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Healthcare MedicineTop 10 Best Healthcare Analytics Software of 2026
Top 10 ranking of healthcare analytics software for hospitals and payers. Includes comparisons and notes on MedeAnalytics and Strata Decision.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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MedeAnalytics is the strongest fit for quality-focused operations teams that need repeatable, API-driven measure analytics with validation checks, whereas Definitive Healthcare suits teams focused on provider and facility market analytics using recurring cohort views.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
MedeAnalytics
Healthcare measure analytics with cohort-based slicing designed for recurring performance reporting workflows.
Built for fits when quality operations teams need repeatable measure analytics with API-driven ingestion and validation checks..
Definitive Healthcare
Editor pickEntity-centric provider and facility intelligence that supports market share and benchmarking across organizations.
Built for fits when operations and analytics teams need provider and facility market analytics with recurring cohorts..
Strata Decision
Editor pickGoverned metric definition workflow ties cohort outputs to traceable configuration, supporting controlled releases across reporting cycles.
Built for fits when healthcare teams need controlled cohort reporting with repeatable measure logic and governed access..
Related reading
Comparison Table
MedeAnalytics
enterpriseHealthcare performance analytics for providers, payers, and employers.
Healthcare measure analytics with cohort-based slicing designed for recurring performance reporting workflows.
MedeAnalytics is built for organizations that need consistent analytics definitions across measure calculation, cohort slicing, and recurring reporting cycles. Measure analytics and cohort analytics are the dominant workflows, which fits quality measure reporting and performance tracking rather than ad hoc dashboards. API-based integration reduces manual file drops, especially when feeding analytics warehouse loads from existing ETL stages. Data validation support helps catch common issues before metrics propagate into downstream reports.
A tradeoff is that best results require disciplined source mapping and stable input feeds so measure outputs stay comparable across reporting cycles. MedeAnalytics fits situations where multiple teams share the same analytic definitions, such as quality operations and care management teams aligning on cohort and performance metrics. It also fits healthcare organizations that need repeatable automation around measure calculation rather than one-time analysis.
- +Healthcare-specific measure analytics supports repeatable quality reporting workflows
- +API-driven integration supports automation-friendly ingestion and metric exports
- +Cohort-based analysis enables consistent comparisons across populations
- +Data validation reduces errors propagating into downstream reporting outputs
- –Requires careful source mapping to keep cohorts and metrics consistent
- –Workflow configuration takes time when multiple teams share analytic definitions
- –Some advanced reporting scenarios depend on integration work with existing data pipelines
- –Limited fit for organizations that only need general BI reporting
Quality operations teams
Automated quality measure calculation cycles
Faster measure reporting with fewer rework
Population health analysts
Cohort comparisons for intervention targeting
More precise care gap focus
Show 2 more scenarios
Data engineering teams
API-driven ingestion into analytics pipelines
Lower manual load and reprocessing
Uses API-based integration patterns to feed analytics inputs into recurring calculations.
Clinical coding operations
Validation before measure-impacting metrics
Reduced metric drift from input errors
Applies validation checks so upstream data issues do not skew measure results.
Best for: Fits when quality operations teams need repeatable measure analytics with API-driven ingestion and validation checks.
More related reading
Definitive Healthcare
enterpriseHealthcare commercial intelligence platform with provider and market analytics.
Entity-centric provider and facility intelligence that supports market share and benchmarking across organizations.
Definitive Healthcare is built around healthcare reference and market datasets that support cross-organization analysis across provider and facility entities. The system supports analytics use cases like market share tracking, benchmarking, and cohort slicing for targeting and performance measurement. Admin oversight is centered on controlled dataset access for analysts and reporting users rather than ad hoc spreadsheets.
A practical tradeoff appears when projects require bespoke clinical data structures or deep interoperability mapping into native FHIR resources. Definitive Healthcare fits teams that prioritize operational and market analytics over building a custom clinical data model from raw messages. It also fits organizations that need recurring reporting on provider performance signals tied to utilization and revenue cycle metrics.
