Genetic and Molecular Diagnostics Support Systems

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Types of genomic and molecular data may need to represent in EHR

  • Immunohistochemistry
  • Flow cytometry
  • Fluorescence in situ hybridization
  • Polymerase chain reaction
  • Gene expression panels
  • Next-generation sequencing panels
  • Whole exome sequencing
  • Whole genome sequencing
  • Methylation panels


Rationale for a separate genomic ancillary system

Storage of raw genetic and molecular information in EHR is not practically feasible

  1. EHR is best utilized to store discrete/actionable data values that need rapid access. EHR is NOT good for large blocks of data that don't require rapid access
    1. Types of laboratory results
      1. Simple data (numbers, binary)
        1. Ex. cholesterol level
      2. Complex, requiring more processing
        1. Ex. microbiology, cultures/sensitivity
      3. Complex, requiring specialist interpretation
        1. Ex. pathology reports, karyotypes
  2. Genetic data is bigger and more complex than traditional laboratory results
    1. Ex. Common data sizes of next generation sequencing for genomics
      1. Whole genome sequencing: 60-350 GB
      2. Whole exome sequencing: 5-20 GB
      3. Targeted sequencing: 100 MB-5 GB
  3. Genetic data is more "unstable" and "uncertain" than traditional laboratory results
    1. Traditional laboratory data
      1. Will still be replicable across different laboratories and accurate over years
    2. Genetic data
      1. Reanalysis of same sample years later yields different results
        1. Better analytic techniques
        2. Better algorithms to detect mutations
        3. Discover new genes that determined to be relevant
        4. Changes to recommended gene lists by professional organizations
      2. Conflicting variant interpretations among commercial laboratories


Genetic and molecular data is more similar to radiology data than to traditional laboratory data.

The best possible solution is for a separate genomic ancillary system akin to PACS for radiology data that feeds into the EHR.


Emerging solutions

Genomic data warehouses

  • Mayo Clinic, internally developed genomic data warehouse [1]
  • Northwestern Medicine, internally developed ancillary genomics systems [2]


Most solutions for hospitals are currently in development and not ready for commercial usage. Most hospital do not have the resources to build out complex internally developed information systems.

Genomic Data Interoperability

Currently, genomic data warehouses are mostly under purview of 3rd party genetic testing laboratories who perform these tests and store the results in their internal databases. Transfer of test results to ordering providers is currently limited to foundational level interoperability through unstructured PDF reports.

An alternative solution to hospitals investing in genomic data warehouses is the use of the FHIR Genomics messaging standard that may incentivize more 3rd party genetic testing companies to transmit their results to providers/hospitals in way that can allows structured representation in the EHR.

References

Warner JL, Jain SK, Levy MA. Integrating cancer genomic data into electronic health records. Genome Med. 2016;8(1). doi:10.1186/s13073-016-0371-3

Starren J, Williams MS, Bottinger EP. Crossing the omic chasm: A time for omic ancillary systems. JAMA- J Am Med Assoc. 2013;309(12):1237-1238. doi:10.1001/jama.2013.1579

Bewicke-Copley F, Arjun Kumar E, Palladino G, Korfi K, Wang J. Applications and analysis of targeted genomic sequencing in cancer studies. Comput Struct Biotechnol J. 2019;17:1348-1359. doi:10.1016/j.csbj.2019.10.004

Liu P, Meng L, Normand EA, et al. Reanalysis of Clinical Exome Sequencing Data. N Engl J Med. 2019;380(25):2478-2480. doi:10.1056/nejmc1812033

Balmaña J, Digiovanni L, Gaddam P, et al. Conflicting interpretation of genetic variants and cancer risk by commercial laboratories as assessed by the prospective registry of multiplex testing. J Clin Oncol. 2016;34(34):4071-4078. doi:10.1200/JCO.2016.68.4316

Horton I, Lin Y, Reed G, Wiepert M, Hart S. Empowering Mayo Clinic Individualized Medicine with Genomic Data Warehousing. J Pers Med 2017, Vol 7, Page 7 [Internet]. 2017 Aug 22 [cited 2021 Sep 1];7(3):7. Available from: https://www.mdpi.com/2075-4426/7/3/7/htm

LV R, ME S, F A, SD P, LJ R-T, JA P, et al. An ancillary genomics system to support the return of pharmacogenomic results. J Am Med Inform Assoc [Internet]. 2019 Feb 19 [cited 2021 Sep 1];26(4):306–10. Available from: https://pubmed.ncbi.nlm.nih.gov/30778576/

Genomics - FHIR v4.0.1 [Internet]. Available from: https://www.hl7.org/fhir/genomics.html


Submitted by Teja Ganta

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