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Electronic Medical Records Boot Camp
2025-06-30 - 2025-07-01    
10:30 am - 5:30 pm
The Electronic Medical Records Boot Camp is a two-day intensive boot camp of seminars and hands-on analytical sessions to provide an overview of electronic health [...]
AI in Healthcare Forum
2025-07-10 - 2025-07-11    
10:00 am - 5:00 pm
Jeff Thomas, Senior Vice President and Chief Technology Officer, shares how the migration not only saved the organization millions of dollars but also led to [...]
28th World Congress on  Nursing, Pharmacology and Healthcare
2025-07-21 - 2025-07-22    
10:00 am - 5:00 pm
To Collaborate Scientific Professionals around the World Conference Date:  July 21-22, 2025
5th World Congress on  Cardiovascular Medicine Pharmacology
2025-07-24 - 2025-07-25    
10:00 am - 5:00 pm
About Conference The 5th World Congress on Cardiovascular Medicine Pharmacology, scheduled for July 24-25, 2025 in Paris, France, invites experts, researchers, and clinicians to explore [...]
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AI in Healthcare Forum
10 Jul 25
New York
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Articles

EHRs should develop as large Information use develops

ehrs

As big data use continues to increase in healthcare, electronic health records will need to evolve simultaneously, researchers from Northwestern University, Geisinger Health System and Mount Sinai School of Medicine write in a viewpoint recently published in the Journal of the American Medical Association. Current EHRs, the authors say, are not built to handle the capacity of data created by current electronic medical tools, a problem that will only continue to grow as data access becomes easier.

“EHRs are designed to facilitate day-to-day patient care,” co-study author Justin Starren, chief of the division of health and biomedical informatics in the department of preventive medicine at Northwestern’s Feinberg School of Medicine, says in an announcement. “EHRs are not designed to store large blocks of data that do not require rapid access, nor are they currently capable of integrating genomics clinical decision support.”

As a temporary solution until more advanced EHRs are developed, Starren and his colleagues suggest using auxiliary systems for the storage of data culled from what they call increasing ‘omics’ research efforts–studies focusing on genomics, epigenomics, proteomics and metabolomics. Groups like the Electronic Medical Records and Genomics (eMERGE) consortium, they say, already are “bridging the chasm” by creating interoperable systems with the ability to integrate large-scale genomic data with clinical workflow.

es to increase in healthcare, electronic health records will need to evolve simultaneously, researchers from Northwestern University, Geisinger Health System and Mount Sinai School of Medicine write in a viewpoint recently published in the Journal of the American Medical Association. Current EHRs, the authors say, are not built to handle the capacity of data created by current electronic medical tools, a problem that will only continue to grow as data access becomes easier.

“EHRs are designed to facilitate day-to-day patient care,” co-study author Justin Starren, chief of the division of health and biomedical informatics in the department of preventive medicine at Northwestern’s Feinberg School of Medicine, says in an announcement. “EHRs are not designed to store large blocks of data that do not require rapid access, nor are they currently capable of integrating genomics clinical decision support.”

As a temporary solution until more advanced EHRs are developed, Starren and his colleagues suggest using auxiliary systems for the storage of data culled from what they call increasing ‘omics’ research efforts–studies focusing on genomics, epigenomics, proteomics and metabolomics. Groups like the Electronic Medical Records and Genomics (eMERGE) consortium, they say, already are “bridging the chasm” by creating interoperable systems with the ability to integrate large-scale genomic data with clinical workflow.

“Omic data are different,” the authors write. “An individual’s germline genetic sequence changes little over a lifetime, but understanding of that sequence is changing rapidly. The 1000 Genomes project has identified tens of millions of different genomic variants; the clinical significance of these variants is mostly unknown, but current understanding is rapidly changing. … This necessitates systems that dynamically reanalyze and reinterpret stored static genomic results in the context of evolving knowledge.”

(source)