Skip to main content

2.5 Data Interoperability in Health

Early Challenges with EMR Adoption​

The journey toward digital health records began with the adoption of EMRs, which aimed to replace paper-based medical records with electronic formats. This transition promised increased efficiency, improved patient care, and enhanced data accessibility. However, the early stages of EMR adoption were marked by fragmented systems that lacked standardized formats for data exchange. Each healthcare provider often implemented its own EMR system, leading to data silos and limited interoperability. The inability to seamlessly share patient data between different systems hindered collaborative care efforts, hindered medical research, and compromised patient safety.

Issues with Interoperability Among EMR Systems​

As the healthcare landscape evolved, the need for interoperability between different EMR systems became increasingly evident. Healthcare organizations expanded their efforts to exchange patient information across hospitals, clinics, and even different regions. However, challenges persisted due to the lack of standardized data formats, varying data models, and different information exchange protocols among EMR vendors. These challenges not only impacted patient care but also posed obstacles to aggregating data for public health research and analytics.

Continued Struggles and Progress​

Recognizing the need for seamless data exchange, various initiatives emerged to promote health data standards, including Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR). These standards aimed to provide a common framework for structuring and exchanging health information. Despite these efforts, the adoption of standardized formats across the entire healthcare ecosystem remained a complex task due to legacy systems, proprietary software, and the sheer diversity of data sources.

In the following sections, we will explore how Python and its libraries have played a pivotal role in addressing these interoperability challenges. By leveraging Python's capabilities, health informatics professionals can bridge data format gaps, integrate diverse data sources, and work towards a more connected and efficient healthcare system.

Who cares about interoperability and its future? TEFCA and HIEs​

TEFCA, which stands for Trusted Exchange Framework and Common Agreement, is an initiative by the Office of the National Coordinator for Health Information Technology (ONC) in the United States. Its goal is to establish a common framework for health information exchange, enabling secure and interoperable sharing of health information across different health information networks, or health information exchanges (HIEs).

HIEs have emerged as a pivotal solution in modern healthcare, addressing the challenge of seamlessly sharing electronic health information across disparate systems, facilities, and organizations. An HIE serves as a centralized platform that facilitates the secure exchange of patient health records, clinical data, test results, and other critical medical information among healthcare providers, hospitals, clinics, and even patients themselves.

The overarching goal of HIEs is to improve patient care, enhance coordination among healthcare providers, and enable data-driven decision-making while maintaining patient privacy and security. Through standardized protocols, secure communication channels, and interoperable data formats, HIEs bridge the gap between various healthcare entities, making it possible to access comprehensive patient information in real-time, regardless of the originating source. As healthcare systems become more digitized and interconnected, the role of HIEs becomes increasingly vital in achieving efficient, data-driven, and patient-centric care delivery.

The TEFCA initiative aims to overcome the challenges related to data fragmentation that have emerged between different HIEs, lack of standardized exchange methods, and varying levels of trust among healthcare organizations. TEFCA outlines a set of principles, policies, technical requirements, and legal frameworks that health information networks should follow to ensure seamless and secure data exchange.

Key aspects of TEFCA include:

  1. Common Agreement: TEFCA defines a "Common Agreement" that outlines the rules, technical specifications, and interoperability standards that participants in health information networks must adhere to. This agreement ensures consistency and mutual understanding among all parties involved.

  2. QHINs and Participants: TEFCA introduces the concept of Qualified Health Information Networks (QHINs) and Participants. QHINs are organizations that meet specific criteria and are authorized to exchange health information across networks. Participants are organizations or entities that connect to QHINs to exchange data.

  3. Interoperability Framework: TEFCA emphasizes the use of standardized APIs and protocols, such as FHIR, to enable interoperability between different health information systems. This allows for consistent and structured data exchange.

  4. Data Privacy and Security: TEFCA prioritizes the protection of patient privacy and data security. It requires participants to implement appropriate security measures and comply with HIPAA regulations.

