On Medicaid claims data analysis


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  • 3paranoid 4 acute episode 5latent 6residual 7 schizo-affective
  • 3paranoid 4 acute episode 5latent 6residual 7 schizo-affective
  • SAS Version 9.1 and SPSS 12.0 SAS to SPSS: SAS 9.1 datasets can now be read directly into SPSS 12.0. There are two methods to transfer data into SPSS from SAS. These methods only work if the SAS dataset is non-compressed. For "point and click" users, Go to File/Open/Data. Then, in the select box, scroll down to "SAS Long File Name (*.sas7bdat)", select from list and then select the file you'd like to convert, and PRESTO, it's in SPSS !! For syntax users, use the syntax (for example)--- GET SAS DATA='c:\\ecls\\algeria.sas7bdat'. Where the SAS Version 9.1 dataset is algeria.sas7bdat and resides in c:\\ecls. If all goes well, the file will appear in SPSS. SPSS to SAS: To get a SPSS dataset into SAS 9.1, the SPSS dataset must to first saved as a SPSS portable file using SPSS. When the dataset is active in SPSS go to: File/Save as/Portable file (.por). In this example, I saved the file as pcweekly.por in the c:\\ecls subdirectory. Then read the file directly into SAS 9.1 using the code: libname mylib spss 'c:\\ecls\\pcweekly.por'; data one; set mylib._first_; run; proc print data=one; run; SAS gives the SPSS dataset the name _first_ for some reason.
  • Generalizable to Medicaid beneficiaries only Sometimes a good thing: Major insurance source for people w/MR Dual eligibles – confusing Utilization data not prevalence data Non-Medicaid-funded treatment/services not included State variation Data Strengths The biggest strength of MSIS is its comprehensiveness and, consequently, its adaptability to a wide range of analyses. Even with our source data sets, which are far less detailed than the source MSIS data, it is possible to divide enrollees into many different groupings (e.g., by age, race, or gender, or a combination of these), and then to observe whether they use a particular type of medical service and the aggregate level of payments for those services. With the full range of MSIS data, even more detailed payment analyses are possible. For example, one could observe utilization and payments for specific classes of drugs, or determine spending per day in a hospital or nursing home for people in different groups.9 Data Weaknesses One weakness of MSIS is that its size and intricacy make it susceptible to delays in submission by states and availability from CMS. Historically, statistical information for Medicaid (namely Form HCFA-2082) takes longer to be compiled than expenditure data from Form CMS- 64. Delays lengthened with the switch to MSIS, although longer delays were expected simply due to the considerable logistical challenges to be overcome at both the federal and state levels in implementing such a large data system. MSIS was new to many states in FFY 1999, and even those states that participated in earlier versions of the system had to adjust to the new process. The process has sped up, however, as CMS continues to develop MSIS infrastructure and methods, and the states become more familiar with the system. A second weakness is also related to the comprehensiveness of MSIS data: with so much information to collect and synthesize, there is a greater likelihood that data will be missing or erroneous. CMS and its contractors also run tests to validate data, check for consistency of data elements over different periods, and verify reasonableness of data. However, the general quality of the data is only as reliable as the data submitted to CMS by states. Some states have been unable to provide some data during the first few years under the new system, including FFY 2002. Some payments are known to be missing or inaccurate in our source files and additional errors may be introduced when CMS compiles data into more aggregated data sets or standardized reports for each state (e.g., through an unnoticed mistake in computer programming). As a result, the tabulations shown in the accompanying tables come with an extensive list of caveats, which are posted as a separate document. One last weakness of MSIS that deserves note is that it does not include all types of Medicaid expenditures. This “weakness” is perhaps more appropriately described as a “ substantive difference” between MSIS and Form CMS-64. Total MSIS payments do not agree with the CMS-64 financial figures because they do not include payments made outside the claims processing system, such as DSH payments. Payments to DSH hospitals typically do not appear in MSIS since states directly reimburse these hospitals and there is no fee-for-service billing. Likewise, the supplemental (UPL) payments discussed above are less likely to appear in MSIS. 7 This
