ACCELERATING CMS OUTCOMES DATA TO NEAR REAL TIME: CHALLENGES & SOLUTIONS

Rosemarie Hakim, PhD

CMS

2 Background

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Medicare data have been available for research for decades

Privacy Act of 1974 allows use of identifiable data for research by a recipient who has provided CMS “with advance adequate written assurance that the record will be used solely as a statistical research or reporting record, and the record is to be transferred in a form that is not individually identifiable”

The Computer Matching and Privacy Protection Act of 1988 allows matching of federal records with non-federal records to produce aggregate statistical data without any personal identifiers

4 What Works Well Today

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Available data

Chronic Condition Warehouse (CCW) A research database that contains

100% Medicare files and.. Medicaid files Assessment files Part D Prescription Drug Event data

for Fee-for-service institutional and non-institutional claims

Linked by a unique, unidentifiable beneficiary key allow analysis across the continuum of care

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CCW contd.

Plan characteristics Pharmacy characteristics Prescriber characteristics Formulary file - beginning with year

2010 CCW data files may be requested for

any of the predefined chronic condition cohorts, or users may request a customized cohort(s) specific to research focus areas.

Chronic Conditions Dashboard

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CCW conditions Acquired Hypothyroidism Acute Myocardial Infarction Alzheimer's Disease Alzheimer's Disease, Related

Disorders, or Senile Dementia

Anemia Asthma Atrial Fibrillation Benign Prostatic Hyperplasia Cancer, Colorectal Cancer, Endometrial Cancer, Breast Cancer, Lung Cancer, Prostate Cataract

Chronic Kidney Disease Chronic Obstructive

Pulmonary Disease Depression Diabetes Glaucoma Heart Failure Hip / Pelvic Fracture Hyperlipidemia Hypertension Ischemic Heart Disease Osteoporosis Rheumatoid Arthritis /

Osteoarthritis Stroke / Transient Ischemic

Attack

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Other data available

Master Beneficiary Annual Summary File Durable Medical Equipment Medicare-Medicaid Linked Enrollee

Analytic Data Source MedPAR (Hospital and SNF) Outpatient Others (see ResDAC.org)

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Strengths of CMS Administrative Data

Clinical validity - accurate and reliable: Admission and discharge dates, diagnoses, procedures, source of care,

demographics, place of residence, date of death, Link to Other CMS Datasets Population Coverage

>98% percent of adults age 65 and over are enrolled in Medicare. > 99% percent of deaths in the US among persons age 65 and older

are accounted > 45 million beneficiaries enrolled in the Medicare program, allowing

for detailed sub-group analysis with high statistical power. Linkage to External Data Sources:

US Census Registries Other providers (e.g. VA, Medicaid) National death index/State vital statistics Surveys (e.g. Health and Retirement Study) Provider Information

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What Is Missing, Broken or Does Not Work Well Today

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Reliance on billing codes

Conditions must be diagnosed to appear in the utilization files Some diseases (hypertension, depression and

diabetes) are underdiagnosed No information on care needed but not provided

Services that providers know will be denied may be not be submitted as bills

Diagnosis information may not be comprehensive enough for detailed analysis

Prevalence may be misinterpreted as incidence: knowing a person has a chronic disease does not reveal how long they have had the condition or the severity of their condition

The Part D prescription drug event file contains no diagnosis codes

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Reliance on billing codes

Different care settings use different coding systems for procedures Inpatient care is coded using ICD-9 procedure codes Physician/supplier and DME data use CPT and

HCPCS codes Hospital outpatient care is a mix of CPT and

revenue center code No physiological measurements or test results Not all beneficiaries have Part D coverage Little information of unknown quality available

about managed care enrollees No information on services for which claims are

not submitted (e.g. immunizations provided at Walgreens)

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Other limitations

Specific programing expertise needed to analyze claims

In most cases, complex statistical techniques needed to correct biases Propensity scores Missing data algorithms Data validation techniques Severity adjusters Sensitivity analyses Complex regressions

16Challenges and solutions

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Research Data Time Lag

CCW data on 2-year lag for general research community

However – closer to real time data are available In 6 months 96.7% of inpatient and 96.9% of

outpatient claims are complete

How to get closer to real time data Affordable Care Act allows qualified entities to

acquire data for the evaluation of the performance of providers of services and suppliers

Data use agreement under a contract with CMS

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Matching Data to Medicare Claims

Deterministic matchingUse unique personal identifiers (UPIs)

present in Medicare claims and in registry/trial data

GoodMatching SSNs

Better Matching SSNs and DOB

Best Matching SSNs, DOB, gender, and

provider

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Matching Data without UPIs

No unique identifiers in data to be matched to claims

Good results can be obtained using non-unique variables: DOB or age Dates (admission, procedure date) Gender Hospital Geographic region Provider Diagnosis

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Matching Data without UPIs contd.

Probabilistic (fuzzy) matchingUses wide range of potential identifiersComputes weights based on sensitivity

& specificity of identifier Weights used to calculate the

probability that 2 records refer to the same entity

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Matching rates

Authors Data source Type of matching

Results

St. Peter et al. 2011

Dialysis Clinical Outcomes Revisited (DCOR) Trial/Medicare

Unique identifiers

Nearly 100%

Brennan et al. 2012

PCI Registry/Medicare

Deterministic 86%

Hammill et al. 2009

Heart failure registry/Medicare

Deterministic 81%

Hammill et al. 2009

Hospital HF records /Medicare

Deterministic 91%

Setoguchi et al. 2012

ICD Registry/Medicare

Deterministic 61%

Setoguchi et al. 2012

ICD Registry/Medicare

Probabilistic 85%

CDC/NCHS 2003-2004 NHANES /Medicare

Probabilistic 98%

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Short term priorities

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Make Good Use of CMS Data Build linking capability into study or

registry Include capability to link to Medicare

claims data in informed consent Plan data collection to include important

linking variablesUse data for long term follow up for IDE

studies and RCTs

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Make Good Use of CMS Data contd.

Develop expertise – use of administrative data is increasing Educational materials on CMS and

ResDAC websitesResDAC gives courses on using CMS

dataDevelop statistical expertise in using

administrative data -

25 Long Term Priorities

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Health Data Initiatives

Office of Information Products and Data Analytics (OIPDA) Develops, manages, uses, and disseminates

data and information resources Goal of improving access to and use of CMS

data Manages the CMS Data Navigator - web-

based search tool CMS’ EHR incentive program – encourages

data interoperability and development of Health Information Exchanges

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Thank you

rosemarie.hakim@cms.hhs.gov

Chronic Conditions Data Warehouse https://

www.ccwdata.org/web/guest/home

ResDAC http://www.resdac.org/

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