must make information submitted
in transparency reports and physician
ownership reports publicly available
on a searchable website

Public Use Files of Part C and D
Reporting Requirements Data
 Federal regulations require Medicare Advantage (MA)
plans and Part D sponsors to report to CMS information
on (among other things):
 Enrollment and Disenrollment (Part C and Part D)
 Grievances (Part C and Part D)
 Special Needs Plans Care Management (Part C)
 Organization Determinations/Reconsiderations (Part C)
 Coverage Determinations and Exceptions (Part D)
 Long-term Care Utilization (Part D)
 Medication Therapy Management Programs (Part D)
 Redeterminations (Part D)

Big Data Trends
 Other Government Data Sources
 Medicare Fraud Strike Force Team
 Data-driven Quality Initiatives
 Other Non-public Government Data Sources
 Government Uses of Data for Compliance and
Enforcement

What Providers and Payers Can Expect
 Scenario 1: Increased Media Exposure
 Scenario 2: Linking Manufacturer Payments Data to
Anti-Kickback Allegations
 Scenario 3: Quality of Care FCA Litigation

Scenario 1:
Increased Media Exposure
See http://time.com/#198/bitter-pill-why-medical-bills-are-killing-us/

Scenario 2: Linking Manufacturer
Payments Data to AK Allegations
 Expect qui tam relators to
attempt to bolster complaints
by “linking” physician payments
to “increased” drug or device
utilization in order to allege
an Anti-Kickback Statute (AKS)
violation

Scenario 3: Quality of
Care FCA Litigation
Linked To Data
 Expect qui tam relators and/or government to contend
payment structures and reporting measures set forth in
various new quality programs materially affect payment
and are thereby conditions of payment—and that
violations triggers False Claims Act (FCA) liability

Care FCA Litigation
Data-driven Quality Initiatives
 Programs resulting from the Patient Protection and
Affordable Care Act (PPACA), the American Recovery
and Reinvestment Act (ARRA) as well as those initiated
by OIG and CMS reflect an increased focus on quality
 Health Information Technology for Economic and Clinical
Health (HITECH) Act established the Electronic Health
Record (EHR) Meaningful Use Program to provide
financial incentives to providers to promote the adoption
and meaningful use of certified EHR technology to
improve patient care (ARRA, Public Law 111-5, Division
A, Title XIII and Division B, Title IV)

Care FCA Litigation
Data-driven Quality Initiatives (cont.)
 PPACA establishes numerous quality-related programs,
potentially exposing providers to increased liability for
quality shortfalls; these include, among others:
 Medicare Physician Quality Reporting Improvements: financial
incentives and penalties for reporting or failure to report Physician
Quality Reporting Initiative (PQRI) measures (PPACA §§ 3002,
3007)
 Value-based Purchasing Program: pays hospitals based upon
how well they perform on specific quality measures (Id. § 3007)

Real World Examples of
Physician Compliance Risk
1. Overuse of -25 modifier
2. Overuse/exclusive use of high level E/M codes
3. Extremely high levels of production
4. Psychiatry time-based codes and use of E/M codes
with same
5. High utilization of specialty-related services (Oncology,
Cardiac)

How Can We Mitigate Risk?
Think like a reporter, a qui tam relator, a MAC, MIC, ZPIC,
RAC, DOJ, and the OIG, etc.
Exercise protections of the PSQIA via a Patient Safety
Organization.

Key Questions
 Are you incorporating data sets in your compliance and
internal audit activities?
 Is data analytics a key part of your monitoring and
auditing plan?
 Are you assessing data analytics capabilities (or lack
thereof) as part of your annual risk assessment?
 Are you evaluating where you are amongst your peers?
 If you are an outlier, is there a legitimate reason why, or
do you need to mitigate an issue through corrective
action?

Resources to Identify Most
Significant Areas of Potential Risk
 OIG Work Plan
 OIG Semi-annual Report to Congress
 OIG Special Fraud Alerts
 OIG and DOJ Announcements
 Corporate Integrity and Deferred Prosecution Agreements
 RAC Audits
 RADV Audits
 Complaints, Investigations, and Audits
 . . . Your Gut!

