Insightful Health

Health Analytics and Data-Driven Transformation in Health and Life Sciences


My Talk at the Healthcare Analytics Symposium 2014

Health Data Management invited me to come back this year for their annual Healthcare Analytics Symposium. Here is a copy of the talk I gave.

If you would like to see the talk I gave during the first year of the event, I covered it in a previous post which you can find here.  Thanks to HDM for the opportunity to share my thoughts and experiences on where we need to go in health analytics.


Live: health analytics on streaming radio

RadioI’ve been invited to appear on an radio show tomorrow (Monday, 7-Oct-2013) called Making Medicine Smarter.  We are going to talk about health analytics, the book, and all things health care.  The show will air live at 2PM Eastern US, and you can hear it by going to  There will also be an after-show text chat on the AllAnalytics message board if you want to dive deeper on any topics.  Hope you can join us!


AllAnalytics article on health analytics


The website was nice enough to publish one of my articles today about the value of health analytics. Hope you enjoy it!


The 5 Most Annoying Data Excuses

data miningI recently had an interesting Twitter exchange with my friend Dan Munro over at Forbes regarding a KevinMD posting,  Quality is a Word that Lacks Universal Meaning.  The article touched on one of my book topics: the industry’s reporting-centric, manufacturing-oriented conceptualization of quality is ambiguous and unreflective of the problem space.  We need to look at quality differently.

Dan raised the concern of data — do we really have the data to support a different view of quality, especially in light of resistance to data collection?

Dan is absolutely right; from a quality perspective, we often are “flying blindfolded and handcuffed.”  But do we really lack data?

Consider that most health organizations have four available data sources:

  • Their own data (though it is often not readily consumable)
  • Partner data (though they may not be sharing it)
  • Purchasable data (though we often just pay for the answer, not the data)
  • Public data (though we often can’t determine how best to use them)

Between these four categories, we are drowning in data: EMRs, claims, referrals, device, billing, CMS, labs, imaging, clinical trials, public health, service utilization, reimbursement, health exchanges, costing, genomics, consumer sentiment, behavioral data, consumer health devices, e-prescribing… and it keeps coming.  It doesn’t look to me like we lack data; it looks like we lack insight.  So why do we lack insight?

Part of the challenge is focus.  Instead of getting smarter on how we use data, we continually shift our attention to the next set of data we need to collect, the next regulation we need to satisfy, the next benchmark we are handed, the next incentive to capture or penalty to avoid, the next dashboard report to build.

A second challenge is lack of process oversight, which I think validates Dan’s question.  We have plenty of processes across health care, and we do manage them.  But we fail to instrument or otherwise empirically characterize those processes in such a way as to offer process-related insights.  Note that meaningful use and HEDIS don’t do this either — they simply establish benchmarks.  Additional instrumentation does not imply we must collect more data — we could use derived or surrogate measures, for example.  But it just needs to be a focus (see above).

But a third challenge is the litany of status quo excuses that leaders hear daily about why we cannot be more data-driven in our decision making:

1. We don’t have enough data.

Of all the data excuses, this one gets the most play.  There are two assumptions behind this state.  First, it assumes that a complete inventory of data options exists, and that inventory does not offer enough to do what you need.  In my experience, most firms do not have a handle on all of their data options, and they do not understand (because they haven’t looked analytically) whether a given data asset is useful in understanding the problem at hand.  There is also an assumption you can determine how much data you need without analytics – which is untrue.  You actually need to do an analysis to determine if you have enough data.

2. The data we have isn’t good enough.

This excuse always interests me because, 99% of the time, it surfaces without any actual analytical work being done.  The data isn’t good enough…good enough for what exactly?  The perception is usually derived from either a) previously struggling with the data on an unrelated project, or b) physically looking at the data, which often appears messy, incomplete, and/or error prone.  Yet all data assets have limitations such as these; the utility of data can only be assessed in the context of the question being asked and the analytical method being used.  And the data never gets better until you use it and make it better.

3. The data exists, but we can’t have access to it.

Our obligations to patient privacy notwithstanding, we have to move beyond the rampant fears in using data.  Rules and contract terms should not prolong patient suffering and/or drive up health costs.  If an organization has data assets that cannot be used for agile innovation, then change the rules, re-write the contracts, change the consent forms, or provision new data sources to compensate.

4. We can’t use the data because of regulations.

This excuse is really a variation of #3, but it has the apparent added weight of the government behind it.  My same opinion applies.  There is no question that we need strong data protections and use provisions — we’ve been facing that for decades.  But no patient I know would rather suffer, die, or face bankruptcy than have their data used responsibly to improve medical decision making.  And if they do, that is fine, but have the patient or sponsor “opt out”, not “opt in.”

