Healthcare data technology and the next phase of connected medical devices

Why healthcare data technology now matters for device strategy

Healthcare data technology is no longer limited to the software behind electronic health records. It now covers the APIs, device interfaces, security controls, analytics pipelines, identity systems, and governance processes that move clinical information among patients, providers, payers, medical devices, and public health systems. For connected medical devices, this shift is significant. A device is increasingly assessed not only on whether it measures or delivers care accurately, but also on whether its data can be trusted, secured, exchanged, and used in clinical workflows.

For hospitals, device manufacturers, and digital health teams, interoperability, cybersecurity, data provenance, and lifecycle maintenance need to be designed in from the start. More coverage of these developments is available in the healthcare technology section.

The policy signals shaping health data exchange

Several U.S. policy developments have made health data exchange more concrete since 2023. The Office of the National Coordinator for Health Information Technology, commonly known as ONC, positioned its HTI-1 final rule around health data, technology, interoperability, algorithm transparency, and information sharing. One major change is that USCDI Version 3 became the baseline standard for the ONC Health IT Certification Program as of January 1, 2026. ONC has also stated that certified health IT supports care delivered by more than 96% of hospitals and 78% of office-based physicians, which means certification changes can quickly influence the practical expectations placed on technology suppliers.

CMS has also moved interoperability from a broad policy goal toward specific API obligations. On January 17, 2024, CMS finalized its Interoperability and Prior Authorization Final Rule, known as CMS-0057-F. The rule requires impacted payers to implement and maintain specified HL7 FHIR APIs, including patient access, provider access, payer-to-payer, and prior authorization capabilities on phased timelines, with important requirements beginning January 1, 2027.

TEFCA adds another layer. ONC has described December 2023 as the point when the first Qualified Health Information Networks were designated and data began flowing among them. TEFCA does not replace device integration work inside hospitals, but it shows the direction of travel: health data exchange is becoming more networked, standards-based, and accountable.

From records to real-time device data

Traditional health IT was built around records: encounters, orders, diagnoses, medications, lab results, claims, and documentation. Connected medical devices add a different kind of information. They may produce continuous waveforms, alarms, device status messages, firmware information, therapy settings, patient-generated measurements, and remote monitoring data. These data streams can be clinically valuable, but they also create integration challenges that ordinary record exchange does not fully solve.

A connected infusion pump, bedside monitor, ventilator, implantable cardiac device, glucose sensor, or home-use diagnostic tool may generate data that is time-sensitive and operationally complex. For a clinician, the value of that data depends on whether it arrives with enough context: which patient, which device, which care setting, which measurement method, which software version, and whether the reading has been validated or modified. Without that context, more data can create more noise rather than better decisions.

Context is as important as connectivity

Device data should be treated as evidence with provenance, not as a generic data feed. Useful provenance can include device identifier, patient association, timestamp source, measurement units, calibration or quality status, software or firmware version, operator action, and whether the data was captured directly from the device or transformed by middleware. These details matter when data is used for clinical documentation, remote monitoring, quality reporting, or machine learning.

This is where healthcare data technology becomes a bridge between engineering and care delivery. Interfaces, middleware, integration engines, cloud platforms, and EHR workflows must preserve meaning as data moves. A system may be able to receive a value, but if it cannot preserve the clinical context, the integration remains technically connected and clinically weak.

Cybersecurity becomes part of data architecture

The security discussion has changed because connected devices now operate in the same data environment as hospital networks, EHR systems, cloud services, and patient-facing apps. The FDA’s June 27, 2025 final guidance on cybersecurity in medical devices focuses on quality system considerations and the content of premarket submissions for devices with cybersecurity risk. It superseded the September 2023 guidance of the same title and reflects the agency’s continuing emphasis on secure design, documentation, and lifecycle management for cyber devices.

HHS has also emphasized the scale of cyber risk in health care. In its December 27, 2024 announcement of a proposed HIPAA Security Rule update, the HHS Office for Civil Rights said reports of large breaches increased by 102% from 2018 through 2023, while the number of individuals affected increased by 1002%. HHS also reported that more than 167 million individuals were affected by large breaches in 2023. As of October 9, 2026, the HHS page for that proposal continued to describe it as a proposed rule while noting that the current Security Rule remains in effect.

For device and health IT teams, the lesson is not simply to add encryption late in development. Cybersecurity has to be built into architecture decisions, supplier management, access control, audit logging, vulnerability handling, update mechanisms, and incident response. NIST’s Cybersecurity Framework 2.0, released on February 26, 2024, and NIST SP 800-66r2 on HIPAA Security Rule implementation are also important references for organizations aligning clinical technology security with broader risk management programs.