  • On Medicaid claims data analysis

    1. 1. Demystifying Medicaid Claims Data for Use in Health/Behavioral Health Services Program Evaluation Elspeth M. Slayter, M.S.W., Ph.D. Salem State College School of Social Work Council on Social Work Education Annual Program Meeting San Antonio, Texas November 7, 2009
    2. 2. A guide to using claims data <ul><li>Introduction to claims </li></ul><ul><li>Best uses </li></ul><ul><li>Strengths & limitations </li></ul><ul><li>Access </li></ul>Slayter, 2009
    3. 3. Introduction: What are claims? <ul><li>Billing records </li></ul><ul><li>Submitted to Medicaid </li></ul><ul><li>Administrative data </li></ul><ul><li>Primary purpose : Getting paid </li></ul>Slayter, 2009
    4. 4. The claims reporting process Slayter, 2009
    5. 5. The claims reporting process Slayter, 2009
    6. 6. 2 primary types of Medicaid claims databases <ul><li>Focus of this presentation : Medicaid Statistical Information System ( MSIS ) </li></ul><ul><ul><li>Federal data vs. state data </li></ul></ul><ul><li>Medicaid Analytic Extract ( MAX ) </li></ul><ul><ul><li>Extracted from (MSIS) </li></ul></ul>Slayter, 2009
    7. 7. 2 primary types of Medicaid claims databases <ul><li>Focus of this presentation : Medicaid Statistical Information System ( MSIS ) </li></ul><ul><ul><li>Person-level data files </li></ul></ul><ul><ul><li>FY 1999 – mandatory reporting by states </li></ul></ul><ul><ul><li>Fiscal year, quarterly </li></ul></ul><ul><ul><ul><li>Lags in state reports by quarter </li></ul></ul></ul><ul><ul><li>Eligibility, utilization, adjudicated expenditures </li></ul></ul>Slayter, 2009
    8. 8. 2 primary types of Medicaid claims databases <ul><li>Medicaid Analytic Extract ( MAX ) </li></ul><ul><ul><li>Extracted from (MSIS) </li></ul></ul><ul><ul><ul><li>Delay </li></ul></ul></ul><ul><ul><li>Person-level data files </li></ul></ul><ul><ul><li>Eligibility, utilization, expenditures </li></ul></ul><ul><ul><li>Calendar year </li></ul></ul>Slayter, 2009
    9. 9. MSIS file structure <ul><li>Eligibility/enrollment data </li></ul><ul><li>4 types of claims files </li></ul><ul><ul><li>Inpatient </li></ul></ul><ul><ul><li>Outpatient </li></ul></ul><ul><ul><li>Long-term Care </li></ul></ul><ul><ul><li>Prescription drugs </li></ul></ul>Slayter, 2009
    10. 10. MSIS eligibility variables <ul><li>Identification number </li></ul><ul><li>Eligibility by month </li></ul><ul><li>Age – Gender – Race – Ethnicity </li></ul><ul><li>Basis of eligibility </li></ul><ul><li>Health insurance </li></ul><ul><li>Plan type </li></ul><ul><li>Medicare dual eligibility </li></ul>Slayter, 2009
    11. 11. MSIS claims variables <ul><li>Diagnostic codes </li></ul><ul><li>Procedure codes </li></ul><ul><ul><li>Local codes </li></ul></ul><ul><li>Date of service </li></ul><ul><li>Place of service - classification of setting </li></ul><ul><li>Expenditures </li></ul><ul><ul><li>Including capitated payments in managed care plans </li></ul></ul><ul><li>Medications </li></ul>Slayter, 2009
    12. 12. International Classification of Diseases Codes <ul><li>ICD-9-CM </li></ul><ul><li>Classification used for diagnoses in U.S. </li></ul><ul><ul><li>Inpatient </li></ul></ul><ul><ul><li>Outpatient </li></ul></ul><ul><ul><li>Physician office </li></ul></ul><ul><li>Diagnosis </li></ul><ul><li>Procedure </li></ul>Slayter, 2009
    13. 13. ICD-9-CM Diagnostic Codes <ul><li>Diagnoses </li></ul><ul><li>E codes: External cause of injury) </li></ul><ul><li>V codes: Supplementary factors influencing health status, contact with services) </li></ul><ul><li>Free listing: </li></ul><ul><ul><li>http://www.icd9data.com/ </li></ul></ul>Slayter, 2009
    14. 14. ‘ Crosswalk ’ between ICD-9-CM and the DSM-IV Slayter, 2009 DSM-IV ICD-9-CM 311.0 - Depressive disorder, not otherwise specified 311.0 - Depressive disorder, not elsewhere classified 314.9 - Attention Deficit Hyperactivity Disorder 314.9 - Unspecified hyperkinetic syndrome 303.0 – Alcohol intoxication 303 - Alcohol dependence syndrome 303.0 – Alcohol intoxication 303.0X - Acute alcoholic intoxication -303.00 - Unspecified -303.01 - Continuous -303.02 - Episodic -303.03 – Remission 303.90 – Alcohol dependence 303.90 Other and unspecified alcohol dependence
