Interoperability: Driving Digital Healthcare

The transformation of the healthcare industry heavily relies on digital data integration and interoperability. Over recent years, the adoption of Electronic Medical and Health Record systems, e-prescribing, device data, patient-generated data, and information related to the social determinants of health has significantly increased the flow of healthcare data. When coupled with Advanced Analytics, Large Language Models (LLMs), and artificial intelligence (AI), this influx of data presents a vast potential for the development of Digital Healthcare Data Products. These products can greatly enhance various aspects of healthcare, including diagnosis accuracy, early disease prevention, care coordination, management of multi-drug interactions, personalized treatment, and the implementation of value-based care.

However, realizing this potential necessitates high-quality datasets, seamless communication across healthcare systems, and the use of standardized data formats that can be processed both by humans and machines. Unfortunately, a significant portion of today's health data remains virtually unusable due to issues of interoperability. Much of this data is locked in isolated databases, trapped within incompatible systems, or concealed behind proprietary software, rendering it difficult to exchange, analyze, and interpret.

The healthcare interoperability ecosystem encompasses individuals, systems, and processes that aim to share, exchange, and access all forms of health information, including discrete data, narratives, and multimedia. Various stakeholders, such as individuals, patients, healthcare providers, hospitals/health systems, researchers, payers, pharmacies, pharmaceutical companies, suppliers, and health information systems, are integral parts of this ecosystem. Each stakeholder plays a crucial role in the creation, exchange, and utilization of health data and information.

Presently, the health data landscape is characterized more by fragmented "small data" rather than cohesive "big, coordinated, and consistent data." To ensure that information from diverse sources can be trusted for its accuracy and completeness, organizations must make efforts to maintain high-quality data that adheres to standardized terminology and formatting wherever possible. This structured and coded data should be made available in a machine-readable format, facilitating analysis and interpretation and ensuring comprehensive documentation that effectively communicates the full patient story to end-users. Consequently, the Interoperability Ecosystem serves as the bedrock for any healthcare data initiative, enabling the creation of domain-specific Data Products through these interoperability standards.

In the realm of health IT, there are over three dozen standards development organizations. Some of these organizations are responsible for creating standards, such as Health Level Seven (HL7), Systematized Nomenclature of Medicine (SNOMED) International, and the Clinical Data Interchange Standards Consortium (CDISC). Others, like Integrating the Healthcare Enterprise, focus on bundling base standards that define specific functions or use cases.

Content Standards: Content standards relate to the data content within exchanges of information.

• Consolidated Clinical Document Architecture (C-CDA): A library of CDA templates represents harmonization of the HL7 Health Story guides and related components of Patient Care Coordination and Continuity of Care Documents, or CCD.

• HL7's Version 2.x (V2): A widely implemented messaging standard that allows the exchange of clinical data between systems.

• HL7 Version 3 Clinical Document Architecture (CDA): An XML-based document markup standard that specifies the structure and semantics of "clinical documents" following six characteristics: persistence, stewardship, potential for authentication, context, wholeness and human readability.

Identifier Standards: Standards to uniquely identify patients or providers.

• Enterprise Master Patient Index (EMPI): A data registry used across a healthcare organization to maintain consistent and accurate data on the patients treated and managed.

• Medical Record Number (MRN): An organization specific code used as a systematic documentation of a patient's history and care during a hospital stay.

• National Council of State Boards of Nursing ID (NCSBN ID): A unique identifier automatically generated for each registered nurse and licensed practical/vocational nurse.

• National Provider ID (NPI): A unique 10-digit number for a healthcare provider to create a standard identification. These NPIs are included in the free NPI Registry.

Transport Standards: Address the format of messages exchanged between computer systems, document architecture, clinical templates, user interface and patient data linkage.

• Digital Imaging and Communications in Medicine (DICOM): The standard for the communication and management of medical imaging information and related data.

• Direct Standard: Defines a set of standards and protocols to allow participants to send authenticated, encrypted health information directly to known, trusted recipients over the internet (XDR and XDM).

• Fast Healthcare Interoperability Resources (FHIR): An HL7 standard for exchanging healthcare information electronically. The basic building blocks of FHIR are "resources," which describe exchangeable health data formats and elements to provide standardization for application programming interfaces (APIs).
  
  

Vocabulary /Terminology Standards: Ad dress the ability to represent concepts in an unambiguous manner between a sender and receiver of information

• Current Procedural Terminology (CT): A code set, maintained by the American Medical Association (AMA), used to bill outpatient and office procedures.

• ICD-10 and ICD-11: The International Statistical Classification of Diseases and Related Health Problems (ICD) is a medical classification list by the World Health Organization (WHO). It contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases.

• Logical Observation Identifiers Names and Codes (LOINC): A universal code system for identifying health measurements, observations and documents.

• National Drug Code (NDC): NDC provides a list of all drugs manufactured, prepared, propagated, compounded or processed for commercial distribution.

• Rad Lex: A unified language of radiology terms for standardized indexing and retrieval of radiology information resources. It unifies and supplements other lexicons and standards, such as SNOMED-Clinical Terms and DICOM.

• RxNorm: A terminology used to normalize names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software.

• Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT): A comprehensive clinical health terminology product. It enables the consistent, processable representation of clinical content in electronic health records (EHRs). These codes often represent the "answer" for a test or measurement to the LOINC "question" code.

The adoption of various interoperability standards fosters a scenario that promotes the use of these base standards through implementation guidance. This guidance describes how multiple standards can work together to support interoperable health information exchange and facilitate the development of operational and analytical Data Products. These efforts yield several benefits:

• Medical Communication: Interoperability enables quicker, more efficient, and repeatable information management and retrieval, reducing medical errors arising from communication and transformational barriers while also decreasing the documentation and data management burden.

• Stakeholder Cooperation: It provides access to Data Products built on common definitions across internal and external stakeholders, addressing a wide range of healthcare issues.

• Research and Innovation: Interoperability improves the use of real-world data for large-scale observational studies and facilitates the creation of new hypotheses using large-language models and AI.

• Governance and Management: It establishes trust in digital assets (e.g., Data Products, Data Marketplaces, Business Drivers, underlying tools, and capabilities) by ensuring the validity of analysis results and building confidence in these healthcare digital assets.

In summary, the success of Digital Healthcare Data hinges on interoperability and standardized data. These elements unlock the full potential of LLMs, AI, and various Data Products, enhancing the communication of medical information, streamlining medical research, and fostering cooperation among stakeholders. Investments in the development of a Common Interoperability Framework and Parser, capable of managing various types and standards of data in diverse forms, types, and shapes, pave the way for an interconnected digital health infrastructure. Such infrastructure breaks down barriers between individuals and organizations, enabling the transformation of digital medical data into meaningful information that ultimately improves the health and well-being of patients.