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Data Quality

Data Quality. Tips and Tricks Wendy Funk Kennell and Associates wfunk@kennellinc.com. Objectives. List several important MHS initiatives that rely upon good MTF data Identify the major MTF-level data products Identify common MHS Data Problems

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Data Quality

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  1. Data Quality Tips and Tricks Wendy Funk Kennell and Associates wfunk@kennellinc.com

  2. Objectives • List several important MHS initiatives that rely upon good MTF data • Identify the major MTF-level data products • Identify common MHS Data Problems 4. Utilize M2 Standard Reports to analyze DQ issues

  3. Transformation of the MHS into a data-driven enterprise! Then: Rudimentary funding Closed organization Production-focused Now: Productivity Population Health PPS & Business Plans Balanced Scorecard MCS Contracts / TFL

  4. Data-Based Clinical Initiatives

  5. Data-Based Clinical Initiatives • Disease Management Initiatives • Asthma and Congestive Heart Failure • Identification of high-risk patients using SIDR, SADR and Claims data • Pop-Health Portal • Preparation of action lists for providers or primary care managers • Uses SIDR, SADR, Lab, Rad, PDTS and Claims • HEDIS measurement, other clinical work

  6. Data-Based Clinical Initiatives • Pharmacy Utilization Review • Pharmacy Data Transaction Service (PDTS) does real-time UR for MHS eligibles • Online since 2001 • (MTF Rx, Retail, TMOP, Paper Claims) • Significant achievement for the MHS! Good coding & person identification

  7. Data-Based Funding Initiatives

  8. Data-Based Funding • Prospective Payment System • O&M budgets; service level • Built-up from Business Plans; with adjustments later…… (HA later in course!) • Based on “workload” from SIDR and SADR • Uses private sector pricing - does not rely on MTF costs Coding on SIDR & SADR are important!

  9. Data-Based Funding • Prospective Payment System • Inpatient Earning are based on days for mental health, and “RWPs” for all other care • Ambulatory earnings are based on RVUs and provider specialty code • Pay attention to procedure and diagnosis codes, provider specialty • PPS Policy continues to evolve.

  10. Inpatient PPS Earnings Example: MTFs code the SIDR & SADR HA Applies PPS Rates MDR adds RVUs and RWPs

  11. Data-Based Funding • GWOT Funding • Additional funding on top of DHP to cover new benefits for guard/reserve • NDAA 2004 extended period of coverage for GWOT-activated members & families • Early eligibility, screening period and extended transitional assistance • Significant increase in eligible population

  12. November 2004 + New Way Extra TAMP 2-4 months Early Elg 60 days Screen Mobilization Period Routine TAMP Old Way Mobilization Period Routine TAMP Lengthened period of eligibility!!!

  13. Growth in Guard and Reserve Population

  14. Watch it Grow! • Includes all eligibles sponsored by guard/reserve; including sponsors

  15. Data-Based Funding • GWOT Guard/Reserve • The DHP earns money from the GWOT fund based on SIDR, SADR, PDTS, and claims data • Direct care costs are measured using “Patient Level Cost Allocation” (PLCA) costing methodology • GWOT Guard/Reserve data in M2

  16. Data-Based Funding • TRICARE Reserve Select • Allows GWOT activated guard/reserve to purchase eligiblity after completion of TAMP. • Same access priority as ADFM, but no Prime. • Must agree to continued service and must pay premiums • Funding using same basic process as NDAA 2004 benefits

  17. Data-Based Funding • TRICARE for Life • Separate fund provides for $$$ to care for Non-AD / ADFM Medicare eligibles • Medicare Eligible Retiree Health Care Fund (MERHCF) • MTF $$$ (earnings) based on SIDR, SADR, PDTS. • Medicare Eligibility from DEERS (from CMS) • More from Mr. Moss later in course

  18. SIDR and SADR Full Costs for Medicare Eligibles

  19. Other Funding • Third Party Collections • CMAC for outpatient and ancillaries • DRG based billing for inpatient • Billing based on CHCS or AHLTA coding • Venture Capital & MISSY • Extra funding available through TMA for CHAMPUS Recapture • Models require MTF SIDR, SADR and EASIV.

  20. External Business & Data

  21. Data-Based Contracts • T-Next TRICARE Managed Care Support Contracts • 3 U.S. “At-Risk” contracts • Enrollment Processing and PCM Assignment • Claims Payment • Managed Care & Much More! • Ongoing provision by TMA of SIDR, SADR,PDTS, Claims and DEERS data

  22. Data-Based Contracts • TRICARE Global Remote, TRICARE Overseas Prime, TRICARE for Life • Claims • TRICARE Retail Pharmacy • Claims & PDTS • TRICARE Mail Order Pharmacy • Claims & PDTS

  23. Data-Based Contracts • TRICARE Dual Eligible Fiscal Intermediary Contract (MERHCF) • Claims • Designated Provider • Managed Care (Health Care) • Enrollment • Capitated with Risk Adjustment

  24. Focus on Data Quality

  25. Data Quality and the MHS • TRICARE Senior Prime • Very poor audits • DoD Financial Statement Problems • Poor data quality cited • Data problems cited repeatedly with MCS Contract ‘disputes’ Significant focus on DQ at TMA and Services.

