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

November 18-19, 2015
Prepared for Becker’s 2015 Annual CEO Strategy Roundtable
Big Data: Implications of Data
Mining for Employed Physician
Compliance Management
Becker’s 2015 Annual CEO Roundtable

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

Big Data Trends
• Trends in the use and public dissemination of
healthcare financial, claims, and quality data
– Publicly Available & Third-Party Data
• Federal Charge Data
• State-level Charge Data
• Physician and other Supplier Public Use File
• Broad Disclosure of Physician Payment Information under
Sunshine Act
• Public Use Files of Part C and D Reporting Requirements
Data
• Other Public or For Purchase Data Sources

Federal Charge Data
• CMS has released hospital-specific data from
2011 comparing the charges for the 100 most
common inpatient services and 30 common
outpatient services
• Inpatient DRG examples:
– Heart Failure & Shock w cc
– G.I. Obstruction w cc
– Transient Ischemia
4

Federal Charge Data (cont.)
• Outpatient examples:
– Level III Endoscopy Upper Airway
– Level I Nerve Injections
– Level 1 Hospital Clinic Visits
See http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-
Reports/Medicare-Provider-Charge-Data/index.html
5

• Numerous states also provide
state-level charge data
• The information and format varies
• Examples:
– Wisconsin, X Facility,
Cesarean Delivery: $12,881
– Tennessee, All Facilities, Rotator Cuff Repair,
Average charge without another procedure: $23,483
– Oregon, X Facility, Esophagitis, gastroent & misc.
digest disorders w/o MCC, Average Charge: $8,546
State-Level Charge Data
6

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 paid through June 30,
2013
7

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
8

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
9

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)
10

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 – Adventist results

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/
13

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
14

FRCP 9(b) & Big Data
• Interplay of Rule 9(b) Motions to Dismiss
and Big Data
15

Rule 9(b) Relator’s Counsel “In Their Own Words”
“Sunshine data instantly provides qui tam attorneys a
host of information that would have been impossible
or very difficult to find before the Act. [One relator’s
counsel] believes the information would, right off the
bat, add credibility to a relator's allegations. Attorneys
will be able to corroborate their client's allegations or
confirm suspicions of widespread conduct by running
a simpl[e] search.”
16

“At the very least, Sunshine data will provide facts to
beef up a plaintiff's complaint. Rule 9(b) of the Federal
Rules of Civil Procedure requires that for ‘alleging
fraud or mistake, a party must state with particularity
the circumstances constituting fraud or mistake.’ [One
relator’s counsel] notes that the exact dates of
transactions and the precise amounts of payments will
add that required specificity.”
See http://www.policymed.com/2014/02/physician-payment-sunshine-act-will-sunshine-data-
help-qui-tam-whistleblowers-and-their-attorneys.html
17

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
18

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)
19

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)
20

Potential Review Results
PQRS/QUALITY REPORTING DETAILED RESULTS
PQRS Results Family Practice Internal Medicine
Other
Specialties
Met 757 247 103
Not Met 545 145 68
PQRS code and/or ICD-9 code not documented 144 56 50
Supporting ICD-9 or additional PQRS code should be reported 99 26 6
A different PQRS code was documented 107 29 7
No documentation received 0 2 4
Corresponding CPT code not supported 195 32 1
Modifier deficiency1 6 0 0
1 Of note, Not Met is counted per transaction or claim line versus the deficiencies listed which include transaction-level
and component-level errors. Modifier deficiency is a component-level error; meaning that the error count in some
instances may also be captured in one of the other categories.

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.

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 9-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-9 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
workflow/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-9-CM
• 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-9 Selection
• Signature Compliance
• Time-Based Code Support
• NPP/Midlevel Provider Compliance
• NCCI/Bundling Compliance
• Other Agreed-Upon Regulatory or
Facility-Specific Areas of Interest
• ICD-10 Documentation Readiness
E/M Compliance Elements General Compliance Elements

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

Identifying Overpayments

Medicare Parts A & B:
Medicare Parts A & B
• 60‐Day Overpayment Proposed Rule
– 10-year look‐back period
– Duty to take affirmative investigative action related to
potential overpayments
53

Medicare Parts C & D:
Medicare Parts C & D
• 60-Day Overpayment Final Rule
– Six-year look-back period
– “[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.”
54

Questions

Thank You!
Denise Hall, RN, BSN
Principal, Healthcare Consulting
Pershing Yoakley & Associates, P.C.
(404) 266-9876
dhall@pyapc.com

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

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