Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 1: An Overview of Requirements

Abstract

BackgroundPersonalized medicine approaches provide opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world including Turkey.ObjectiveThe objective of this study was to concentrate on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluate the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP incorporated NHIS-T for prostate cancer.MethodsFor the first article of this miniseries, the scientific literature is reviewed and the requirements of SNP data integration into EMRs/EHRs are extracted and presented.ResultsIn the literature, basic requirements of genomic-enabled EMRs/EHRs are listed as incorporating genotype data and its clinical interpretation into EMRs/EHRs, developing accurate and accessible clinicogenomic interpretation resources (knowledge bases), interpreting and reinterpreting of variant data, and immersing of clinicogenomic information into the medical decision processes. In this section, we have analyzed these requirements under the subtitles of terminology standards, interoperability standards, clinicogenomic knowledge bases, defining clinical significance, and clinicogenomic decision support.ConclusionsIn order to integrate structured genotype and phenotype data into any system, there is a need to determine data components, terminology standards, and identifiers of clinicogenomic information. Also, we need to determine interoperability standards to share information between different information systems of stakeholders, and develop decision support capability to interpret genomic variations based on the knowledge bases via different assessment approaches.

Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 3: An Evaluation of SNP Incorporated National Health Information System of Turkey for Prostate Cancer

“…The healthy individuals, who had a higher risk than controls, namely “06-hu56B3B6”, had basal cell carcinoma, and “21-huAC827A” had hypogonadism, that is a low level of testosterone. And both of these clinical conditions are known to decrease the prostate cancer risk [ 1 , 2 ]. Also, “06-hu56B3B6” and “17-huA720D3” used several risky, protective drugs and supplements regarding prostate cancer risk.…”

“…First the literature review for SNP data incorporated electronic medical record (EMR)/electronic health record (EHR)s is presented. In addition, the requirements for the EMR/EHR systems in terms of the standardizations of terminologies and messaging are reviewed [ 1 ]. The need for a structured knowledge base, decision support approaches, systems for reporting, and risk assessment are addressed as well.…”

Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 2: The Incorporation of SNP into the National Health Information System of Turkey

“…After that, we argued the possible architectural extensions with complementary capabilities for a SNP data incorporated NHIS-T in the light of literature review and characteristics of NHIS-T [ 8 ].…”

“…The aim of this miniseries is to present how the incorporation of personal single nucleotide polymorphism (SNP) data into the NHIS-T would make disease risk assessment possible, and to evaluate the performance of various predictive models for a specific medical condition (eg, prostate cancer). The requirements of SNP data integrated with EMRs/EHRs from scientific literature have been reviewed in the previous part of this miniseries [ 8 ], and here we will focus on extending the capabilities of the NHIS-T via incorporating SNP and clinicogenomic data for disease risk assessment. In the final part of the miniseries, we will evaluate the proposed complementary capabilities with real data from prostate cancer cases [ 9 ].…”

Application of Nanotechnology for Sensitive Detection of Low-Abundance Single-Nucleotide Variations in Genomic DNA: A Review

“…Essentially, the presence of a heterozygote allele may not change the risk of developing a disease, but a homozygote allele of the same SNP significantly affects susceptibility to a complex polygenic disease [ 67 ]. Recently, germline and somatic sequence SNPs in coding and/or noncoding DNA regions have been largely investigated for their role in the onset of cancer [ 68 , 69 ], inflammatory diseases [ 70 , 71 ], eye diseases, cardiovascular diseases [ 72 , 73 ], kidney diseases, endocrine disorders, skin diseases [ 74 , 75 ], psychological diseases and anxiety disorders, gynecological diseases, etc.…”