Provider Predictive Modeling: Utilizing SNF Data to Mitigate Risk

Transcription

1 HCCA April 22, 2013 Provider Predictive Modeling: Utilizing SNF Data to Mitigate Risk Shawn Halcsik DPT, MEd, OCS, RAC CT, CPC, CHC Vice President of Compliance Evergreen Rehabilitation Paula G. Sanders Esq. Principal & Chair, Health Care Practice Post & Schell, P.C. Shawn Halcsik DPT, MEd, OCS, RAC CT, CPC, CHC is the VP of Compliance at Evergreen Rehabilitation, a long term care contract therapy provider. In addition to bringing her vast experience as a physical therapist to the role, she also spent 3 ½ years as a Senior Medical Reviewer at a Medicare PSC where she provided subject matter expertise in coding, reimbursement, documentation, and Medicare regulations to internal and external customers including FBI, OIG, AG, and AUSA; performed pre/post pay review to identify overpayments, cost savings, and fraud/abuse issues; participated in onsite audits; and provided medical review perspective to data analysis, allegation triage, and special fraud/abuse proactive identification projects. 2 Paula G. Sanders, Esq., Principal and Chair of Post & Schell s health care practice group, focuses her national practice exclusively on health care law. She represents clients on both substantive and procedural aspects of health facility regulation, such as surveys; licensure; Medicare/Medicaid; compliance; RAC, MIC, PERM, CERT and ZPIC audits; accreditation; payment matters; HIPAA; fraud and abuse, False Claims Act investigations and voluntary disclosures. She vigorously advocates for her clients before multiple regulatory and law enforcement agencies and is especially successful at coordinating an integrated response to her clients issues. 3 1

2 4 Objectives Overview of Center for Program Integrity (CPI) and Fraud Prevention System (FPS) Identify SNF claims data used by CMS, FI/MACs, RACs and ZPICs Understand how to use your data to perform internal predictive modeling and create your own risk score Learn to analyze data and not get caught up in tunnel vision to identify areas of risk Be able to answer the question: What does my claims data profile say about me? 5 Health Care Fraud and Abuse (HCFAC) Program $4.2 billion recovered in 2012 Return on investment (ROI): $7.90 for every $1 > $23 billion returned to Medicare Trust Fund since 1997 Department of Health and Human Services & Department of Justice Health Care Fraud and Abuse Control Program Annual Report for Fiscal Year 2012 (2/11/2013); rt2012.pdf 6 2

3 Center for Program Integrity (CPI) Established April 2010 by CMS Mission is to ensure that correct payments are made to legitimate providers for covered, appropriate, and reasonable services for eligible beneficiaries Enhance efforts to screen enrolling providers and suppliers Detect aberrant, improper, or potentially fraudulent billing patterns and take quick actions against providers suspected of fraud 7 Fraud Prevention System (FPS) Required by Small Business Jobs Act Implemented July 2011 to all ZPIC geographic zones Analyzes Medicare claims data using models of fraudulent behavior Generates automatic alerts on specific claims and providers Alerts are prioritized for review and investigation by program integrity analysts 8 Fraud Prevention System (FPS) Predictive Analytic Model Categories Rules Based Anomaly Detection Predictive Models As of July 1, Rules Based 8 Anomaly Detection 3 Predictive 9 3

4 First Year Results CMS Report to Congress Was due September 30, 2012 Issued December 14, 2012 Category $ Millions Estimated Actual Savings 31.8 Estimated Projected Savings 83.6 Total Estimated Savings Total Costs 34.7 Estimated Return on Investment: 3.3 to 1 10 First Year Results: OIG & GAO Reports Did not fully comply with the requirements for reporting actual and projected improper payments recovered and avoided in the Medicare Fee for Service program and its return on investment Methodology for savings calculations included some invalid assumptions that may have affected the accuracy of reported amounts (100% fraud) CMS has not defined or measured quantifiable benefits or established appropriate performance goals Has integrated the FPS into its overall fraud prevention strategy but not the payment processing system FPS will strengthen the efforts to combat fraud, waste, and abuse in the Medicare Fee for Service program 11 First Year Results: GAO Report ZPIC Feedback FPS has not fundamentally changed the way in which they investigate fraud FPS has not significantly sped up investigations or enabled quicker administrative actions FPS provides broad indicators Beware of false positives Often require additional investigative steps Provides data to support analysis of leads Near real time claims data Time sensitive interviews Verification of tips and complaints 12 4