  5. Data Use and Consent: TEFCA promotes patient engagement and control over their health data by defining requirements for obtaining patient consent and managing data use permissions.

TEFCA's role in overseeing HIEs is to create a unified ecosystem where health data can flow securely and efficiently across different health information networks. While TEFCA is still evolving, its framework aims to address the challenges of health data interoperability, improve patient care coordination, and facilitate research and public health efforts. As TEFCA continues to develop, it will likely play a significant role in shaping the future of health information exchange in the United States.

Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR): A Brief Overview​

Health Level Seven (HL7):​

HL7, or Health Level Seven, is an internationally recognized set of standards for the exchange, integration, sharing, and retrieval of electronic health information. The HL7 standards were initially developed in 1987 by the Health Level Seven International organization, which is a not-for-profit, ANSI-accredited standards developing organization. HL7 focuses on defining various messaging and document standards to facilitate the exchange of clinical, administrative, and financial information among healthcare systems. HL7 has gone through multiple versions, with the most prominent being HL7 v2 and HL7 v3.

Fast Healthcare Interoperability Resources (FHIR):​

FHIR, or Fast Healthcare Interoperability Resources, is a modern interoperability standard developed by Health Level Seven International (HL7). FHIR was introduced as a response to the limitations of previous HL7 standards. Unlike its predecessors, FHIR is designed to be more web-friendly, utilizing a RESTful architecture that allows for easier and more flexible data exchange. FHIR uses modern web technologies such as JSON and XML to enable seamless integration between various healthcare systems. It focuses on modular components called "resources" that can represent discrete pieces of health information. FHIR has gained widespread adoption due to its flexibility, simplicity, and ability to cater to modern healthcare requirements.

Key Differences and Similarities:​

While both HL7 and FHIR aim to facilitate health data interoperability, they differ in their approaches. HL7 encompasses a range of standards and versions, some of which are considered complex and may require extensive implementation efforts.

FHIR, on the other hand, streamlines data exchange with its explitic structure (schema) that it provides. FHIR's simplicity and alignment with modern web standards have contributed to its rapid adoption across the healthcare industry. FHIR is, in fact, developed under the umbrella of HL7 International. FHIR leverages the expertise and established reputation of HL7 to promote data interoperability. It represents a more user-friendly, web-oriented evolution of HL7 standards, embracing the need for seamless integration in today's healthcare landscape.

HL7 and Python​

Python is a powerful language for working with HL7 (Health Level 7) messages, which are widely used in healthcare for data exchange between different systems. Python offers several open-source packages that facilitate parsing, manipulating, and generating HL7 messages. Some of the notable Python packages for HL7 include:

Common packages​

  1. python-hl7: The python-hl7 package provides functionalities for parsing and creating HL7 messages. It allows you to navigate through different segments and fields in an HL7 message. You can use it to extract specific information, modify messages, and generate new messages.

  2. hl7apy: hl7apy is a Python library specifically designed for parsing and creating HL7 messages. It supports various HL7 versions and offers a high-level API for handling messages and segments, making it easier to work with complex HL7 structures.

  3. hl7tools: The hl7tools package focuses on HL7 version 2.x messages. It provides tools for parsing, validating, and generating HL7 messages. It also includes utilities for working with HL7 message profiles.

  4. PyHL7: PyHL7 is another Python library for parsing and generating HL7 messages. It's designed to be easy to use and offers methods for handling different HL7 segments and fields.

HL7 Message Structure​

Now, let's delve into a hypothetical scenario where these Python packages come to life. Imagine you're tasked with processing an HL7 message that needs to be exchanged between two distinct healthcare systems. In this example, we'll consider a simplified scenario involving a patient's admission to a hospital.

The HL7 message contains vital patient information, such as name, admission date, and diagnosis. Using the chosen Python package, let's navigate through the HL7 message, extract key data, and potentially make modifications or generate a new message based on the needs of the receiving system.

