PMI data volumes will put a premium on interoperability
Genomic, EHR data and more will require better exchange capabilities, says Andy Wiesenthal.
As the Precision Medicine Initiative ramps up to enroll 1 million Americans in a national cohort, the sheer volume and complexity of big data it will produce—including that from electronic health records—will require an emphasis on access- and query-based exchange instead of the actual transfer of large datasets.
That’s the conclusion a Health IT Standards Committee task force on precision medicine, which presented its final recommendations during a May 17 joint HIT Policy-Standards Committee meeting.
According to Andy Wiesenthal, co-chair of the task force, participants in the PMI cohort will voluntarily enroll in the study, sharing a broad range of data, including EHRs, genomic information, as well as mobile health data on their lifestyle habits and environmental exposures over many years.
“The volumes of data inherent in these queries and these exchanges would be well beyond what is currently encountered in our newborn but growing EHR ecosystem,” said Wiesenthal. “There’s going to be lots of data sources that we are not currently including, especially data directly from patients.”
In addition, he warned that genomic and other data from research platforms, laboratories and disease registries will also be included as part of the National Institutes of Health’s landmark longitudinal study. “Some of the data sits in EHRs, but some of it sits outside of EHRs today,” Wiesenthal said.
Further, he asserted that it’s particularly important to recognize that genetic data “while it’s not static, it remains quite valuable over long periods of time” as opposed to other types of data such as lab tests. As a result, he argued that the “validity and quality of the data must be preserved over time.”
When it comes to interoperability, Leslie Kelly Hall, co-chair of the task force, said that the group recommends that the Office of the National Coordinator for Health IT provide a PMI addendum to ONC’s Interoperability Roadmap, including an inventory of all data flows envisioned for PMI and the standards being used.
“We also want to engage stakeholders to accelerate the definition of minimum data set and standards for patient-generated data or phenotypic data to be sent to the NIH,” she said. “We also want to provide ongoing guidance from technical expert panels, considering this Roadmap effort, and inform the research community on interoperability with EHRs and standards in general, so that we don’t end up having just disconnected ecosystems.”
The task force recommended that PMI consider high value, non-EHR data sources to promote the completeness of the longitudinal patient information. In particular, the group encouraged the use of standard application programming interfaces (APIs) to get medication history and adherence data, not just from patients themselves but also from pharmacy benefit managers and retail pharmacies.
PMI should “consider the challenges in pulling data from commercial and hospital labs, and explore practical differences between lab data in the EHR versus data directly from the labs to determine which set might have greater fidelity and value,” Hall added.
She indicated that the task force had discussions about how claims data can “enhance the understanding of the patient and the sources” and the panel encourages the use of claims data as well. In addition, the group argued that PMI should consider means of patient-mediated data donation to reduce problems with matching.
“The more data you have, the more likely things can be matched to a patient,” concluded Hall. “Also, we felt that the participants’ access to their aggregated information will promote participation and retention. So data return should offer dynamic and compelling visualizations to promote its use. And patients should have access to computable, raw genetic testing and sequencing data.”
In the near term, the task force recommended that PMI should accelerate individual’s abilities to retrieve their information. The group’s recommendations included a call for patient-facing portals that enable participants to access all data types and that the portals should allow for the use of apps and APIs based on existing and emerging standards such as HL7’s Fast Healthcare Interoperability Resources (FHIR).
That’s the conclusion a Health IT Standards Committee task force on precision medicine, which presented its final recommendations during a May 17 joint HIT Policy-Standards Committee meeting.
According to Andy Wiesenthal, co-chair of the task force, participants in the PMI cohort will voluntarily enroll in the study, sharing a broad range of data, including EHRs, genomic information, as well as mobile health data on their lifestyle habits and environmental exposures over many years.
“The volumes of data inherent in these queries and these exchanges would be well beyond what is currently encountered in our newborn but growing EHR ecosystem,” said Wiesenthal. “There’s going to be lots of data sources that we are not currently including, especially data directly from patients.”In addition, he warned that genomic and other data from research platforms, laboratories and disease registries will also be included as part of the National Institutes of Health’s landmark longitudinal study. “Some of the data sits in EHRs, but some of it sits outside of EHRs today,” Wiesenthal said.
Further, he asserted that it’s particularly important to recognize that genetic data “while it’s not static, it remains quite valuable over long periods of time” as opposed to other types of data such as lab tests. As a result, he argued that the “validity and quality of the data must be preserved over time.”
When it comes to interoperability, Leslie Kelly Hall, co-chair of the task force, said that the group recommends that the Office of the National Coordinator for Health IT provide a PMI addendum to ONC’s Interoperability Roadmap, including an inventory of all data flows envisioned for PMI and the standards being used.
“We also want to engage stakeholders to accelerate the definition of minimum data set and standards for patient-generated data or phenotypic data to be sent to the NIH,” she said. “We also want to provide ongoing guidance from technical expert panels, considering this Roadmap effort, and inform the research community on interoperability with EHRs and standards in general, so that we don’t end up having just disconnected ecosystems.”
The task force recommended that PMI consider high value, non-EHR data sources to promote the completeness of the longitudinal patient information. In particular, the group encouraged the use of standard application programming interfaces (APIs) to get medication history and adherence data, not just from patients themselves but also from pharmacy benefit managers and retail pharmacies.
PMI should “consider the challenges in pulling data from commercial and hospital labs, and explore practical differences between lab data in the EHR versus data directly from the labs to determine which set might have greater fidelity and value,” Hall added.
She indicated that the task force had discussions about how claims data can “enhance the understanding of the patient and the sources” and the panel encourages the use of claims data as well. In addition, the group argued that PMI should consider means of patient-mediated data donation to reduce problems with matching.
“The more data you have, the more likely things can be matched to a patient,” concluded Hall. “Also, we felt that the participants’ access to their aggregated information will promote participation and retention. So data return should offer dynamic and compelling visualizations to promote its use. And patients should have access to computable, raw genetic testing and sequencing data.”
In the near term, the task force recommended that PMI should accelerate individual’s abilities to retrieve their information. The group’s recommendations included a call for patient-facing portals that enable participants to access all data types and that the portals should allow for the use of apps and APIs based on existing and emerging standards such as HL7’s Fast Healthcare Interoperability Resources (FHIR).
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