AI Utilization for Transparency and Trust in Healthcare: Implementing Provenance Standards for Exchanges

The future of Healthcare: Tracking AI’s influence on Your medical Records

May 3, 2024

A quiet revolution is underway in healthcare, one powered by Artificial Intelligence. But, wiht increasing reliance on AI for diagnoses, treatment plans, and even data entry, a crucial question arises: how do we know what parts of your medical record were created by a machine and what parts were authored by a human? A new project is developing standards to answer exactly that, bringing unprecedented transparency to the intersection of AI and patient care.

The Spectrum of AI Involvement

The level of AI influence in healthcare isn’t a simple binary. It’s a complex spectrum, demanding nuanced tracking.Experts are now categorizing AI’s role into three distinct levels:

• AI-Authored Data: Information generated entirely by an AI, with no human intervention.
• AI-Recommended Data: Suggestions provided by an AI that were then reviewed and approved by a medical professional.
• AI-Assisted Data: Instances where AI tools aided a human, but the final decisions and data entry were primarily human-driven.

Recognizing these distinctions is vital for maintaining trust and accountability in healthcare.Addressing these complexities requires innovative solutions in data management and standardization.

Data Tagging: A Simple Flag for AI Influence

A foundational approach involves data tagging – the practice of adding a simple identifier to medical data to indicate whether and how AI was involved in its creation. This tag serves as a crucial flag, alerting healthcare providers and researchers to potential AI influence. While it doesn’t detail the “how” or “why,” it signals the need for further scrutiny if needed.

Did You Know? Data tags are not intended to replace thorough analysis, but to prompt it. A tag can signal that the data might not be suitable for training future AI models, or that its origin requires deeper examination.

Provenance: unpacking the AI’s Footprint

Going beyond simple tagging, the concept of ‘Provenance’ offers a detailed audit trail of AI involvement.This includes tracking the specific AI model used, its version, the prompts applied, and all associated parameters. Leveraging the HL7 Provenance standard, which builds upon the W3C PROV framework, is key to achieving this level of detail.

The aim is to create a comprehensive record enabling a deep understanding of the AI’s contribution. This detailed record is useful in several ways, including verifying the output, identifying the source of potential errors, and re-evaluating decisions made based on AI-generated or assisted data. The use of model cards and similar standards is also being considered.

Focusing on Detail: Element-Level Tracking

The need for granularity extends to the level of individual data elements within a medical record. Not all information within a complex resource like a Care Plan will be AI-influenced. The ability to track AI’s impact on specific elements, rather than tagging entire resources, is crucial. Both data tagging and provenance solutions are designed to support this element-level approach.

Feature	Data Tagging	Provenance
Level of Detail	General AI interaction	Specific AI model, version, prompts
Complexity	Simple, easy to implement	More complex, requires robust infrastructure
Focus	Flagging AI influence	Detailed audit trail

Addressing Implementation Challenges

One concern raised during recent healthcare technology connectathons centers on the perceived complexity of the Provenance model. Though, experts believe this is primarily an educational hurdle. Utilizing data tags to initially indicate AI influence can direct users to examine the corresponding Provenance records, streamlining the process.

Other strategies include embedding Provenance data directly within resources or leveraging the _revinclude parameter in FHIR search to automatically include related Provenance information.

Ongoing Growth and Collaboration

The HL7 implementation guide for AI transparency is currently in development, with a publicly accessible CI build available here. Collaboration and input from the healthcare community are crucial to ensure the standards meet diverse needs.

Pro Tip: If you’re involved in healthcare data management, explore the ongoing work of HL7 and contribute to the development of these critical standards.

The Broader Context: AI and Trust in Healthcare

The push for AI transparency isn’t isolated to data tagging and provenance. It reflects a growing global concern about responsible AI development and deployment. Regulations like the EU AI act are beginning to mandate transparency and accountability for AI systems used in high-risk applications, including healthcare.

Furthermore, organizations like the Data and trust Alliance are working to establish provenance standards for datasets used in AI training, ensuring the integrity and reliability of the underlying data. These efforts are all converging towards a future where AI is used ethically and responsibly in healthcare.

Frequently Asked Questions About AI Transparency in Healthcare

What are your thoughts on AI and its role in healthcare? How can we ensure that these technologies are used responsibly and ethically to benefit all patients?

Share your comments and join the conversation below!

How might the implementation of provenance standards,as described in the text,directly impact patient trust and informed decision-making regarding AI-driven healthcare?