- +Broad provider and facility datasets enable multi-entity market benchmarking
- +Cohort and segment workflows support repeatable targeting and performance views
- +Reporting outputs cover revenue cycle and utilization style questions
- +Admin controls help restrict dataset access by user group
- –Clinical interoperability and native FHIR mapping depth is limited for complex builds
- –Data refresh and lineage require active governance to avoid stale cohort logic
- –Advanced custom analytics often depend on analyst-led configuration
- –Some specialized workflows need extra modeling work outside the UI
Revenue cycle analytics teams
Benchmark performance by provider cohorts
Faster variance analysis
Market strategy teams
Measure share and referral patterns
More precise market plans
Show 2 more scenarios
Operations analytics teams
Track service line utilization trends
Earlier capacity signals
Slice cohorts by facility attributes and monitor changes in utilization-focused measures over time.
Data governance leads
Standardize recurring analytic cohorts
Reduced reporting drift
Maintain consistent cohort definitions and dataset permissions for recurring reporting workflows.
Best for: Fits when operations and analytics teams need provider and facility market analytics with recurring cohorts.
Strata Decision
enterpriseHealthcare financial analytics and decision support for hospitals and health systems.
Governed metric definition workflow ties cohort outputs to traceable configuration, supporting controlled releases across reporting cycles.
Strata Decision is a strong fit when healthcare analytics needs are tied to recurring measure reporting and cross-functional operational reporting. Core outputs include configurable cohorts, scheduled reporting views, and traceable metric definitions that reduce ambiguity between clinical and operational stakeholders. Integration depth is a key differentiator, with API-based ingestion and transformation patterns that support analytics warehouse or data mart buildouts. Governance features include role-based permissions and audit-style activity logging that help with internal controls for sensitive healthcare data.
A tradeoff is that deeper configuration of metric rules and data mappings requires analysts to follow a defined setup workflow rather than relying on fully automated defaults. It works well for teams that have recurring measure deliverables and need controlled changes to definitions across releases. It is less suitable for ad hoc exploration that requires rapid, one-off metric definitions without configuration overhead.
- +API-based ingestion supports controlled analytics updates
- +Cohort and metric configuration supports repeatable measure workflows
- +Role-based access and activity tracing support internal governance
- +Automated schedules reduce manual reporting overhead
- –Metric rule setup needs analyst time and careful change control
- –Ad hoc metric iteration is slower than low-configuration BI tools
- –Complex integrations may require dedicated integration engineering
- –Some advanced workflows depend on documented configuration patterns
Quality analytics teams
Manage recurring measure reporting cohorts
Fewer definition disagreements
Population health managers
Run operational care gap reporting
More consistent follow-up
Show 2 more scenarios
Analytics engineering teams
Automate data refresh via API
Reduced manual exports
API-based ingestion and transformation patterns support updates from analytics warehouses and data marts.
Healthcare compliance leads
Govern multi-user access and changes
Stronger internal auditability
Role-based permissions and activity tracing support controlled administration for sensitive data workflows.
Best for: Fits when healthcare teams need controlled cohort reporting with repeatable measure logic and governed access.
Innovaccer
enterpriseHealthcare data activation platform unifying patient records for analytics and care management.
HEDIS and CMS Star Ratings analytics with care gap closure outputs tied to operational reporting workflows.
Innovaccer is a healthcare analytics software solution focused on connecting clinical and operational data to analytics workflows across care delivery and revenue cycle. Its strengths center on data integration, quality measure analytics, and population health management use cases that rely on structured interoperability mapping and repeatable reporting pipelines.
The system adds automation through configurable rules and analytics outputs that downstream teams can reuse for operational actions. Governance controls include role-based access and audit logging that support multi-team administration of sensitive healthcare data.
- +Quality measure analytics built for HEDIS reporting and CMS Star Ratings workflows
- +Integration to analytics warehouse buildouts with repeatable pipelines and validation
- +Role-based access controls and audit logging for operational visibility
- +API-based integration surface for extending analytics into external systems
- –Interoperability mapping requires ongoing governance to keep mappings current
- –Automation configuration can require specialist support for complex care programs
- –Readiness work is needed to standardize source data before modeling
- –Advanced analytics deployment may take longer for organizations without an integration team
Best for: Fits when healthcare systems need governed analytics workflows spanning quality reporting and population health operations.
