http://pharmaweblog.com/blog/2006/09/11/open-source-drug-rd/ Pharmaceutical and biotech science and business Thu, 28 Aug 2008 19:51:25 +0000 http://wordpress.org/?v=2.0.2 http://pharmaweblog.com/blog/2006/09/11/open-source-drug-rd/#comment-63 Mon, 25 Jun 2007 19:52:39 +0000 http://pharmaweblog.com/blog/2006/09/11/open-source-drug-rd/#comment-63 [...] Last September, I argued in this blog for a data infrastructure, which would be supported by a public-private partnership, capable of capturing real-world, “N of 1″ clinical experiments.  Such experiments collectively represent a huge potential repository of clinical scientific evidence.  Today, experimental evidence coming from routine clinic encounters is underutilized.  Case reports, case series, and retrospective chart reviews, have gained a reputation as weak evidence of causation (typically described as anecdotal evidence) and have thus fallen into disfavor.  Too bad, because such anecdotes have historically frequently been the impetus for exploration of a new use for a drug or a signal of an adverse effect that was previously unseen or unrecognized as therapy-related.  In contrast, with the rise of managed care and the need for drug and device manufacturers to demonstrate a favorable benefit to risk balance for new therapies, studies that make use of databases linking diagnoses, outcomes and therapies are becoming more common.  Such studies are likely to become increasingly important for delivering improved safety surveillance of therapies and would benefit from larger, more demographically diverse patient pools.  They would also benefit from a larger amount of clean contextual metadata (data about data).  For instance, researchers might now be able to tell when a prescription was first filled by searching PBM or insurance records, but it would be more difficult to ascertain the day the prescribed drug was stopped.  However, perhaps the prescribing physician noted in the patient chart (the Electronic Health Record, EHR, in my utopican example) that the drug was stopped 20 days after filling the prescription because of a rash that developed and was restarted after a 30-day off period without reoccurrence of a rash.  Charts may be abstracted for smallish studies, but you can’t manually abstract charts on a million patients.  Plus, today’s abstractions (e.g. CPT coding) are lossy mechanisms for transforming information into a usable form. [...] […] Last September, I argued in this blog for a data infrastructure, which would be supported by a public-private partnership, capable of capturing real-world, “N of 1″ clinical experiments.  Such experiments collectively represent a huge potential repository of clinical scientific evidence.  Today, experimental evidence coming from routine clinic encounters is underutilized.  Case reports, case series, and retrospective chart reviews, have gained a reputation as weak evidence of causation (typically described as anecdotal evidence) and have thus fallen into disfavor.  Too bad, because such anecdotes have historically frequently been the impetus for exploration of a new use for a drug or a signal of an adverse effect that was previously unseen or unrecognized as therapy-related.  In contrast, with the rise of managed care and the need for drug and device manufacturers to demonstrate a favorable benefit to risk balance for new therapies, studies that make use of databases linking diagnoses, outcomes and therapies are becoming more common.  Such studies are likely to become increasingly important for delivering improved safety surveillance of therapies and would benefit from larger, more demographically diverse patient pools.  They would also benefit from a larger amount of clean contextual metadata (data about data).  For instance, researchers might now be able to tell when a prescription was first filled by searching PBM or insurance records, but it would be more difficult to ascertain the day the prescribed drug was stopped.  However, perhaps the prescribing physician noted in the patient chart (the Electronic Health Record, EHR, in my utopican example) that the drug was stopped 20 days after filling the prescription because of a rash that developed and was restarted after a 30-day off period without reoccurrence of a rash.  Charts may be abstracted for smallish studies, but you can’t manually abstract charts on a million patients.  Plus, today’s abstractions (e.g. CPT coding) are lossy mechanisms for transforming information into a usable form. […]

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