Phenotype is how something looks, acts, or behaves; in contrast to DNA sequence, which is fundamentally discrete and universal, phenotype tends to be much "messier," more challenging to reliably assess. Two investigators across the world can easily agree on the exact DNA sequence within a specific cell, say, but might come to very different conclusions about how the cell behaves in culture.

The greatest challenge may also be the most important: measuring complex human phenotypes, such as how a patient is experiencing a particular disease, or responding to a given treatment. Too often, and quite understandably, the approaches used by physicians and medical researchers have been relatively simple, episodic assessments -- measuring a patient's blood sodium, or blood pressure, for example, at the time of an annual physical. Such evaluations can provide important and useful information, but rarely capture the complexity of a patient's health and experience over time.

We envision improved measurement of phenotype as the underlying basis for the next generation of medical progress. Improved measurements of patients can guide -- immediately -- the treatment approach used by physicians, who often have very little visibility into what happens after a patient leaves the office. Better measurement can also guide medical product development, focusing attention on a patient's true unmet needs.

The FDA, to its credit, has recognized the need for improved measurement, and has been an early champion of the need for better "assessment science." Speaking at a conference on the subject last year, the director the FDA's Center for Drug Evaluation and Research, Dr. Janet Woodcock, noted (PDF, p.13) that "the identification, development, and qualification of new clinical trial outcome assessments has not been aggressively pursued by the scientific community," adding that "the consequent lack of assessment tools has been impeding, I think, the development of new drugs because we really, in many cases, don't know how to measure the impacts, both for good and ill, of the drugs we test in people."

The ability to measure with greater precision the real-world impact of a patient's illness would also enable improved assessment of the impact of both treatment approaches and of the providers themselves, giving us an opportunity to better assess the value of each, and to enable the iterative improvement of patient care and health delivery.

The improved measurement of complex patient phenotypes will also provide enormous benefit to basic researchers, enabling them to link this new information with existing, rich genetic data to form coherent datasets that can help identify key underpinnings of disease, and enable researchers to develop more targeted, and in many cases more personalized, interventions. Integrated datasets will also fuel increasingly sophisticated computer-driven "in silico" modeling approaches, capturing the benefits of empiric, "big data" technologies and approaches already used to great effect in other industries and disciplines.