Download - Building a Digital Quality Ecosystem
Building a Digital Quality EcosystemSes s ion 190 , Augus t 12, 2021
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Executive Vice President, Quality Measurement and Research Group, NCQAMichael S. Barr, MD, MBA, MACP
DISCLAIMER: The views and opinions expressed in this presentation are solely those of the author/presenter and do not necessarily represent any policy or position of HIMSS.
Brad Ryan, MDChief Product Officer, NCQA
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Welcome
Chief Product Officer, NCQABrad Ryan, MD
Executive Vice President, Quality Measurement and Research Group, NCQA
Michael S. Barr, MD, MBA, MACP
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Conflict of Interest
Michael S. Barr, MD, MBA, MACP and Brad Ryan, MD have no real or apparent
conflicts of interest to report.
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Agenda• The environment
• Measure roadmap
• Use cases for digital measures for value-based payment models
• Problems with current environments and barriers
• Public-private digital quality ecosystem model
• The alignment model as the solution
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Learning Objectives
• Explain public/private digital quality utility and ecosystem need and benefits
• Compare/contrast the ecosystem to the current, more limited quality
enterprise
• Describe challenges and steps needed to move to this ecosystem
• Prepare attendees to meet challenges and thrive in the ecosystem
• Generate excitement and broader participation in collaborating on and
moving to the new ecosystem
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The Environment
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Move to Digital
Focus on Health Equity
Growth in Virtual and Home Care
Challenges in Value-Based Payments
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Quality Content Today – Implementation Burden and Cost
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Inefficiencies
Practice GuidelinesGuideline TextProgram Goals
Measurement
Measure Specifications
Data Collection, Transfer,
AggregationReports, CDS, Care
Gaps, Analytics
Fragmented, narrative,
disconnected
Variation; inconsistent validation
Retrospective; limited by data;
“specs” only
High cost; manual; duplicative effort and
infrastructure
~$20B per year
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Public-Private Digital Quality Ecosystem Model
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Shared Tools, Infrastructure, Approaches
Data Collection, Transfer,
Aggregation
FHIR/QI Core, eCR, CCD
Measurement
dQM, eCQM
Practice Guidelines
CPG, CDS
• Integrated, end-to-end• Multi-plan/program• Efficient
Evidence-Based Medicine
Research
Data Science
Moving From Linear and Fragmented to Connected and Consistent
Standards and Utilities
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Reduced burden and cost
Integrated and consistent across ecosystem
Trusted by all stakeholders
Timely insights for care
Relevant insights at the level of action
Benefits of Digital Quality
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CMS Vision for Future of Digital Quality Measurement
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• dQMs as software for end-to-end measure calculation and reporting
• Data collected from providers, payers, CMS, and other organizations via FHIR-based
Application Programming Interfaces (APIs)
• Pro-competitive and open-source system
• Data quality strengthened and tested
• dQMs audited and manual processes to compare data against original data source
(e.g., the medical record) automated where possible
• Data collection from third-party aggregators (e.g., HIEs and clinical registries)
expanded and policies established for data aggregation, measure calculation and
data integrity
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The Journey from Paper to Digital Quality Measures
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Paper Quality Measures (pQMs)Data from claims, manual chart extractions and patient experience surveys
Electronic Clinical Quality Measures (eCQMs)Data primarily from electronic health records (EHRs)
Digital Quality Measures (dQMs)Data from EHRs, registries, HIEs, claims, devices, surveys, etc.
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Paper to Software: Reduced Cost and BurdenMoving From People and Paper to Software
1.Democratizes access to organizations with less capabilities by enabling centralized code control and “push” updates
2.Improves quality and speed for launches and updates
Computable Specifications
Reduces duplicative interpretation, development, implementation, and maintenance of measures and content
Reduces redundant systems and infrastructure costs
Executable Software
Designed from the start for digital clinical data collection, mapping, and use across levels of measurement and source systems
Reduces burden and enables data quality reporting solutions
Data Collection and Reporting
Enables users to easily adapt, configure, and stratify measures and content for use in their applications
Reduces administrative effort for changes while preserving integrity
Modular Configuration
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The Path to A Better Measurement Ecosystem
Measures that support multiple
use cases
• High interest in use for accountability
• Value-based purchasing
Measures that address priority
populations
• Prevention and population health
• High need, high cost
• Health equity
Measures that align across levels
• Misalignment contributes to burden
• Uncoordinated approach to quality improvement
Measures that encourage
electronic data use
• Care coordination requires data sharing
• Infrastructure improved but could be better
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Digital Measures For Multiple Quality Uses
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By supporting broader use cases, we can enable higher value, lower-burden quality solutions
Adjudicate and manage contractual and incentive-based payments
Payment
Provide trusted visibility and periodic reporting between stakeholders
Reporting
Identify and prioritize improvement opportunities
Optimization
Choose, develop, and deploy measures
Performance Measurement
See performance and understand drivers
Performance Management
Operations and decision support for QI/PI
Performance Improvement
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Supporting Multiple Levels of Accountability Through Alignment
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CollaborationCooperationCoordination
Connecting the dots across the delivery system
A measurement framework to focus reporting on what matters at each level of the delivery system
Managed Care: Meso LevelManage delivery of evidence-based care
State & Federal: Macro LevelSet priorities and direct resources through regulations and financial support
Facility/Provider: Micro LevelProvide evidence-based treatment and services to support whole-person care
Reference: Olin, S., Freed, G., Hudson, S., Applegate, M. (n.d.) Aligning to Improve Pediatric Healthcare Quality. Academic Pediatrics- Under Review.
