Building healthcare AI without rebuilding your data platform

AI is transforming every corner of healthcare — from diagnosis, medical imaging, billing, and clinical documentation to clinical trials. Yet with 37% of hospitals running at a loss or on margins of 2% or less, organizations are under intense pressure to improve efficiency. 

At Fivetran, investments like our Epic EMR connector help organizations such as Inova Health and Sharp Healthcare keep healthcare data AI-ready, compliant, and accessible. Combined with phData's expertise designing and scaling modern data platforms for healthcare and life sciences (HCLS), Fivetran enables enterprises to modernize data integration, governance, and AI adoption, helping organizations deploy AI across the business.

With a modern, AI-ready data foundation like this in place, healthcare organizations are putting AI to work across the business. CFOs are using it to streamline billing and operations, while clinicians rely on it to document visits, support diagnoses, accelerate research, and improve patient care. From clinical documentation and diagnostics to clinical trials and revenue cycle management, AI is delivering measurable value in every part of the organization. Some key areas include:

• Clinical assistants and scribes: By utilizing ambient AI, providers can automate medical note-taking and securely summarize conversations in real time, drastically reducing post-shift charting times and ensuring clinical data is captured accurately and consistently for downstream analytics. 
• Predictive patient monitoring: Wearables and AI agents continuously track patient vitals and health data. These systems identify risk patterns or predict flare-ups of chronic conditions hours before human symptoms escalate.
• Diagnostics and imaging: AI tools process medical imaging like MRIs and X-rays with incredible precision. They can flag micro-anomalies human eyes might miss. 
• Intelligent patient engagement: AI-driven virtual assistants provide 24/7 symptom checking and self-service booking, allowing patients to easily schedule appointments and receive personalized reminders to reduce no-shows
• Administrative and revenue cycle management: AI agents take over complex back-office workflows, from insurance verification to automated medical coding, which speeds up reimbursements and significantly reduces claim denials.

Fivetran plays a critical role in these solutions as it can seamlessly capture and move raw data and diagnosis information to a common platform, providing executives with insights across every aspect of their business and allowing a combination of experience and AI to transform every piece. 

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AI’s data challenge

Many healthcare companies built their core data infrastructure in the past 10+ years, using the prevailing best practices of the time, but these dated solutions are now holding them back. The first challenge is speed and rigidity: many ETL pipelines took months to build and were designed for an era when implementations took weeks and data warehouses were updated weekly—or, at best, daily—on a fixed enterprise scheduler.

The second challenge of these legacy ETL pipelines is that they are predominantly coded to pull a specific subset of data to address a limited set of use cases. This means as new use cases are defined on the same source data, the entire data-engineering process has to start from scratch again. That was fine when data was just feeding dashboards. It is not fine when AI agents need fresh, complete data around the clock to influence clinical decisions, automate workflows, and surface operational insights in real time. The infrastructure gap is not a technical inconvenience; it is actively slowing down every AI initiative in the business.

A better way

The shift from ETL to ELT isn't just a technical preference; it's what makes an AI-ready data architecture possible. The top 2 measures when creating pipelines for AI are time to deliver the data and how fresh it is. Fivetran pipelines are configured in minutes and run themselves — continuous replication, automatic schema handling, no maintenance overhead. All data that is moved by Fivetran is encrypted, and where necessary, this can be backed by a BAA.

Out of the box, Fivetran removes the operational burden that slows down many data teams by having the following capabilities built in:

• Everything, not just what you planned for. Fivetran replicates all objects from a source, not just the ones a specific use case needs today. When new questions emerge, the data is already there.
• Always current. Change data capture (CDC) keeps data continuously fresh across every pipeline, so operational dashboards and AI agents are working from the latest data.
• Self-managing as systems evolve. When source schemas change, a column is added, or a table is restructured, Fivetran handles it automatically. No reprocessing, no pipeline failures, just a notification.