  26. Data Quality and the MHS • Data Quality Management Control • Data Quality Managers • Data Quality Course • Data Quality IPT (Functional) • Commander’s statements and review lists • Data Quality Standard Reports for M2

  27. Data Quality and the MHS • Redesign of IM/IT Process • Functional responsibility for business rules • Requirements vetted through IM (Services, HA/TMA, DEERS, Others) • Requirements documented • Significant re-engineering of data feeds • Reduce burden on the source systems • Process it once, ship it out where needed!

  28. Data CEIS IDB Mart (edit checks M include parser & logical edits) T F AIR Force C (uses AFVAL edits) H RCMAS Legacy RCMAS C V1 SAS Army V2 DMIS (uses PASBA S Processor edits) Navy DMIS-SS (none, may use PASBA edits) Inpatient Data Record Flow 10/98

  29. Until 6/30/99 Data CEIS IDB Mart (edit checks IDBR M include parser & (min edits) logical edits) Feed Node RPU RLP RLP T SAS (min (min F edits) edits) EDW EDW Stars AIR Force (data transformed ETL C (uses to FAM-D standard) AFVAL edits) H RCMAS Legacy RCMAS C V1 SAS Army V2 DMIS (uses PASBA S Processor edits) Navy ARS DMIS-SS PASBA (none, may use PASBA edits) Inpatient Data Record Flow4/99

  30. MDR Inpatient Data Record FlowToday Data Mart M T F C H C Data Mart S Simplicity……….

  31. Basic Information Systems

  32. Types of Systems • MHS is a complex business • Deliver healthcare • Process Claims • Managed Care • Complex data needs; multiple ways to view the business • More than 9 million eligibles; terrabytes of data

  33. Types of Systems

  34. Types of Systems Real-Time Periodic Updates Transactional Warehouse Data Mart

  35. Types of Systems • Quality • Real time systems are harder to fix • Must often stop the real-time mission to correct known errors • Usually too big a price to pay for a business Cleaning is usually designed into warehouse functions

  36. Types of Systems • Using the data • Transactional systems are not generally designed for analysis purposes • Data Warehouses are generally used by skilledprogrammers with significant data expertise • Data Marts are designed for analytical purposes generally, intended to be easy to use

  37. Types of Systems • MHS operates a complex set of systems to meet different business requirements • New systems are generally built with routine systems models (transactional, warehouse, mart) • Older systems aren’t that way!

  38. Types of Systems • MHS Data Repository • MHS Business Data Warehouse • Receives data from transactional systems and other data marts • Processes, cleans, archives • Limited access • MDR provides data to most other corporate business systems • Services and External Entities as well

  39. Types of Systems • The “M2”: • Data Mart • Contains a subset of MDR data • Contains many data files from MTFs • Significant functional involvement in development and maintenance • 1100+ users at all levels in the MHS • Ad-hoc querying or “Corporate Reports”

  40. Types of Systems • The “M2”: • M2 contains a family of corporate reports designed for data quality enhancements • Reports are written to resemble DQ metrics wherever possible • Additional reports about important data problems are also included • Report documentation is provided in your handouts

  41. Types of Systems • The “M2”: • Most DQ reports contain data for all MTFs • Some have prompted filters (you tell M2 your DMIS ID and hit run) • Reports will be updated as data files are updated • Can also be modified and/or updated by the M2 user • Examples use the reports! • Help Desk info provided in previous presentation

  42. Remainder of Presentation • Description of systems • Output data files • DQ Issues or Considerations • Use of M2 Corporate Reports to aid in DQ Management at the MTF

  43. The MTF Data Environment

  44. MTF Data Environment • Many systems at each MTF • Service specific systems • TMA Systems • Service Systems provide data to some TMA Systems • Personnel • Financial

  45. MTF Data World! • Composite Health Care System (CHCS) • Primary operational system supporting MTFs • Hospital Management / Administration • Clinical Coding • Communicates with DEERS, other MTF-level systems • 100+ separate systems with no common database • Extremely important to MTF operations…

  46. CHCS Data captured as a part of doing business Appointing Registration Admitting Billing (Inpat) Ordering Ancillaries Utilization Review Workload Capture Etc…… Real time data store about health care delivery, revenues, providers, patients, clinics and wards, etc…… LOCAL DATA ONLY!

  47. MTF Data World! • Composite Health Care System (CHCS) • Legacy Status • Much of the functionality of CHCS is being built in other systems • Enrollment Processing, Primary Care Manager Assignments now done with DEERS Online Enrollment System (DOES) • Deployment of AHLTA is underway to replace the ambulatory data module and enhance clinical data • Referral, Appointing underway

  48. CHCS is the local “Hub” Financial CHCS Pharmacy DEERS AHLTA Billing

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