HL7 messages are typically transmitted in plain text files. The common file format for HL7 messages is a simple text file with each segment represented as a line of text. The segments and fields within the message are delimited by specific characters, with the pipe character | being the most commonly used delimiter.

The general format of an HL7 message includes the following components:

  1. Message Header (MSH Segment): The MSH segment is the first segment of an HL7 message and contains metadata about the message. It specifies delimiters, encoding characters, sender information, receiver information, message timestamp, message type, and version.

  2. Segments: Following the MSH segment, the message consists of various segments that hold specific pieces of information. Each segment starts with a three-letter code indicating the segment type (e.g., PID for patient identification, OBX for observation/result, PV1 for patient visit information).

  3. Fields: Within each segment, data is organized into fields, which are separated by the delimiter. Fields contain different pieces of information related to the segment type. For example, the PID segment includes fields for patient name, date of birth, address, and more.

  4. Subfields: Some fields can be further divided into subfields using the repetition separator ~ and component separator ^. Subfields help organize complex data, such as the patient's name broken down into last name, first name, middle name, and suffix.

  5. *Repeating Segments: *In some cases, segments like OBX (observation/result) can be repeated to accommodate multiple observations or measurements associated with a single test.

  6. Escape Sequences: HL7 messages may also include escape sequences to handle special characters within fields, ensuring that they are treated as text and not as delimiters.

Types of HL7 Messages​

HL7 messages are categorized into various types, each serving a specific purpose in healthcare data exchange. Here are some common types of HL7 messages:

ADT (Admit, Discharge, Transfer)​

The ADT message type is used to convey patient demographic and admission-related information. It includes details about patient admissions, discharges, and transfers between different healthcare facilities. ADT messages are crucial for maintaining accurate patient records and tracking their movements within the healthcare system.

ORM (Order Message)​

ORM messages are used for ordering medical services, such as tests, procedures, and medications. These messages facilitate communication between various departments within a healthcare facility, ensuring that patient orders are accurately transmitted and executed.

ORU (Observation Result)​

ORU messages convey the results of medical observations, tests, and procedures. They include information about laboratory results, radiology reports, and other diagnostic findings. ORU messages play a vital role in communicating clinical data to healthcare providers for decision-making.

SIU (Scheduling Information Unsolicited)​

SIU messages are used to communicate scheduling and appointment information. They inform healthcare providers and facilities about upcoming patient appointments, changes in scheduling, and other scheduling-related updates.

MDM (Medical Document Management)​

MDM messages handle the exchange of medical documents, such as medical reports, images, and other clinical documents. These messages ensure that healthcare providers have access to accurate and up-to-date patient documents.

ACK (Acknowledgment)​

ACK messages are used to acknowledge the receipt and processing of other HL7 messages. They provide confirmation that a message was successfully received and processed, helping to ensure the integrity of data exchange.

DFT (Detail Financial Transactions)​

DFT messages handle financial transactions related to healthcare services. They include information about charges, payments, and billing for medical services rendered.

These are just a few examples of the many types of HL7 messages used in healthcare data exchange. Each message type serves a specific purpose and contributes to the seamless communication of healthcare information among different systems and entities.

So here's an example of how a general HL7 message might look:

MSH|^~\&|SendingApp|SendingFac|ReceivingApp|ReceivingFac|20230830140012||ADT^A01|123456|P|2.5
PID|1||123456|Doe^John^C||19800101|M|||123 Main St.^Apt 4B^New York^NY^10001||555-123-4567||S||123456789|123-45-6789

In this example, the MSH segment sets the message header, and the PID segment provides patient identification information. The segments and fields are delimited by the pipe character (|), and the repetition separator (~) and component separator (^) are used for subfields.

The general format and conventions of HL7 messages allow for structured data exchange between different healthcare systems, making it a widely used standard for health data interoperability.