• Inventory and visibility: organizations need to know which devices, interfaces, software versions, and cloud services handle protected health information or safety-relevant data.
• Secure data movement: authentication, authorization, encryption, and logging should be applied consistently across device, middleware, API, and EHR layers.
• Lifecycle maintenance: connected products require vulnerability monitoring, patch planning, coordinated disclosure processes, and postmarket risk reassessment.
• Clinical safety alignment: security controls should protect confidentiality and integrity without interrupting essential clinical use.

How standards change procurement and integration

Standards are becoming procurement requirements, not just technical preferences. FHIR is central to many modern health data exchange policies, while USCDI defines important classes and elements for interoperable health information. However, device teams should not assume that FHIR alone solves every integration problem. FHIR is a powerful framework, but device data may still require careful profile selection, terminology mapping, observation modeling, alarm handling, and workflow design.

The most useful procurement conversations now go beyond whether a product has an API. Buyers increasingly need to ask what data the API exposes, whether it uses recognized standards, how identity and consent are handled, whether data provenance is preserved, how updates are managed, and whether cybersecurity documentation can support regulatory, operational, and risk reviews. See also: clinical equipment.

For medical device manufacturers, interoperability should be planned before commercialization. For providers, interoperability should be tested before deployment. The cost of late integration work is not only technical debt; it may also include workflow disruption, duplicate documentation, weak data quality, and security exposure.

Governance turns data into operational value

Healthcare organizations often focus on connectivity first and governance later. That sequence is risky. Data governance determines who can access information, which data is trusted for clinical use, how long data is retained, how patient requests are handled, how errors are corrected, and how information moves between care, billing, research, quality, and public health use cases.

Governance is also becoming more important because device data can feed analytics and predictive tools. ONC’s HTI-1 rule introduced transparency requirements for artificial intelligence and other predictive algorithms that are part of certified health IT. The broader implication is that organizations need to understand not just the output of a model, but the data that supports it. If device data is incomplete, delayed, biased toward certain populations, or separated from clinical context, downstream analytics can be misleading.

Good governance does not mean slowing innovation. It means making the rules visible. A remote monitoring program, for example, should define which readings trigger review, who receives alerts, how false positives are handled, whether patients can see the same data, and how device malfunction is separated from patient deterioration. These choices are operational, clinical, technical, and ethical at the same time.

A practical roadmap for device and healthcare teams

The following roadmap is an editorial synthesis of current regulatory and industry signals. It is not a substitute for legal, regulatory, or clinical engineering advice, but it can help teams structure decisions around healthcare data technology and connected medical devices.

1. Map the data journey. Identify where data is created, transformed, stored, displayed, exchanged, and deleted. Include device, gateway, mobile app, cloud, EHR, analytics, and payer-facing systems where applicable.
2. Classify data by risk. Separate safety-critical measurements, protected health information, operational telemetry, administrative data, and research datasets. Each category may require different controls.
3. Design for provenance. Preserve device identity, timestamp source, units, software version, user action, and transformation history when clinically relevant.
4. Use standards deliberately. Prefer FHIR, USCDI-aligned data elements, recognized terminology, and documented implementation guides where they fit the use case. Do not force standards in ways that erase clinical meaning.
5. Build cybersecurity into the lifecycle. Treat threat modeling, vulnerability management, access control, logging, and update mechanisms as design requirements, not deployment tasks.
6. Test workflow impact. Validate how data appears to clinicians, patients, biomedical engineering teams, and compliance staff. A technically correct feed can still fail if it creates alert fatigue or ambiguous responsibilities.
7. Prepare for change. Regulations, standards, and security expectations will continue to evolve. Contracts and architectures should allow updates without requiring full system replacement.

The organizations that benefit most from healthcare data technology will be those that treat data as a regulated clinical asset. Connected devices can improve monitoring, coordination, and decision support, but only when data exchange is paired with security, context, governance, and workflow discipline.

Frequently asked questions

What is healthcare data technology?

Healthcare data technology refers to the systems and methods used to collect, secure, exchange, analyze, and govern health information. It includes EHR platforms, APIs, interoperability standards, medical device interfaces, analytics tools, cloud infrastructure, identity management, cybersecurity controls, and data governance processes.

How does healthcare data technology relate to medical devices?

Connected medical devices increasingly generate data that must move into clinical workflows, patient apps, remote monitoring platforms, analytics systems, and sometimes payer or public health processes. Healthcare data technology determines whether that movement is secure, standardized, reliable, and clinically meaningful.

Is FHIR enough to integrate medical device data?

FHIR is important, but it is not enough by itself. Device data also needs correct terminology, units, timestamps, patient matching, provenance, cybersecurity controls, workflow design, and governance. A FHIR API can move data, but organizations still need to decide how that data should be trusted and used.

What changed by 2026?

By 2026, USCDI v3 had become the ONC certification baseline, FDA medical device cybersecurity expectations had been updated through the 2025 final guidance, and CMS payer API requirements were moving toward major 2027 implementation dates. Together, these developments make interoperability, security, and governance more central to connected device strategy.