Hubble: One API to Solve Medical Data Silos and Pave the Way for AI Agents

The "Last Mile" Problem of Medical Data

In an era where digital transformation is sweeping across every industry, medical records remain stuck in a surprisingly primitive state. Fax machines, phone menu systems, and patient portals whose passwords no one can remember — these seemingly last-century technologies are still the primary channels for accessing medical records.

The continued use of fax machines in healthcare may seem absurd, but there are deep-rooted reasons behind it. Fax transmission has long been legally regarded as a "secure" communication method compliant with HIPAA privacy regulations, since it's a point-to-point transmission that doesn't pass through internet intermediaries. More importantly, faxing doesn't require both sender and receiver to use the same software system — in an ecosystem composed of thousands of different IT systems, it has ironically become the "lowest common denominator" universal solution. It's estimated that the U.S. healthcare system still sends approximately 9 billion pages of faxes per year. Many small clinics and independent practitioners, constrained by limited IT budgets, continue to treat fax as the default method for transmitting medical records.

Hubble, which recently landed at #5 on the Product Hunt leaderboard, targets exactly this pain point. Its tagline cuts straight to the core: "Retrieve medical records that other APIs can't." The product appeared simultaneously in the Developer Tools, Artificial Intelligence, and Health categories, garnering 92 upvotes and 13 comments — a clear signal of strong demand from the developer community for medical data interoperability.

Why Is Medical Data So Hard to Access?

The fragmentation of medical data is no accident. A single patient's complete medical history can be scattered across multiple hospitals, clinics, laboratories, and imaging centers. Each institution uses a different Electronic Health Record (EHR) system, adopts different data formats, and follows different privacy compliance processes. To make matters worse, many institutions still rely on fax as their primary method for transmitting records.

The fragmented EHR landscape has deep historical roots. In the U.S. market, vendors like Epic, Cerner (now Oracle Health), MEDITECH, and Allscripts hold major market share, but their data formats and interface protocols have long lacked unified standards. Statistics show that the U.S. has over 700,000 healthcare service providers using hundreds of different EHR products. The HITECH provisions within the 2009 American Recovery and Reinvestment Act used financial incentives to drive large-scale EHR adoption, but in the rush to deploy quickly, interoperability took a back seat — ultimately creating today's landscape of proliferating "data silos." Even within the same EHR vendor's system, data migration between different versions is far from straightforward.

This fragmented state has led to an awkward reality: even in an era of rapidly advancing AI, reliably retrieving a patient's complete cross-institutional medical records programmatically (via API) remains an extremely challenging task. Many existing medical data APIs can only cover institutions that have adopted standardized systems, leaving them powerless against records "hidden behind fax lines."

Hubble's Solution: One Verification, Complete Aggregation

Hubble's core approach can be summarized as "patient-authorized data aggregation." The workflow is clean and straightforward:

• One-time patient identity verification: Patients verify their identity just once to complete authorization.
• Cross-institutional record aggregation: Hubble handles collecting medical records from all of a patient's healthcare providers, regardless of what systems or transmission methods those institutions use.
• Unified API response: All aggregated records are returned through a single, unified API endpoint, ready for developers' AI agents to consume directly.

The elegance of this design lies in encapsulating all the complexity behind the scenes — handling faxes, navigating phone trees, logging into various portals. Developers no longer need to write custom integration logic for each institution; instead, they build against a single standard interface.

Built for the AI Agent Era

Here's a notable detail: Hubble explicitly positions its output target as "AI agents." This is a remarkably forward-looking stance. AI agents are AI systems capable of autonomously perceiving their environment, formulating plans, and executing multi-step tasks — distinct from traditional single input-output models. Powered by Large Language Model (LLM) technology, AI agents typically possess core capabilities such as tool use, memory management, and reasoning & planning. In a healthcare scenario, a typical AI agent workflow might look like this: upon receiving the instruction "assess the surgical risk for Patient X," the agent autonomously calls APIs to retrieve the patient's medical records, lab results, and imaging reports, then comprehensively analyzes various indicators to generate a risk assessment report with flagged abnormalities requiring the physician's special attention. Frameworks like LangChain, AutoGen, and CrewAI provide the technical infrastructure for building such agents.

As LLMs and autonomous agent technologies mature, the healthcare sector is seeing a surge of AI application scenarios that require access to patient records:

• Intelligent pre-diagnosis and condition summary generation: AI automatically synthesizes multi-source records to help physicians quickly grasp a patient's full picture
• Medication conflict and medical history risk auto-screening: Cross-institutional data integration enables more comprehensive drug interaction detection
• Automated insurance claims review: Structured medical record data dramatically improves claims processing efficiency
• Clinical research patient screening and data extraction: Research institutions can more efficiently match eligible trial participants

The common prerequisite for all these applications is the ability to access complete medical records in a structured, programmable way. Hubble essentially lays the "foundational data pipeline" for these AI applications, freeing agents from the bottleneck of data acquisition. By positioning itself as the "data supply layer" for these agents, Hubble needs to deliver not just raw medical record data, but output in agent-friendly formats (such as structured JSON or semantically annotated text) that enable AI to efficiently parse and leverage the information.