Communication Flow and Standard Acknowledgments in HL7​

HL7 (Health Level 7) messages follow a specific communication flow and utilize standard acknowledgments to ensure accurate and reliable data exchange between systems. The communication flow involves the use of control characters and the Minimal Lower Level Protocol (MLLP) to encapsulate and transmit HL7 messages over TCP/IP connections.

Communication control characters are essential for maintaining message integrity during transmission. The communication flow includes the following key steps:

  1. Start of Block (SOB): A single vertical tab character (Hex 0x0B) is used to indicate the beginning of a block at the start of the message.

  2. Message Content: The HL7 message content, consisting of segments, fields, and data, follows after the SOB character.

  3. Segment Termination: Each segment within the HL7 message is terminated by the carriage return character (Hex 0x0D).

  4. End of Block and Termination: The end of the block is indicated by the Hex 0x1C character (FS), followed by the carriage return character (Hex 0x0D) at the end of the message.

Standard Acknowledgments​

HL7 messages are designed to include standard acknowledgments (ACK) to ensure the receipt and processing of messages by receiving applications. Acknowledgments play a crucial role in maintaining data integrity and continuity:

  • MSH Segment: The MSH (Message Header) segment contains information about the sending and receiving applications, including the Message Control ID that uniquely identifies each HL7 message.

  • MSA Segment: The MSA (Message Acknowledgment) segment indicates whether the message was accepted or rejected. It includes an acknowledgment status code that indicates the outcome of message processing. Common acknowledgment statuses include:

    • AA (Positive Acknowledgment): The message was successfully processed.
    • AE (Application Error): There is a processing problem that needs to be corrected before resending.
    • AR (Application Reject): There is an issue with the message structure or receiving application.

Let’s say an inbound HL7 ADT (Admit, Discharge, Transfer) message came in with the following MSH (message header).

MSH|^~\&|EPICADT|DH|LABADT|DH|201301011226||ADT^A01|HL7MSG00001|P|2.3|

If the message was accepted and acknowledged, then the response ACK message (following original mode) will look like this:

MSH|^~\&|LABADT|DH|EPICADT|DH|201301011228||ACK^A01^ACK |HL7ACK00001|P|2.3
MSA|AA|HL7MSG00001

Note the following:

  1. The source and destination systems have been switched;
  2. MSA segment contains the “AA” value which indicates acceptance; and
  3. MSA segment also contains the ID of the original message.

Importance of Acknowledgments​

Acknowledgments are essential for maintaining the reliability of HL7 data exchange. Sending applications rely on ACK messages to determine the success or failure of message processing. This information helps manage the communication flow and ensures that data is not lost in transmission.

By adhering to the communication flow and using standard acknowledgments, HL7 messages can be reliably transmitted, received, and processed across different healthcare systems and entities.

Python Examples​

Below is a high-level pseudocode representation of how you could approach this scenario using one of the popular Python HL7 packages:

# Import the relevant package
import python_hl7

# Load the HL7 message
hl7_message = "MSH|^~\&|SendingApp|SendingFac|ReceivingApp|ReceivingFac|20230830140012||ADT^A01|123456|P|2.5"

# Parse the HL7 message
parsed_message = python_hl7.parse(hl7_message)

# Access segments and fields
patient_name = parsed_message["PID"][5][0][0]
admission_date = parsed_message["PV1"][19][0][0]

# Print extracted information
print("Patient Name:", patient_name)
print("Admission Date:", admission_date)

This example showcases a basic workflow for handling an HL7 message using the chosen Python package. Of course, real-world scenarios can be more complex, involving additional segments and data. By leveraging these Python HL7 packages, you're equipped to bridge data format gaps and facilitate seamless healthcare data exchange.