    15. 15. ‘ Crosswalk ’ between ICD-9-CM and the DSM-IV: Schizophrenia Slayter, 2009 DSM-IV ICD-9-CM -- 295.XX - Schizophrenic disorders -- 295.0 – Unspecified -- 295.1– Sub-chronic -- 295.2 – Chronic -- 295.3 – Sub-chronic with acute exacerbations -- 295.4 – Chronic with exacerbations -- 295.5 – Remission -- 295.X0 – Simple 295.10 - Schizophrenia, disorganized type 295.X1 – Disorganized 295.70 - Schizoaffective disorder 295.X 7– Schizoaffective type
    16. 16. Validity of diagnostic codes: Intellectual disability (ID) <ul><li>ICD-0-CM Codes: 317-319 </li></ul><ul><li>Unlikely to be a false positive </li></ul><ul><li>More prone to false negatives </li></ul><ul><ul><li>See: Walkup, Sambamoorthi, and Crystal 1999 </li></ul></ul><ul><li>My experience with ID/substance abuse research </li></ul><ul><ul><li>Majority had mild or moderate MR or NOS </li></ul></ul><ul><ul><li>Severe and profound – posited clinical error </li></ul></ul>Slayter, 2009
    17. 17. ICD-9-CM Procedure Codes <ul><li>Procedures conducted – variation by state </li></ul><ul><ul><li>CPTs - Current Procedural Terminology </li></ul></ul><ul><ul><ul><li>Subset of health care common procedures coding system ( HCPCS ) codes </li></ul></ul></ul><ul><ul><li>DRGs - Diagnosis-Related Group </li></ul></ul><ul><ul><li>Local - Established when insurer prefers suppliers use a local code to identify a service (for which no HCPCS code exists) ( Controversial ) </li></ul></ul>Slayter, 2009
    18. 18. Exemplar procedure codes Slayter, 2009 Code Description 90801 Psychiatric diagnostic interview examinations 90804 Individual psychotherapy 20-30 minutes 90806 Individual psychotherapy 45-50 minutes 90808 Individual psychotherapy 75-80 minutes 90853 Group psychotherapy 90862 Pharmacological management
    19. 19. Standard file structure Slayter, 2009 Client Health status rating Current mental health diagnosis Index score Client 1 Good Intellectual disability 33 Client 2 Fair Oppositional Defiant Disorder 21 Client 3 Poor Schizophrenia 15 Client 4 Good None 2 Client 5 excellent Depression 66
    20. 20. MSIS File Structure Slayter, 2009 Date of service Primary diagnostic code Secondary diagnostic code Service location Procedure code Expenditure 1 06/01/09 311.00 303.00 21 90806 4,500.00 1 10/02/09 295.00 311.00 54 90806 211.00 1 11/14/09 237.00 317.00 21 90843 123.00 2 6/01/09 831.00 318.00 20 90811 55.00 2 6/21/09 245.00 318.10 05 90804 345.00 3 11/7/09 313.00 317.00 32 90807 211.00
    21. 21. Developing variables for data analysis Slayter, 2009
    22. 22. Best uses of claims data <ul><li>Population monitoring </li></ul><ul><li>Benchmarking </li></ul><ul><li>Expenditure studies </li></ul>Slayter, 2009
    23. 23. Best uses of claims data: Population monitoring <ul><li>Population </li></ul><ul><ul><li>State </li></ul></ul><ul><ul><li>Agency </li></ul></ul><ul><li>Rare and elusive populations </li></ul><ul><ul><li>People with intellectual disabilities and substance abuse </li></ul></ul><ul><ul><li>Firesetting </li></ul></ul><ul><ul><li>Sex offending </li></ul></ul>Slayter, 2009
    24. 24. Population monitoring: Exemplars <ul><li>Child injury </li></ul><ul><li>Prevalence </li></ul><ul><li>Incidence </li></ul><ul><li>Expenditures </li></ul><ul><li>Children with intellectual disability </li></ul><ul><li>Substance use disorders among: </li></ul><ul><li>People with Autism </li></ul><ul><li>People with dual eligibility for Medicare </li></ul><ul><li>People by racial and ethnic status </li></ul>Slayter, 2009
    25. 25. Best uses of claims data: Benchmarking <ul><li>Quality of care </li></ul><ul><li>Performance measurement </li></ul><ul><ul><li>State-specific measures </li></ul></ul><ul><ul><li>Agency-specific measures </li></ul></ul><ul><li>Healthcare Effectiveness Data and Information Set (HEDIS) measures </li></ul><ul><ul><li>Types of measures </li></ul></ul><ul><ul><ul><li>Anti-depression medication management </li></ul></ul></ul>Slayter, 2009
    26. 26. Benchmarking: Exemplars <ul><li>The Washington Circle </li></ul><ul><li>Measures in development </li></ul><ul><ul><li>Screening and brief intervention </li></ul></ul><ul><ul><li>Medication-assisted treatment </li></ul></ul><ul><ul><li>Recovery management </li></ul></ul><ul><li>Substance use disorder process measures </li></ul><ul><ul><li>Identification </li></ul></ul><ul><ul><li>Initiation </li></ul></ul><ul><ul><li>Engagement </li></ul></ul>Slayter, 2009
