scholarly journals Assessing the readiness of precision medicine interoperabilty: An exploratory study of the National Institutes of Health genetic testing registry

2017 ◽  
Vol 24 (4) ◽  
pp. 323 ◽  
Author(s):  
Jay G Ronquillo ◽  
Chunhua Weng ◽  
William T Lester
Keyword(s):  
Genetic Testing ◽  
Precision Medicine ◽  
Healthcare Providers ◽  
National Institutes Of Health ◽  
Snomed Ct ◽  
Genetic Tests ◽  
Test Characteristics ◽  
Current State ◽  
Starting Point ◽  
Systematized Nomenclature Of Medicine

Background:  Precision medicine involves three major innovations currently taking place in healthcare:  electronic health records, genomics, and big data.  A major challenge for healthcare providers, however, is understanding the readiness for practical application of initiatives like precision medicine.Objective:  To better understand the current state and challenges of precision medicine interoperability using a national genetic testing registry as a starting point, placed in the context of established interoperability formats.Methods:  We performed an exploratory analysis of the National Institutes of Health Genetic Testing Registry.  Relevant standards included Health Level Seven International Version 3 Implementation Guide for Family History, the Human Genome Organization Gene Nomenclature Committee (HGNC) database, and Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT).  We analyzed the distribution of genetic testing laboratories, genetic test characteristics, and standardized genome/clinical code mappings, stratified by laboratory setting.Results: There were a total of 25472 genetic tests from 240 laboratories testing for approximately 3632 distinct genes.  Most tests focused on diagnosis, mutation confirmation, and/or risk assessment of germline mutations that could be passed to offspring.  Genes were successfully mapped to all HGNC identifiers, but less than half of tests mapped to SNOMED CT codes, highlighting significant gaps when linking genetic tests to standardized clinical codes that explain the medical motivations behind test ordering.  Conclusion:  While precision medicine could potentially transform healthcare, successful practical and clinical application will first require the comprehensive and responsible adoption of interoperable standards, terminologies, and formats across all aspects of the precision medicine pipeline.

scholarly journals Promoting Safe and Effective Genetic Tests in the United States: Work of the Task Force on Genetic Testing

1999 ◽  
Vol 45 (5) ◽  
pp. 732-738 ◽  
Author(s):  
Neil A Holtzman
Keyword(s):  
United States ◽  
Genetic Testing ◽  
Human Services ◽  
Task Force ◽  
Healthcare Providers ◽  
The United States ◽  
Appropriate Use ◽  
Genetic Tests ◽  
Health And Human Services ◽  
Other Information

Abstract The Task Force on Genetic Testing was created to review genetic testing in the United States and, when necessary, to make recommendations to ensure the development of safe and effective genetic tests. A survey to explore the state of genetic testing was undertaken for the Task Force and completed in early 1995. The survey, as well as literature reports and other information collected for the Task Force, showed problems affecting safety and effectiveness, as defined by the Task Force: validity and utility of predictive tests, laboratory quality, and appropriate use by healthcare providers and consumers. On the basis of these findings, the Task Force made several recommendations to ensure safe and effective genetic testing. The Secretary of Health and Human Services followed up one recommendation by creating the Secretary’s Advisory Committee on Genetic Testing. One of its functions will be to implement other recommendations of the Task Force.

Standardizing Physiologic Assessment Data to Enable Big Data Analytics

2016 ◽  
Vol 39 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Susan A. Matney ◽  
Theresa (Tess) Settergren ◽  
Jane M. Carrington ◽  
Rachel L. Richesson ◽  
Amy Sheide ◽  
...  
Keyword(s):  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Snomed Ct ◽  
Assessment Data ◽  
Nursing Assessment ◽  
Coding Systems ◽  
Current State ◽  
Data Elements ◽  
Systematized Nomenclature Of Medicine

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

Pharmacogenomic testing and prescribing patterns for patients with cancer in a large national precision medicine cohort

2021 ◽  
pp. jmedgenet-2021-108112
Author(s):  
Jay G Ronquillo ◽  
William T Lester
Keyword(s):  
Genetic Testing ◽  
Observational Study ◽  
Precision Medicine ◽  
Standard Of Care ◽  
National Institutes Of Health ◽  
Prescribing Patterns ◽  
Age Group ◽  
Patients With Cancer ◽  
Testing Practices ◽  
Population Databases

Population databases could help patients with cancer and providers better understand current pharmacogenomic prescribing and testing practices. This retrospective observational study analysed patients with cancer, drugs with pharmacogenomic evidence and related genetic testing in the National Institutes of Health All of Us database. Most patients with cancer (19 633 (88.3%) vs 2590 (11.7%)) received ≥1 drug and 36 (0.2%) received genetic testing, with a significant association between receiving ≥1 drug and age group (p<0.001), but not sex (p=0.612), race (p=0.232) or ethnicity (p=0.971). Drugs with pharmacogenomic evidence—but not genetic testing—were common for patients with cancer, reflecting key gaps preventing precision medicine from becoming standard of care.

