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Geneo
AI Visibility Report
01/14/2026
Live Analysis:
ChatGPT_

AI Visibility Report for
value-basedcarevsfee-for-service

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Brand Performance Across AI Platforms
All 15 brands referenced across AI platforms for this prompt
YouTube
1
3
Sentiment:
Score:95
Cleveland Clinic
0
1
Sentiment:
Score:55
AMA
0
1
Sentiment:
Score:55
4CMS
0
1
Sentiment:
Score:55
5Elevance Health
0
1
Sentiment:
Score:55
Referenced Domains Analysis
All 19 domains referenced across AI platforms for this prompt
ChatGPT
Perplexity
Google AIO
#1youtube.com faviconyoutube.com
ChatGPT:
1
Perplexity:
1
Google AIO:
1
3
#2cms.gov faviconcms.gov
ChatGPT:
0
Perplexity:
1
Google AIO:
0
1
#3getvim.com favicongetvim.com
ChatGPT:
0
Perplexity:
0
Google AIO:
1
1
#4aledade.com faviconaledade.com
ChatGPT:
0
Perplexity:
0
Google AIO:
1
1
#5medrina.com faviconmedrina.com
ChatGPT:
0
Perplexity:
0
Google AIO:
1
1

AI Search Engine Responses

Compare how different AI search engines respond to this query

ChatGPT

2141 Characters

BRAND (15)

YouTube
Cleveland Clinic
AMA
CMS
Elevance Health
Aledade
Vim
Health Catalyst
Raven Health
Veradigm
Innovaccer
Commonwealth Fund
Net Health
Medrina
CVS Health

SUMMARY

ChatGPT provides a structured educational overview of both payment models, clearly defining Fee-for-Service as payment per individual service and Value-Based Care as outcome-focused compensation. The response emphasizes the potential drawbacks of FFS including fragmented care and cost escalation, while beginning to explain VBC's payment structure. The explanation is methodical and focuses on helping readers understand the fundamental differences between the two approaches.

Perplexity

1622 Characters

BRAND (15)

YouTube
Cleveland Clinic
AMA
CMS
Elevance Health
Aledade
Vim
Health Catalyst
Raven Health
Veradigm
Innovaccer
Commonwealth Fund
Net Health
Medrina
CVS Health

SUMMARY

Perplexity presents a detailed comparative table format that directly contrasts Value-Based Care and Fee-for-Service across multiple dimensions including payment structure, financial incentives, care approach, and coordination. The response emphasizes VBC's focus on patient outcomes and population health versus FFS's volume-based, transactional approach. It highlights how VBC promotes enhanced collaboration while FFS operates in silos with limited coordination between providers.

Google AIO

1564 Characters

BRAND (15)

YouTube
Cleveland Clinic
AMA
CMS
Elevance Health
Aledade
Vim
Health Catalyst
Raven Health
Veradigm
Innovaccer
Commonwealth Fund
Net Health
Medrina
CVS Health

SUMMARY

Google AIO delivers a comprehensive overview that succinctly captures the core distinction between volume-based FFS and quality-focused VBC payment models. The response covers key aspects including payment structures, incentives, focus areas, and administrative considerations. It emphasizes VBC's shift toward keeping people healthy and improving satisfaction while noting the complexity of data requirements and increased financial risk for providers under value-based models.

Strategic Insights & Recommendations

Dominant Brand

No specific healthcare brands are prominently featured across the platform responses, with the focus remaining on explaining the conceptual differences between payment models.

Platform Gap

ChatGPT provides the most educational foundation while Perplexity offers the most structured comparison and Google AIO delivers the most concise comprehensive overview.

Link Opportunity

Google AIO provides the most extensive linking opportunities with 11 links compared to Perplexity's 9 and ChatGPT's single link, suggesting stronger potential for driving traffic to authoritative healthcare sources.

Key Takeaways for This Prompt

All platforms consistently position Value-Based Care as outcome-focused versus Fee-for-Service's volume-based approach.

Perplexity's table format provides the clearest side-by-side comparison for decision-makers evaluating payment models.

Google AIO uniquely mentions the complexity and financial risk challenges associated with implementing Value-Based Care.

ChatGPT's educational approach makes it most suitable for audiences new to healthcare payment model concepts.

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