Presentation Transcript

1. OSA Investment inData Analytics

   NSAA Annual Conference, Monterey, CA June 12, 2013
2. Laura Marlin, First Deputy Auditor OSA Priorities, Data Analytics Investment and Introductions
3. Auditor Bump’s Priorities Use Resources Strategically Focus resources on high-risk areas Promote Professionalism Strengthen professionalism, capacity of staff Provide Solutions Champion reforms that make government more efficient, more effective, and more accountable Communicate Across Audiences Issue more meaningful and accessible reports on audits, investigations and local impact reviews
4. Why invest in Data Analytics Using Resources Strategically Risk-based audit selection Enhanced risk analysis in audits Data-driven, proactive fraud investigations Provide Solutions Focus on systemic issues More meaningful recommendations
5. Introduction of Presenters Paul Albano, Director Medicaid and Health Care Audits Allie Alland, Director Bureau of Special Investigations Kleber Gallardo, Consultant Big Data and Analytics Paul McLaughlin, Director Information Technology
6. Paul Albano, Director Medicaid Audit Unit Impact of Data Analytics On the Audit Process
7. Discussion Points Audit Approach Prior to Data Analytics Current Audit Approach Example of Recent Audit Success Collaboration Between Audit, Legal and Investigative Departments
8. Audit Approach Prior to Data Analytics Selecting the Audit Selection process was blind to potential waste, fraud, or abuse Conducting the Audit Hit or miss approach; majority of files and claims not tested Audit results and recommendations Systemic problems not addressed (e.g. deficiencies in claims processing system, program regulations, etc.)
9. 2. Current Audit Approach Service Provider Selection Process: Internet search to identify potential audit areas Download all paid claims for selected areas Analyze claims data for Red Flagssuch as: duplicate payments unusual dollar volume unusual services level pricing variations Current process targets high risk providers and potential audit issues
10. Current Audit Approach (cont’d) Significant Changes Focus on validatingRed Flags Target specific member files and claims for review Ask pointed questions Seek out underlying causes Focused approach reduces field work time and increases efficiency and effectiveness
11. Current Audit Approach (cont’d) Audit Results and Recommendations: Greater financial impact Recommend program changes to reflect “best practices” Recommend system edits to identify and deny unallowable claims Refer potential instances of fraud to investigators and prosecutors
12. 3. Current Audit Successes Medicaid Claims for Drug Testing $7.8 Million: Excessive testing $4.5 Million: Unbundled services $2.3 Million: Unnecessary testing $1.3 Million: Unallowable testing $314,000: Duplicate payments $16.2 Million Total
13. 4. Audit Collaboration Auditors Identified Major Issues (Dr. Dental) Excessive fluoride treatments Unallowable tooth restorations Unallowable case management Unallowable detailed oral screenings Questionable denture implants and x-rays
14. Audit Collaboration (cont’d) Bureau of Special Investigations (BSI) Yellow Book suggests working with investigators on potential fraud cases Audit operations, BSI, Legal staff collaborated Audit suspended Dr. Dental case turned over to BSI
15. Allie Alland, Director Bureau of Special Investigations Data Analytics and Proactive Fraud Identification
16. Targeted investigation was derived from OSA’s dental audit with a focus on Dr. Dental BSI expanded time frame from Audit’s sample Audit scope: January 2008 - December 2010 BSI scope: January 2006 - December 2012 Data Analytics Driven Investigation
17. Dental procedure codes Dr. Dental and peer dental providers Bank records Individual patients Tax analysis Provider service and childbirth Geographic analysis Other toothaches Dr. Dental Data Analysis Approach
18. Observation: Within the audit sample, many of Dr. Dental’s patients received frequent D9110 (emergency palliative treatment of dental pain) and D0160 (detailed oral evaluations for chemotherapy, transplant, amputation patients) services Investigation goal: Determine whether frequency of 2 specific codes was common for all MassHealth patients or only Dr. Dental’s Data analytical steps: BSI queried claims data for all MassHealth patients who received D0160 and D9110 services from January 1, 2006, through December 31, 2012 and separated Dr. Dental’s from all other MassHealth providers for comparison Determination: Following graph analysis shows that Dr. Dental’s patients received significantly more frequent D9110 and D0160 treatments than other MassHealth members Dental Codes – D9110 & D0160
19. Code D9110- (Emergency Palliative Treatment)
20. Code D0160- (Detailed Evaluations for Chemotherapy, Transplant, Amputation Patients)
21. Using what was learned from this billing pattern, the MassHealth claims database was queried for other dental codes during the 7 year time frame Two dentists emerged as billing for a high number of D5520 (replacement of missing or broken teeth) claims per patient A single dental practitioner submitted more claims than both Boston University School of Dental Medicine and Tufts Dental Clinic While this analysis did not directly benefit the Dr. Dental investigation, it provides the basis for future investigations of additional dental providers Dental Provider Comparisons
22. Dr. Dental’s bank records show transactions between Dr. Dental and her patients Analysis showed checks to 5 of her patients, Patient C received $3,743.38 in 3 years Following the Money Checks made payable to a patient from a dental provider