5 ZPIC Proactive Analysis Beyond FPS Peer Comparisons Weighted Risk Score by provider type Incorporate Multiple Pieces of Claims Data Identify Outliers Identify Trends Provider Profiles Time Studies Beneficiary Utilization 13 What does my claims data profile say about me? 14 Build Your Profile & Risk Score Build your provider profile Use the same claim data as CMS, FI/MACs, RACs, and ZPICs Consider use of additional non claim data to provide contextual and background information Determine your risk score Overall, Part A, Part B Base on national or state benchmarks when available Develop internal benchmarks when needed Update in response to regulatory changes Evaluate and Explain 15 5

6 SNF MEDICARE DATA: Part A 16 Two Different Profiles Provider 1 Total 40 patients Average LOS 60 RU% 93 RV% 6.9 RH%.13 RM%.04 Provider 2 Total 5990 patients Average LOS RU% 94.2 RV% 4.0 RH%.90 RM% OIG Report 12/2010: Questionable Billing By SNFs SNFs increasingly billed for higher paying RUGS from 2006 to 2008 even though beneficiary characteristics remained largely unchanged Ultra high therapy RUGS increased from 17% in 2006 to 28% in 2008, resulting in payments increasing by nearly 90% from $5.7 billion to $10.7 billion Higher level of assistance with ADLs For profit SNFs 18 6

7 OIG Report 11/2012: Inappropriate Payments to Skilled Nursing Facilities Cost Medicare More Than a Billion Dollars in 2009 SNFs billed one quarter of all claims in error in 2009, resulting in $1.5 billion in inappropriate Medicare payments. 20.3% claims were upcoded 2.5% downcoded 2.1% did not meet Medicare coverage requirements. SNFs misreported information on the MDS for 47 percent of claims 30.3% Therapy (i.e., physical, occupational, speech) 16.8% Special Care (e.g., intravenous medication, tracheostomy care) 6.5% Activities of Daily Living (e.g., bed mobility, eating) 4.8% Oral/Nutritional Status (e.g., parenteral feeding) 2.4% Skin Conditions and Treatments (e.g., ulcers, wound dressings) CMS should use its Fraud Prevention System to identify and target SNFs that have a high percentage of claims for ultrahigh therapy and for high levels of assistance with activities of daily living. 19 SNF Part A Data RUGs Ultra High Therapy* ADL score Length of Stay Type of Assessment* COT Discharge Destination Claim Status/Location (Rejections) 20 SNF Part A Data: Benchmarks FY 2011 FY2012 QTR 1 FY2012 QTR 1 & 2 FY 2012 QTR 1, 2, & 3 Ultra High Rehabilitation ( 720 minutes of therapy per week) Very High Rehabilitation ( minutes of therapy per week) High Rehabilitation ( minutes of therapy per week) Medium Rehabilitation ( minutes of therapy per week) Low Rehabilitation ( minutes of therapy per week) 44.9% 46.7% 46.2% 46.9% 26.9% 27.3% 26.7% 26.2% 10.8% 10.4% 10.7% 10.5% 7.6% 6.3% 6.6% 6.5% 0.1%.1%.1% 0.1% Fee for ServicePayment/SNFPPS/Spotlight.html 21 7

