Accelerating CMS Outcomes Data to Near Real Time: Challenges & Solutions

1. Accelerating CMS Outcomes Data to Near Real Time: Challenges & Solutions Rosemarie Hakim, PhD CMS

2. Background

3. 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

5. 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

6. 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

7. 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

8. Medicare – ccw condition period prevalence , 2010

9. Cardiovascular conditions- Trends

10. 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)

11. 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

12. What Is Missing, Broken or Does Not Work Well Today

13. 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 neededbut 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

14. 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)

15. 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

16. Challenges and solutions

17. 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

18. Matching Data to Medicare Claims • Deterministic matching • Use unique personal identifiers (UPIs) present in Medicare claims and in registry/trial data • Good • Matching SSNs • Better • Matching SSNs and DOB • Best • Matching SSNs, DOB, gender, and provider

19. 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

20. Matching Data without UPIs contd. • Probabilistic (fuzzy) matching • Uses wide range of potential identifiers • Computes weights based on sensitivity & specificity of identifier • Weights used to calculate the probability that 2 records refer to the same entity

21. Matching rates

22. Short term priorities

23. 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 variables • Use data for long term follow up for IDE studies and RCTs

24. Make Good Use of CMS Data contd. • Develop expertise – use of administrative data is increasing • Educational materials on CMS and ResDAC websites • ResDAC gives courses on using CMS data • Develop statistical expertise in using administrative data -

25. Long Term Priorities

26. 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

27. Thank you rosemarie.hakim@cms.hhs.gov Chronic Conditions Data Warehouse https://www.ccwdata.org/web/guest/home ResDAC http://www.resdac.org/