The Park Street Active Fact Store

Making Clinical Computing And Interoperation Possible


The Data Warehouse:

Where Your Data Goes To Die?

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Knowledge: Locked Away In Antiquated Formats?

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Interoperability: Merely a Theory?

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Park Street's Active Fact Store makes both clinical knowledge and patient data computable, uniting them seamlessly in a patient-centered, ontology-driven repository that performs superbly at scale. The Active Fact Store links content, computation and data to people and applications in the real world.

Making Knowledge Computable

Park Street has invented a solution for managing ontologies using relational technology. It provides for simple expression and efficient execution of the most complex knowledge base queries, seamlessly joined to patient data, without recursion.

Knowledge models are fundamentally concerned with structure — expressed in the web of relationships among concepts and the hierarchy of the underlying class system.

In common enterprise technologies, structures are hard-coded. Relational databases express structure in the relationships among tables, for example. But the variety and complexity of knowledge structures makes hard-coding an infeasible solution. Knowledge structures also can be challenging to navigate, as this requires recursive programming techniques, which are difficult to develop and perform poorly in relational technology.

Park Street's knowledge base relies on the construction of a special structure — the "transitive closure" — in essence, a list of all the possible paths through the knowledge base. Our algorithms automatically build and maintain the transitive closure, incrementally and in real time, with each change to the graph. Using the transitive closure, queries don't need recursion. Straightforward SQL is all that's needed.

Making Data Computable

Park Street's technologies unify and standardize data from diverse clinical systems, organize patient facts according to industry standard ontologies, and transform them to a representation suitable for advanced computing and analytics.

Patient data kept in clinical systems is largely incomputable. It is stored in human readable format for documentation purposes, and is not intended for use in computing or analytics. Financial and social data are stored in entirely separate systems. Park Street's tools provide the following services to make patient data computable:

  • Unification – bringing together data from disparate sources and databases
  • Harmonization – creating a single representation to support computing
  • Transformation – from documentation to computable data
  • Simplification – creating an elegant, inherently simple rubric for data management
  • Standardization – organizing data around common ontologies and coding systems
  • Semantics – adding the meaning and behavior of facts
  • Annotation – extracting inaccessible information from textual facts
  • Clinical computation – providing tools that support the specialized nature of clinical computing
  • Self-service – allowing end users to obtain data without expert support

Active Fact Store

Park Street's Active Fact Store brings together clinical, administrative, operational, genomic, and personal data from myriad data sources, with an architecture and toolset that make patient health data computable.

Using our proprietary knowledge representation technology, Park Street has pioneered a strategy for enterprise data warehouse design that's completely patient-centered and knowledge base driven. Our systems unify and standardize data from diverse clinical systems, organize patient facts according to industry standard ontologies, and transform them to a representation suitable for advanced computing. Textual analysis is applied to enhance basic facts, driving out latent or hidden information that would not otherwise be available for computing. Database-resident tools address problems unique to clinical informatics and provide a framework for the convenient expression and efficient execution of complex queries. Desktop and mobile end-user applications address the challenges of making data from the repository available to users throughout the enterprise.

Image Intro
The Active Fact Store (click to enlarge)


Park Street's knowledge representation system provides an efficient repository for ontologies that capitalizes on all of the advantages of relational database systems. The knowledge base relies upon proprietary technology for managing directed graphs and semantic networks in the relational environment, allowing developers to implement sophisticated queries using pure declarative SQL rather than complex, inefficient algorithmic solutions.


Clinical ontologies and coding systems are available from the National Library of Medicine and other sources — but not in a form that can be easily used for any practical purpose. Park Street has defined a complete informatics database using its ONTX knowledge representation system, and has developed the tools required to load and update the database with a full set of essential ontologies.


The Park Street Active Fact Store provides a complete longitudinal record of patient facts using a single, simple representation. In the Park Street database, every patient fact is represented as the intersection of a knowledge base concept with a patient or visit at a point in time. Types of patient facts include administrative data (enrollment, benefits, claims, payments), registration events (admissions, discharges, and transfers), diagnoses, procedures, orders, results, medications, observations, assessments, documents, images, and more.


