{"id":708,"date":"2016-01-27T23:59:15","date_gmt":"2016-01-27T23:59:15","guid":{"rendered":"https:\/\/www.concert.co\/the-promise-of-data-science-and-the-challenges-of-the-clinical-workflow-notes-from-pmwc16\/"},"modified":"2024-04-18T21:03:03","modified_gmt":"2024-04-18T21:03:03","slug":"the-promise-of-data-science-and-the-challenges-of-the-clinical-workflow-notes-from-pmwc16","status":"publish","type":"post","link":"https:\/\/www.concert.co\/blog\/the-promise-of-data-science-and-the-challenges-of-the-clinical-workflow-notes-from-pmwc16\/","title":{"rendered":"The Promise of Data Science and the Challenges of the Clinical Workflow (notes from #PMWC16)"},"content":{"rendered":"

One of the most exciting things to behold at this year\u2019s <\/span>Personalized Medicine World Conference \u00a0<\/a>is the increasingly innovative ways that companies and healthcare providers are combining disparate data sources and deploying analytic tools to generate insights that are useful and actionable for clinicians. <\/span><\/p>\n

This is no small feat.<\/p>\n

It won\u2019t surprise anyone reading this blog post\u00a0that the great promise of personalized medicine \u2013 namely, the ability to identify and deliver the optimal treatment for an individual patient based on her genetic code and other individual attributes \u2013 has been tempered by the difficulty of processing the enormous data associated with the human genetic and phenotypic makeup to generate information that is actually useful to clinicians on a practical level.<\/p>\n

In other words, we know the data contain\u00a0insights, they\u2019re just hard to generate.<\/p>\n

So, it\u2019s encouraging to see how innovators are pulling together disparate data sets in new and unique ways to overcome these barriers.\u00a0For example, we heard more about an interesting partnership between the team at <\/span>Genospace<\/a> and Washington, DC-based <\/span>Inova Health System<\/a>, which has collected thousands of whole genomic sequences along with corresponding clinical data from their patient populations, and has engaged Genospace to <\/span>layer analytic tools <\/a>\u00a0that leverage this petabyte of data and make it useful to the clinicians treating Inova\u2019s 2 million+ patient population.\u00a0<\/span><\/p>\n

And, this is just one example. We heard others from organizations like <\/span>Numedii<\/a>, <\/span>Syapse<\/a> and Oracle<\/a>.\u00a0<\/span>These perfectly highlight the practical challenge. In this era of ever-expanding data, incorporating its insights into the clinical workflow is a formidable challenge.<\/p>\n

This was the <\/span>exact topic<\/a> our founder, Mark Harris, chose to explore in his talk at PMWC16.<\/span><\/p>\n

As readers know, we focus on one particular segment \u2013 genetic testing \u2013 and came face-to-face with this reality\u00a0at a very early stage. We built a comprehensive database of every genetic testing product on the market, and when we turned to deliver that into hospitals, we discovered the layers of complexity and variation\u00a0in ordering workflows. Each hospital was different from the next in ways that were difficult\u00a0to predict\u00a0(or even imagine).<\/p>\n

We learned quickly that we needed to become students of the workflow at each institution, approaching their genetic test ordering struggles with empathy and a sharp pencil. (Trust us, it is <\/span>not <\/span><\/i>easy to be a send-out lab manager at a hospital.)<\/span><\/p>\n

Once we did, it was incredible what we learned \u2013 not to mention the number of features we built\u00a0\u2013 adapting to different workflows. Now, fast forward a couple of years, and we\u2019ve installed our tools at enormous health systems, high-quality regional hospitals, and everything in between. Far from being a distracting challenge, incorporating our tools into the clinical workflow was the gateway into greater adoption and greater impact.<\/p>\n

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And, if you\u2019re wondering\u00a0what we\u2019re up to now, we\u2019re focused on two things:<\/p>\n