Medicine is complicated, and rightfully so. The complexity of the human anatomy and physiology lends itself to a dense pathology, with a myriad of patient symptoms & experiences. Even with the quality and quantity of clinical research growing impressively, a comprehensive and exhaustive digital framework, a pre-requisite to automation, remains out of reach.

Consequently, we see dozens of clinical specialties/sub-specialties and a geographically sprawling heterogeneity of medical sites. Unsurprisingly, this results in non-standard patient paths across a fragmented provider landscape—medical journeys rarely follow straight lines. With so many different journeys and types of providers, each with idiosyncratic clinical and operational flows, we live in a world with a proportionally varied set of internal IT systems and software. Though there is an observable degree of convergent evolution, these technological stacks often lack shared feature-sets, data structures, and ontologies. Given the heavily regulated and politically entrenched nature of healthcare, combined with the stringent privacy and compliance standards associated with sensitive patient data, health technology exists in a uniquely constrained ecosystem. Unlike other industries where software development follows a compounding trajectory—iteratively building upon existing frameworks to accelerate innovation—healthcare software remains largely siloed, with fragmented systems inhibiting cumulative progress.

The status quo is one where technological incumbents, especially EHR systems, have built monopolies on the back of regulatory capture.

This structural inertia is exacerbated by the vast volume of clinical data accumulated over a patient’s lifetime and its critical role in diagnosis and treatment. The switching costs associated with migrating to alternative software are prohibitively high, effectively locking providers and patients into legacy systems. As a result, the status quo persists, with diminishing incentives for innovation over time. Interoperability, whether driven by federal regulatory mandates or by privately accrued network effects, is essential. Specifically, if we seek to harness the full potential of free-market technology and innovation in healthcare, provider systems (today, primarily EHRs) must be capable of seamless data exchange, interpretation, and utilization across platforms and stakeholders, all while maintaining the required levels of privacy and security.

The case for interoperability is well-established. Over the past decade, a number of nationwide initiatives have sought to design and implement interoperable infrastructure, most notably through legislative mandates. These mandates manifest practically in the form of Health Level 7 International (HL7) data standards and Fast Healthcare Interoperability Resources (FHIR) APIs. However, significant non-technical, “human” barriers continue to impede widespread adoption, including incumbent lobbying, prolonged compliance cycles for certification and security, and the presence of adverse actors. While these challenges persist, in this paper, we focus on the technical obstacles that remain within our academic locus of control. Despite many EHRs and related systems nominally adhering to HL7 standards and exposing FHIR APIs, they fail to achieve the level of flexibility required for real-world healthcare workflows. We contend that this is due to:

• The complexity and variability of data schemas across EHRs
• Inconsistencies arising from manual errors and workflow idiosyncrasies
• FHIR/HL7 formats prioritizing static data exchange over dynamic, real-time, and bidirectional synchronization (outdated standards)
• Legacy infrastructure (e.g., on-prem databases, COBOL-based systems) lacking native support for API-based communication
  Any viable technical approach must incorporate mechanisms to mitigate these challenges if the goal is true, scalable interoperability.

Any viable technical approach must incorporate mechanisms to mitigate these challenges if the goal is true, scalable interoperability.

Achieving seamless interoperability requires a deep understanding of both standardized data exchange protocols and the specific architecture of the EHR system being integrated. Our approach to building an interoperability engine on Athenahealth’s rails begins with leveraging FHIR APIs, the current baseline for modern healthcare data exchange. Although we use Athenahealth’s base FHIR resources to configure and initialize our engine, we take precise steps to engineer an architecture capable of serving as a comprehensive abstraction layer a step-function beyond modern standards—eventually, deprecating the need for rigid data structures prescribed by outdated frameworks.

FHIR, developed by HL7, is designed to provide a lightweight, flexible, and modular approach to exchanging healthcare data. Unlike earlier HL7 v2 and v3 standards, which were primarily direct message based, FHIR employs RESTful APIs, making it more compatible with modern web-based applications.

