Seven Reasons Why That Beautiful Informatics Architecture Won’t Deliver

Every organization has one: the informatics diagram.  It flows from left to right, starting with a few source systems and ending with users doing incredible things in browsers and mobile devices.  It looks well thought out, comprehensive, and flexible.  I’ve created a fair number of these pictures myself.  They often make sense even to the least technical people in the room.

Typical Informatics Architecture

 

There is only one problem with the standard picture: real transformation from advanced analytics won’t work that way.  Here are the reasons why:

1.  Data will be federated.  I know we don’t like to hear that.  We like the control and convenience of our own data buckets.  And we will have plenty of data to manage.  But not all of it.

2.  Your data is not analyzable.  I know many people think it is, but it’s not.  The data are incomplete and inconsistent.  It lacks clinical context of some primary measures and endpoints.  The structure does not lend itself to real analytics, and you are missing information that your analysts will need.

3.  All data are not created equal, and you don’t know a priori which is valuable.  If you operate under the assumption that you amass as much information as possible on an ongoing basis, you should also assume that you will be amassing costs on an ongoing basis — costs that are not tied to any specific return on investment.  That doesn’t make sense.  You need a mechanism for understanding what data is actually important.

4.  Extremely high data volumes require extremely high analytical computing capacity.  If you really need high-performance computing (i.e., your questions require analyzing these high data volumes), that infrastructure is a very different architecture.  If you don’t really need high-performance computing (i.e., your questions can be answered with more modest data sets), then your plan (and diagram) needs to account for data triage (see #3).

5.  There is no provision in that diagram for a learning system.  Insightful intelligence in health — like any other discipline — is iterative.  You need a feedback loop from the users and their work back into the architecture and data, a way of capturing what we learn that should both influence and contribute to subsequent analytical exercises and insights.

6.  Clinical decision support in the future will not be purely based on business rules and published practice guidelines.  First, it takes too long to deploy those practices and rules (10 years or more by some estimates).  Second, the question is not whether practice guidelines and rules add value; the question is whether any standardized guideline can reflect the natural heterogeneity that exists in patient populations.  We already know that the multiplicity of factors influencing a given patient’s health outcomes and costs are beyond the cognitive capacity of single medical practitioners, and much more extensive than can easily be documented in standardized treatment plans.  The future of medical best practices will be driven by real-world, real-time analytics — predictive models that do not supplant business rules and policies, but that more accurately inform and guide collaborative care teams (including patients and family members) about treatment options and outcome propensities based on “patients that look like this one”, not broad-based patient populations.

7.  The challenges aren’t technical.  Though these diagrams serve well to educate people about the opportunity, the challenges are not related to hardware, software, or networking.  Organizations need new competencies.  Cultures will need to evolve: a tolerance for exploring the unknown, a comfort in risk taking, a thirst for knowledge, a willingness and ability to operate in real-time, and a discipline for managing electronic information and insights as evolutionary assets.

In a future post, I’ll share some ideas for what an advanced analytics architecture for health informatics might look like in the future.

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Does health care need “big data” or “big insights?”

Data tapeI’m thinking of starting my own medical condition:

Big Data Fatigue Syndrome (BDFS): a cognitive disorder characterized by feelings of frustration, disbelief, and growing apathy caused by repeated exposure to over-hyped technology concepts.  Occasionally accompanied by recurring fantasies of slapping publishers.

In the midst of our mad rush to amass yottabytes of “big data” as the cure-all for health care (see my Twitter feed for example articles), I wonder if it might be possible to pause briefly and ask one question:

What exactly are you going to do with the data?

Don’t get me wrong: I am a huge advocate of the opportunity in big data (I actually believe it could be revolutionary).  But it strikes me that health and life sciences has not really mastered “small data,” yet everyone seems excited to discuss big data.  I suppose it is no different in other industries — the hype is rolling along, with Gartner estimating 2013 spending of $34B.  That’s more than ice cream money.

Yet there are a few things we’ve learned in other industries and data experiences that might be applicable to health care:

1.  If you don’t know what you are going to do with data, there is no way you will collect it properly.  Hint: EMR implementers, you might want to look into this.

2.  “More” and “Better” are two different and often unrelated concepts.

3.  “More” increases costs regardless of how it is used (i.e., storage, cleaning, administration, integration architectures, software licenses, etc.).

4.  “Better”, when used properly, increases return on investment (i.e., increased efficacy, productivity, cost containment and avoidance, revenue maximization)

5.  If the “more” is not already inherently “better”, it can only become “better” by incurring additional costs.

“More” is a quantitative assessment – one petabyte is more than 500 terabytes.  “Better” is a qualitative assessment – it requires context in order to assess.  In the world of analytics, that context is directly related to the question you are trying to answer. Without that context, “more” can only ever be “more.”

In a conference I spoke at in July, I posed this question to the audience: do we really want “big data,” or should we be focused on “big insights?” Based on the reaction in the room, I think the question resonated with a lot of health executives.  If we raised the caliber of questions we are asking, we would undoubtedly find big data has a dramatic role to play.  For example, I’ve written before that big data presents a new opportunity in the science we practice.  What sorts of clinical questions could we answer using this analytics-oriented approach…investigations that could potentially offer immediate benefits to patients and physicians?  Could we, for example, model a 3-factor relationship between disease prevalence, socio-economic status, and geography in order to better optimize the design of clinical trials?  Could we mine behavioral propensities to look for non-genomic indicators of treatment efficacy?  Could we predict (not just detect) epidemiological progressions based on real-time consumer data feeds?

For each of these, the question itself opens up a more meaningful dialogue.  How exactly could we analyze that?  What data could we potentially use?  How much data would we likely need?  What would be the limitations of the data, and how might we address those limitations?  What other questions would we need to answer in order to feel confident in our findings?  These questions put us on the road to delivering real value from our data assets, regardless of their size or source.

The discussions we need to be having should be around the insights that could provide the biggest impacts across health and life sciences.  Let’s define “better” before we decide how much “bigger.”

Starting Fresh

Welcome to my new blog!  My name is Jason Burke, and I’m a strategist, technologist, and writer focused on how data, technology, and analytics can be used to transform the health and life sciences ecosystem.  My LinkedIn profile is public if you are interested in the places I’ve worked and the experiences I’ve had.

For those that have followed my blogging since my start way back in 2008, thanks for continuing to read and contribute to the conversation (and if you wish to browse my prior blog posts, they are still available at SAS.com, just click here for the feed).  For those that are new, I hope some of the things I share will be at least thought provoking, and I would welcome your participation in the dialogue.

Here is what you can expect from me on this blog:

  1. I don’t accept the status quo.  I believe health care — the entire ecosystem — is broken…but not beyond repair or hope.  I write about what interests me, which often focuses on the potential for evolution of the industry — the business, the science, the technology — towards a more effective health delivery system.
  2. My view of the problem space — and therefore my writing — tends to be multi-disciplinary.  Topics include the evolving disciplines of medicine, clinical informatics, advanced analytics, strategy development, innovation, software architectures, big data, performance  management…the list goes on, as the field of opportunities for health improvement is huge.
  3. I don’t pick sides.  I’m not interested in the politics of health care — I don’t write about them, and I don’t participate in the ideological debates of conservative vs. liberal policies.  I’m interested in researching, exploring, and discussing real-world issues based on real-world information, not financial and social philosophies.

So welcome aboard, it should be an interesting ride!