MILI 6990: Using Insurance Claims Data for Health

Market Opportunity Analysis

Adrine Chung, MBA and Stephan Dunning, MBAChronic Disease Research Group, Minneapolis

Medical Research Foundation

AKA - Steve called in a favor

AgendaI. Our Background and CDRGII. Introduction to Claims DataIII. Utilization of Claims Data IV. Market OpportunitiesV. MILI Program – Students and Affiliates

I. Background: CDRG Mission

Impacting public policy and clinical careRespected for independence and quality

Multispecialty with a focus on Chronic Diseases

Private non-profit research organization

The Chronic Disease Research Group pursues its commitment to public health by advancing knowledge about chronic disease to improve patient care and outcomes.

I. Background: CDRG Organizational Hierarchy

Hennepin Healthcare System, Inc.: Operating Hennepin County Medical Center in Minneapolis, MN, a nationally recognized academic medical center employing 400+ healthcare providers. The physicians also have faculty appointments at the University of Minnesota.

Minneapolis Medical Research Foundation (MMRF): Private, non-profit research subsidiary of Hennepin Healthcare System, Inc.

Chronic Disease Research Group (CDRG): Operational division within MMRF employing more than 65 staff.

I. Background: CDRG Programs

Scientific Registry of Transplant Recipients

Health Resources and Services Administration

(HRSA) Contract

Analyzes data and simulates for policy

development, creates reports of programs, and

provides data for evaluation of solid organ

transplantation in U.S.

United States Renal Data System

National Institute of Diabetes and Digestive

and Kidney Disease (NIDDK)

Collects, analyzes, and distributes information about end-stage renal disease (ESRD) in the

United States

Chronic Disease Research Group

Various (sponsored, grants, independent)

Public health research in nephrology, cardiology,

oncology, pharmacoepidemiology, and geriatric medicine

I. Background: Knowledge Factory

II. Intro to Claims Data: Overview

• Claims – billable interactions between:• covered patients and the healthcare delivery• health care or service provider and the payer

II. Intro to Claims: EMR vs. Claims

Claims EMR

Scope of Data Information from all doctors/providers caring for a patient

Only the portion of care provided by doctors/providers using the EMR

Scope of Patients Insured only Uninsured and insured

Data Elements Diagnosis, procedures as coded

Lab results, vital signs, free text, habits, problem list

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

II. Intro to Claims: Source of Claims Data

• Commercial Claims (i.e. United Health, MarketScan)• Medicare

o Limited (LDS) o Research Identifiable (RIF)o USRDS (ESRD)

• Medicaid• Linked Datasets (i.e. SEER-Medicare)

II. Intro to Claims: Commercial vs. Medicare

Feature Medicare CommercialEnrollment Elderly and disabled

(Compulsory at age 65 and ESRD)

Coverage is until death

Traditionally employer based, insurance exchanges emerging (ACA)

Coverage may change with employment (affects follow-up)

Data Elements Medical services, prescription drug, laboratory billing (no results)

Medical services, prescription drug, laboratory billing and results provided through limited contracts with laboratories

Demographic Race, gender, and region well represented. Age is >65 years (unless ESRD)

Limitations to region depending on dataset. Greater range for age (including pediatric)

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%)

II. Intro to Claims: Medicare

HEALTH INSURANCE CLAIM FORM

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

II. Intro to Claims: DRGs• Part A Hospital Claims

o 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.

o The position of codes matters for payment. That is, not all diagnosis and procedure code are created equal.