23. Analysis and Progression of Patient C Patient C and daughter received emergency treatment on all 16 visits 27 visits in 3 months; 16 shared with Patient C’s daughter Dr. Dental’s payments to Patient C led to an investigation of Patient C’s bank accts Patient C and her family were receiving additional state benefits Patient C and her3 children were all patients of Dr. Dental Data analytics led BSI to proactively identify inconsistencies with Patient C’s public assistance and bank records. As a result, BSI opened a case on this subject; matter is ongoing
24. Select lines from Personal Income Tax Returns
25. Dr. Dental’s practice suffered a significant income decrease from 2010 to 2011 (approx. 35%). This corresponds with provider’s decrease in claims Dr. Dental’s husbandwas paid by her company (Husband’s Job #2) Dr. Dental owns dental practice (reports income loss on corporate tax returns) Dr. Dental claimed family members as dependents, 2 of which were her sons Take Aways from Tax Analysis
26. Childbirth and Treatment on Surrounding Days Claims data indicates treatment performed by Dr. Dental on the days surrounding childbirth
27. Treatment Provided to People Not in the United States Verified with Department of Homeland Security, a patient was outside the United States on 2 days that the patient was treated by Dr. Dental
28. BSI ran death match queries to determine if Dr. Dental treated any patients post death Queried husband’s patients vs. Dr. Dental’s patients to identify possible overlap of patient population Reviewed MassHealth pharmaceutical claims to identify Dr. Dental’s amount of DEA Class prescriptions (i.e. narcotics) Data analysis on denied claims to identify irregular billing practices BSI Investigation ongoing Other Toothaches
29. Kleber Gallardo, Consultant Big Data and Analytics OSA Data Analytics Engine
30. How we got here? Business and Technology Identify Need Pilot Identify and Access Data Sources Build Analytics Engine Infrastructure Collaborative Process Hire and Train Team Data Warehouse and Data Analytics Software Put Text Here
31. Audit Data Analytics Process Preprocess data Collect, clean and store Take action based on results OSA Data Analyst reviews output OSA Analytics Engine Machine learning, statistics, KDD, data mining, risk scoring, and others Auditor interprets results Search for patterns queries, rules, neural nets Data analysis results Stand Alone Data sources Databases, flat files of different types Revise/refine Analysis OSA Integrated Data Warehouse Results feedback
32. Audit Risk Matrix FINANCIAL OR SYSTEMIC RISK LEVEL MEDIUM HIGH HIGH CRITICAL CRITICAL Marginal Minor Moderate Major Severe Almost Certain MEDIUM MEDIUM HIGH HIGH CRITICAL Likely LOW MEDIUM MEDIUM HIGH CRITICAL EVENT LIKELIHOOD LOW MEDIUM MEDIUM MEDIUM HIGH Possible LOW LOW MEDIUM MEDIUM HIGH Unlikely Rare
33. Pharmacy Claims Example Claims Members Paid 170M 2M 5B Filter for year 2012 1 25M 0.9M $577M Get Paid Claims 2 14M 0.7M $541 3 Filter Narcotics 0.67M 0.16M $9.5M Calculate Risk Score with Analytics Engine 4 114K 6K $7.4M Filter Paid > $250/Year 5 Select Members with Risk Score greater than 25 5K 130 $1.4M 6
34. OSA Analytics Engine
35. Paul McLaughlin, Director Information Technology Data Analytics at the OSA Present and Future
36. The Broad Toolset of Analytics Data analytics allows the OSA to employ a range of new and expanded strategies Paul and Allie discussed two very different strategies of using data analytics Telescopic and microscopic
37. Telescopic Use Paul focused the tools on the universe of dentists and identified those behaving in a suspicious manner
38. Microscopic Use Allie’s example took the tool and focused it on a single individual ,and with the use of analytic tools, expanded the case to include other behaviors, acts and people
39. Pre-Data Analytics? We used traditional tools (ACL, Excel, Access)to analyze data Relied on auditees to provide us with data We developed clusters of expertise in analysis and the available analytic tools
40. Data Analytics 1.0 The OSA is currently at a level of analytics use and deployment what we call data analytics 1.0 Initial Analytics provided by a small group of very skilled individuals Blocks or sets of enhanced data are delivered to the audit team Feedback from the audit and investigative teams is used to improve algorithms Algorithms are stored in an algorithm library for reuse
41. Data Analytics 2.0 Data analytics engine is fully developed Expanded group of users is trained to use the data analytics engine Algorithms are used to create packets to be used in standard audit testing and investigative processes Addition of several data sources
42. Data Analytics 3.0 Engine is fully deployed across the OSA (visualization tools) Group of up to 30-40 power users trained and given access to the data analytics engine Additional data sources added Predictive analytics/trends and projections Optimization
43. Access + Experience = Success Skills and Experience with Data Results Access to Data
44. Summary Data analytics provides enhanced and or enriched data for auditors, investigations and other OSA users Audit and investigative results lead to better models, enhanced algorithms and more effective packages Improved models, algorithms and packages lead to more precise and more meaningful audits or investigations Velocity will increase: Timeliness X Impact = Velocity
45. Questions? Panel and Audience Interaction