Industry Significance and Potential Challenges

From an industry perspective, Hubble operates in a space that is a direct extension of the long-standing challenge of healthcare data interoperability. The FHIR standard in the U.S. and various Health Information Exchange (HIE) networks have been trying to solve the data flow problem for years, but coverage breadth and real-world effectiveness have remained uneven.

FHIR (Fast Healthcare Interoperability Resources) is the next-generation healthcare data exchange standard developed by HL7, the international health information standards organization. Unlike its predecessors HL7 v2 and CDA, FHIR adopts a modern web technology architecture, built on RESTful API design and using JSON and XML as data formats, significantly lowering the barrier to entry for developers. In 2020, the U.S. Department of Health and Human Services (HHS) issued the ONC Final Rule (under the 21st Century Cures Act), mandating that EHR vendors support FHIR R4 standard patient data access APIs. However, FHIR's real-world implementation remains inconsistent: some vendors have achieved only the bare minimum of compliance, with slow API response times and incomplete data fields being common issues. Furthermore, the FHIR standard currently covers primarily institutions that are already digitized and connected to the standards ecosystem — it can't reach institutions that still rely on fax or paper-based processes. HIE network coverage is similarly uneven, with participation rates below 50% in some states.

Through its differentiated positioning of "retrieving records that others can't," Hubble aims to fill the coverage gaps left by existing solutions.

Key Areas to Watch

Despite the highly compelling product concept, the unique nature of the medical data space presents challenges that cannot be ignored:

Compliance and privacy: Handling medical records requires strict adherence to regulations like HIPAA. HIPAA (Health Insurance Portability and Accountability Act) is the cornerstone law for medical data privacy protection in the U.S. Its Privacy Rule governs the conditions for using and disclosing Protected Health Information (PHI), while its Security Rule sets specific requirements for technical safeguards protecting electronic PHI. Under the HIPAA framework, third-party access to patient records typically requires patients to sign a written authorization that explicitly specifies the scope, purpose, and recipient of the information. Hubble's "one-time verification" model may technically involve HIPAA's Business Associate Agreement (BAA) mechanism — where Hubble, as a business associate, processes PHI on behalf of patients or covered entities. Ensuring this authorization chain is legally complete and valid, without subjecting users to lengthy signing processes, is critical to the product's ability to scale. Additionally, individual states may have privacy regulations stricter than HIPAA (such as California's CMIA), further increasing compliance complexity.

Data quality and structuring: The degree of structure and accuracy of records extracted from faxes and scanned documents directly impacts the reliability of downstream AI applications. This often requires deep support from technologies like OCR and NLP. Optical Character Recognition (OCR) converts text in images to machine-readable text, but medical documents frequently contain handwriting, blurry prints, and complex tables. While traditional OCR can achieve over 99% accuracy on high-quality printed text, accuracy can drop to 70%-80% on handwritten prescriptions and low-resolution fax documents. Natural Language Processing (NLP) then works on top of OCR output: recognizing medical terminology and abbreviations (e.g., "q.i.d." meaning four times daily), extracting diagnosis codes (ICD-10), identifying drug names and dosages, parsing timeline relationships, and more. In recent years, Transformer-based medical LLMs (such as Med-PaLM and GatorTron) have significantly improved medical NLP performance, but achieving clinical-grade accuracy still requires extensive domain expert involvement in annotation and validation. Data quality issues have a cascading effect — a single OCR error can lead to NLP misinterpretation, which in turn affects the decision quality of downstream AI agents.

Sustainability of coverage breadth: "Retrieving what other APIs can't" is a powerful selling point, but maintaining this coverage advantage requires continuous investment of operational resources to integrate with a massive number of dispersed healthcare institutions.

Conclusion: Data Accessibility Is the Real Bottleneck for Healthcare AI

Hubble's emergence reflects a repeatedly validated principle in AI application deployment: the real bottleneck is often not the model itself, but data accessibility. As healthcare AI capabilities grow increasingly powerful, the "last mile" of data supply becomes the critical factor determining whether applications can truly create value.

By encapsulating complex multi-source medical record aggregation into a clean API, Hubble lets developers focus on building the AI agent applications above, rather than getting mired in the swamp of fax lines and portal logins. For developers with ambitions in the healthcare AI space, the value of this kind of infrastructure tooling will only become more pronounced as agent-based applications proliferate.