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Aligning bundles of measures across levels: Example
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Goal is harmonization, not replication
Lauren Niles & Serene Olin (May 2021)https://www.ncqa.org/wp-content/uploads/2021/07/20210701_Behavioral_Health_Quality_Framework_NCQA_White_Paper.pdf
Measure Bundle for Population Goal: Reducing Opioid-Related Mortality
Federal &State
Outcome: Opioid-related death
Process: Follow-up post emergency department for OUD
Structure: Prior authorization for MOUD, reimbursement for telehealth
Managed Care
Facility/Provider
Outcome: Repeat opioid overdose/poisoning events
Process: Treatment continuity, care coordination for high-risk members
Structure: BH network adequacy, coverage of non-opioid pain therapy
Member experience
Outcome: Treatment dropout/show rates
Process: Access to MOUD, treatment engagement, preventive and chronic care management for patients with OUD, care coordination
Structure: Waivered providers, telehealth infrastructure, care team communication infrastructure
Patient experience
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This evolution is necessary but not sufficient
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The technology is the “easy” part
Incentives drive adoption
Quality is a good business case- Value-based payments and incentives- Need for stakeholder alignment- Data already flowing
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Questions
BUILDING A DIGITAL QUALITY ECOSYSTEM
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Thank you!Michael S Barr, MD, MBA, MACP, FRCPExecutive Vice President, Quality Measurement & Research GroupNational Committee for Quality AssuranceEmail: [email protected]: @barrmsLinkedIn: www.linkedin.com/in/michaelsbarr
Brad Ryan, MDChief Product OfficerNational Committee for Quality AssuranceEmail: [email protected]: www.linkedin.com/in/bradryanmd/
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Acronyms and Terms Used
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Those you might be less familiar with
API An application programming interface (API) is a computing interface that defines interactions between multiple software or mixed hardware-software intermediaries.
ASP Average Selling Price, the price at which a certain class of good or service is typically sold.
CCD Continuity of Care Document, based on the HL7 CDA architecture is a "document standard“ for transferring clinical data between organizations.
CDS Clinical Decision Support (Rules, etc.)
CPG, CPG Hub
Clinical Practice Guideline. CPG Hub refers to a repository or database of CPGs.
DAV Data Aggregator Validation, a new NCQA product validating HIEs and related entities.
DMC Digital Measurement Community
DMR Digital Measurement Roadmap, NCQA’s plan to move to digitized measure specifications.
dQM Digital Quality Measure
eCQM Electronic Clinical Quality Measure
ECPG Electronic Clinical Practice Guideline
eCR electronic Case Report
ETL (Data ETL) Extract/Transform/Load. This is a process to extract data from the source systems, enforce data quality and consistency standards, conform data so that separate sources can be used together, and deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions.
FHIR (FHIR/QI Core)
The HL7® FHIR® (Fast Healthcare Interoperability Resources 1 ) standard defines how healthcare information can be exchanged between different computer systems regardless of how it is stored in those systems. QICore defines a set of FHIR profiles with extensions and bindings needed to create interoperable, quality-focused applications.
IMAS Internal Measure Authoring System
MaaS Measures-as-a-Service, a proposed NCQA product
NLP Natural Language Processing, but also refers to a pilot NCQA program for validating NLP in the context of HEDIS measurement
Reporting Solutions A proposed NCQA product
VBC Value-based care
VBP Value-based payment
VS Value Set, a list of codes and corresponding terms, from standard clinical vocabularies (such as SNOMED CT®, RxNorm, LOINC® and others), often representing a clinical concept such as “Heart Failure” or “Primary Care Visit”