Fivetran ingests structured, semi-structured, and unstructured data—from claims records and clinical notes to imaging metadata—into a single open data layer that any downstream AI or analytics tool can use. This eliminates duplicate data pipelines, keeps data consistent, and lets healthcare organizations adopt new technologies without rebuilding their infrastructure.

phData has seen this approach succeed in regulated healthcare and life sciences environments, where organizations need to modernize legacy reporting, reduce manual integration, and build scalable AI-ready data platforms. In one global biopharma deployment, Fivetran served as the managed ingestion layer into Snowflake, providing a repeatable way to land governed source data while making trusted data available faster across clinical, commercial, manufacturing, and operational teams—without every new use case requiring custom engineering.

Business-first experience

The measure of a successful data foundation is simple: how quickly can a previously unknown new business question get answered without requiring an engineering project? For most healthcare organizations today, the answer is weeks or months. With the right architecture in place, it becomes hours or minutes.

It starts with fast access to data. Fivetran's 750+ prebuilt connectors can be configured in minutes, making sources like Epic or Salesforce available in as little as 15 minutes. The more data sources connected and refreshed, the faster organizations can make informed decisions.

Once data is available, dbt transforms it into AI-ready models. dbt Wizard accelerates development by turning business requirements into production-ready models while generating tests and documentation, giving AI agents the context they need through catalogs or Agent Schema.

With Fivetran continuously ingesting fresh data and dbt managing transformations, organizations can keep analytics and AI up to date. dbt State further reduces latency by running only the transformations affected by upstream changes instead of rebuilding entire pipelines. In environments with thousands of transformations, this delivers typically 40% lower compute costs, but more importantly for our agents, an average of 32% runtime savings, giving both AI agents and people faster access to trusted, current data.

Intelligence platforms are not enough

Life would be simpler if that were the end of the story, but patient data originates in and is used across CRMs and EMR platforms, which means a key requirement is not just to centralize data, but to sync trusted, up-to-date data back into those systems. The final piece of the puzzle is Fivetran Activations, which, much like Fivetran connectors, can be configured in minutes and can read from tables to create, update, and delete objects in those platforms. That means administrators, doctors, and nurses can all work from the same current information.

Risks

A common concern with any healthcare data modernization project is whether the platform can connect to the industry's specialized systems. Fivetran addresses this with a production-ready Epic connector, native support for widely used platforms like Salesforce, and expanding healthcare and life sciences coverage, including Veeva Vault. For systems without a prebuilt connector, AI-powered connector generation can create API integrations in minutes. If it is more involved, Fivetran’s AI-enabled Connector SDK allows any data sources that can be accessed via Python to be configured, tested, and deployed in ever-shortening cycles.

For regulated healthcare organizations, the challenge is not just connecting data — it's doing so within established governance processes. Together, Fivetran and phData enable rapid data ingestion while ensuring new connectors and data sources follow the validation, documentation, approval, and change-management practices required in healthcare and life sciences. The result is a modern, AI-ready data foundation that improves agility without compromising compliance.

The time to move is now

The organizations pulling ahead in healthcare AI aren't necessarily the biggest or the best-funded. They're the ones who recognized the data foundation problem early and solved it. 

Inova Health is a good example — what looked like a 4-year transformation was done in 6 months, and their teams now have the real-time data access to make faster, better-informed decisions across the organization.

That kind of progress is achievable because organizations no longer need to carry the full burden of building and maintaining every pipeline pattern themselves. With Fivetran providing the ingestion foundation and phData helping design, implement, and operationalize the broader platform — or AI agent harness — around it, healthcare teams can move faster without sacrificing trust, governance, or long-term scalability.

The question is not whether AI will reshape your organization. It already is. The question is whether your data will be ready when it matters.

Continue the conversation with Fivetran and phData on September 22, where we'll explore practical strategies for building trusted, AI-ready healthcare data foundations.

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