Using Data Effectively
 Considerations when designing an effective data
analytics function:
 Availability of data
 Accessibility to the data
 Timeliness to gain access to the data
 Quality of the data
 Expertise of those using the data
 Corporate support for the program
 Privacy and Privilege considerations

Physician Compliance Monitoring
Making the information come to you…

Making Physician Compliance
Manageable AND Meaningful
Targeted
Physician Probes
Effective use of physician analytics
allows a physician compliance
program to be extremely detailed
while remaining efficient and
cost-effective.
Analytics Suite
on All Employed Physicians
Focused
Physician
Reviews

Typical Areas of Focus
“REV $”“PHYS ALIGN”“CODING”
•Area/Metric
•Area/Metric
•Area/Metric
•Area/Metric
•Area/Metric
•Area/Metric
•Area/Metric
•Area/Metric
•Area/Metric
Develop unique areas of focus, metrics to measure, and thresholds to assess
compliance and risk. This is an active, fluid initiative.

Other Customized Analytics:
Getting “Outside of the Box”
In addition to a number of analytics to evaluate certain “expected” areas
of physician utilization (e.g., E/M bell curves), consider other topical ways
to assess physicians based upon a customized list of targeted service
areas to determine if “outlier” patterns exist. Some example focus areas
include:
CODING
PHYS
ALIGN
REV $
• Critical Care Service Utilization
• 25-Modified E/M Services
• Preventive Medicine Services (e.g., ratio of G-code to 10-code use)
• Extended Discharge Day Management Services
• Incident-To/Split Shared Services
• Time Studies/Work RVU Analysis
• EP Study Utilization
• Long-term Drug Use ICD-10 Code Utilization

Physician Analytics Suite Examples

E/M Distribution (Bell Curve) Analysis
CODING
PHYS
ALIGN
REV $

Benchmark Specialty
Procedural Service Mix Analysis
CODING
PHYS
ALIGN
REV $
Physician
Rank
Percent
CPT/HCPCS
Codes
Appended CPT/HCPCS Brief Description
Neurosurgery
Benchmark
Rank
Neurosurgery
Benchmark
Rank
Percent
of Total
Benchmark
Units CPT/HCPCS Brief Description
Physician
Rank
1 23% 99232 Subsequent hospital care 8 1 14% 99213 Office/outpatient visit est 63
2 15% 99222 Initial hospital care 16 2 7% 99214 Office/outpatient visit est 55
3 14% 99231 Subsequent hospital care 7 3 6% 99212 Office/outpatient visit est -
4 7% 99223 Initial hospital care 13 4 5% 99204 Office/outpatient visit new -
5 5% 63047 Removal of spinal lamina 28 5 5% 99203 Office/outpatient visit new -
6 3% 99233 Subsequent hospital care 21 6 4% J2323 Natalizumab injection -
7 2% 63048 Remove spinal lamina add-on 12 7 3% 99231 Subsequent hospital care 3
8 2% 22851 Apply spine prosth device 14 8 3% 99232 Subsequent hospital care 1
9 2% 22551 Neck spine fuse&remov bel c2 37 9 3% J0585 Injection,onabotulinumtoxinA -
10 2% 99221 Initial hospital care 24 10 2% G8447 Pt vis doc use EHR cer ATCB -
11 2% 61781 Scan proc cranial intra - 11 2% 99205 Office/outpatient visit new -
12 1% 22614 Spine fusion extra segment 17 12 2% 63048 Remove spinal lamina add-on 7
13 1% 22552 Addl neck spine fusion 46 13 2% 99223 Initial hospital care 4
14 1% 61312 Open skull for drainage - 14 2% 22851 Apply spine prosth device 8
15 1% 22845 Insert spine fixation device 33 15 2% 99215 Office/outpatient visit est -
Specialty Benchmark Comparison
PHYSICIAN
Specialty Benchmark Comparison
NEUROSURGERY