FHIR’s core principles include:

• Resource-Oriented Architecture: Data is structured into discrete resources (e.g., Patient, Practitioner, Appointment, DocumentReference) that are individually retrievable via API endpoints.
• RESTful Communication: APIs use standard HTTP methods—GET, POST, PUT, and DELETE—to facilitate data retrieval and modification.
• JSON and XML Serialization: Data is formatted in JSON or XML, ensuring widespread compatibility across different technology stacks.
• Extensibility and Profiles: FHIR allows custom extensions to accommodate practice-specific variations while maintaining a baseline of standardized fields.

Most modern EHR systems expose FHIR R4 APIs, which provide endpoints for core clinical and administrative data. However, while FHIR is a standardized protocol, its implementation varies significantly across vendors, often requiring extensive customization and reconciliation.

Athenahealth (Athena), as a more recently developed cloud-based EHR (relatively), does provide an API framework (alongside documentation) that supports data exchange for key clinical and administrative workflows. However, its implementation exhibits several unique characteristics and constraints that shape our approach to integration. Their API ecosystem is primarily RESTful, aligning with modern interoperability standards. However, unlike some competitors (e.g., Epic, which has a more rigid API framework), Athena offers greater configurability in certain domains. Ostensibly, these are beneficial to clinicians—in practice, these configurations in Athena’s native schemas cement their utilization and are not designed to be shared cross-system.

A truly interoperable schema maintains data integrity and context, while allowing for fluid transformation by external parties, critically, in real-time. To focus this paper and achieve such a level of “complete” interoperability, our engine aims to synchronize the following key datasets between Athena and external systems with such a standard in mind:

1. Providers and Departments

Athena does not provide a dedicated API endpoint for direct provider queries that can be systematically applied to all practices. Instead, comprehensive and accurate provider data must be inferred from appointment records, clinical documentation, and scheduling metadata. Departmental data is similarly fragmented, often embedded within practice-level configurations rather than existing as a dedicated, queryable resource. For example, Departments can be configured to be locations, groups within a location, credentialed provider groups, or divisions. Unsurprisingly, such ambiguous definitions result in idiosyncratic interpretations e.g. “providers” vs. “departments” vs. “provider groups”.

2. Patient Demographics and Registration Information

The Patient API supports retrieval and modification of demographics, contact details, and registration status. However, Athena follows a strict authorization model, requiring explicit patient consent for certain data fields. Additionally, duplicate patient records and manual input errors introduce discrepancies, necessitating intelligent record-linking mechanisms within our engine.

3. Patient Insurance Details

While Athena provides insurance data within the broader patient records, the format and granularity of this data vary across implementations. Insurance details, including payer IDs, plan names, network tiers, and relevant benefit types, are often inconsistently structured, requiring schema normalization to ensure consistent processing. In addition, for practices leveraging Athena’s billing/RCM tools, insurance details are stored as Athena packages, an Athena-specific standardization of payers and plans, relevant for filing claims. These packages are stored in internal Athena look-up tables and do not correspond cleanly with unstructured inputs. Under status quo, most software solutions map to Athena packages using Athena’s best-match algorithm, a black-box from an infrastructure/algorithmic standpoint, making it especially prone to accuracy/matching errors. This obscured mapping problem is borne out empirically.

4. Clinical Documents

Clinical documentation within Athena follow FHIR DocumentReference structures, supporting structured and unstructured data (e.g., progress notes, lab results, imaging reports). However, Athena’s implementation diverges from standard FHIR guidelines in metadata representation, requiring bespoke translation layers to align documents with external schemas.

5. Appointments and Scheduling (Practice Calendar)

The Appointments API allows real-time scheduling, rescheduling, and cancellation, but it exhibits asynchronous behavior, meaning updates are not always immediately reflected. Additionally, appointment types and provider schedules are often stored in non-standardized formats, requiring custom parsing and normalization.

