Research Report
From healthcare dissonance to harmony
Scaling intelligent MedTech through data synchronization
5-minute read
September 7, 2026
In brief
- MedTech is creating new sources of value from data, but fragmented data exchange makes that value difficult to scale.
- Our research identifies four systemic barriers that create dissonance across the healthcare ecosystem and make data collaboration harder to scale.
- Three levers can help MedTech leaders scale: design for predictability, ground value in measurable results and institutionalize execution.
Playing from different scores
In a well-functioning orchestra, every musician reads from the same score. In healthcare today, providers, payers and MedTech companies often approach data sharing with different priorities, constraints and expectations. The result is friction across an ecosystem that increasingly depends on them working together.
For MedTech, the stakes are rising. Intelligence is becoming a primary source of differentiation as companies use data to improve clinical decision support, outcomes measurement and evidence generation. Yet scaling that value remains difficult. Regulatory uncertainty, uneven operational readiness, trust gaps and differing definitions of value create dissonance across the ecosystem. Addressing these barriers requires coordinated leadership across MedTech companies, providers and payers.
50%
of MedTech executives report difficulty accessing the data they need
88%
cite lengthy compliance reviews as their greatest regulatory hurdle
73%
of providers say their IT is only moderately prepared or less for future interoperability
The four sources of dissonance
Our research identifies four systemic barriers. Each amplifies the others and each plays out differently for providers, payers and MedTech companies creating dissonance across the healthcare ecosystem.
Dissonance 1: Regulatory and legal uncertainty
Inconsistent rules, interpretations and partner expectations slow data sharing. Standardized regulatory positions can reduce uncertainty, shorten reviews and enable more repeatable execution.
- Providers hold the liability if data is misused, so they default to caution. This often leads to delays rather than an outright “no”.
- Payers face strict rules about how data can be shared and used. Even data that protects patient identity is carefully reviewed.
- MedTech relies heavily on tightly regulated data: 89% of MedTech senior executives say EMR/EHR and patient-level data are critical to their solutions.
DATA
97%
of providers rank privacy and security as their top barrier
Dissonance 2: Operational and technical misalignment
Even when incentives align, aging systems and disconnected data can make data exchange slow and difficult to scale.
- Providers often rely on aging, siloed systems. Even with EHR integration, separate imaging, lab and operational systems may not connect.
- Payers often have data spread across different systems, with small teams manually pulling it together.
- MedTech continues to invest heavily in technology, while operating capabilities critical to scale remain underinvested.
DATA
89%
of provider executives cite legacy integration as their main obstacle to data access
Dissonance 3: Limited trust and partnerships
Trust has become essential to data sharing. Even when the technology is in place, concerns about how data will be used can slow a partnership.
- Providers, cautious after past breaches, want to know how their data will be used and protected. Proposals that overlook their day-to-day realities can lose momentum early.
- Payers weigh both regulatory and economic risk, approving narrow access and blocking reuse when the value isn't clear.
- MedTech companies are broadening their partnerships, with collaborations with startups, regulatory bodies and third-party data aggregators growing rapidly.
DATA
55%
of provider executives trust MedTech companies to use their data responsibly
Dissonance 4: Value misalignment
Providers, payers and MedTech companies may agree on the promise of innovation, but they often define success differently. Partnerships move forward when the value is clear to everyone involved.
- Providers want concrete results: time saved, burden removed and outcomes improved. Broad promises of innovation are not enough.
- Payers want measurable results in cost, quality and Star Ratings, plus a clear path from pilot to scale.
- MedTech companies can struggle to align internally on value, making it harder to present a clear value story to partners.
DATA
Only 23%
of MedTech executives report having a mature, enterprise-wide governance model.
Striking the right chord: three levers to reset the data foundation
Regulatory uncertainty, operational gaps, limited trust and differing views of value cannot be addressed in isolation. Three levers can help MedTech leaders address them together and build more durable data partnerships.
Not every segment starts in the same place
Data-exchange maturity varies across MedTech companies. Some segments are still navigating basic contracting and access; others are grappling with value alignment and data ownership at scale. A segment's maturity shapes the challenges it faces and which of the three levers leaders should prioritize first.
Early-stage segments should prioritize designing for predictability, with clear rules for data access, use and governance; mid-stage segments should prioritize building repeatable execution models so partnerships can scale beyond individual sites; and late-stage segments should focus on measurable value and aligning incentives across partners.
Applying the wrong lever at the wrong stage wastes capital and credibility, so leaders who correctly assess their maturity can move faster and invest more precisely.
The cost of standing still
MedTech's shift to data- and AI-enabled growth depends as much on leadership decisions as it does on technology. Companies continue to favor customization over standardization risk, making each partnership harder, slower and more expensive than the last, with AI remaining in pilots. Those that build for repeatability can make data partnerships easier to scale and turn data-enabled innovation into lasting value.