Targeted Physician Probes
Special Data Analytics for High-risk Concerns

New vs. Established Patient E/M Services
CODING
REV $
Physician
Ratio
Est Patient E/M
to
New Patient E/M
PHYSICIAN
Ratio
Est Patient E/M
to
New Patient E/M
BENCHMARK
Percent
Variance
Dashboard
>=50%
>=35%
>=20%
Physician A 1.3 3.6 177%
Physician E 0.9 2.4 176%
Physician I 1.7 3.6 112%
Physician C 1.2 2.4 100%
Physician B 3.2 4.0 25%

Focused Benchmark Analysis:
Modifier Use
Physician
Modifier Use
> 30%
Above Benchmark
Modifier Use
> 25%
Above Benchmark
Modifier Use
> 20%
Above Benchmark
Physician A 25, 80 59
Physician B 51 22
Physician C 51 51
Physician D 80 59 51
Physician E 25 22
Physician F 22 25
Physician G 25
Physician H 59 25 80
Physician I 80 59
25 Significant separately identifiable E/M service
59 Distinct procedural service
80 Surgical assistant
22 Increased procedural service
CODING
PHYS
ALIGN
REV $

Physician Productivity Analysis:
Addressing Work Relative Value
CODING
PHYS
ALIGN
REV $
Physician Specialty Work RVUs
Weighted
Average Work
RVU per Unit
90th
Percentile
Work RVUs per
MGMA
Work RVUs
as a % of
90th
Percentile
Dashboard
>200%
>150%
>100%
Physician A Geriatrics 20,658 1.43 6,194 334%
Physician B Hospitalist 21,666 1.03 6,901 314%
Physician C Endocrinology 16,232 0.94 6,801 239%
Physician D Geriatrics 14,163 1.58 6,194 229%
Physician E General Surgery 18,179 2.63 10,730 169%
Physician F Gynecology/Oncology 16,233 1.24 10,775 151%
Physician G OB/GYN 16,022 1.88 10,432 154%
Physician H Gastroenterology 15,609 1.75 12,604 124%
Physician I Hospitalist 9,244 1.80 6,901 134%
Physician J Family Medicine 7,790 0.35 7,082 110%
Physician K Plastic/Reconstructive Surgery 6,551 1.87 11,411 57%
Physician L Psychiatry 3,819 1.34 6,189 62%

Physician Productivity Analysis:
Work RVUs
CODING
PHYS
ALIGN
REV $

Place of Service Impact Analysis
The Office of Inspector General reports the following in its HHS OIG
Work Plan for Fiscal Year 2014:
“Federal regulations provide for different levels of payments to
physicians depending on where services are performed (42 CFR
§414.32). Medicare pays a physician a higher amount when a service is
performed in a non-facility setting, such as a physician’s office, than it
does when the service is performed in a hospital outpatient
department…”
CODING
REV $
Physician
SORTED BY
CLIENT Billed in
Non-Facility ($$) Setting
Benchmark Billed in
Facility ($) Setting
CLIENT | Benchmark
Place of Service
Match
Dashboard Reimbursement
Higher Based upon CLIENT
Compared to Benchmark
Place of Service
Physician D 70% 30%
Physician A 61% 39%
Physician G 1% 76%
Physician C 0% 100%
Physician O 0% 77%
Physician K 0% 51%

Non-Physician Practitioner
(NPP) Collaboration “Probe” Analysis
Define physicians who may collaborate with NPPs to perform
incident-to, split/shared E/M visit and post-operative follow-up
services.
CODING
PHYS
ALIGN
REV $
Physician
SORTED BY
Percent
Billing Provider = MD
and
Rendering Provider = MLP
Dashboard
>=50%
>=35%
>=20%
Physician B 55%
Physician A 47%
Physician C 35%
Physician D 33%
Physician G 20%
Physician K 15%
Physician O 0%