8 SNF Part A Data: Benchmarks FY 2011 FY2012 QTR 1 FY2012 QTR 1 & 2 FY 2012 QTR 1, 2, & 3 Individual 91.8% 99% 99.5% 99.5% Concurrent.8% 1%.4%.4% Group 7.4% 0%.1%.1% Scheduled PPS assessment Start of Therapy (SOT) assessment End of Therapy (EOT) assessment (w/o Resumption) Change of Therapy (COT) assessment 95% 85% 84% 84% 2% 2% 2% 2% 3% 3% 3% 3% N/A 10% 11% 11% 22 SNF Part A Data Benchmarks: Claims Oct 1, 2011 June 30, 2012 Urban Rural TOTAL % RUX 203,296 22, , % RUL 148,881 20, , % RVX 91,253 19, , % RVL 93,471 18, , % RHX 42,607 11,474 54, % RHL 41,735 11,947 53, % RMX 37,392 10,159 47, % RML 21,107 7,184 28, % RLX % RUC 5,660,209 1,137,530 6,797, % RUB 7,792,839 1,014,207 8,807, % RUA 4,039, ,451 4,908, % RVC 2,936, ,164 3,824, % RVB 3,523, ,493 4,284, % RVA 2,538, ,720 3,348, % RHC 1,223, ,709 1,717, % RHB 1,168, ,344 1,529, % RHA 903, ,705 1,331, % RMC 816, ,825 1,147, % RMB 678, , , % RMA 542, , , % 23 Provider Profile Fac *RU Score *RU% +RV% RUG days Site Prod Avg Avg Avg Avg Avg Avg $/Clai $/Clai $/Clai units/vunits/ units/ *Part Part A m m m isitpart B part visit B avg avg Mont Mont Mont LOS LOS h PT h OT h ST PT B OT visitpart *% B ST COT *YTD % KX A % B % C % D % E % F % G % H % 24 8

9 Red Flags Facility A Score 30 RU% 71.1% RU+RV% 100% Part A Average 75 days Length of Stay % COT 0% 25 Big Picture vs. Tunnel Vision Facility D F H Score RU% 55.4% 72.1% 18.1% RUG days Part A Average Length of Stay % COT Facility D: Documentation Review and TMR excellent payment % 26 Two Different Stories Provider 1 Total 40 patients Average LOS 60 RU% 93 RV% 6.9 RH%.13 RM%.04 Provider 2 Total 5990 patients Average LOS RU% 94.2 RV% 4.0 RH%.90 RM%

10 SNF MEDICARE DATA: Part B Distribution of Spending in Outpatient Therapy by Setting 4.00% 2.00% 11.00% SNF 37.00% PT Private Prac 16.00% HOPD ORF, CORF, & HHA 30.00% Phys and Nonphys PP OT and SLP PP 29 OIG Report 12/2010: Questionable billing For OP Therapy Medicare expenditures increased 133% between 2000 and 2009 from $2.1 billion to $4.9 billion while the number of Medicare beneficiaries receiving outpatient therapy only increased 26% from 3.6 million to 4.5 million PT services accounted for 74% ($3.6 billion) OT services accounted for 19% ($945 million) SLP services accounted for 7% ($328 million) 30 10

11 OIG Report 12/2010: Questionable billing For OP Therapy Average number of outpatient therapy services per beneficiary that providers indicated would exceed an annual cap. OIG calculated the average number of services (units) per beneficiary that had the KX modifier. Percentage of outpatient therapy beneficiaries whose providers indicated that an annual cap would be exceeded on the beneficiaries first date of service in OIG identified beneficiaries whose providers billed Medicare using the KX modifier on the beneficiaries first date of service in calendar year Percentage of outpatient therapy beneficiaries whose providers were paid for services that exceeded one of the annual caps. 31 OIG Report 12/2010: Questionable billing For OP Therapy Percentage of outpatient therapy beneficiaries whose providers were paid for more than 8 hours of outpatient therapy provided in a single day Average Medicare payment per beneficiary who received outpatient therapy from multiple providers. OIG identified beneficiaries who received outpatient therapy from more than one provider in 2009 and calculated the average reimbursement per beneficiary in Percentage of outpatient therapy beneficiaries whose providers were paid for services provided throughout the year. OIG identified beneficiaries who received outpatient therapy during all four quarters of Manual Medical Review (MMR) of Claims > $3,700 Medicare Administrative Contractors (MACs) will conduct prepayment review until 3/31/2013 Demonstration project pre payment review by RACs (Recovery Audit Contractors) effective 4/1/2013: Florida, California, Michigan, Texas, New York, Louisiana, Illinois, Pennsylvania, Ohio, North Carolina and Missouri Post payment review will be conducted by the RAC in the other states 33 11

