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Presentation on theme: "ACCELERATING CMS OUTCOMES DATA TO NEAR REAL TIME: CHALLENGES & SOLUTIONS Rosemarie Hakim, PhD CMS."— Presentation transcript:


2 Background 2

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 3

4 What Works Well Today 4

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 5

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 6

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 7

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10 Other data available 10  Master Beneficiary Annual Summary File  Durable Medical Equipment  Medicare-Medicaid Linked Enrollee Analytic Data Source  MedPAR (Hospital and SNF)  Outpatient  Others (see

11 Strengths of CMS Administrative Data 11  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 12

13 Reliance on billing codes 13  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

14 Reliance on billing codes 14  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 15  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 16

17 Research Data Time Lag 17  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 18  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 19  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. 20  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 21 AuthorsData sourceType of matching Results St. Peter et al. 2011Dialysis Clinical Outcomes Revisited (DCOR) Trial/Medicare Unique identifiersNearly 100% Brennan et al. 2012PCI Registry/MedicareDeterministic86% Hammill et al. 2009Heart failure registry/Medicare Deterministic81% Hammill et al. 2009Hospital HF records /Medicare Deterministic91% Setoguchi et al. 2012ICD Registry/MedicareDeterministic61% Setoguchi et al. 2012ICD Registry/MedicareProbabilistic85% CDC/NCHS2003-2004 NHANES /Medicare Probabilistic98%

22 Short term priorities 22

23 Make Good Use of CMS Data 23  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. 24  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 25

26 Health Data Initiatives 26  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 27 Chronic Conditions Data Warehouse ResDAC

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