Toward knowing the whole human: next-generation sequencing for personalized medicine

Will the future of knowledge work automation transform personalized medicine?

Today, we live in a world of ‘information overload’ which demands high level of knowledge-based work. However, advances in computer hardware and software have opened possibilities to automate ‘routine cognitive tasks’ for knowledge processing. Engineering intelligent software systems that can process large data sets using unstructured commands and subtle judgments and have the ability to learn ‘on the fly’ are a significant step towards automation of knowledge work. The applications of this technology for high throughput genomic analysis, database updating, reporting clinically significant variants, and diagnostic imaging purposes are explored using case studies.

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Automation of ‘routine cognitive processes’ is the need of the hour.

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Expanding biomedical literature, clinical, and research data poses significant problem.

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Knowledge work automation will be beneficial for translating data into information.

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Automated data mining, linking, and annotation will accelerate translational research.

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Artificial intelligence and semantics can automate data intensive tasks in genomics.

Challenges for implementing next-generation sequencing-based genome diagnostics: it's also the people, not just the machines

The scope of next-generation DNA sequencing (NGS) is transitioning from research to diagnostics (and beyond), but the conditions for routine clinical application have not been clearly defined. Technological limitations for sequencing a patient's DNA fast and affordably are rapidly disappearing. At the same time, more and more is known about the role of DNA variation in disease susceptibility, disease development and response to treatment. Consequently, more and more pediatricians, cardiologists and other medical specialists would like to apply NGS-based diagnostics. The standard, comprehensive and easy-to-handle genetic test these specialists are looking for, however, is not yet available. Molecular diagnostic laboratories have started to implement NGS into their routine workflows, but are also becoming increasingly aware that the context in which they operate is changing. It becomes apparent that the major challenges are not in the technology, but rather in anticipating the changing scope and scale. Developing the infrastructure to sustainably perform NGS-based diagnostics in a changing technological, clinical and societal context is therefore more relevant than defining minimal performance criteria or standard analysis pipelines. Implementing NGS-based diagnostics comes with novel applications, emerging service models and reconfiguration of professional roles, and should thus be considered in the context of future healthcare. Here, we present the key elements for transition of NGS from research to diagnostics.

The current and future state of pharmacogenomics medical education in the USA

Healthcare professionals (e.g., physicians, physician assistants, pharmacists, nurses and genetic counselors) believe pharmacogenomics (PGx) is essential to personalized medicine; however, they still lack confidence prescribing, dosing, interacting with other healthcare professionals and counseling patients with regard to PGx. This is due to the inadequate incorporation of PGx content into professional curricula. Compared with other health professions, Doctor of Pharmacy programs have integrated more PGx content. Unlike other healthcare professionals, pharmacists have extensive training in pharmacology, drug selection, drug dosage, drug-drug interactions and are uniquely accessible to patients. We suggest pharmacists are the best poised to facilitate incorporating PGx into therapeutic decision-making. Based on our experience as undergraduate and pharmacy PGx educators, we further reflect on our experience educating future healthcare professionals on PGx.