The Park Street data acquisition system is a high-performance framework for receiving or extracting data, transforming and aligning it to standard ontologies or custom knowledge models, and preparing it for export or bulk load. It includes a complete job execution framework that orchestrates multiple-step ETL jobs, supports serial and parallel execution of sub-tasks and individual task steps, and handles dependencies and job eligibility checking.


The core data model of the Park Street generalized data warehouse is de-identified according to HIPAA standards, so that all analytic queries can be executed without exposing personal health information. Identified data is available for administrative and operational uses, but is segregated so that only users and applications with appropriate rights are allowed to access PHI or re-identify patient facts.


The Park Street architecture includes sophisticated support for the temporal aspects of clinical informatics. Database-resident state machine tools are provided to transform event streams into knowledge base defined maps of patient state over time. Starmaker end-user applications enable enterprise-wide self-service for clinical, administrative, and research users who need data for analysis but lack database navigation skills.


Traditional repository designs transform data to an enterprise data model as it is received, which can limit flexibility and create operational difficulties. Late binding designs reflect the modeling found in source systems, reducing data acquisition challenges but moving the burden of data transformation and normalization to each and every use of data. Because the Active Fact Store represents the complexity of patient facts using highly flexible knowledge structures, it avoids the brittleness of early binding while offering analysts a query-ready data resource.


Most healthcare institutions already own tools for data analysis and visualization. The most serious challenge involves getting data into the hands of analysts so that they can put their tools to work. Park Street's Starmaker tool allows users to specify data requirements with simple drag-and-drop techniques. Behind the scenes, Starmaker orchestrates a sequence of fact selection algorithms to find and stitch together disparate fact types. Starmaker provides access to the advanced computing capabilities of the Active Fact Store, simplifying its abstractions and adapting it to the perspective of untrained users and common reporting tools.


Interoperability is merely a statement of potential — it's done once data has been put on the wire, before any useful computing has taken place. Park Street provides tools that support the realities of interoperation, allowing applications and services to make sense of data received and to use it in real clinical computing.

FHIR Proxy

Park Street delivers the terminology services missing from standard FHIR implementations.

Sophisticated terminology services are required to make sense of FHIR data and to execute real-world computing tasks that use it. However, standard FHIR servers do not provide the necessary support, leaving the problem to systems receiving FHIR data. The Park Street FHIR Proxy system stands between FHIR servers and data consumers, adding the missing terminology operations and creating a single, simple, standard interface to FHIR data.


There is NO value in a FHIR server that does not have native, integrated terms capability.

Author A Prominent Expert Informatics and Interoperability


Terminology services are essential to clinical computing.

Coding standards for clinical data are highly granular. This supports a robust scheme for organizing and categorizing individual atoms of data in a way that recognizes the extreme complexity of the clinical domain.

Because codes are so granular, clinical computing invariably takes place at a higher level than that of individual codes. Value sets are used to provide useful, semantically relevant groupings of codes to represent higher-level clinical concepts.

An extensive collection of extensional (enumerated) value sets is maintained by authorities such as the NLM and the CDC, forming the basis for most standardized measures. It is impractical, though, to expect that value sets will be available and ready to support every conceivable clinical query. Sophisticated terminology operations, including mapping, transformation, subsumption, and navigation, are required to generate value sets on demand.


Native, integrated terminology services are essential to FHIR.

The success of FHIR is premised upon the use of standard code systems to describe clinical data.

When FHIR servers do not integrate terminology operations callers must supply that functionality. Even the use of simple value sets often involves complex code translation and vocabulary navigation. The burden of such operations would pose a significant obstacle to the implementation of most FHIR-driven applications and analytics.

What's worse — lack of support for terminology operations means that callers must interact extensively with FHIR servers. In doing so they become exposed to the implementation details and differences among vendor implementations. The construction of write-once, run-anywhere applications is a practical impossibility.

Integrated terminology services that allow callers a simple, streamlined way to interact with FHIR servers are required to provide callers with a single, reliable, standard interface to clinical data.