Transcript Using Insurance Claims Data for Health Market Opportunity Analysis

MILI 6990: Using
Insurance Claims Data for
Health Market
Opportunity Analysis
AKA - Steve called in a favor
Adrine Chung, MBA and Stephan Dunning, MBA
Chronic Disease Research Group, Minneapolis
Medical Research Foundation
Agenda
I.
II.
III.
IV.
V.
Our Background and CDRG
Introduction to Claims Data
Utilization of Claims Data
Market Opportunities
MILI Program – Students and Affiliates
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.
Private
non-profit
research
organization
Impacting
public policy
and clinical
care
Multispecialty
with a focus
on Chronic
Diseases
Respected for
independence
and quality
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
Chronic Disease Research
Group
National Institute of
Diabetes and Digestive
and Kidney Disease
(NIDDK)
Various (sponsored,
grants, independent)
Collects, analyzes, and
distributes information
about end-stage renal
disease (ESRD) in the
United States
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
Commercial
Elderly and disabled
(Compulsory at age 65 and
ESRD)
Traditionally employer based,
insurance exchanges emerging
(ACA)
Coverage is until death
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)
Enrollment
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 nondrug-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
• 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
Limitations
• 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 contracts
o Data lag: 9 months for Part A/Part B and 15 for Part D
• Commercially-insured claims data:
o OptumInsights, MarketScan, Medco, PharMetrics
o 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 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)
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)
III. Utilization of Claims
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
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].
Commercially
insured
Medicare
US adults
Female
breast
25.6 (24.7,
26.4)
35.4 (33.8,
37.0)
89.8 (87.0,
92.6)
Prostate
4.8 (4.4,
5.1)
36.3 (34.6,
37.9)
61.1 (58.6,
63.7)
Lung
7.8 (7.3,
8.2)
15.7 (14.6,
16.8)
34.8 (33.0,
36.6)
Multiple
Myeloma
10.8 (10.3,
11.4)
22.5 (21.2,
23.8)
49.2 (47.1,
51.4)
Li et al, presented a the American Society of Clinical Oncology, 2009
Other
11.5 (10.9,
12.0)
18.6 (17.5,
19.8)
44.7 (42.7,
46.7)
All cancers
60.4 (59.1,
61.7)
128.5 (125.5,
131.6)
279.7 (274.6,
284.8)
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.
Results: Event Free Survival
(%)
Repeat Revasc.
Death/Repeat Revasc
100
100
90
90
90
80
80
80
70
70
70
60
60
60
50
CAB
40
DES
30
20
BMS
50
CAB
40
DES
30
20
BMS
Axis Title
100
Axis Title
Axis Title
All Cause Mortality
50
CAB
40
DES
30
20
10
10
10
0
0
0
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.
BMS
Zzzzzz?!
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
o Differing findings on including Oral Drugs in Bundle
IV. Market Opportunities
• Data Linkages:
o
o
o
o
o
o
o
o
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
IV. Market Opportunities
• Business Opportunities with Claims:
User/Purpose
Project Type
Marketing
Market sizing, medical service process or
flow, sales estimates
Finance
Revenue projections, baseline
opportunity
Regulatory
Safety monitoring, risk assessment
• Users:
o
o
o
o
o
Insurance/Payers
Providers
Pharma/Device/Biotech
Policy-makers
Quality
V. MILI Students and
Affiliates
•
•
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•
MILISA
MILI Specialization
MILI Affiliates/Alumni
MILI Valuation Lab
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