How the NIH Genetic Testing Registry and Medical Genetics Summaries can help oncologists adopt pharmacogenetics guidelines.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14141-e14141
Author(s):  
Adriana Jose Malheiro ◽  
Laura Charlotte Dean ◽  
Marilu A Hoeppner ◽  
Vitaliy Lyoshin ◽  
Baoshan Gu ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Cancer Diagnostics ◽  
Medical Genetics ◽  
National Institutes Of Health ◽  
Genetic Tests ◽  
Medical Specialties ◽  
Cancer Management ◽  
Extract Content ◽  
The Us ◽  
The Relationship

e14141 Background: Oncology is one of the few medical specialties where pharmacogenetic dosing is becoming a routine part of patient management. In the US, oncology drugs account for one-third of all drugs with pharmacogenetic data in their labeling. The NIH Genetic Testing Registry (GTR, www.ncbi.nlm.nih.gov/gtr ) is a freely available database of orderable genetic tests voluntarily provided by laboratories, and supports easy identification of both diagnostic and therapy-based tests. Medical Genetics Summaries (MGS, www.ncbi.nlm.nih.gov/books/NBK61999 ) presents actionable pharmacogenetic information by collating guidelines from authoritative professional (e.g. FDA, CPIC) and medical (e.g. ASCO) societies. MGS is regularly updated and can be found by searching PubMed. To increase oncologist adoption of pharmacogenetic recommendations, we analyzed the relationship between genetic tests in GTR and practice guidelines in MGS. Methods: The GTR database was queried to extract content, and we consulted the FDA Table of Pharmacogenomic Biomarkers in Drug Labeling. Results: Of the 90 oncology drugs that have pharmacogenetic information in their labeling, 29 drugs have at least one test for drug response in GTR. The oncology drugs most tested are mercaptopurine and irinotecan with 13 tests each, followed by thioguanine (12) and tamoxifen (9). For the remaining 61 drugs, only one drug (Pertuzumab) has a guideline, which is summarized in MGS. In total, MGS offers 10 summaries focused on oncology drugs. The 90 drug labels mention 49 distinct biomarkers (e.g. genes and variants). All 49 biomarkers can be interrogated using tests via the GTR. In total, GTR has approximately 3,500 tests for cancer diagnostics, 500 tests for cancer management, and 37 pharmacogenetic tests for cancer drugs. Conclusions: Searching for a genetic test in GTR by biomarker (100% coverage) is more efficient than by drug name (32% coverage) because of how laboratories describe their tests. Clinicians need to quickly find the best pharmacogenetic test and how tests are represented in the community affect the ability to search and find them. This presentation will show busy clinicians how to optimally use GTR and MGS. This work was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health.

scholarly journals A Simple Practical Guide to Genomic Diagnostics in a Pediatric Setting

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 818
Author(s):  
Alan Taylor ◽  
Zeinab Alloub ◽  
Ahmad Abou Tayoun
Keyword(s):  
Genetic Testing ◽  
Genetic Test ◽  
Diagnostic Yield ◽  
Financial Burden ◽  
Healthcare Providers ◽  
Cost Effective ◽  
Turnaround Time ◽  
Test Characteristics ◽  
Clinical Scenarios ◽  
Uncertain Significance

With limited access to trained clinical geneticists and/or genetic counselors in the majority of healthcare systems globally, and the expanding use of genetic testing in all specialties of medicine, many healthcare providers do not receive the relevant support to order the most appropriate genetic test for their patients. Therefore, it is essential to educate all healthcare providers about the basic concepts of genetic testing and how to properly utilize this testing for each patient. Here, we review the various genetic testing strategies and their utilization based on different clinical scenarios, and test characteristics, such as the types of genetic variation identified by each test, turnaround time, and diagnostic yield for different clinical indications. Additional considerations such as test cost, insurance reimbursement, and interpretation of variants of uncertain significance are also discussed. The goal of this review is to aid healthcare providers in utilizing the most appropriate, fastest, and most cost-effective genetic test for their patients, thereby increasing the likelihood of a timely diagnosis and reducing the financial burden on the healthcare system by eliminating unnecessary and redundant testing.