Clarify Health
enterpriseHealthcare analytics platform linking clinical, claims, and social determinants data.
Audit logging tied to reporting and workflow actions, supporting traceability for measure analytics operations.
Clarify Health applies healthcare analytics to improve care delivery and performance tracking through measure-focused reporting. It organizes claims-derived and clinical-derived datasets for quality measure analytics, including HEDIS and related performance views.
The product connects to upstream systems with an API and supports operational automation so data refresh and reporting pipelines can run on a schedule. Governance features include audit logging and role-based access controls for controlled access to datasets and reporting outputs.
- +Measure-centric analytics built for HEDIS workflows and performance tracking
- +API-based integration supports repeatable data pipelines for reporting
- +Audit logging records data and workflow activity for compliance traceability
- +RBAC supports controlled access across reporting and datasets
- –Cohort configuration can require structured data preparation and mapping effort
- –Readiness depends on upstream data completeness and documentation quality
- –Automation controls can be harder to tune without a strong analytics operations role
Best for: Fits when analytics teams need measure-driven performance reporting with controlled access and repeatable pipelines.
Cotiviti
enterprisePayment accuracy and healthcare analytics for payers and providers.
A validation-first pipeline that prepares claims data for consistent analytics and measure-oriented performance outputs.
Cotiviti applies healthcare claims analytics and payment-related decisioning to help organizations reduce avoidable errors and improve payment accuracy. The core workflow centers on ingesting payer and provider data, validating key fields, and running rule-driven and model-driven analytics for quality and risk signals.
Cotiviti also supports reporting needs tied to measure performance and operational oversight using standardized outputs from its validation and analytics pipelines. Automation is expressed through repeatable batch processing and integration patterns that feed downstream reporting and decision workflows.
- +Claims-focused analytics designed for payment and measure performance oversight
- +Repeatable validation and analytics workflows for consistent results at scale
- +Integration-ready outputs for downstream reporting and operational decisioning
- +Strong fit for governance-heavy analytics cycles with documented processing steps
- –Requires dedicated integration work to map source feeds into Cotiviti inputs
- –Less suited for ad hoc self-serve analytics without an established data pipeline
- –Model and rules configuration typically depends on implementation support
- –Reporting flexibility can lag teams that need highly custom dashboards
Best for: Fits when healthcare organizations need claims analytics with validation-first processing for payment and quality performance workflows.
Qventus
enterpriseHealthcare operations analytics platform for hospital capacity and throughput optimization.
Case- and task-aware automation that applies analytics outputs to operational workflows in near real time.
Qventus targets healthcare operations analytics with tight coupling between reporting and execution, rather than standalone dashboards.
Automated decisioning is designed around case handling, so metrics can drive changes to queues, tasks, and statuses.
API-based integration patterns support bringing operational and clinical signals into analytics and pushing outputs back to execution systems.
Administration includes RBAC and audit-oriented traceability for automated runs inside workflow management.
- +Workflow-linked analytics that map operational metrics to case actions
- +Automation rules connect reporting triggers to queue and task state changes
- +API-based integration supports bidirectional data movement with external systems
- +Role-based access controls help separate operational and analytic responsibilities
- –Advanced configuration for orchestration takes more admin attention
- –Clinical analytics depth depends on the availability and quality of sourced data
- –Cohort-style analytics require clear alignment to the operational case model
- –External reporting compatibility can require custom data extracts
Best for: Fits when analytics teams need workflow automation tied to utilization, referral, or care coordination case handling.
Trilliant Health
enterpriseHealthcare market analytics platform combining claims, consumer, and provider data.
Measure-focused performance analytics designed for operational follow-up across recurring quality and population workflows.
Trilliant Health focuses healthcare analytics on payer and provider performance workflows built around network and care delivery measurement. It supports population and quality-oriented reporting with cohorting, benchmarking, and measure-oriented insights used by care management and analytics teams.
Configuration centers on mapping clinical and claims-derived inputs into reporting structures, then operationalizing results into recurring reviews. Integration depth is geared toward API and batch-style data movement into analytics environments used for ongoing measure tracking and performance follow-up.