    27. 27. Benchmarking: Exemplar Slayter, 2009
    28. 28. Best uses of claims data: Expenditures <ul><li>Population-specific expenditure estimates for use in cost-effectiveness analysis </li></ul><ul><ul><li>Administrative costs not included </li></ul></ul><ul><ul><li>Disproportionate Share Hospital (DSH) payments not included </li></ul></ul><ul><li>Risk adjustment </li></ul><ul><li>A guide to expenditure analysis in Medicaid </li></ul>Slayter, 2009
    29. 29. Expenditure analyses: Exemplars <ul><li>Prescription drug expenditures ( click here ) </li></ul><ul><li>County-level analyses ( click here ) </li></ul><ul><li>Statewide behavioral health expenditures ( click here ) </li></ul><ul><li>Cost-effectiveness of schizophrenia treatment ( click here ) </li></ul>Slayter, 2009
    30. 30. Strengths and limitations: Purpose <ul><li>Primary source of all clinical insight but c odes are at times “ questionable accuracy, completeness, meaningfulness and clinical scope ” (Iezzoni 2002:348) </li></ul><ul><li>“… codes are not meant to tell stories, rather to generate reimbursement …” (Iezzoni 2002:348) </li></ul><ul><ul><li>But…chart-claims comparison studies </li></ul></ul>Slayter, 2009
    31. 31. Strengths and limitations: Complete clinical picture <ul><li>Availability of code types </li></ul><ul><li>Reimbursed for procedures </li></ul><ul><ul><li>May not capture all client diagnoses </li></ul></ul><ul><ul><li>Primary vs. secondary codes </li></ul></ul><ul><li>Impact of ‘churning’ </li></ul><ul><ul><li>Need for episode approach </li></ul></ul>Slayter, 2009
    32. 32. Strengths and limitations: Stigmatized conditions <ul><li>Responsiveness of providers to stigmatized conditions </li></ul><ul><ul><li>Substance use disorders </li></ul></ul><ul><ul><li>Mental illnesses </li></ul></ul><ul><ul><li>HIV-related conditions </li></ul></ul><ul><ul><li>Intellectual disability </li></ul></ul><ul><li>Example : Pregnant mother with substance abuse </li></ul>Slayter, 2009
    33. 33. Strengths and limitations: Managed care data <ul><li>Availability of detailed expenditure data </li></ul><ul><li>Cascading payment systems in carve-outs </li></ul><ul><li>Shadow claims on encounters </li></ul><ul><li>See: Garnick, Hodgkin, and Horgan 2002 </li></ul>Slayter, 2009
    34. 34. Reframing limitations: Slayter, 2009 Strengths: Limitations: Access to data about low-income populations by key policy categories Generalizable only to people with Medicaid coverage, non-churners Ability to generalize to within-state Medicaid populations In states using local codes, can behard to compare to national studies Ability to generalize to statewide Medicaid populations Can be hard to compare to other states, national studies Data are screened for “a practical level of quality and consistency” by CMS Clinical validity is still questionable at times Good client or population-specific cost estimates Data do not include DSH ot supplemental payments or administrative costs (but could link data sources) Can be linked with other data sources Can be complicated, time-consuming Baseline data from a range of service locations from which to guide smaller, clinical studies Does not provide context of service environment, treatment
    35. 35. Take-home messages: <ul><li>Avoid ‘gold standard’ bias : Don’t discount the data outright - start from the areas of strength </li></ul><ul><ul><li>Work around the limitations to find answerable, important questions </li></ul></ul><ul><li>Speak to the experts : Work with people who understand the culture of coding </li></ul>Slayter, 2009
    36. 36. Access to Medicaid claims data <ul><li>Your agency </li></ul><ul><li>State Medicaid authority </li></ul><ul><li>Centers for Medicare and Medicaid Services (CMS) </li></ul><ul><ul><li>Federal Medicaid authority </li></ul></ul><ul><ul><li>ResDac </li></ul></ul><ul><li>Private research groups already holding the data (need data re-use approval from CMS) </li></ul>Slayter, 2009
    37. 37. Access: Need for a good SAS programmer, or… <ul><li>Experience with Medicaid claims is a must </li></ul><ul><li>Program that converts SAS to SPSS </li></ul><ul><li>Partner with your agency’s MIS person </li></ul><ul><li>Partner with someone in your state Medicaid authority? </li></ul>Slayter, 2009
    38. 38. Your ideas: How would you use claims data to conduct behavioral health-related research? Slayter, 2009