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Extending Synthea to Generate FHIR AuditEvent Resources for the PICA Project

Overview

For the PICA project, there is a need to extend Synthea to generate the FHIR AuditEvent resource. The FHIR AuditEvent resource serves as a structured representation of auditable events within a healthcare system, crucial for reconstructing processes from various perspectives, particularly within a patient-centric view.

Purpose of the FHIR AuditEvent Resource in PICA

The FHIR AuditEvent resource within the context of the PICA project supports multiple perspectives essential for analyzing and reconstructing healthcare processes:

  1. Core Perspective: Attributes such as ae.patient and ae.occurredDateTime provide essential details to reconstruct processes, containing the minimum information required for this purpose.

  2. Conformance Perspective: Tasks conducted are documented with machine-readable codes within the AuditEvents using the ae.code attribute, which refer to a standardized log of operational events.

  3. Patient-Visit Perspective: AuditEvents reference an Encounter resource using the ae.encounter attribute, grouping multiple events related to a patient visit. This enables the analysis of patient pathways during encounters, such as hospital stays.

  4. Care-Pathway Perspective: AuditEvents document pathways followed by patients, such as defined or recorded CarePlans using the ae.basedOn attribute. This allows for the analysis of specific processes followed by patients.

  5. Actor Perspective: Agents participating in process steps are identified within AuditEvents, allowing for the analysis of processes from multiple perspectives.

Implementation Strategy

To achieve the generation of the FHIR AuditEvent resource, we need to extend Synthea's post-processing capabilities. This involves the creation of custom scripts that process the generated FHIR bundles and create corresponding AuditEvent resources.

Steps to Implement

  1. Define the AuditEvent Structure: Identify and define the necessary attributes for the AuditEvent resource, such as ae.patient, ae.occurredDateTime, ae.code, ae.encounter, ae.basedOn, and the actor information.

  2. Modify Synthea's Post-Processing: Create scripts to post-process the generated FHIR bundles from Synthea. These scripts will:

    • Extract relevant information from existing resources (e.g., Patient, Encounter, CarePlan,Procedures,Observations, ...).
    • Generate new AuditEvent resources based on the extracted data.
    • Link AuditEvent resources to other resources within the bundle.

  3. Integration: Integrate the scripts into the Synthea workflow.

Relationships Between FHIR Resources in the PICA Project

The following diagram illustrates the relationships between various FHIR resources within the context of the PICA project. These resources are interconnected to provide a comprehensive representation of auditable events, patient care, and clinical data.

resource-relationships

Key Resources and Their Relationships

AuditEvent

The AuditEvent resource captures auditable events within the healthcare system. It references multiple other resources to provide a detailed log of activities and their context. Key attributes include:

  • FullUrl: Unique identifier for the AuditEvent.
  • resourceType: Specifies the type of resource (AuditEvent).
  • code: Machine-readable code documenting the type of event.
  • occurredDateTime: Timestamp of when the event occurred.
  • recorded: Timestamp of when the event was recorded.
  • patient: Reference to the Patient resource.
  • encounter: Reference to the Encounter resource.
  • basedOn: Reference to the CarePlan resource.
  • agent: Information about the agent involved in the event.

Patient

The Patient resource represents individual patients. Key attributes include:

  • FullUrl: Unique identifier for the Patient.
  • resourceType: Specifies the type of resource (Patient).

Encounter

The Encounter resource represents an interaction between a patient and healthcare provider(s). It groups multiple events related to a patient visit. Key attributes include:

  • FullUrl: Unique identifier for the Encounter.
  • resourceType: Specifies the type of resource (Encounter).
  • basedOn: Reference to the CarePlan resource.
  • subject: Reference to the Patient resource.
  • participant: Participants involved in the encounter.

CarePlan

The CarePlan resource outlines the management plan for a patient's healthcare. Key attributes include:

  • FullUrl: Unique identifier for the CarePlan.

Observation

The Observation resource captures measurements and assertions about a patient's health. Key attributes include:

  • FullUrl: Unique identifier for the Observation.
  • resourceType: Specifies the type of resource (Observation).
  • code: Code representing the type of observation.
  • issued: Timestamp when the observation was made.
  • subject: Reference to the Patient resource.
  • encounter: Reference to the Encounter resource.

DiagnosticReport

The DiagnosticReport resource represents the findings and interpretation of diagnostic tests. Key attributes include:

  • FullUrl: Unique identifier for the DiagnosticReport.
  • resourceType: Specifies the type of resource (DiagnosticReport).
  • code: Code representing the type of diagnostic report.
  • issued: Timestamp when the report was issued.
  • subject: Reference to the Patient resource.
  • encounter: Reference to the Encounter resource.

