California Community Choices Data Warehouse Study Presentation to Choices Advisory Committee

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California Community ChoicesData Warehouse Study
Presentation to Choices Advisory Committee

• March 11, 2009
• David Zingmond, M.D., Ph.D., University of
  California Los Angeles
• Kate Wilber, Ph.D., University of Southern
  California

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Reason for the Study

• Better assess and improve service delivery and
  potential cost efficiencies
• By identifying.
• Models
• Opportunities
• Barriers
• To integrating
• consumer demographics,
• cost and service utilization data
• Across multiple long-term service and support
  programs and funding sources

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Data are in Silos

• Information and coordination are scattered across
  different programs and sites
• Issues include
• Ownership of data
• Sharing of data (including protecting patient
  confidentiality)
• Value added ways of combining these data
• Coordinating payment (using unified reporting
  to segue to global budgeting/payment)

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Development of an Electronic Data Warehouse

• Incorporates data across programs and funding
  sources
• Considers cost saving initiatives and service
  delivery improvements
• Enables the state to
• develop consumer profiles using characteristics
  of service users
• propose critical pathways responsive to consumers
  of long-term service
• include supports at home, in the workplace or in
  a facility.

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Goals

• Define what demographic data are available that
  describe users of long-term services and supports
  in California
• across state, federal and locally funded health
  care and social service programs, including
  Medicare, Medicaid, Older Americans/Californians
  Acts, Rehabilitation Act, Lanterman Acts and
  others.
• Identify promising practices in other states that
  could inform Californias efforts to describe and
  serve consumers of long-term services and
  supports.

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Goals Continued

• Identify how California can access and use data
  to inform state policy decisions for health care,
  public health, preventive health and long-term
  services and supports.
• Identify data analysis modeling opportunities
  using an integrated data warehouse in California.
• Identify barriers that would need to be overcome
  in order to make a data warehouse available in
  California.

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Better Measurement of Quality is an Important
Goal

• Quality varies across settings
• Actively improving quality or directing patients
  to higher quality providers

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Data Warehouse Study Timeline

• Establish Contract April 2009
• Initiate Study Activities May 2009
• Submit Detailed Work Plan June 2009
• Submit Draft Report April 2010
• Submit Final Report May 2010
• Disseminate Report July 2010

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Background

• SB 910 Californias Strategic Plan for an Aging
  Population Getting Ready for the Aging Boomers
• requested the University of California develop a
  comprehensive long term master plan for
  Californias aging population, including a
  longitudinal database of older Californians.
• California Aging Reporting System (CARS)
  Readiness Survey

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SB 910 Element 7 Infrastructure!

• Data and Data Systems
• Create a Comprehensive/Integrated Database on
  Aging and Disabled Californians. From this base
  develop
• No Wrong Door Care Navigation System, including
• Implementation of a California 211 System
• Integrated, One-Stop Consumer Information System
• Secure communication system for qualified health
  care professionals to use to communicate and
  coordinate needs and services, and share plans of
  coordinated care
• Longitudinal Data Base for program and policy
  decision making
• Require the dramatic improvement of data
  collection,
• concurrent with data base design (specifics
  p.91-92 of Plan)

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Uniform Assessment (UAT) in San Mateo

• Includes all programs/services
• provided within San Mateos continuum of care
• Currently being inserted into The Q System an
  electronic case management system
• Standardizes the content quality of assessments
• Tracks outcomes across time for individuals and
  client groups
• Tracks changes in the population over time
• Monitoring changes in frailty
• Provides standardized response to needs
• Provides aggregate data for policy making and
  planning

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Build on Other States Resources and Promising
Practices

• Examples
• Michigan_Data_Warehouse_MAR_2003.PDF
• Michigan_ChoiceNet_MAR_2007.pdf
• Florida_SDCA_Data_Warehouse.pdf
• OHIO_Final_Report_DEC_2006.pdf
• VA_Corporate_Data_Warehouse_2007.ppt
• LTC_Summit_HITQualityHITSummit_JUN_2007.ppt
• Role_of_HIT_in_LTC_Reform_JUN_2008.ppt
• SHP_briefing_using_HIT_MAR_2008.pdf
• CMS_MedicaidTransformationGrants_HITLessonsLearned
  _JAN_2009.pdf
• Comparison of Assessment Tools-FINAL.xls

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Scope of Work

• Complete an inventory of available electronic or
  manual systems, databases and demographic data
• Identify lessons-learned from the Long-term Care
  Integration planning at the state and local level
  that could inform a Data Warehouse initiative.
• Identify lessons-learned that are applicable to
  California from other states
• Identify promising practices/current data
  integration initiatives in CA (e.g., CARS and San
  Mateos UAT)

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SOW Continued

• Identify technical aspects and potential state
  benefits of a data center
• Identify costs and consequences of not having a
  data warehouse.
• Identify and work with key stakeholders to
  identify and prioritize key needs and priorities
  for a data warehouse (client, provider, state
  level)

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SOW Continued

• Identify barriers to establishing and maintaining
  a data warehouse (client, provider, state level).
• Provide recommendations for establishment of a
  data warehouse in CA, including costs, activities
  and timeline.

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Products from a good data center

• Overall and broken down costs, leading to global
  budgeting
• Review of quality by condition, intervention,
  etc.
• Detailed longitudinal descriptions of patient
  care (quality, utilization, and cost) and
  outcomes
• Decreased barriers to sharing of data, including
  administrative support for data requests gt
  Coordinated sharing of data to interested parties

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Data Warehouse Study

• Questions
• Input