Now to better understand this, lets break apart how a standard HL7 message that is a little more complex might appear:

MSH|^~\&|SendingApp|SendingFac|ReceivingApp|ReceivingFac|20230830140012||ADT^A01|123456|P|2.5
PID|1||123456|Doe^John^C||19800101|M|||123 Main St.^Apt 4B^New York^NY^10001||555-123-4567||S||123456789|123-45-6789
PV1|1|O|Room123^Bed1^FacilityA|||||||Doe^Jane^C|||||||||Service123^DoctorA^FacilityA|||||||||||||||||||||||||20230830
OBR|1|123456^Lab1|123456^Lab1|CBC^Complete Blood Count|||20230830143000|||||||||Doe^Jane^C|||20230830150000
OBX|1|NM|WBC^White Blood Cell Count||5.2|10^9/L|4.5-11.0||||F|||20230830151500
OBX|2|NM|RBC^Red Blood Cell Count||4.8|10^12/L|4.2-5.8||||F|||20230830151500
OBX|3|NM|HGB^Hemoglobin||14.2|g/dL|12.0-16.0||||F|||20230830151500
OBX|4|NM|HCT^Hematocrit||42.5|%|37.0-47.0||||F|||20230830151500

In this example, the HL7 message represents the following:

  • The message header (MSH segment) indicates the sender and receiver, message type, timestamp, and version.
  • The patient identification (PID segment) contains personal information like patient ID, name, date of birth, address, phone number, social security number, and more.
  • The patient visit information (PV1 segment) captures details about the patient's encounter, such as room, bed, facility, and attending physician.
  • The lab test order (OBR segment) contains information about the lab test, including the test code, date, and responsible clinician.
  • The lab test results (OBX segments) provide specific measurements and observations from the lab test, including values, units, reference ranges, and flags.

And here is how we would parse this code in python: 

from hl7apy.parser import parse_message

# Sample HL7 message
hl7_message = (
    "MSH|^~\\&|SendingApp|SendingFac|ReceivingApp|ReceivingFac|20230830140012||ADT^A01|123456|P|2.5\n"
    "PID|1||123456|Doe^John^C||19800101|M|||123 Main St.^Apt 4B^New York^NY^10001||555-123-4567||S||123456789|123-45-6789\n"
    "PV1|1|O|Room123^Bed1^FacilityA|||||||Doe^Jane^C|||||||||Service123^DoctorA^FacilityA|||||||||||||||||||||||||20230830\n"
    "OBR|1|123456^Lab1|123456^Lab1|CBC^Complete Blood Count|||20230830143000|||||||||Doe^Jane^C|||20230830150000\n"
    "OBX|1|NM|WBC^White Blood Cell Count||5.2|10^9/L|4.5-11.0||||F|||20230830151500\n"
    "OBX|2|NM|RBC^Red Blood Cell Count||4.8|10^12/L|4.2-5.8||||F|||20230830151500\n"
    "OBX|3|NM|HGB^Hemoglobin||14.2|g/dL|12.0-16.0||||F|||20230830151500\n"
    "OBX|4|NM|HCT^Hematocrit||42.5|%|37.0-47.0||||F|||20230830151500"
)

# Parse the HL7 message
parsed_message = parse_message(hl7_message)

# Access segments and fields
msr_segment = parsed_message.MSH
pid_segment = parsed_message.PID
pv1_segment = parsed_message.PV1
obr_segment = parsed_message.OBR
obx_segments = parsed_message.OBX

# Access specific fields within segments
patient_name = pid_segment.PID_5.value
date_of_birth = pid_segment.PID_7.value
admission_location = pv1_segment.PV1_3.value
ordering_provider = obr_segment.OBR_16.value

# Print parsed values
print("Patient Name:", patient_name)
print("Date of Birth:", date_of_birth)
print("Admission Location:", admission_location)
print("Ordering Provider:", ordering_provider)

# Print OBX segment data
for obx_segment in obx_segments:
    observation = obx_segment.OBX_3.value
    result_value = obx_segment.OBX_5.value
    reference_range = obx_segment.OBX_7.value
    print("Observation:", observation)
    print("Result Value:", result_value)
    print("Reference Range:", reference_range)
    print()

This complex HL7 message demonstrates how various segments come together to convey comprehensive patient data and lab results. Using Python and the appropriate HL7 package, you can parse, manipulate, and extract meaningful information from such messages to facilitate efficient healthcare data interoperability.