12 SNF Part B Data KX Modifier Usage* $3700 = ADR Length Of Stay* Units per Visit Dollars/Claim or Episode* Beneficiary Episode # Codes Sets Billed (static) Claim Status/Locations (rejections) Time Code Study* Functional Limit Reporting 34 Medicare Part B Data Benchmarks Outpatient Therapy Spending and Usage in 2011 # Therapy Users (Million) Total Spending (Billion) Share of Spending Mean Spending Per User Mean Visits / User Users Above Cap Mean Spending on Users Who Exceed Cap PT 4.3 $4.1 71% $ % $3013 ST.6 $.5 10% $ OT 1.1 $1.1 19% $ % $3026 Total 4.9 $5.7 $ MEDPAC Report November Provider Profile Fac *RU Score *RU% +RV% RUG days Site Prod Avg Avg Avg Avg Avg Avg $/Clai $/Clai $/Clai units/vunits/ units/ *Part Part A m m m isitpart B part visit B avg avg Mont Mont Mont LOS LOS h PT h OT h ST PT B OT visitpart *% B ST COT *YTD % KX A % B % C % D % E % F % G % H % 36 12

13 Red Flags Facility A Score 30 Part B avg LOS Avg $/Claim Month PT 1384 Avg $/Claim Month OT 1316 YTD % KX Big Picture vs. Tunnel Vision Facility D Score 24 Part B avg LOS 36.1 Avg $/Claim Month PT 1019 YTD % KX 62.5 Documentation Review and MMR Pre auth excellent payment % 38 KX

14 Drill Down on KX and 3700 KX % / $3700 % Facility Peer Comparison Discipline Drill Down Therapist Drill Down MMR ADR Denial Rate Facility Discipline Therapist Impacted by Length of Stay, Units/Visit, and Code Sets Billed 40 Length Of Stay 60 Part B Length of Stay (4/2012 9/2012) Average visits Code Sets Billed Date Range: 7/1/2012 9/30/2012 PT OT Code Description Minutes Units Minutes Units PT evaluation OT evaluation Ultrasound therapy Therapeutic exercises Neuromuscular reeducation Gait training therapy Therapeutic activities Self care management training G0283 Electric stim other than wound

15 Code Sets Billed Units G Code Sets Billed Provider Code Set Percent of Visits Length of Stay Time/Visit Visits/Day A B C OT: X 2, X 2, x 1, G0283 OT: X 2, X 2, x 1, G0283 OT: X 2, X 2, x 1, G % 30 visits 1 hr 15 minutes 100% 10 visits 1 hr 15 minutes 25% 10 visits 1 hr 15 minutes Timed Codes = 99.4% = 85% Know what your productivity reports say about you 45 15

16 1 2 Timed Codes = 99.4% = 144% Know what your productivity reports say about you 46 Non Claim Data ADRs Denials Documentation Due Reports Part B Clinician Involvement Billing Errors/Line Item Denials Modifier Diagnosis Documentation Audit Results 47 Provider Response to CMS Screening All Claims on Front End Know your data profile as good as, if not better, than the CMS, ZPICs, FI/MACs, and RACs Identify trends and outliers that require further drill down and evaluation Utilize to develop Audit and Monitoring Plan Evaluate and Re evaluate documentation and billing practice Be prepared to respond to documentation requests and audits Know when to involve legal counsel 48 16

17 Legal Counsel Involvement Ensure attorney client protections Develop appropriate responses to discovered problems Review your contracts Remember 60 day repayment rule 49 Conclusion Audit and monitor Assess your risks Consult with counsel as necessary Train and implement Report and refund as necessary 50 Questions? Shawn Halcsik Paula G. Sanders