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MILI 6990 : Using Insurance Claims Data for Health Market Opportunity Analysis

MILI 6990 : Using Insurance Claims Data for Health Market Opportunity Analysis. Adrine Chung, MBA and Stephan Dunning, MBA Chronic Disease Research Group, Minneapolis Medical Research Foundation. AKA - Steve called in a favor. Agenda. Our Background and CDRG Introduction to Claims Data

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MILI 6990 : Using Insurance Claims Data for Health Market Opportunity Analysis

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  1. MILI 6990: Using Insurance Claims Data for Health Market Opportunity Analysis Adrine Chung, MBA and Stephan Dunning, MBA Chronic Disease Research Group, Minneapolis Medical Research Foundation AKA - Steve called in a favor

  2. Agenda • Our Background and CDRG • Introduction to Claims Data • Utilization of Claims Data • Market Opportunities • MILI Program – Students and Affiliates

  3. I. Background: CDRG Mission The Chronic Disease Research Group pursues its commitment to public health by advancing knowledge about chronic disease to improve patient care and outcomes.

  4. I. Background: CDRG Organizational Hierarchy

  5. I. Background: CDRG Programs

  6. I. Background: Knowledge Factory

  7. II. Intro to Claims Data: Overview • Claims – billable interactions between: • covered patients and the healthcare delivery • health care or service provider and the payer

  8. II. Intro to Claims: EMR vs. Claims • Other Limitations of EMRs – • Lack of standardization – “If you’ve seen one EMR, you’ve seen one…” • Inconsistent data entry • Single site of patient care

  9. II. Intro to Claims: Source of Claims Data • Commercial Claims (i.e. United Health, MarketScan) • Medicare • Limited (LDS) • Research Identifiable (RIF) • USRDS (ESRD) • Medicaid • Linked Datasets (i.e. SEER-Medicare)

  10. II. Intro to Claims: Commercial vs. Medicare

  11. II. Intro to Claims Data: Medicare • Part A – hospital care, skilled nursing facility care, nursing home care, hospice, and home health services • Part B – physician visits, ambulance services, durable medical equipment, mental health, preventative services • Part D – prescription drug coverage (70%)

  12. II. Intro to Claims: Medicare

  13. HEALTH INSURANCE CLAIM FORM

  14. II. Intro to Claims Data: Coding • ICD 9 – International Classification of Diseases, Version 9 (diagnoses) • XXX.XX – AMI 410.X, PTCA 00.66 • X matters • CPT 4 – Current Procedural Terminology, Version 4 (procedures) • 5 digits, 0 matters • i.e. PTCA 92982 • NDC- Food and Drug Administration’s Nation Drug Code directory (Drugs) • 10 digit number with 3 segments

  15. II. Intro to Claims: DRGs • Part A Hospital Claims • ICD-9 and CPT codes associated with the hospitalization episode are processed through “grouping” algorithms to result in a single Diagnosis Related Group (DRG) for payment from CMS. • The position of codes matters for payment. That is, not all diagnosis and procedure code are created equal.

  16. II. Intro to Claims: ICD 9 to ICD 10

  17. II. Intro to Claims: Health Data Representation

  18. II. Intro to Claims: Strengths and Limitations • Primary source of all clinical insight but codes are at times“ questionable accuracy, completeness, meaningfulness and clinical scope” • “…codes are not meant to tell stories, rather to generate reimbursement…” • (Iezzoni 2002:348)

  19. II. Intro to Claims: Access to Data • Medicare & Medicaid: • Research Data Assistance Center (ResDAC) • Aggregate-level data through private research groups that use CMS with approval (i.e. CDRG and University of Minnesota) • Direct for federally funded contracts • Data lag: 9 months for Part A/Part B and 15 for Part D • Commercially-insured claims data: • OptumInsights, MarketScan, Medco, PharMetrics • Data updated quarterly

  20. III. Utilization of Claims Data • Market Research • Quality Improvement- QIP • Fraud Detection • Drug Safety Signal Detection (FDA Sentinel) • Post-market Safety and Surveillance • Health Economics and Outcome Research (CDRG’s Core) • Comparative Effectiveness • Clinical • Economic • Value • Clinical Trial Supplement

