Overcoming obstacles to AI-ready healthcare data

If artificial intelligence (AI) is going to remain the top news story for 2023, it has to continue to advance important initiatives across the healthcare spectrum, such as:

  • Accurately diagnosing certain conditions or predicting the risk of a patient contracting them
  • Rapid drug discovery 
  • Advancing precision medicine

The challenge with AI; however, is that it is only as good as its source data and the quality of its model, large language or otherwise. To reap the rewards of AI, researchers need to sample from the highest quality and most comprehensive data, which can be difficult to procure.

There are six main obstacles to obtaining AI-ready data for healthcare:

  1. Fragmentation 
    While there is a proliferation of healthcare data available, the entities producing this data only feature one aspect of the patient journey, such as pharmacy claims or lab results. To properly develop healthcare-related AI models, a broader array of patient-centric data needs to be sourced from unique entities, including medical and pharmacy claims, electronic health records, lab, and genetics data.

  2. Interoperability
    The patient’s identity needs to be accurately resolved across all of the data sources being utilized in your AI model, otherwise, results will suffer from fragmented views of patient journeys, leading to inaccurate correlations. 

  3. Privacy
    When incorporating a variety of patient-centric data into an AI model, you have to ensure that patient privacy is maintained and that processes adhere to HIPAA and other privacy regulations.

  4. Governance
    Data providers are increasingly concerned with the governance of their data and how it is being used. This has led to some data providers pulling their data from resellers or aggregators, leaving clients of those organizations with no recourse. Therefore, you need to ensure proper management of the data usage rights for all of the data types sourced for your AI model. Additionally, if you’re incorporating first-party data collected from clinical trials, wearables or apps, you also have to be mindful of patient permissions.

  5. Provenance
    To be able to run your fit-for-purpose AI models, you need transaction-level data; however, many resellers or data aggregators force clients to use their closed network platforms. The inability to understand how a platform sources its data or the limitations on adding new data sources that are critical to the research is a major drawback of this approach. For regulatory submissions and buy-in from payers considering a therapy, researchers need more transparent means to understand where data has been sourced and how it was generated.

  6. Diversity
    To help mitigate potential biases and ensure your algorithms effectively address health disparities, you need to incorporate data sources that reflect the populations being treated. Often, this requires the inclusion of social determinants of health, such as race and ethnicity.

Overcoming these obstacles takes time and resources to manage, slowing your research and delaying progress.

Synchronizing AI-ready healthcare data

To take full advantage of innovations in AI and increase speed to insight, you need a synchronized solution that eliminates all of these obstacles and provides high-quality, AI-ready data that enables research from day one.

HealthVerity built its business on the foundational elements of Identity, Privacy, Governance and Exchange, better known as the HealthVerity IPGE approach. By synchronizing these elements with our transformational technologies we address each of the obstacles to optimizing an AI-ready data strategy: 

  • Identity - In the massively fragmented healthcare data environment, your AI models cannot rely on legacy tokenization technologies, rife with high error rates and inconsistent patient identities. False positives (where two or more individuals are assigned the same token, appearing as one person) or false negatives (where a single individual is assigned multiple tokens, appearing as more than one person in the data) lead to lower quality AI results. HealthVerity synchronizes patient identities over time and across data sources, using a cutting-edge approach that resolves each individual as a unique but persistent HealthVerity ID that is 10x more accurate than a traditional token. This creates a single source of truth that reliably syncs patient records across unlimited data sources.

  • Privacy - Today’s regulatory environment requires hypervigilance to ensure data-driven strategies remain compliant with HIPAA across all procured data sources. HealthVerity has synchronized HIPAA compliance across our entire data ecosystem by achieving Expert Determination from an independent, third-party examiner—not an internal source with potential conflicts of interest—so you don’t have to waste two to five months getting a HIPAA certification.

  • Governance - When working with identifiable data, whether from clinical trials, wearables, apps or for commercial outreach, managing the permissions can be a dizzying task. HealthVerity consolidates and manages patient consents and data rights across the enterprise for a single source of truth, ensuring compliant actions.

  • Exchange - We have built relationships with leading data providers across the U.S. who trust the IPGE approach and, thus, trust us to host, de-identify and make interoperable their data, creating the nation’s largest healthcare and consumer data ecosystem and providing full transparency and data provenance.

    Additionally, because we host the data in our ecosystem, we are able to synchronize all aspects of contracting, licensing and procurement, providing a single contract for multiple data sources and the ability to design an initial, low-risk approach.

    This vast data ecosystem provides the most diverse and novel data sources, representative of the full spectrum of populations, as well as social determinants of health data. All of this data is fully interoperable, with a common data model and patient identity accurately resolved, making the data research ready from day one. Additionally, this transaction-level data is delivered directly to your analytics environment of choice.

The HealthVerity approach that synchronizes unparalleled identity management with built-in privacy compliance and data governance, provides the ability to discover and exchange a near limitless combination of AI-ready data at a record pace, while providing the flexibility to license only the data you need with pricing that meets your budget.

Discover AI-ready data

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