With these 5 data-sets as initial requirements for comprehensive integration, let’s outline the explicit advantages and limitations of leveraging Athena as our choice foundation for an end-to-end interoperability engine.

Advantages:

• Cloud-Based Architecture: Unlike on-premise EHRs, Athena is fully cloud-hosted, enabling faster API interactions and centralized updates without localized hardware dependencies.
• Native Snowflake Integration for Analytics: Athenahealth’s use of Snowflake as the backend for its Data View product provides direct access to a performant, cloud-based data warehouse. This enables efficient querying, simplifies ETL workflows, and allows for scalable data extraction at high volumes—removing the need to scrape from operational APIs.
• Relatively Open API Access: While some EHR vendors severely restrict API usage, Athena provides relatively permissive API access for third-party integrations, reducing bureaucratic hurdles in implementation.
• Continuous Updates: Athena actively enhances its FHIR and REST APIs, ensuring compatibility with evolving regulatory standards (e.g., ONC Cures Act compliance). This is reflected in the detail of their documentation and support channels/guidelines.

Limitations:

• Fragmented Provider and Department Data: The lack of dedicated provider/department interpretations, thus APIs, necessitates indirect retrieval methods, increasing complexity in synchronization.
• Data View Access Constraints: Despite its architectural advantages, the Data View product introduces variability in access and sync timing across practices. Some datasets are subject to delayed replication or practice-specific permissions, limiting its utility for near-real-time synchronization and requiring fallbacks to primary API sources in time-sensitive workflows.
• Schema Inconsistencies: Unlike Epic, which enforces a more rigid data structure, Athena allows more practice-specific customization, leading to high variability across implementations.
• Limited Real-Time Event Hooks: While Athena provides some event driven notifications, it does not support real-time webhooks for all data sets, requiring periodic data polling to maintain parity.

Given the aforementioned constraints, our design is subject to several key architectural principles to ensure robust data synchronization:

1. Dynamic Schema Mapping and Normalization: Our translation layer must continuously learn and adapt to practice-specific schemas, ensuring that customized data fields are correctly interpreted and mapped across systems. This includes challenging variants specific to clinical operations, e.g. “credentialed provider groups departments”.
2. Intelligent Data Reconciliation: By leveraging historical data patterns, our engine needs to automatically detect and resolve issues such as duplicate patient records, missing insurance details, and inconsistencies in clinical documentation.
3. Hybrid Synchronization Model: We must account for Athena’s lack of real-time webhooks, limiting real-time bidirectional capabilities. In the following sections, we proceed with a dual layer sync strategy: Online API Calls for immediate updates for time-sensitive transactions (e.g., appointment booking, insurance validation) and Offline Batch Jobs, nightly crons which reconcile historical inconsistencies and validate data accuracy against external records.
4. Error Handling and Data Validation: Given Athena’s variability in response structures, our engine needs to implement adaptive error correction mechanisms that analyze non-standard response types (e.g., malformed insurance data, missing appointment metadata) and automatically apply contextual fixes. This is distinct from the data inconsistency that we outline in (1).

While Athena’s data model introduces heterogeneity across practices, its architectural openness, native Snowflake integration, and evolving standards make it a strategically sound foundation for building reusable interoperability infrastructure. Our engine is explicitly designed to absorb these structural inconsistencies through layered translation, schema reconciliation, and adaptive synchronization logic—allowing us to treat Athena as both a proving ground and a scalable template for broader EHR integration.

At the foundation of our architecture is a robust data warehousing strategy built on Snowflake, a cloud-native platform chosen for both its alignment with Athena’s Data View product and its architectural advantages. While Snowflake’s compatibility with Athena simplifies schema design and reduces impedance mismatch, our choice is grounded in deeper technical considerations.