Benchmark Physician
Time Study Analysis
Physicians with “higher than expected” FTE-equivalent levels often
collaborate with NPPs, nursing and other ancillary staff to engage in the
work flow/practice patterns necessary to support high utilization levels.
CODING
PHYS
ALIGN
REV $
Physician
Total
Professional
Service Time
(in Hours)
FTE-Equivalent
(Based upon 2,000
Annual Hours)
Dashboard
>=3.0
>=2.5
>=2.0
<2
Physician B 9,702 4.85
Physician A 9,616 4.81
Physician C 6,803 3.40
Physician D 4,995 2.50
Physician G 4,306 2.15
Physician K 4,211 2.11
Physician N 2,683 1.34
Physician O 2,386 1.19
Best calculated using the current Medicare Physician Time Study and
2,000 total annual hours per full-time equivalent.

PHYS
ALIGN
Gross And Net Revenue
“Pulse Check” Analysis
Use data to gain a high-level understanding of any potential areas of
revenue “vulnerability.”
REV $

Outcome:
“At a Glance” Reporting
CODING
PHYS
ALIGN
REV $
Specialty Physician
Total Work
RVU
Benchmark
Comparison
Total Work
RVUs by
Service Type
Weighted
Average Work
RVU per Unit
by Service
Type
Productivity
Stability Probe
E/M Services
Total Days
Worked by Day
of the Week
Average Daily
Billed Service
Hours by Day
of the Week
Benchmark
Physician
Time Study
Analytics
Physician A
Physician B
Physician C
Physician D
Physician E
Physician F
Physician G
Physician H
Physician I
Physician J
Physician K
Physician L
Physician M
Physician N
Physician O
Physician P
Physician Q
Physician R
Electrophysiology
Interventional Cardiology

Next Steps:
Focused Physician Reviews
No more annual 10 chart provider review
compliance plan commitments!!!
Grading or Compliance Rate Considerations
Feedback During Review Process
Trending
Corrective Action Plans

Coding and Documentation Review
Guidelines
 CPT
 ICD-10-CM
 HCPCS
 1995/1997 Documentation
Guidelines for E/M Services
 Medicare/Medicaid/Other
Gov’t
 State and Federal
Documentation
 Explanation of Benefits
 CMS 1500
 Medical Record
VS.

Coding and Documentation Review
• Chief Complaint
• History of Present Illness
• History Level
• Review of Systems
• Examination
• Past, Family and/or Social
History
• Medical Decision Making Level
• Modifier Usage
• CPT Selection
• Modifier Usage
• ICD-10 Selection
• Signature Compliance
• Time-based Code Support
• NPP/Mid-level Provider Compliance
• NCCI/Bundling Compliance
• Other Agreed-upon Regulatory or
Facility-specific Areas of Interest
E/M Compliance Elements General Compliance Elements

Potential Review Results
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%100.00%
All Internal Medicine
Physician A
Physician B
Physician C
Physician D
Physician E
Physician F
Physician G
Physician H
Physician I
Physician J
Physician K
Physician L
Physician M
Physician N
Physician O
Physician P
Physician Q
Physician R
Physician S
Physician T
Physician U
Compliance
Missing Provider Signature
Not Documented
Missed Opportunity to Bill
Bundled
Insufficient Documentation to Bill
Overcoded
Undercoded
Inaccurate CPT/HCPCS Assigned
INTERNAL MEDICINE SNAPSHOT – PHYSICIAN CODING DEFICIENCY FINDINGS
(In Compliance Rate Order)