II. Intro to Claims: ICD 9 to ICD 10

ICD-9 (Procedure Codes)

ICD-10-PCS (Procedure Codes)

Number of Characters 3-4 Numeric 7 Alphanumeric

Number of Codes ~4,000 ~90,000

Example of mapping: “PTCA of two coronary arteries, with insertion of two coronary stents”

00.66 (PTCA), 00.41 (Procedure on two vessels), 00.46 (insertion of two vascular stents), 36.06 (insertion of non-drug-eluting coronary artery stents)

02713DZ (dilation of coronary artery, two sites using intraluminal device, percutaneous approach)

II. Intro to Claims: Health Data Representation

II. Intro to Claims: Strengths and Limitations

Strengths Limitations

• Clinical validity – information about covered services

• Demographic information (if available)

• Population Coverage (different strengths for different datasets)

• Cost effective in comparison to chart reviews or clinical trials

• Underdiagnosed diseases (diabetes, depression, hypertension)

• Incomprehensive disease and severity information

• Incidence vs. prevalence• Limited clinical information• Limit to reimbursed services• Limit to number of codes reported

• 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)

II. Intro to Claims: Access to Data

• Medicare & Medicaid:o Research Data Assistance Center (ResDAC)o Aggregate-level data through private research groups that

use CMS with approval (i.e. CDRG and University of Minnesota)

o Direct for federally funded contractso Data lag: 9 months for Part A/Part B and 15 for Part D

• Commercially-insured claims data:o OptumInsights, MarketScan, Medco, PharMetricso Data updated quarterly

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)

o Comparative Effectiveness• Clinical• Economic• Value

o Clinical Trial Supplement

III. Utilization of Claims DataPopulation 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)

Source: 2012 USRDS Annual Data Report, Figure 11.5 (Volume 2)

III. Utilization of Claims DataTotal Medicare dollars spent on ESRD, by type of service

Prevalence of Recognized Bone Metastases in the US Adult Population

Methods: o 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).

o 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.

o BM prevalence was extrapolated to the national commercially insured population aged 18-64 years and to the traditional Medicare population aged ≥65 years.

o 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

III. Utilization of Claims

• 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

Female breast Prostate Lung Multiple

Myeloma Other All cancers

Commercially insured

25.6 (24.7, 26.4)

4.8 (4.4, 5.1)

7.8 (7.3, 8.2)

10.8 (10.3, 11.4)

11.5 (10.9, 12.0)

60.4 (59.1, 61.7)

Medicare 35.4 (33.8, 37.0)

36.3 (34.6, 37.9)

15.7 (14.6, 16.8)

22.5 (21.2, 23.8)

18.6 (17.5, 19.8)

128.5 (125.5, 131.6)

US adults 89.8 (87.0, 92.6)

61.1 (58.6, 63.7)

34.8 (33.0, 36.6)

49.2 (47.1, 51.4)

44.7 (42.7, 46.7)

279.7 (274.6, 284.8)

Results

III. Utilization of ClaimsLong-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.

Results: Event Free Survival (%)

Month1

Month 6

Month 12

Month 240

102030405060708090

100

All Cause Mortality

CABDESBMSAxis Title

Month1

Month 6

Month 12

Month 240

102030405060708090

100

Repeat Revasc.

CABDESBMSAxis Title

Month1

Month 6

Month 12

Month 240

102030405060708090

100

Death/Repeat Revasc

CABDESBMSAxis Title

Herzog et al, presented at the American Society of Nephrology, 2009.

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.

Zzzzzz?!

III. Utilization of Claims DataBenchmarking• 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

o Differing findings on including Oral Drugs in Bundle

IV. Market Opportunities• Data Linkages:

o US Censuso Cancer Registries (SEER)o Other Providers (VA, Medicaid)o National death index/vital statisticso Surveys (MCBS, NHANES, Health and Retirement Study)o Provider Informationo EHRo Clinical Trial Data

IV. Market Opportunities• Business Opportunities with Claims:

• Users: o Insurance/Payerso Providerso Pharma/Device/Biotecho Policy-makerso Quality

User/Purpose Project Type

Marketing Market sizing, medical service process or flow, sales estimates

Finance Revenue projections, baseline opportunity

Regulatory Safety monitoring, risk assessment

V. MILI Students and Affiliates

• MILISA• MILI Specialization• MILI Affiliates/Alumni• MILI Valuation Lab

Tying It Together: MILI DC Field Trip

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