Modern application architectures have shifted away from tightly coupled monoliths in favor of distributed microservices. This decomposition improves modularity and deployment velocity but fragments the data layer across services. Transactional systems optimized for low-latency reads and writes (OLTP) do not provide the consistency, indexing, or historical introspection needed for analytical use cases (OLAP). A centralized warehouse becomes the reconciliation point—a system of record for cross-domain joins, audit trails, and convenient computation.

Snowflake separates compute and storage into independent layers, enabling concurrent access patterns. Analytical workloads do not compete with ingestion pipelines. ETL, backfills, machine learning pipelines, and dash-boarding can all operate independently on the same physical data. The result is an architecture where multiple virtual warehouses act on a single logical truth, maintaining durability and auditability without sacrificing parallelism.

Schema Portability and Declarative Configuration

To ensure schema portability and extensibility across practices as well as other applications that may choose to leverage our foundational model, we define our data transformations using dbt (data build tool), a SQL-based framework for analytics engineering. Rather than hard-coding schema definitions or maintaining one-off ETL scripts, we express all transformations as modular, version-controlled models that can be parameterized for different clients, orgs, and environments.

dbt allows us to express transformation logic as code, structure it with dependencies and lineage, and track its evolution in git. This enables understandable communication of views and tables that can evolve safely across time and teams. Downstream applications can extend or override default models without modifying core logic—enabling interoperability not just at the data level, but at the transformation level.

Adaptive Ingestion for Cross-EHR Compatibility

While our primary source system is Athena, Snowflake’s ingestion flexibility enables broad compatibility with structured and semi-structured formats. CSVs, TSVs, JSON, and XML files can be loaded directly. For unstructured formats (e.g., PDFs), we rely on pre-processing with OCR pipelines before transformation. OCR solutions like AWS Textract or other more intelligent document extraction services can be leveraged interchangeably.

Document-oriented sources like MongoDB require deeper restructuring due to their lack of fixed schema (this extends to all non-relational DB formats). To accommodate these, we implement translation macros within dbt that flatten nested documents, infer typed columns, and de-normalize reference paths. These macros are portable across collections and generalize well to other NoSQL sources.

As a result, the warehouse does more than centralize Athena data—it acts as a schema-normalizing interface for any upstream EHR or partner system, transforming domain-specific records into interoperable tabular formats under a common abstraction model.

This section focuses on specific implementation details, with the goal of operationalizing the architectural theory discussed above.

Ingestion and Raw Layer Design

Because Athena houses their Data View in Snowflake, our transfer process can be simple—direct Snowflake-to-Snowflake replication. For data from other EHR sources, we propose using Fivetran for managed ELT and change data capture (CDC), especially given that most EHR data stores live within Redshift, BigQuery, or S3, all with native connectors in Fivetran. The core EHR data transfer can be merged with operational databases (Postgres) and document stores (MongoDB) into Snowflake with minimal engineering overhead. All data lands first in the Raw layer of our Snowflake environment, segmented by source system (e.g., athena_dev, odata_dev, postgres_dev).

This is where the data—identified and unmodified—is staged for downstream processing. Snowflake’s ability to scale compute elastically allows us to index and query this layer in real time, even while long-running transformation jobs are executed in parallel.

Canonical Schema and De-Identification

We use dbt to map raw tables from source schemas into a unified canonical schema that reflects Superscript’s internal ontology—standardizing column names, typing systems, and entity relationships across EHRs. The configuration models used can be replaced or re-structured for application requirements distinct from our own.

As part of this initial transformation, all personally identifiable information (PII) is hashed and de-identified. The de-identification step is enforced centrally across all dbt projects and designed to preserve referential integrity across systems. For instance, a patient record in Athena and a user record in our Postgres environment that refer to the same individual will resolve to the same de-identified superscript_user_id, governed by a master index.

The original PII-rich raw data is retained alongside the de-identified tables within the Bronze layer, but is governed by strict access controls and encryption policies. Downstream models operate solely on de-identified data unless PII access is explicitly required.