Additional HL7 Message Examples​

  1. HL7 - Official Confluence Page:

    • Link: HL7 Confluence
    • This page is unique because it shows raw HL7 messages, as well as mappings to convert said messages to FHIR

  2. Sample HL7 Messages from STCHOME

  3. Sample HL7 Messages from WebChart

FHIR and Python​

Python offers a variety of tools and libraries for working with FHIR (Fast Healthcare Interoperability Resources) data, which has gained traction as a modern standard for healthcare data exchange.

Structure​

FHIR (Fast Healthcare Interoperability Resources) follows a structured format designed to facilitate the exchange of healthcare information between different systems. FHIR resources are the building blocks of the standard, representing individual pieces of healthcare data. Each resource is defined by a set of attributes, and these resources can be interconnected to represent more complex healthcare concepts. Here's a general overview of the structure and formatting of FHIR:

FHIR Resource Structure

  1. Resource Type: Each FHIR resource has a resource type that defines the kind of information it represents. Examples include "Patient," "Observation," "Medication," etc.

  2. Resource ID: Every FHIR resource has a unique identifier within a system. This ID helps in distinguishing and referencing the resource.

  3. Resource Attributes: FHIR resources consist of various attributes that capture different aspects of the data. Attributes are represented as key-value pairs, where the key is the attribute name, and the value is the corresponding data.

  4. Nested Elements: Some attributes may have nested elements, forming a hierarchical structure. These nested elements can capture more detailed information.

FHIR Resource Format

FHIR resources are typically represented in structured formats like JSON and XML. JSON is commonly used due to its simplicity and ease of use in web-based applications and APIs. Here's an example of how a FHIR resource, such as a Patient, may look in JSON format:

{
  "resourceType": "Patient",
  "id": "example",
  "identifier": [
    {
      "use": "usual",
      "system": "http://example.com/ids",
      "value": "12345"
    }
  ],
  "active": true,
  "name": [
    {
      "use": "official",
      "family": "Doe",
      "given": ["John"]
    }
  ],
  "gender": "male",
  "birthDate": "1970-04-09"
}

In this JSON representation, you can see the clear structure: the resource type, attributes, and nested elements are all organized. The "resourceType" field indicates the type of resource (in this case, "Patient"), followed by attributes such as "id", "identifier", "active", "name", "gender", and "birthDate". Some attributes, like "name", contain nested elements, like "given" and "family".

This structured format allows FHIR resources to be easily understood by systems and applications that need to exchange healthcare data.

Common packages​

Here are some Python packages that can help you work with FHIR data effectively:

  1. fhir.resources: This package provides a Pythonic way to work with FHIR resources. It includes classes that represent FHIR resources, making it easy to create, manipulate, and validate FHIR data.

  2. fhir-parser: The fhir-parser package is a Python library that can parse FHIR profiles and definitions. It allows you to access FHIR resources and elements from profiles, which can be useful for data validation and transformation.

  3. fhirpathpy: FHIRPath is a query language for FHIR resources. The fhirpathpy library enables you to use FHIRPath expressions to query and manipulate FHIR data.

  4. pyfhir: pyfhir is designed for working with FHIR data structures. It provides an intuitive way to create, modify, and validate FHIR resources. Additionally, it offers utilities for working with extensions, coding systems, and references.

  5. fhir.resources.stu3: If you're specifically working with FHIR version STU3, this package provides a set of Python classes that correspond to FHIR resources and data types defined in the standard. It simplifies the process of handling FHIR data structures.