- +Strong measurement and reporting workflow for quality and population performance
- +Cohorting and benchmarking support recurring care management analytics loops
- +Integration oriented toward analytics ingestion via API and batch-style pipelines
- +Audit-friendly operationalization for recurring performance monitoring cycles
- –Measure configuration and mappings require disciplined data governance
- –Cohort and output customization can take time for non-analytics teams
- –Automation coverage depends on how upstream data feeds are operationalized
- –Reporting depth favors managed workflows over ad hoc visualization
Best for: Fits when analytics teams need measure-oriented cohorting and benchmarking with governed data mappings.
Arcadia
enterprisePopulation health analytics platform aggregating clinical and claims data.
API-driven cohort run automation with reusable definitions tied to configured interoperability mappings.
Arcadia turns healthcare datasets into analytics-ready cohorts and measures for operations and quality workflows. It focuses on interoperability mapping and data ingestion patterns that support analytics warehouse and downstream reporting use cases.
Arcadia also provides an API surface for automation and integration with existing data pipelines and dashboards. Administrative controls support governance needs through RBAC and activity visibility across configured connections and models.
- +API-first workflow hooks for automating cohort runs and exporting results
- +Interoperability mapping tools reduce friction when standardizing mixed source data
- +Cohort definitions and measures are reusable across multiple reporting tasks
- +Governance controls include RBAC plus audit-friendly activity traces
- –Requires careful configuration of source feeds before analytics outputs stabilize
- –Automation needs some engineering support to scale multi-system throughput
- –Modeling flexibility can add overhead for teams that only need basic dashboards
- –Advanced measure tuning takes iteration rather than a single guided setup
Best for: Fits when analytics teams need API-driven cohort and quality measure workflows across multiple source systems.
LeanTaaS
enterprisePredictive analytics platform for hospital resource optimization including OR and infusion scheduling.
Quality and risk workflow automation that turns sourced clinical data into refreshable analytics outputs for downstream reporting.
LeanTaaS targets healthcare analytics teams that need population-level insight without building every report and integration from scratch. It focuses on quality measure and risk workflows that start from sourced healthcare data and end in usable analytics outputs. LeanTaaS also supports interoperability mapping and automation hooks through an integration and API surface for downstream consumption.
- +Quality measure analytics workflows that align with common reporting needs
- +Interoperability mapping helps reduce friction between source formats and analytics
- +API-based integration supports feeding analytics into external systems
- +Automation options support repeatable refresh patterns
- –Requires careful configuration to keep cohort definitions stable across refreshes
- –Complex data sourcing can slow initial onboarding for nonstandard schemas
- –RBAC boundaries and governance controls need deliberate admin setup
- –Some advanced modeling outcomes depend on upstream data completeness
Best for: Fits when care organizations need repeatable quality analytics and risk views with integration into existing reporting stacks.
Conclusion
After evaluating 10 healthcare medicine, MedeAnalytics stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right healthcare analytics software
Healthcare analytics software in this guide centers on repeatable measurement, cohort logic, and workflow automation that connect clinical, claims, and reporting outputs back into daily operations. The covered tools include MedeAnalytics, Definitive Healthcare, Strata Decision, Innovaccer, Clarify Health, Cotiviti, Qventus, Trilliant Health, Arcadia, and LeanTaaS.
The evaluation focus tracks how each platform handles governed metric and cohort configuration, auditability, and API-driven ingestion for downstream pipelines. Tool differences show up in measure-centric cohort workflows like MedeAnalytics, governed releases like Strata Decision, and case-aware operational automation like Qventus.
Healthcare analytics software for governed cohorts, measure performance reporting, and operational automation
Healthcare analytics software builds analytics outputs from sourced clinical data, claims data, and interoperability mappings so teams can run recurring quality and population workflows. Tools like MedeAnalytics emphasize healthcare measure analytics with cohort-based slicing designed for repeatable performance reporting.
Other platforms prioritize governance and traceability for change control and auditing. Strata Decision ties metric and cohort configuration to governed release workflows for controlled reporting cycles, while Clarify Health focuses on audit logging tied to reporting and workflow actions for measure analytics operations.
