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 ManagementBecker’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

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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

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http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/index.html

http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/index.html



• 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

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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

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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

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http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Physician-and-Other-Supplier.html

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

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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)

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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/

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http://time.com/#198/bitter-pill-why-medical-bills-are-killing-us/

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

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

and Big Data


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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.”

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“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

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http://www.policymed.com/2014/02/physician-payment-sunshine-act-will-sunshine-data-help-qui-tam-whistleblowers-and-their-attorneys.html

http://www.policymed.com/2014/02/physician-payment-sunshine-act-will-sunshine-data-help-qui-tam-whistleblowers-and-their-attorneys.html



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

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Scenario 3: Quality ofCare 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)

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Scenario 3: Quality of 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)

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Potential Review ResultsPQRS/QUALITY REPORTING DETAILED RESULTS

PQRS Results Family Practice Internal MedicineOther

Specialties

Met 757 247 103Not Met 545 145 68PQRS code and/or ICD-9 code not documented 144 56 50Supporting ICD-9 or additional PQRS code should be reported 99 26 6A different PQRS code was documented 107 29 7No documentation received 0 2 4Corresponding CPT code not supported 195 32 1Modifier deficiency1 6 0 01 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 modifier2. Overuse/exclusive use of high level E/M

codes3. Extremely high levels of production4. Psychiatry time-based codes and use of E/M

codes with same5. 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 MonitoringMaking 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 Suiteon 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

PHYSALIGN

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

PHYSALIGN

REV $



Benchmark Specialty Procedural Service Mix Analysis

CODING

PHYSALIGN

REV $

PhysicianRank

PercentCPT/HCPCS

CodesAppended CPT/HCPCS Brief Description

Neurosurgery Benchmark

Rank

Neurosurgery Benchmark

Rank

Percentof Total

BenchmarkUnits CPT/HCPCS Brief Description

PhysicianRank

1 23% 99232 Subsequent hospital care 8 1 14% 99213 Offi ce/outpatient visit est 632 15% 99222 Initial hospital care 16 2 7% 99214 Offi ce/outpatient visit est 553 14% 99231 Subsequent hospital care 7 3 6% 99212 Offi ce/outpatient visit est -4 7% 99223 Initial hospital care 13 4 5% 99204 Offi ce/outpatient visit new -5 5% 63047 Removal of spinal lamina 28 5 5% 99203 Offi ce/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 38 2% 22851 Apply spine prosth device 14 8 3% 99232 Subsequent hospital care 19 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 Offi ce/outpatient visit new -12 1% 22614 Spine fusion extra segment 17 12 2% 63048 Remove spinal lamina add-on 713 1% 22552 Addl neck spine fusion 46 13 2% 99223 Initial hospital care 414 1% 61312 Open skull for drainage - 14 2% 22851 Apply spine prosth device 815 1% 22845 Insert spine fixation device 33 15 2% 99215 Offi ce/outpatient visit est -

Specialty Benchmark ComparisonPHYSICIAN

Specialty Benchmark ComparisonNEUROSURGERY



Targeted Physician ProbesSpecial Data Analytics for High Risk Concerns



New vs. Established Patient E/M Services

CODING

REV $

Physician

RatioEst Patient E/M

toNew Patient E/M

PHYSICIAN

RatioEst Patient E/M

toNew Patient E/M

BENCHMARKPercentVariance

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

PHYSALIGN

REV $



Physician Productivity Analysis:Addressing Work Relative Value

CODING

PHYSALIGN

REV $

Physician Specialty Work RVUs

Weighted Average Work RVU per Unit

90th Percentile Work RVUs per

MGMA

Work RVUsas a % of

90th Percentile

Dashboard>200%>150%>100%

Physician A Geriatrics 20,658 1.43 6,194 334%

Physician B Hospital ist 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 Hospital ist 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

PHYSALIGN

REV $



Place Of Service Impact AnalysisThe 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 BYCLIENT Billed in

Non-Facility ($$) SettingBenchmark Billed inFacility ($) Setting

CLIENT | BenchmarkPlace 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

PHYSALIGN

REV $

Physician

SORTED BYPercent

Billing Provider = MDand

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

PHYSALIGN

REV $

Physician

TotalProfessionalService 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.



PHYSALIGN

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

PHYSALIGN

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 APhysician BPhysician CPhysician DPhysician EPhysician FPhysician GPhysician HPhysician IPhysician JPhysician KPhysician LPhysician MPhysician NPhysician OPhysician PPhysician QPhysician 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



All Internal MedicinePhysician APhysician BPhysician CPhysician DPhysician EPhysician FPhysician GPhysician HPhysician IPhysician JPhysician KPhysician L

Physician MPhysician NPhysician OPhysician PPhysician QPhysician RPhysician SPhysician TPhysician U

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%

ComplianceMissing Provider SignatureNot DocumentedMissed Opportunity to BillBundledInsufficient Documentation to BillOvercodedUndercodedInaccurate CPT/HCPCS Assigned

Potential Review ResultsINTERNAL MEDICINE SNAPSHOT – PHYSICIAN CODING DEFICIENCY FINDINGS(In Compliance Rate Order)



Potential Review ResultsFamily 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



Potential Review ResultsTOTAL AND SPECIALTY GROUPING ERROR COUNTS



Potential Review ResultsE/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%Insuffi cient 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 PracticeE/M Coding Detailed Results

Internal MedicineE/M Coding Detailed Results

Other Specialties E/M Coding Detailed Results



Potential Review ResultsPROCEDURAL CODING DETAILED RESULTS



Identifying Overpayments



Medicare Parts A & B: Identifying Overpayments

Medicare Parts A & B• 60‐Day Overpayment Proposed Rule

– 10-year look‐back period

– Duty to take affirmative investigative action related to potential overpayments

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Medicare Parts C & D: Identifying Overpayments

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.”

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Questions



Thank You!

Denise Hall, RN, BSNPrincipal, Healthcare Consulting

Pershing Yoakley & Associates, P.C.(404) 266-9876

[email protected]

mailto:[email protected]

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

Healthcare

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