Transformations, QA, and Promotion Pipelines

From the de-identified base layer, we build all downstream data products via chained dbt models. These transformations encode business logic, analytics requirements, and feature engineering for use in Superscript applications. For example, insurance verification or payer-negotiated rate models applied to Athena data are written as SQL macros in dbt, versioned via Git, and compiled into clean, reproducible “pricing” tables.

We maintain a classic three-environment structure—Dev, QA, and Prod— mapped to dbt feature branches and CI/CD pipelines:

• Developers create models in isolated dbt environments tied to Git feature branches.
• On push, CI workflows test and materialize models into the Dev database.
• Merging into main-dev triggers CD pipelines that promote models to QA, cloning all referenced tables for controlled testing.
• Once UAT passes, QA → Prod promotion automates the creation of final models and tables used in live Superscript products and dashboards.

Cross-database joins are supported at all layers, enabling us to output models that combine records across EHR sources (e.g., a unified pricing table combining Athena and OData) or keep logic source-scoped (e.g., Athena specific intake form coverage). This gives us modularity without sacrificing schema clarity or code reuse.

Extensibility

This pipeline structure not only allows for rapid iteration and version control, but also serves as a platform abstraction layer: any partner, downstream integrator, or Superscript service can interface with our canonical schema without knowing the underlying source system. New data sources—relational or document-based—can be onboarded simply by extending the dbt pipeline: new raw sources land in Snowflake, dbt applies standard transformations, and new de-identified outputs flow into the same combined analytics surface as existing data. This foundation allows us to abstract away source-specific complexity while enabling controlled data exchange at scale.

One of the foundational challenges in designing a scalable interoperability engine lies in defining a schema that is simultaneously expressive, sustainable, and adaptable across source systems. Our goal was to build a canonical data model that reflects the minimal unit of semantic meaning for each entity we care about—patients, appointments, insurance, procedures—while also remaining manipulable and extensible to support downstream applications.

Our schema is intentionally decoupled from any particular domain interface. While it is built to support internal Superscript systems, we intentionally avoid hardwiring application logic into the schema definitions. This preserves its utility as a shared interface.

All transformations are written in modular dbt models, with each layer corresponding to a clear semantic shift: raw → normalized → canonical. The normalized layer handles source-specific naming, typing, and null handling; the canonical layer introduces shared semantics and standardized identifiers (alongside “nice-to-haves” like time-stamping). This layered design enables portability, without requiring recompilation.

While standardization handles the 80% case, the remaining 20% (highly variable and often organization-specific edge cases) demands disproportionate effort and engineering precision. In practice, edge case resolution is the true test of any interoperability engine.

This layer absorbs the entropy of the real world.

We explicitly separate our standardization logic from edge case handling, recognizing that many edge cases reflect not only schema violations but mismatches in upstream semantics, data entry practices, and organizational ontologies. As such, our system includes a dedicated suite of transformations, validations, and overrides tuned to the failure modes we’ve observed in production. This layer is built on a foundation of:

• Multi-stage waterfall joins: Matching records across sources using cascading rulesets (e.g., exact match on first_name, last_name, dob, phone, then fuzzy phonetic match, then administrative override).
• Data science-informed heuristics: We ran cluster analyses, string distance functions, and nullity pattern profiling to isolate structural anomalies and inform merge logic (e.g., high-frequency duplicate names with inconsistent identifiers).
• Temporal inference and shadow joins: Identifying rescheduled or canceled appointments by temporal proximity and overlapping metadata rather than by primary keys, which are often non-deterministic.
• Schema disambiguation utilities: For example, differentiating between “departments”, “provider groups”, and “divisions” depending on how a practice encodes them across different contexts.

The most common edge cases include:

• Inconsistent naming conventions across organizations.
• Duplicate enterprise records due to upstream patient merges or administrative overrides.
• Null values in key join fields (e.g., missing phone numbers, incomplete insurance metadata).
• Dangling or duplicated appointments resulting from reschedules, late cancels, or double-booked slots.

