Data Mining: Using Data to Get Results. Catherine (Kate) H. Clark, CPC, CRCE-I Vice President Kohler HealthCare Consulting, Inc.

Transcription

1 Data Mining: Using Data to Get Results Catherine (Kate) H. Clark, CPC, CRCE-I Vice President Kohler HealthCare Consulting, Inc. 1

2 Session Agenda Organizational Pictures with Compliance Challenges Why is this Relevant? What is Data Mining? Data Mining Options Low-Cost, High-Yield How to Use Your Data Case Study Example Questions & Answers 2

3 One Picture From Two Lenses 3

4 Let s Paint a Picture First Lens 4

5 Let s Paint a Picture Second Lens 5

6 Challenges Impacting Your Lens Which picture you can paint tells what you are realistically able to assess and address Our frame of reference (lens) changes drastically based on the picture, and Our effectiveness is correlated to the things we know and are able to understand Knowing how to evolve our picture is key 6

7 Challenges Impacting Your Lens Budget constraints correlate to organizational size, Board support and commitment, and ability to independently evaluate risk-areas within the organization Organizational size and complexity create unique hurdles Small organizations tend to have lower budgets, fewer staff resources, and job duties may include compliance and noncompliance responsibilities Larger organizations tend to have greater challenges with relationship building, initiative momentum, and clinical leadership buy-in at the operational levels 7

8 What is Data Mining? Merriam-Webster s Definition: Data mining is the practice of searching through large amounts of computerized data to find useful patterns or trends Broadly speaking, data mining techniques may be classified by what they can do Description and visualization Association and clustering Predictive modeling Source: Journal of Healthcare Information Management, Vol. 19, No 2, Data Mining applications in Healthcare, Hian Chye Koh and Gerald Tan 8

9 What is Data Mining? Pyramid of Data Mining Lots of raw data available in rapid velocity Little information Less insight Insight Information Raw Data 9

10 Why Data Mining? RAC Activity Approximately 1.1 million Medical records requested through the 3 rd quarter of 2013 (from RAC activity inception) $2.5 billion in denials were reported through the 3 rd quarter of 2013 (from RAC activity inception) Source: Results of AHA RACTrac Survey, 3 rd Quarter 2013, November 21,

11 Data Mining Applications in Healthcare Detection of compliance issues including fraud and abuse Strategic planning Operations management Assessment of treatment effectiveness Predictive modeling of disease Denials Trending/Tracking/Targeting 11

12 Data Mining Options Buy or build your own platform Many foundational architecture options for enterprise wide data warehouses - but anticipate consolidation Many IT departments are swamped with ICD-10 implementation, EHR rollouts, and Meaningful Use Cost and timing If you don t have a platform use someone else s 12

13 Data Mining Options Elements to Consider Include the edits used by the MACs, RACs, NCCI, clinical indicators, OIG focus issues, and MedPAR data for your entity Decide whether review will be one-time or ongoing/continuous review Use your final 837 version claims for data mining. 837 files are generally like between payers 835 file details may vary drastically between payers Line vs. claim level actions Inclusion of all OP Codes (HCPCS/CPT and modifiers) 13

14 Data Mining Options Clarify What You Want to Do Target your area of inquiry Compliance Clinical Documentation Improvement Denials Management Legal Defense Strategic Planning Define your population Medicare only Inpatient like payers Others 14

15 Let s ask ourselves, How can I create a better picture on a pauper's budget? 15

16 Low-Cost, High-Yield Option GS*HC* * * *125123* *X*005010X223A2~ ST*837*0001*005010X223A2~ BHT*0019*00*482123* *125123*CH~ NM1*41*2*CLAIM.MD*****46* ~ PER*IC*EDI NM1*40*2*CLEARHOUSE*****46* ~ HL*1**20*1~ PRV*BI*PXC*123D00000X~ NM1*85*2*BYRON MEMORIAL HOSPITAL O*****XX* ~ N3*1475 FM WEST MAIN STREET~ N4*APPLE*LA* ~ REF*EI* ~ HL*2*1*22*0~ SBR*P*18*******MA~ NM1*IL*1*PATIENT*BYRON*A***MI* A~ N3*2419 FM 123 YOUR STREET~ N4*APPLE*LA*12351~ DMG*D8* *F~ NM1*PR*2*MEDICARE*****PI*12311~ N3*PO BOX ~ N4*PAYER*LA*12306~ CLM*1855* ***13:A:1**A*Y*Y~ LX*1~ SV2*0360*HC:64483:LT*7110*UN*1**0~ Scrubbed to de-identify; for example use only Example of layout File has been compared to: Hieroglyphics Scrambled eggs Greek Among others Occasionally you can recognize a meaningful term What are You??? 16

