

Comprehend (YC W11) wants to cure data woes for pharma companies - rmorrison
http://pandodaily.com/2013/01/08/comprehend-wants-to-cure-data-woes-for-pharma-companies/

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epistasis
This is an interesting side to to push on, but I haven't heard many people
claiming this is where pharma has data woes. The real data woes are from high-
throughput biological data such as gene expression and now sequencing, and
that's a field that's crowded with tiny startups (and though my startup has
great tools for this, surviving on pharma does not seem like a sustainable
business model).

This other side of information management, clinical trials, is typically
handled by Oracle's offerings in the area. However, I haven't heard many
complaints about the data side of clinical trials, it's the actual patient
accrual and patient handling that's typically expensive on clinical trials.
That's not to say that Oracle has solved all the issues. As there are more
trials with a bent towards personalized medicine, accruing the patients with
the desired traits is going to get more difficult, and will require the
participation and coordination of many many more different trial sites. Having
additional trial sites is the real cost, as all the sites have to use exactly
the same treatment and data collection protocols, and these protocols almost
certainly differ from the hospital's or office's standard protocols. And
trials also require the doctors to attend training sessions fro their
particular data collection routines, even if they have previously learned
similar techniques, which is extremely wasteful, but necessary without proper
certification schemes. _These_ are the areas that a forward thinking startup
can really get ahead of Oracle, so even though I don't see much indication in
this PR about it, hopefully Comprehend is thinking in that direction.

That said, typical Silicon Valley startups are woefully naive about healthcare
(not necessarily a bad thing!!), and typical startups here underestimate the
difficulty of working their way into long-standing relationships, and of
changing practices in a field that is change-averse without proper evidence.
Additionally it can be extremely difficult to see the areas that can use
improvement unless you've studied the terrain carefully, and by the time
you've found pain points that you can address, you may have lost the helpful
parts of naivete that let you look past all the myriad hurdles in the way of
making progress.

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siculars
I'm under the impression that these guys are in the pharma space because that
is where the money is for a service provider. Pharma underwrites a lot of this
stuff. Once you have a toehold in the larger health care space you can branch
out and expand to other opportunities. If I were to approach healthcare on the
whole I could think of worse places in the pipeline to start than big pharma.

There is a difference between bioinformatics such as genetic sequencing and
its better half, analysis, and medical /clinical/research informatics. Imho,
the current ability to sequence vast amounts of dna far outstrips our ability
to analyze it. Better, quicker analysis will come in the form of better
algorithms and through machine learning/data mining.

Medical informatics, the practice of managing patient information is a
separate problem. Here the problem is connecting different systems that were
not meant to be connected when designed, and great tools for parsing and
presenting that data by non-programmers. I'm working with tools like Amalga
and RedCap right now which each play in their own space but do provide a
service. They could both be... better. The field is wide open for disruption.

Programmer time is the limiting resource factor in making better things happen
for a lot of different institutions. The other major limiting factor is simply
institutional politics. No amount of silicon valley magic will make that go
away.

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polskibus
What's unique about Comprehend compared to all other BI tools? Tableau was
mentioned in the article, but there are plenty of other applications like
QlikView or MS SSAS, etc.

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rmorrison
Rick from Comprehend here:

Comprehend isn't a BI tool. If all of a company's data is well-structured in a
single database, with a non-changing data structure and without missing or
null data, then it's easy to use BI tools like Tableau, Spotfire, Excel, etc.

But this is not the case in the modern day enterprise. Instead, there are
dozens of different data collection systems, with different and changing data
structures. This is what Comprehend's core technology was built to handle, in
real-time.

In order to get similar functionality, companies are typically relying on
teams of programmers to manually write and run scripts over and over again.
Or, they're trying to put in place data warehouses, which rarely contain
everything required and are often out of date.

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polskibus
What you described is just standard ETLs in any Data Warehouse system - again
in my opinion a typical functionality in a BI stack. QlikView tries to do it
in-memory and live but it puts strain on OLTP systems for example freezing
cash machines (seen it!). Every system requires maintenance, question is
where's Comprehend's maintenance effort placed?

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jkbyc
This article is surprisingly information-free with respect to what they are
actually doing. It mostly says that clinical trials are lengthy and costly...