Now lets look at an example of how FHIR data looks and how you can use the fhir.resources package in Python to work with it:

Python Examples​

To demonstrate working with FHIR data in Python, let's explore a simple case involving a FHIR Patient resource. The following is a sample FHIR Patient resource in JSON format:

{
  "resourceType": "Patient",
  "id": "example",
  "text": {
    "status": "generated",
    "div": "<div xmlns=\"http://www.w3.org/1999/xhtml\">Patient example</div>"
  },
  "identifier": [
    {
      "use": "usual",
      "system": "http://example.com/ids",
      "value": "12345"
    }
  ],
  "active": true,
  "name": [
    {
      "use": "official",
      "family": "Doe",
      "given": ["John"]
    }
  ],
  "gender": "male",
  "birthDate": "1970-04-09"
}

Let's use the fhir.resources package in Python to interact with this FHIR Patient resource:

from fhir.resources.patient import Patient

# Create a new Patient resource
patient = Patient(
    id="example",
    identifier=[{"use": "usual", "system": "http://example.com/ids", "value": "12345"}],
    active=True,
    name=[{"use": "official", "family": "Doe", "given": ["John"]}],
    gender="male",
    birthDate="1970-04-09"
)

# Print patient information
print("Patient ID:", patient.id)
print("Identifier:", patient.identifier[0].value)
print("Active:", patient.active)
print("Name:", patient.name[0].given[0], patient.name[0].family)
print("Gender:", patient.gender)
print("Birth Date:", patient.birthDate)

In this example, we create a Patient instance using the fhir.resources package and populate it with relevant information. We then access and print various attributes of the Patient resource. This demonstrates how you can use the fhir.resources package to work with FHIR resources in a Pythonic manner.

Additional FHIR examples from fhir.org​

  1. Observation - Glucose Level
  • FHIR Link: Observation Example - Glucose
  • This example demonstrates how to represent an observation of a patient's glucose level. It includes information such as the code for glucose, the measurement value, reference ranges, and the time of the observation.
  1. Medication
  • FHIR Link: Medication Example
  • This example showcases how to represent a medication using FHIR. It includes details about the medication, its dosage form, and ingredients.
  1. Condition - Sepsis
  • FHIR Link: Condition Example - Sepsis
  • This example illustrates how to represent a patient's condition using FHIR. It includes information about the condition, its clinical status, onset date, and supporting evidence.

These examples serve as official resource examples for understanding how FHIR resources are structured, what attributes are included, and how different healthcare concepts are represented using standardized formats. By studying these examples, developers and data scientists can gain insights into how to create FHIR-compliant data and integrate it into their applications or systems.

Other FHIR data examples:​

FHIR-Compatible Databases and Tools​

  1. FHIRBase

    • Link: FHIRBase
    • Description: FHIRBase is a database solution specifically designed for FHIR data. It stores FHIR resources natively and provides SQL-like query capabilities for FHIR data retrieval and manipulation.

  2. Microsoft FHIR Server

    • Link: Microsoft FHIR Server
    • Description: The Microsoft FHIR Server is an open-source project that offers a robust FHIR server implementation. It's designed to support FHIR data storage, retrieval, and exchange, and it can be easily deployed in various environments.

  3. Linux for Health FHIR Module

    • Link: Linux for Health FHIR Module
    • Description: The Linux for Health FHIR module is part of the Linux for Health project. It enables the integration of FHIR capabilities into the Linux for Health ecosystem, allowing interoperability with FHIR data.

Tools for FHIR Data Extraction​

  1. Bulk Data Server
    • Link: Bulk Data Server
    • Description: The Bulk Data Server is an open-source project by SMART Health IT that facilitates the bulk export of FHIR data. It supports the standardized FHIR Bulk Data API for retrieving large volumes of data efficiently.

These FHIR-compatible databases and tools provide valuable resources for managing and interacting with FHIR data. They allow healthcare organizations and developers to store, query, and exchange FHIR resources in a standardized and efficient manner. Additionally, tools like the Bulk Data Server streamline the process of extracting data from FHIR databases, making it easier to work with large datasets.