MedicationRequest

The MedicationRequest resource documents a request for medication for a patient. Key attributes include:

  • FullUrl: Unique identifier for the MedicationRequest.
  • resourceType: Specifies the type of resource (MedicationRequest).
  • medicationCodeableConcept: Code representing the medication.
  • authoredOn: Timestamp when the request was made.
  • subject: Reference to the Patient resource.
  • encounter: Reference to the Encounter resource.

Condition

The Condition resource represents the clinical conditions or diagnoses of a patient. Key attributes include:

  • FullUrl: Unique identifier for the Condition.
  • resourceType: Specifies the type of resource (Condition).
  • code: Code representing the condition.
  • recordedDate: Timestamp when the condition was recorded.
  • subject: Reference to the Patient resource.
  • encounter: Reference to the Encounter resource.

Procedure

The Procedure resource documents actions performed on a patient. Key attributes include:

  • FullUrl: Unique identifier for the Procedure.
  • resourceType: Specifies the type of resource (Procedure).
  • code: Code representing the procedure.
  • subject: Reference to the Patient resource.
  • encounter: Reference to the Encounter resource.
  • period: Time period during which the procedure was performed.

Summary of Relationships

  • AuditEvent references:
    • Patient (via patient attribute)
    • Encounter (via encounter attribute)
    • CarePlan (via basedOn attribute)
  • Encounter references:
    • Patient (via subject attribute)
    • CarePlan (via basedOn attribute)
  • Observation, DiagnosticReport, MedicationRequest, Condition, Procedure reference:
    • Patient (via subject attribute)
    • Encounter (via encounter attribute)

These relationships ensure a structured and comprehensive representation of healthcare events, facilitating detailed audits and analyses of patient care pathways and clinical processes within the PICA project.

Implementing a Rule-Based System for FHIR AuditEvent Generation

Rule-based systems are a fundamental technology in the field of artificial intelligence (AI) and computer science. They are designed to solve problems by applying specific, predefined rules to input data to produce outputs. These systems are widely used in various domains, including expert systems, decision support systems, and natural language processing, among others.

1. Explicit Rules and Decision Logic

At its core, a rule-based system operates based on a set of explicitly defined rules. These rules determine how input data is processed to produce the desired output. In this script, the rules are implemented through various functions and conditional statements.

Example: Rule for Encounter Resource

The rule for processing an Encounter resource involves extracting the recorded date from resource["period"]["start"]. Each resource type has its own specific rules for extracting relevant fields.

2. Conditional Logic and Flow Control

The script uses conditional logic extensively to control the flow of data processing. These conditionals determine which rules to apply based on the type and attributes of the input data.

Example: Extracting References

The function for extracting references applies different conditions to extract references from data["entry"] based on the resource type and its relationships, guiding how the data should be processed.

3. Pattern Matching

Pattern matching involves identifying specific structures or sequences in the data to apply the corresponding rules. The script matches resource types and their attributes to determine how to process each entry.

Example: Matching Resource Types

A loop identifies the type of each resource and then calls a processing function with the appropriate parameters. This processing function contains the rules for handling each type of resource.

4. Data Transformation

The primary function of this script is to transform FHIR JSON resources into AuditEvent resources. Each transformation follows specific rules that are determined by the type and attributes of the input data.

Example: Creating AuditEvent

The function constructs an AuditEvent resource based on the provided parameters, which are populated according to the rules defined in other parts of the script.

5. Deterministic Behavior

A hallmark of rule-based systems is their deterministic nature. Given the same input, the system will always produce the same output, as it strictly follows the predefined rules without any learning or adaptation.

Why

  • Predictable and Repeatable: The behavior is predictable and repeatable due to the explicit rules.
  • Non-Adaptive: The script does not learn from data or adapt its behavior based on new inputs.
  • Complexity Managed by Rules: The complexity of processing different types of FHIR resources is managed by the set of rules encoded in the functions.

Other approaches

Other approaches can be found here link. Here are other approaches that were expiremented with such as seq2seq models and large language models, however these did not yield the results expected for the creation of the FHIR AuditEvent resource

Conclusion

By extending Synthea to generate FHIR AuditEvent resources, the PICA project can effectively document and analyze healthcare processes from various perspectives. This enhancement ensures that the synthetic data generated is not only comprehensive but also aligned with the audit and conformance requirements of healthcare systems.

while this approach does not involve machine learning or statistical models, it fits the broader category of rule-based systems that are an essential component of the AI domain, especially for tasks that require deterministic data processing and transformation based on predefined criteria.

This rule-based approach ensures that the FHIR AuditEvent resources are generated consistently and accurately, supporting the various perspectives required by the PICA project to reconstruct and analyze patient-centric processes.