A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

Abstract

BackgroundIn recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.DiscussionCritical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.SummaryA national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.

Decision time for clinical decision support systems

“…These methods will gain in importance as complex, high-volume, genomic data become commonplace in clinical practice, and application of these techniques will play an important role in achieving the benefi ts of personalised medicine. 29 An important tenet of human computer interaction -the domain of computer science that deals with the interaction between users and computers -is to design with the end user in mind. In the case of CDSSs, computer scientists often develop software to manipulate complex clinical concepts that they do not fully understand for clinicians who clearly understand these concepts but who may not well understand the technologies and methods that underpin CDSSs.…”

Pharmacogenomics for Precision Medicine in the Era of Collaborative Co-creation and Crowdsourcing

“…The major impact of pharmacogenomic maps would be in how they could be potentially integrated into Electronic Medical Records, Decision Support Systems and Research Databases [43]. The future could see new standards enabled with the highest encryption, offering tiered release of relevant information to all stakeholders-the patient, physicians, researchers, and planners.…”

Informatics and clinical genome sequencing: opening the black box

“…40 In genomic terms, this might equate to: a database (or databases) of genotype-phenotype associations, an analysis pipeline to prioritize a list of candidate variants of interest in a particular patient, and a user-friendly portal for inputting, accessing, and visualizing patient data. Standardized representation of genomic and nongenomic patient data is essential to ensure reliable computer-based interpretation and processing, 41 and robust epidemiological data and statistical methods are required to ensure evidence-based analysis.…”