Governed cohort logic, measure workflows, and automation surfaces
Healthcare analytics software succeeds when cohort and metric definitions stay consistent across recurring reporting runs. The tools in this guide differ most in how they structure governed measure logic, track change across cycles, and push results into downstream workflows.
API-driven ingestion and recurring run automation
MedeAnalytics uses API-driven integration and validation checks for repeatable performance reporting workflows. Arcadia provides API-first workflow hooks for automating cohort runs and exporting results across multiple source systems.
Governed metric definition and controlled releases
Strata Decision ties cohort outputs to traceable configuration so metric updates can ship with controlled releases across reporting cycles. Qventus links analytics outputs to queue and task state changes so operational teams act on triggers rather than waiting for scheduled reports.
Measure-centric workflows built for quality reporting
Innovaccer delivers HEDIS and CMS Star Ratings analytics with care gap closure outputs designed for operational reporting workflows. Clarify Health focuses on measure-centric analytics workflows for HEDIS performance tracking with controlled access.
Validation-first claims pipelines for measure-oriented performance
Cotiviti prepares claims data through a validation-first pipeline that produces consistent measure-oriented performance outputs. MedeAnalytics also supports repeatable quality reporting workflows but emphasizes healthcare measure analytics with cohort-based slicing for recurring operational performance views.
Interoperability mapping support to stabilize outputs
Arcadia includes interoperability mapping tools that reduce friction when standardizing mixed source data. LeanTaaS adds interoperability mapping to reduce friction between source formats while refreshable outputs depend on stable cohort definitions.
Audit and traceability for reporting workflow actions
Clarify Health ties audit logging to reporting and workflow actions so measure analytics operations keep traceability. Qventus emphasizes workflow-linked analytics where rule triggers move case states, which reduces manual interpretation between reporting and action.
Pick a tool by governance depth and how analytics results enter operations
Choosing healthcare analytics software requires mapping the analytics workflow to the organizational control model. Some platforms optimize for repeatable measure and cohort reporting definitions, while others optimize for traceable change control or for pushing outputs directly into operational task queues.
Select governed measure and cohort definitions for repeatable quality reporting
Choose MedeAnalytics when recurring performance reporting needs healthcare-specific measure analytics with cohort-based slicing and API-driven ingestion plus validation checks. Choose Trilliant Health when the workflow focus is measure-oriented cohorting and benchmarking for recurring quality and population performance loops with governed data mappings.
Choose traceable change control when multiple teams share analytics definitions
Choose Strata Decision when governed metric definition workflows require traceable configuration and controlled releases across reporting cycles. Choose Clarify Health when audit logging tied to reporting and workflow actions must support traceability for measure analytics operations.
Choose operational automation when analytics must drive near real-time case actions
Choose Qventus when analytics outputs need to map to case actions with workflow-linked analytics that connect triggers to queue and task state changes. Choose LeanTaaS when repeatable quality analytics and risk views must refresh into existing reporting stacks with automation that depends on stable cohort definitions across refreshes.
Choose validation-first claims analytics when consistency drives payment and quality oversight
Choose Cotiviti when claims analytics requires validation-first processing to produce consistent analytics and measure-oriented performance outputs. Choose Innovaccer when HEDIS and CMS Star Ratings care gap closure outputs must align with operational reporting workflows and validation in analytics warehouse buildouts.
Choose market intelligence views when benchmarking across entities is the primary analytics goal
Choose Definitive Healthcare when provider and facility market analytics require entity-centric benchmarking across organizations with cohort and segment workflows. Choose Arcadia when multi-system cohort and quality measure workflows need API-driven cohort run automation tied to configured interoperability mappings.
Reject tools that cannot match required interoperability governance depth
Choose Innovaccer with extra governance effort when interoperability mapping requires ongoing governance to keep mappings current for complex care programs. Choose Definitive Healthcare when clinical interoperability and native mapping depth are not sufficient for complex builds and data refresh plus lineage require active governance to avoid stale cohort logic.
