Trilogy Logo Trilogy Tidings
 August 2018
in this issue
     Developing new stuff is a constant challenge -- and a dire business necessity. I address the challenge from two directions: screening your ideas and exploring how data science might help.


Screen Your New-Product Ideas Sooner -- Not Later

I spent nearly 20 years of my corporate life in R&D and business development roles. Great success was sweet; modest success was bitter-sweet; commercial failure was bitter indeed. In every one of those outcomes the R&D itself was at least adequate and worthy of back-patting. What made the difference in the success of commercialization? Adequate screening of our new-product ideas.
In hindsight this factor's importance should have been obvious. I suppose it was at some academic level. The problem: We almost never did an adequate job of screening because we left out a few steps of the screening process, either because we forgot (or avoided) some key steps or were constrained by edicts "from above."
Look, the steps are not rocket science. They are well known by most. Unfortunately they are all too easy to ignore or address at gut level without rigorous analysis. As proof of their obviousness, here are my top-10 screening criteria for early-stage product concepts. (Several of them relate specifically to medical products, but most are generally applicable criteria.) You may have a different take on these, but that really doesn't matter.
What really matters is that your organization adopts its own list and rigorously addresses every criterion on that list before expending significant time and money on product development and commercialization. You will not regret it. And commercial success will be sweet indeed.
More on Data Science and Artificial Intelligence
I continue to learn about, and work on, these interesting technologies - whatever you choose to call them - because I think they will be important to many medtech and life science market players.
Data Science

(I actually did some work in AI in the mid-70s under contract to the Veterans Administration attempting to move the bar in automatic speech recognition. We did move the bar, but not quite up to Alexa level!)
Here are a few of the relevant publications I've reviewed lately:
After reviewing this stuff I thought of one potential application (which now seems obvious) related to my own corporate experience. Let's say you're in the clinical diagnostics business offering a bunch of biochemical assays. In addition to reporting analyte values, wouldn't it be nice to connect those findings to some knowledge of the patient's clinical history in order to also report some suggested diagnoses and/or clinical interventions? Sure it would, if/when electronic health records (EHRs) become more uniform and widely embraced. Maybe not now, but someday.
This is just one of many potential opportunities. OK, let's say you're just starting out in this data science arena. Exactly how should you move forward? Here are my suggested steps:
  1. Learn enough about these various technologies and their potential applications to get beyond a blank stare
  2. Try to imagine how one or more of these technologies might enhance your own product/service offerings or improve your internal processes
  3. Reach out to appropriate tech specialists to test the feasibility of your imagined concepts
  4. If test results are encouraging, hire, execute and commercialize as/when appropriate
Along the way, let me know if you'd like to collaborate on Step 2. I'd be pleased to participate and add some value.      
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ContactInfoJoseph J. Kalinowski, Principal