scholarly journals Recent Developments in Clinical Terminologies — SNOMED CT, LOINC, and RxNorm

2018 ◽  
Vol 27 (01) ◽  
pp. 129-139 ◽  
Author(s):  
Oliver Bodenreider ◽  
Ronald Cornet ◽  
Daniel Vreeman
Keyword(s):  
Snomed Ct ◽  
Standard Drug ◽  
Funding Sources ◽  
Current State ◽  
Development Teams ◽  
Recent Developments ◽  
Clinical Terminology ◽  
Systematized Nomenclature Of Medicine ◽  
Evolutionary Paths ◽  
Clinical Terminologies

Objective: To discuss recent developments in clinical terminologies. SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) is the world's largest clinical terminology, developed by an international consortium. LOINC (Logical Observation Identifiers, Names, and Codes) is an international terminology widely used for clinical and laboratory observations. RxNorm is the standard drug terminology in the U.S. Methods and results: We present a brief review of the history, current state, and future development of SNOMED CT, LOINC and RxNorm. We also analyze their similarities and differences, and outline areas for greater interoperability among them. Conclusions: With different starting points, representation formalisms, funding sources, and evolutionary paths, SNOMED CT, LOINC, and RxNorm have evolved over the past few decades into three major clinical terminologies supporting key use cases in clinical practice. Despite their differences, partnerships have been created among their development teams to facilitate interoperability and minimize duplication of effort.

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

scholarly journals Integrating Social Determinants of Health to Precision Medicine through Digital Transformation: An Exploratory Roadmap

2021 ◽  
Vol 18 (9) ◽  
pp. 5018
Author(s):  
Ik-Whan G. Kwon ◽  
Sung-Ho Kim ◽  
David Martin
Keyword(s):  
Precision Medicine ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Information Technologies ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Determinants Of Health ◽  
Collaborative Tools ◽  
High Level

The COVID-19 pandemic has altered healthcare delivery platforms from traditional face-to-face formats to online care through digital tools. The healthcare industry saw a rapid adoption of digital collaborative tools to provide care to patients, regardless of where patients or clinicians were located, while mitigating the risk of exposure to the coronavirus. Information technologies now allow healthcare providers to continue a high level of care for their patients through virtual visits, and to collaborate with other providers in the networks. Population health can be improved by social determinants of health and precision medicine working together. However, these two health-enhancing constructs work independently, resulting in suboptimal health results. This paper argues that artificial intelligence can provide clinical–community linkage that enhances overall population health. An exploratory roadmap is proposed.

scholarly journals A System for Phenotype Harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

2021 ◽  
Author(s):  
Adrienne M Stilp ◽  
Leslie S Emery ◽  
Jai G Broome ◽  
Erin J Buth ◽  
Alyna T Khan ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Association Studies ◽  
Controlled Vocabulary ◽  
National Institutes Of Health ◽  
Design Data ◽  
Data Set ◽  
Data Repositories ◽  
Phenotype Data ◽  
National Heart Lung ◽  
Centralized System

Abstract Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institute’s Trans-Omics for Precision Medicine program, which is generating genomic and other omics data for &gt;80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 studies per phenotype (participants recruited 1948-2012). We discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

scholarly journals Effectiveness of the Interventions Utilized in Genetic Counseling

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Jeeyae Choi ◽  
Hyeoneui Kim
Keyword(s):  
Genetic Counseling ◽  
Genetic Testing ◽  
Information Technologies ◽  
Decision Aids ◽  
Healthcare Providers ◽  
Experimental Studies ◽  
Well Being ◽  
Diagnostic Tools ◽  
Effective Decision ◽  
Single Intervention

Background. Advances in genetic science and biotechnology accumulated huge knowledge of genes and various genetic tests and diagnostic tools for healthcare providers including nurses. Genetic counseling became important to assist patients making decisions about obtaining genetic testing or preventive measures. Method. This review was conducted to describe the counseling topics, various interventions adopted in genetic counseling, and their effectiveness. Experimental studies (N=39) published between 1999 and 2012 were synthesized. Results. The most frequently covered topic was benefits and limitations of genetic testing on breast cancer ovarian and colorectal cancers. Most of researchers focused on evaluating cognitive aspect and psychological well-being. Conclusion. No single intervention was consistently reported to be effective. Decision aids enhanced with information technologies have potential to improve the outcomes of genetic counseling by providing tailored information and facilitating active engagement of patients in information uptake. Clinical Implication. When nurses are familiar with topics and interventions of genetic counseling, they are well positioned to provide genetic/genomic information to the patient and families.

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