Teams that get the most value from governed analytics workflows
Healthcare analytics software fits best when teams run recurring measure logic and need consistent outputs across reporting cycles. The tools in this guide separate operational reporting automation from event-driven case execution and separate validation-first claims pipelines from market benchmarking workflows.
Quality operations teams running HEDIS and CMS Star Ratings reporting
Innovaccer and Clarify Health align with measure-centric workflows for HEDIS tracking and care gap closure outputs, with API-driven integration for repeatable reporting pipelines.
Analytics teams coordinating shared measure definitions across departments
Strata Decision supports governed metric definition workflows tied to traceable configuration for controlled releases, while Clarify Health provides audit logging tied to reporting and workflow actions.
Population health and care management teams acting on analytics triggers
Qventus maps analytics outputs to case actions by connecting reporting triggers to queue and task state changes for near real-time execution.
Revenue cycle and claims analytics teams needing validation-first consistency
Cotiviti provides claims-focused analytics built around repeatable validation-first processing for consistent measure-oriented performance outputs.
Operations teams focused on provider and facility market benchmarking
Definitive Healthcare provides entity-centric provider and facility intelligence that supports market share benchmarking with recurring cohorts and performance views.
Common failure points during healthcare analytics platform selection
Several predictable mistakes cause healthcare analytics projects to miss schedule and output consistency targets. These pitfalls typically appear when teams underestimate source mapping effort, overestimate ad hoc iteration speed, or treat governance as optional instead of part of the workflow.
Assuming cohort and metric definitions will stay stable without source mapping governance
MedeAnalytics requires careful source mapping to keep cohorts and metrics consistent, and LeanTaaS requires careful configuration to keep cohort definitions stable across refreshes.
Underestimating analyst time needed for governed metric rule setup and change control
Strata Decision requires analyst time and careful change control for metric rule setup, which makes ad hoc metric iteration slower than low-configuration BI tools.
Selecting a platform that cannot sustain interoperability mapping governance as source complexity grows
Innovaccer needs ongoing governance to keep interoperability mappings current, and Definitive Healthcare reports limited clinical interoperability and native mapping depth for complex builds.
Expecting near real-time operational action from a tool built for scheduled reporting
Qventus is designed for workflow automation tied to utilization and care coordination case handling, while tools such as Cotiviti focus on validation-first claims analytics and are less suited for ad hoc self-serve analytics without an established pipeline.
Overlooking upstream data completeness and documentation quality when measuring readiness
Clarify Health flags that cohort configuration readiness depends on upstream data completeness and documentation quality, which can slow measure-driven performance reporting.
How We Selected and Ranked These Tools
We evaluated MedeAnalytics, Definitive Healthcare, Strata Decision, Innovaccer, Clarify Health, Cotiviti, Qventus, Trilliant Health, Arcadia, and LeanTaaS against feature coverage and workflow fit for governed cohort and measure reporting. Features counted for 40% of the score, ease of use counted for 30%, and value counted for 30%.
MedeAnalytics ranked highest because healthcare measure analytics with cohort-based slicing was built for recurring performance reporting workflows and because API-driven ingestion plus validation checks supported automation-friendly pipelines. The scoring also reflected that MedeAnalytics requires careful source mapping to keep cohorts and metrics consistent, which reduced ease relative to tools that treat mapping as a lighter step.
Frequently Asked Questions About healthcare analytics software
How do healthcare analytics tools handle API-based integration and automation between source systems and analytics outputs?
Which tools provide governed access controls and audit logging for multi-user administration of healthcare data?
How does data migration typically work when moving from existing analytics spreadsheets or warehouse tables into a healthcare analytics data model?
When teams need clinical and administrative interoperability mapping for analytics, which platforms center the mapping workflow?
What breaks if a healthcare analytics workflow lacks documented measure logic and traceable configuration?
Which platform fit signals differentiate quality measure reporting workflows from claims analytics and payment-related validation workflows?
How do healthcare analytics tools support quality measures that require cohorting and recurring reporting cycles?
What tradeoff should teams expect when choosing between workflow automation and strictly reporting-focused analytics?
How do admin controls differ when supporting multiple workspaces, configured connections, and operational governance?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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