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Provenance and Precision Medicine November 27, 2020

by Diana Proud-Madruga 

“A personalized approach based on a patient's or pathogen’s unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles…Provenance must be preserved and reported to promote transparency and reproducibility in complex analyses. Standards must be established to reliably communicate genomic data between databases and individual scientists.”

Robust and reproducible data analysis is key to successful personalized medicine and genomic initiatives. In order to be reproducible, the origin and history of research data must be maintained. Current systems rely on data stored with incomplete provenance records and in different computing languages. It is essential that users know the provenance of healthcare information in order to make trustworthy decisions before relying upon that information.

Two key goals of Data Provenance (as defined by the ONC HIT S&I Provenance Initiative in 2014) are:

  • Improve the visibility of the source of, and alterations to, health information.
  • Improve the confidence that healthcare stakeholders have in the authenticity, reliability, and trustworthiness of shared data.

In 2020, the HL7 Privacy and Security Architecture Framework (PSAF) Volume 3 – Provenance Domain Analysis Model refined and clarified that first goal as follows:

  • Improve the visibility of health information permutations from creation to exchange, integration, and use across multiple health information systems.

Precision medicine research partners work with health-related data that comes from many sources, including: doctors, patients, health apps, wearables, and multiple databases including “curated” databases that do not receive their experimental data directly. Health-related data which has provenance information that is modeled, stored, and exchanged in a standardized way would enable any healthcare scientist to trace the origin of:

  • A dataset (e.g., an image, lab results, fitness activity, or treatment information and results)
  • A document (e.g., physician notes, clinical codes, or patient provided information)
  • A device (e.g., wearable medical device, or fitness trackers)

It also enables healthcare scientists to learn about the people and organizations involved and assess the reliability, quality, and usefulness of the dataset, document, or device. As the amount of data from and in these databases grows, patterns will emerge that would not be visible at a smaller scale. However, that growth in data comes with an increasing imperative to know that the data is trustworthy. Accurate, reliable, and standards-based provenance information is fundamental to understanding the “who, what, when, where, why, and how” of the data in the curated database in order to help determine the quality of information and, therefore, the quality and reliability of the conclusions based on that information.

Electrosoft works closely with VHA and the HL7 Working Groups that develop healthcare security and privacy standards. One of the program’s goals is to ensure VHA has access to standards that safeguard the security and privacy of our Veterans’ health information while still enabling the interoperability needed to deliver world-class care.


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