AI Utilization for Clarity and Trust in Healthcare: Implementing Provenance Standards for Exchanges

The Growing Need for AI Transparency in Healthcare

Artificial intelligence (AI) is rapidly transforming healthcare,offering incredible potential for improved diagnostics,personalized treatment plans,and streamlined operations.Though, this progress hinges on building and maintaining trust. Patients and healthcare professionals alike need to understand how AI arrives at it’s conclusions. This is where AI transparency and robust provenance standards become critical. As noted in recent research (Hurley et al., 2025), transparency directly supports informed patient decision-making. Without it, adoption and effective utilization of AI in healthcare will be severely limited.

Understanding Provenance in AI Healthcare Exchanges

Provenance, in the context of AI, refers to the complete history of an AI model’s lifecycle – from data collection and training to deployment and ongoing monitoring. It’s essentially a detailed audit trail. For healthcare exchanges, this means documenting:

* Data sources: Where did the data used to train the AI model originate? (e.g., EHRs, imaging databases, genomic data)

* Data Preprocessing: What steps were taken to clean, transform, and prepare the data?

* Model Development: Which algorithms were used, and what were the key parameters?

* Validation & Testing: How was the model validated, and what were its performance metrics across diverse patient populations?

* Deployment & Monitoring: How is the model being used in clinical practice, and how is its performance being continuously monitored for drift or bias?

* Version Control: Tracking different versions of the model and associated data.

Implementing these standards isn’t just about technical documentation; it’s about establishing accountability and fostering confidence in AI-driven healthcare solutions. Data lineage is a core component of provenance.

Key Technologies Enabling Provenance

several technologies are emerging to facilitate the implementation of provenance standards:

1. Blockchain: Offers an immutable and obvious record of data transactions and model updates. While not a silver bullet, it can enhance security and auditability.
2. Knowledge Graphs: Represent relationships between data elements, algorithms, and clinical outcomes, providing a contextual understanding of AI decision-making.
3. Metadata Management Systems: Centralized repositories for storing and managing metadata related to AI models and data assets.
4. Explainable AI (XAI) Techniques: Methods for making AI models more interpretable, allowing users to understand why a particular prediction was made. (e.g., SHAP values, LIME).
5. Federated Learning: Allows model training on decentralized datasets without exchanging the data itself, preserving patient privacy while still benefiting from diverse data sources.

Implementing Provenance Standards: A Practical Guide

Successfully integrating provenance requires a multi-faceted approach:

* Define Clear Standards: Adopt or develop industry-wide provenance standards for AI in healthcare. Collaboration between regulatory bodies, healthcare providers, and AI developers is crucial.

* Invest in Infrastructure: Implement the necessary technological infrastructure to capture, store, and manage provenance data.

* establish Governance Policies: Develop clear policies and procedures for data access,model validation,and ongoing monitoring.

* Training & Education: Educate healthcare professionals and AI developers on the importance of provenance and how to implement it effectively.

* Prioritize Interoperability: Ensure provenance data can be easily exchanged between different healthcare systems and AI platforms. HL7 FHIR standards are particularly relevant here.

* Focus on Data Quality: Provenance is only as good as the data it tracks. Invest in data quality initiatives to ensure accuracy and completeness.

Benefits of AI Provenance in Healthcare

The benefits of prioritizing provenance extend beyond simply building trust:

* Improved patient Safety: Identifying and mitigating potential biases or errors in AI models.

* Enhanced Regulatory Compliance: Meeting increasingly stringent regulatory requirements for AI in healthcare (e.g., FDA guidelines).

* Faster Innovation: Facilitating the responsible development and deployment of new AI-powered healthcare solutions.

* Reduced Legal Risk: Providing a clear audit trail in case of adverse events or legal challenges.

* Increased Adoption: Boosting confidence among healthcare professionals and patients, leading to wider adoption of AI technologies.

* Better Clinical Decision Support: Providing clinicians with the information they need to understand and trust AI-driven recommendations.

Real-World Examples & Emerging Trends

Several organizations are already exploring provenance solutions. For example, some hospitals are using blockchain to track the provenance of medical images used for AI-powered diagnostics. Research institutions are developing knowledge graphs to map the relationships between genomic data, clinical phenotypes, and treatment outcomes.

Looking ahead, we can expect to see:

* Increased Automation: Automated tools for capturing and managing provenance data.

* Standardized Provenance Metadata: Development of standardized metadata schemas for AI in healthcare.

* Integration with EHRs: Seamless integration of provenance data into electronic health records.

* Patient-Controlled Provenance: Empowering patients to access and control their own AI provenance data.

Reference:

Hurley, et al. (2025). Challenges in Pursuing AI Transparency. *Journal