Family Practice Internal Medicine Other Specialties
Provider Compliance
Dashboard
<60%
61-89%
90-100% Provider Compliance
Dashboard
<60%
61-89%
90-100% Provider Compliance
Dashboard
<60%
61-89%
90-100%
Physician A 90% Physician A 83% Physician A 85%
Physician B 89% Physician B 80% Physician B 75%
Physician C 88% Physician C 79% Physician C 71%
Physician D 86% Physician D 75% Physician D 68%
Physician E 76% Physician E 75% Physician E 66%
Physician F 75% Physician F 75% Physician F 65%
Physician G 75% Physician G 75% Physician G 63%
Physician H 74% Physician H 72% Physician H 60%
Physician I 74% Physician I 68% Physician I 60%
Physician J 73% Physician J 67% Physician J 58%
Physician K 71% Physician K 65% Physician K 53%
Physician L 71% Physician L 62% Physician L 52%
Physician M 69% Physician M 61% Physician M 50%
Physician N 69% Physician N 53% Physician N 50%
Physician O 68% Physician O 45% Physician O 40%
Physician P 65% Physician P 43% Physician P 36%
Physician Q 65% Physician Q 40% Physician Q 30%
Physician R 65% Physician R 40% Physician R 27%
Physician S 64% Physician S 37% Physician S 24%
Physician T 63% Physician T 36% Physician T 18%
Physician U 62% Physician U 20% Physician U 7%
Physician V 61% Physician V 5%
Physician W 59%
Physician X 59%
Physician Y 58%
Physician Z 58%
Physician AA 58%
Physician AB 57%
Physician AC 57%
Physician AD 57%
Physician AE 55%
Physician AF 54%
Physician AG 54%
Physician AH 53%
Physician AI 52%
Physician AJ 52%
Physician AK 48%
Physician AL 47%
Physician AM 45%
Physician AN 43%
Physician AO 40%
Physician AP 38%
Physician AQ 37%
Physician AR 35%
Physician AS 34%
Physician AT 33%
Physician AU 31%
Physician AV 24%
COMPLIANCE RATES PER PROVIDER

TOTAL AND SPECIALTY GROUPING ERROR COUNTS

E/M CODING DETAILED RESULTS
Met 267 55% Met 127 61% Met 70 39%
Not Met 217 45% Not Met 81 39% Not Met 111 61%
Undercoded 95 20% Inaccurate CPT/HCPCS Assigned 2 1% Inaccurate CPT/HCPCS Assigned 9 5%
Insufficient Documentation to Bill 74 15% Insufficient Documentation to Bill 13 6% Insufficient Documentation to Bill 9 5%
Overcoded 35 7% Missing Provider Signature 1 0.5% Missing Provider Signature 6 3%
Not Documented 6 1% Not Documented 17 8% Not Documented 28 15%
Bundled 4 1% Overcoded 39 19% Overcoded 52 29%
Inaccurate CPT/HCPCS Assigned 2 0.4% Undercoded 9 4% Undercoded 7 4%
Missing Provider Signature 1 0.2%
Family Practice
E/M Coding Detailed Results
Internal Medicine
Other Specialties

PROCEDURAL CODING DETAILED RESULTS

Medicare Parts A & B:
Identifying Overpayments
Medicare Parts A & B
 60‐Day Overpayment Final Rule
 CMS’ new guidance clarifies that an overpayment
has not been “identified” under the 60-day rule
until a provider has or should have, through
“reasonable diligence,” quantified the
overpayment
 Six-year look‐back period
 Duty to take affirmative investigative action related to
potential overpayments

Medicare Parts C & D:
Identifying Overpayments
Medicare Parts C & D
 60-Day Overpayment
 Six-year look-back period – advised
 “[I]f an MA organization or Part D sponsor has
received information that an overpayment may exist,
the organization must exercise reasonable diligence to
determine the accuracy of this information, that is, to
determine if there is an identified overpayment ... ‘‘day
one’’ of the 60-day period is the day after the date on
which organization has determined that it has
identified the existence of an overpayment.”

Questions

Thank You!
Kristen Lilly, MHA, RHIA®, CPHQ
Manager, Healthcare Consulting
Pershing Yoakley & Associates, P.C.
(404) 266-9876
klilly@pyapc.com

Big Data: Implications of Data Mining for Employed Physician Compliance Management

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Big Data: Implications of Data Mining for Employed Physician Compliance Management