To manage these conditions, we built a layered validation and resolution system, designed to both operate autonomously and allow for convenient human review when thresholds of ambiguity are exceeded. Logs and intermediate state tables capture every override or inferred link for auditability.

Many of these workflows originated from intensive empirical research and trial-and-error debugging: tracing failures across environments, analyzing latent join cardinalities, and building reference datasets that map known anomalies. In this sense, our edge case handling is not just procedural—it encodes months of organizational learning about how healthcare data actually behaves.

We do not claim this layer is exhaustive, nor can it be made so in static form. This foundation is designed to evolve. As new patterns emerge, overrides can be introduced without mutating the canonical schema or interrupting downstream consumption. In this way, edge case handling becomes not a barrier to interoperability, but an asset—an ever-growing layer of resilience in the face of systemic fragmentation.

Outside of the foundational data, we need to ensure that our engine enables real-time read and write functionality by leveraging Athena’s RESTful and FHIR-compliant APIs. These interfaces allow our system to perform both atomic and idempotent operations against patient records, appointments, and administrative entities (the full set of which is described in earlier sections).

While similar integrations have been built before, our contribution lies in extensibility. By anchoring read/write functionality to a stable, canonical schema, we abstract away source-specific inconsistencies and make these actions composable—allowing downstream services or applications to execute writes using a unified interface.

In cases where certain actions are not exposed directly via Athena’s API surface, we simulate equivalent outcomes through action chains: coordinated sequences of permissible operations that together achieve the desired result. While not yet rigorously tested across edge conditions, this technique lays the groundwork for future automation and makes the developer interface significantly more expressive.

The goal is not just interoperability but usability.

Our hope is that this layer provides the necessary abstraction to enable broader developer communities (internal or third-party) to build rich, application-level workflows without having to manage raw API complexity, especially when the API documentation and extensibility lives within incumbents that have little incentive to keep up with the frontier (including Athena).

Achieving interoperability at scale requires more than real-time operations. Specifically, it requires the ability to reconcile large volumes of historical data into our centralized model. Our system addresses this cold-start problem through a modular, fault-tolerant series of backfill scripts.

At the core of this layer is a batch-processing architecture that mirrors standard large-scale data ingestion patterns. Patients are processed in parallelized chunks ensuring fault isolation, memory efficiency, and most importantly, observability. Each sub-batch is handled by a callable job, which generates a backfill script for insertion into our system.

For each patient record, the engine attempts to first resolve identity via deterministic joins (e.g., first name, last name, DOB, phone number). This waterfalls into a series of fuzzy matching/record-linking scripts. When no canonical match is found, a new record is generated. Metadata (demographics, insurance, intake forms) is fetched and standardized, and patient-linked appointments are processed for ingestion. Where appointment records lack explicit EHR visit IDs or fall within a defined temporal proximity (e.g., overlapping within 15 minutes), a collapse rule is applied to de-duplicate and normalize overlapping entries.

Though this can be implemented differently across use-cases, all of our application-relevant services are invoked at the appointment-level, dependent on patient insurance, provider NPI, and service—triggering parallel gateway calls to relevant third-party services (e.g. Clearinghouses, Billing Entities). If cache hits exist, responses are short-circuited for performance; otherwise, full jobs are executed. These calls inform our downstream set of services.

All transformations are logged at the row level and stored under a unique migration artifact. Each batch is exported to a well-structured script directory, with metadata on the number of insertions, table-level diffs, and unresolved anomalies. These logs enable downstream data quality checks (DQ), enforcing row-count reconciliation and referential integrity.

In aggregate, the backfill system serves as both a bootstrap mechanism and a long-term maintenance layer. It ensures parity not only at the start of a client lifecycle but across organizational transitions, EHR migrations, and data recovery workflows. Because it reuses the same validation, schema, and error handling pipelines as our real-time logic, it also guarantees consistency between historical and forward-facing states.