  21. III. Utilization of Claims Data Population Monitoring • Political, administrative, demographic populations (state based, dual eligible, VA) • Disease monitoring (incidence, prevalence, and medical expenditures) Adjusted incident rates of ESRD per million population, 2010, by HSA Source: 2012 USRDS Annual Data Report: Figure 1.3 (Volume 2)

  22. III. Utilization of Claims Data Total Medicare dollars spent on ESRD, by type of service Source: 2012 USRDS Annual Data Report, Figure 11.5 (Volume 2)

  23. III. Utilization of Claims Prevalence of Recognized Bone Metastases in the US Adult Population Methods:  • All available claims from 2004-2008 were studied in 2 point-prevalent cohorts with insurance coverage on Dec 31, 2008: • 1) persons aged 18-64 years enrolled in commercial plans (MarketScan) and • 2) persons aged ≥65 years enrolled in traditional Medicare (Medicare 5% sample).  • Presence of BM was defined by 1 inpatient or 2 outpatient claims in any 1-year interval with a diagnosis of BM or 1 claim for zoledronic acid or pamidronate with a qualifying diagnosis for cancer. • BM prevalence was extrapolated to the national commercially insured population aged 18-64 years and to the traditional Medicare population aged ≥65 years. • Applying age/sex-specific rates to the 2008 US census population, we estimated BM prevalence in the US adult population overall and for select cancers. Li et al, presented a the American Society of Clinical Oncology, 2009

  24. Results • In the commercially insured and Medicare cohorts, we identified 9,502 (in 18.2 million) and 6,427 (in 1.3 million) BM cases, respectively. • We estimated there were 279,679 US adults with recognized BM on Dec 31, 2008. Estimates by cancer type are shown in the table [N (95% CI), in thousands].  Li et al, presented a the American Society of Clinical Oncology, 2009

  25. III. Utilization of Claims Long-Term Survival and Repeat Revascularization in US Dialysis Patients after Surgical versus Percutaneous Coronary Intervention (ASN Renal Week 2009) Methods • Searched United States Renal Data System claims database to identify 4,351 dialysis pts having coronary artery bypass surgery,(CAB), bare metal stents (BMS), or drug-eluting stents (DES) in 2005. • Outcomes of Long-term event-free survival for all-cause mortality, repeat revascularization (CAB or PCI), and the combined event of death or repeat revascularization was estimated by Kaplan-Meier method.

  26. Results: Event Free Survival (%) Conclusion: Data suggest that DES provide the best first year survival in dialysis pts, but CAB patients have better un-adjusted long-term survival and lower risk of repeat coronary revascularization. Herzog et al, presented at the American Society of Nephrology, 2009.

  27. Zzzzzz?!

  28. III. Utilization of Claims Data Benchmarking • Quality of care: ESRD Quality Incentive Program (QIP), Hospital Readmission Penalty • Performance measurement: State-specific, Agency-specific, Facility-specific measures (Transplant Program-specific Reports, Dialysis Facility Compare, etc) • Accountable Care Organization – performance monitoring and payment/penalty system Evaluating Policy • CBO, GAO – Cost assessment of ESRD Bundle • Differing findings on including Oral Drugs in Bundle

  29. IV. Market Opportunities • Data Linkages: • US Census • Cancer Registries (SEER) • Other Providers (VA, Medicaid) • National death index/vital statistics • Surveys (MCBS, NHANES, Health and Retirement Study) • Provider Information • EHR • Clinical Trial Data

  30. IV. Market Opportunities • Business Opportunities with Claims: • Users: • Insurance/Payers • Providers • Pharma/Device/Biotech • Policy-makers • Quality

  31. V. MILI Students and Affiliates • MILISA • MILI Specialization • MILI Affiliates/Alumni • MILI Valuation Lab

  32. Tying It Together: MILI DC Field Trip March 17 -19 Sign up today! http://bit.ly/YDS8jK

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