17 Low-Cost, High-Yield Option Reported data fields can be analyzed in comparison in an apples-to-apples function Can be combined with MEDPAR analytics of local and/or State level peer groupings for Medicare Approximately 18 months behind PHI scrubbed, but provider numbers remain Similar to data mined by CMS, OIG, and State Medicaid Fraud Investigation organizations Internal (build and/or buy) External (one time or ongoing) 17

18 Low-Cost, High-Yield Process Locate & Review Apply 837/835 Data Determine ICD-10 Interpret Analyze 18

19 Low-Cost, High-Yield Option Locate & Review Final billed version of 837 (Internal vs. Clearinghouse)? Final billed version of 835 (maintained for all payers)? Consider Corrections to claims by PFS (internal, vendor, payer systems) What access do you have ($ from 3 rd Party vendor) 19

20 Low-Cost, High-Yield Option Determine Which data (besides CMS) maintains line item payment detail? Do resources exist to develop and maintain internally or will this be outsourced? Is this a one time (or annual) snapshot or will you be able to import additional data moving forward (Monthly, Quarterly, Real-Time?) 20

21 Low-Cost, High-Yield Option Analyze Editing can include, but is not limited to: Inpatient CMS MCE Outpatient CMS OCE RAC (All Regions) LCD and NCD Revenue Opportunities Hot Topics related to OIG, CMS, Medicaid, Customized for Known Issues Internally (Endless possibilities..) Review multiple facilities in a system or a single facility 21

22 Low-Cost, High-Yield (Dashboard Examples) Drill Down Element RAC Edit Concerns 22

23 Low-Cost, High-Yield (Dashboard Examples) Drill Down Element Sepsis DRG Review 23

24 Low-Cost, High-Yield (Dashboard Examples) Drill Down Element Single MCC/CC 24

25 Low-Cost, High-Yield (Dashboard Examples) Drill Down Element Actual Codes Reported on Claim 25

26 Low-Cost, High-Yield Option Interpret What issues were confirmed? What issues were you previously unaware of? What MUST be included in your Audit Plan? What CAN you delegate to other committees to audit? What are you going to monitor? Start to create an action plan for compliance and other staff members 26

27 Low-Cost, High-Yield Option Apply Implement priority auditing for risks which require immediate attention Utilize internal resources more precisely (investigations) Regularly report findings to Board Members and Senior Leadership with confidence Manage compliance activities and leverage organizational staff: Example - Work with Clinical Directors (CDM) and HIM on charge/coding related matters (Delegate/Monitor) Now start to gather next round of data. 27

28 Case Study: Clinical Documentation Improvement A hospital system wanted the ability to determine if and how any CDI improvements would impact revenue Task: Provide the hospital system with clinical service levels and DRG level opportunities Goal #1: Identify the DRGs most likely to benefit from clinical documentation improvement (CDI) by hospital Goal #2: Determine net financial targeted improvements estimated on the target DRGs identified

29 Case Study: Clinical Documentation Improvement Approach: Utilized 837 files to provide a CDI opportunity range of $3M to $6M The lower end of range is generated from a set of conservative assumptions The higher end of the range is developed from an expected value The full value of the opportunity was in excess of $10M The opportunity was provided to the client systemwide, by hospital, by hospital service, and by DRG. While this client did not require it, information is easily made available by physician as well Note: Data Adjusted for Confidentiality

30 Closing/Additional Comments What Can Data Mining Do? Quickly review (query) new hot topics Multiuse vials billed inappropriately; risk exists that the OIG may show up to audit in next 1-2 years? RAC DRG-Pair targets and risk assessment; how much cash reserve is potentially impacted? Monitor performance improvement activities Quality Measurements/Committees Revenue Integrity Activities 30

31 Additional Comments Additional Applications (cont d): Expand your reach further and deeper through dissemination of information to impacted areas Quickly move to the actual patient claim level with Revenue Integrity (lost $) Manage audit activities through integrated secure online process Including the use of remote audit staff Enhanced reporting (side-by-sides) Future: Integrate into ICD-10 Implementation and Monitoring 31

32 Questions? 32

33 Thank You! Catherine (Kate) Clark, CPC, CRCE-I Vice President Kohler HealthCare Consulting, Inc. Direct: Office: