data in pace and how it relates to the world
TRANSCRIPT
The Times They Are a Changin’ …
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The way data are collected and stored is
being dismantled
Relational databases are
passé
Ivory towers are being
dismantled
Authority from where to
draw conclusions is
shifting
The sweep is broad and far
reaching
Disease outbreak signals from social media
Reinhart, Rogoff study and
economic growth
“Changin” times impact Data in PACE
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• CMS mandates PACE organization data collection and submission. It is prescriptive
• On Lok offers DataPACE, a data management and benchmarking service
1989 DataPACE
1st Generation
• New data collection developed by PACE Data Management Steering Committee to supplement DataPACE which by now is optional following the Balanced Budget Act of 1997
• State regulators for states where data are mandated have input into data domains
2006 DataPACE2
2nd Generation
• Data elements determined independently by NPA and representatives from PACE organizations with no regulatory input
• Data elements selected based on their ability to serve PACE operations and objectives
2016 Common Data
Set (CDS) 3rd Generation
Common Data Set – A Brief Primer
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Goals and Objectives
Data Sets Created
Process to Develop the CDS
Generate standard quality indicators Support PACE organizations internal quality
improvement programs Allow for efficient, meaningful, timely and
accurate benchmarking Accurately describe and compare PACE
enrollees to other populations to demonstrate PACE value
Support the improvement and universal adoption of PACE specific Electronic Health Record systems
Common Data Set – A Brief Primer
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Goals and Objectives
Data Sets Created
Process to Develop the CDS
Analyzed current data standards and practices in PACE, as well as existing geriatric assessment tools
Identified issues in existing data flow and designed the optimal future data flow process
Constituted a Data Steering Committee with representation from all types of PACE organizations and functional areas
Selected data elements of relevance to PACE Created file layouts in partnership with IT
professionals from PACE organizations Currently, soliciting and incorporating inputs
from PACE organizations to arrive at standardized definitions of services
Common Data Set – A Brief Primer
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Goals and Objectives
Data Sets Created
Process to Develop the CDS
CDS I: about 100 elements of assessment and demographic data
CDS II: about 60 elements of service and utilization data
Common Data Set is developed in collaboration with PACE organizations, yet most major aspects are aligned with different data initiatives
occurring simultaneously in the healthcare arena
Convergence with Other Data Initiatives in Healthcare
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Impact Act
Tool for Functional Assessment
Standardized Items
Quality Measures proposed by
Econometrica
Provides validation that best practices were used
The Improving Medicare Post-Acute Care Transformation Act of 2014 is a law intended to change and improve Medicare’s post-acute services and reporting
PAC providers and assessment tools used Long term care hospitals - LCDS Home health agencies - OASIS Skilled nursing facilities - MDS Inpatient rehab facilities - IRF-PAI
What is the Impact Act
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Requires post-acute care providers (PAC)* to report standardized patient assessment data, data on quality measures and data on resource use and other
measures
Requires the data to be inter-operable to allow for exchange among PAC and other providers to give them access to longitudinal information to facilitate
coordinated care and improve Medicare beneficiary outcomes
Modify PAC assessment instruments applicable to PAC providers for the submission of standardized patient assessment data on such providers and
enable data comparison across all providers
Convergence with Impact Act and why the Act is Relevant for PACE
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POs use the same assessment tools as the PAC
providers or draw from them
CDS has been built using data
items from these same assessment
tools
As Impact Act aligns the different
assessment tools, CDS will be
calibrated towards this alignment
The lack of comparable information across PAC settings undermines the ability to evaluate and differentiate appropriate care settings for and by individuals and their caregivers
Common Features between the Impact Act and CDS Inability to Compare Across Settings is Major Impetus for the Impact Act and CDS
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Co
mm
on
Data Set
Imp
act
Act
The lack of comparable data across POs undermines the ability to develop comparative consistency in benchmarking and distinguish PACE value from other options for duals. In addition, there is inability to:
Create a profile of the PACE participant
Provide direction for PACE specific EHRs
Require submission of standardized assessment data by PACs
CMS, however will not replace standardized instruments with one common assessment tool
Common Features between the Impact Act and CDS Collection of Standardized Assessment Data – Similar Philosophy
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Co
mm
on
Data Set
Imp
act
Act
Requires submission of standardized assessment data by POs
CDS created keeping in mind POs need to use assessment tools best suited for their program
Functional Status
Cognitive Function
Special Services
Medical Conditions
and Impediment
Data in Similar Categories will be Collected
Common Features between the Impact Act and CDS Data is Participant-centric and Supports Quality Strategies that are Similar
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Co
mm
on
Data Set
Imp
act
Act
The principal is that data follows the person from setting to setting
Makes possible access to longitudinal information for all providers to facilitate coordinated care and improved outcomes
Data is participant centric: each data element is tied to a participant
Makes possible access to longitudinal information to allow for development of modalities of care for optimal care and intervention
Cognitive and
functional status and
changes
Skin integrity
and changes
Medication recon-
ciliation
Incidence of major
falls
Quality Domains
Care preference
of an individual
Common Features between the Impact Act and CDS Organization of Data in Domains
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Quality Measure Domains
Assessment Categories
Other DEL Domains
CDS I Participant
Characteristics
CDS II Participant Experience
The data element library will be a centralized repository of assessment data mapped to HIT vocabularies, domains etc.
In the CDS, variations will be mapped to derive a standardized native data set
Imp
act
Act
C
om
mo
n D
ata Set
Common Data Set is developed internally and independently at PACE, yet most major aspects are aligned with different data initiatives
occurring simultaneously in the healthcare arena
Convergence with Other Data Initiatives in Healthcare
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Impact Act
Tool for Functional Assessment
Standardized Items
Quality Measures proposed by
Econometrica
Provides validation that best practices were used
Based on the post-acute care settings in the Continuity Assessment Record and Evaluation (CARE) tool, it is a demonstration funded by CMS
What is the Tool for Functional Assessment Standardized Items (FASI)
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Requires testing the reliability of a setting-agnostic, interoperable set of data elements from the CARE tool called “items”, that can support standardized
assessment of individuals across the continuum of care in community based long term services and supports programs
Intended for use amongst various populations: Elders (65 years and older); younger adults (18-64) with physical disabilities and adults of any age with
intellectual or developmental disabilities
Assessment data is intended for use for multiple purposes like use of standardized items to determine individual eligibility for state programs or to help
determine levels of care
Interoperability: Data elements support standardized assessment of individuals across continuum of care in community based long term services and supports programs
Multiple purposes for data
Individual eligibility for programs
Determine levels of care
Common Features between the FASI and CDS
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Co
mm
on
Data Set
F A
S I
Data is participant-centric for participant condition and for all services by all providers in all locations
Multiple purposes for data
Quality measures
Analytics to determine optimal interventions
Requires testing the reliability of a setting-agnostic, interoperable set of data elements from the CARE tool called “items”, that can support standardized
assessment of individuals across the continuum of care in community based long term services and supports programs
Intended for use amongst various populations: Elders (65 years and older); younger adults (18-64) with physical disabilities and adults of any age with
intellectual or developmental disabilities
Assessment data is intended for use for multiple purposes like use of standardized items to determine individual eligibility for state programs or to help
determine levels of care
Common Data Set is developed internally and independently at PACE, yet most major aspects are aligned with different data initiatives
occurring simultaneously in the healthcare arena
Convergence with Other Data Initiatives in Healthcare
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Impact Act
Tool for Functional Assessment
Standardized Items
Quality Measures proposed by
Econometrica
Provides validation that best practices were used
A private research and management consulting firm that provides analyses, modeling, and economic evaluations for clients in the private and public sectors
What is Econometrica
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CMS contracted with Econometrica to adapt, implement, and maintain quality measures for the nationwide PACE program. The contract name is Development, Implementation, and Maintenance of Quality Measures for the Programs of All-
Inclusive Care for the Elderly
The primary objectives of this project is to analyze existing quality measure sets to determine the extent to which they can be modified, refined, or enhanced to
be appropriate to the uniqueness of the PACE program
and organization
Focus on three areas of measurement - Falls, Falls with Injury, and Pressure Injury
Quality Care and Improved Outcomes Objective of Econometrica
measures is to help CMS provide oversight
Develop prevention measures which CMS and Econometrica believe are vital to ensuring quality of care for PACE participants
Cognizant about burden extra data collection imposes
Common Features between Econometrica and CDS
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Co
mm
on
Data Set
Eco
no
met
rica
Quality Improvement and demonstrate PACE value Objective of CDS is to capture
data elements that are needed to create meaningful measures
CDS is flexible and can accommodate changes required to support Econometrica methodology
CDS has been designed to minimize data burden by incorporating existing data structures
• Federal: CMS continues its push to fully utilize EDR for risk adjustment
- CMS’ goal is to transition entirely from using diagnoses submitted to RAPS to using diagnoses from encounter data and they intend to continue transitioning away from a reliance on RAPS data for calculating risk scores.
- Currently in year 2 of transition to full EDR for MA risk adjustment
- Expectation is that risk scores will be 100% encounter data/FFS-based in 2020 (PY2019).
• States: Effort continues towards the Managed Care Model in the absence of FFS data in establishing Medicaid rates; increasingly reliant on EDR
- Recently released (May 6, 2016) Medicaid Managed Care final rule heavily influenced by the Medicaid Statistical Information System (MSIS) and Transformed-Medicaid Statistical Information System (T-MSIS)
- Provisions in final rule that relate to routine reporting of state encounter data as a condition for receiving federal matching payments for medical assistance
- States have graduated 3-year time period to be compliant with encounter data reporting requirements
Looking Ahead - Encounter Data Reporting
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