5. The data is not structured in a way that is useful; it would take too much time.

So it is too much work to innovate?  If you do not have easily consumable data assets, then maybe it is time to start treating data and analytics more seriously.  It is possible to create meaningful, agile analytical assets and insights, but it doesn’t happen accidentally, and it doesn’t happen without work.

The hard part of these five excuses is they all hold an element of truth to them.  But when we accept those challenges as barriers, we don’t progress.  And to Dan’s point, there is justifiable, growing resistance to performance metric pile-on.  Most practitioners I know do not believe there is a strong association between existing quality measures (MU, HEDIS, etc.) and real-world, patient-centered outcomes and costs.  And though that conclusion is overly broad, I think there will be growing evidence to support that view.  For example, two recent studies —  one in the Journal of General Internal Medicine, another in Health Affairs — call into question the association between readmission rates and quality.

The fact that we are using data inadequately does not mean we should not be using data.  And it doesn’t mean we need to re-double our efforts around meaningful use and physician education.  It means we need to become smarter about the questions we ask,  more focused in the priorities we set for our organizations, and predictive through the tools we give practitioners and patients to make more informed decisions.

In my opinion, we should be building and deploying comprehensive, predictive quality models that we know improve outcomes and costs; not justifying retrospective metrics that we hope might help.


Top 10 questions about the health analytics book

Health Analytics book coverWell, today is the day — Health Analytics: Gaining the Insights to Transform Health Care is finally out!  I promised to cover the book in more detail, so today I thought I would take the opportunity to answer some of the more common questions that I get asked about the book.

What is the book about?

The book is about transforming health care and life sciences through data-driven innovations.  It describes a roadmap for growing organizational capabilities across a broad range of insights such as health outcomes analysis, clinical research, financial management, customer engagement, and personalized medicine.  There is an About Health Analytics page on my blog that describes the book in more detail.

Who is the book written for?

I mainly wrote the book for industry leaders who are curious about how data can help them transform their organizations to become more innovative and competitive: value-oriented in delivery, and evidence-based in practice.  It is not a technical book; you don’t need to know anything about statistics or analytics to read this book.  It is mainly designed for non-technical professionals within providers, payers, pharmaceutical, biotechnology, and regulatory organizations who are trying to develop strategies and roadmaps for becoming more information driven.

Why did I write this book?

At the time I made the decision to write the book, part of my job was sharing the opportunities in analytics with current and potential customers.  Over time, it became apparent that there was not really a resource that I could easily point to that captured the landscape.  Also, I had spent more than 6 years studying the field of analytics across health and life sciences, and I had learned quite a bit about what was — and was NOT — happening in the industry.  I began to feel like I had something to say — maybe unique and valuable, maybe not — but something to contribute to the discussion of health transformation.  I was also looking for a vehicle to showcase some of the talent in my team (for example, Dr. Graham Hughes contributed a fantastic chapter to the book called “Best Care, First Time, Every Time”).  So writing a book seemed like a good idea at the time.

How did I get a publisher?

That was the easiest part of the process for me.  My employer at the time, SAS, has a well-established publishing group — they function both as an independent publisher, as well as partner to other publishers.  This book would not have happened without them, as I would have had no idea how to sell a book concept.  They did a great job in helping me develop the book idea, work through how to communicate the idea, and figure out where and how it made the most sense to take the idea forward.  If you are interested in writing any books — business or technical — covering the field of analytics and data, SAS is a great place to start.  In the end, Wiley opted to publish this, and it has been a really great experience.

How long did it take?

That’s hard to measure, as I tended to write in bursts.  In elapsed time, it took a little more than a year, which seems like a long time until you have a contract with deadlines, and then it becomes a very fast 12 months.  Since I already had tons of research on the topics I was writing about, my time was mostly spent actually developing the content.

What was the hardest part of writing?

The hardest part for me was managing the scope of the book.  I wanted a book that was a) easily readable by non-technical executives; b) covered the landscape of opportunities in analytics, not just a single perspective; and c) wasn’t purely conceptual, but instead offered some real-world perspective on the problem spaces.  Trying to strike the right balance between depth, breadth, length, and impact was really hard.  If I tried to cover all of the opportunities properly, I would end up with a book 2,000 pages long that no senior leader would have the time or desire to read.  But surveys are often superficial, and so much of the analytics opportunity requires showing the value hidden in the complexity (e.g., predictive modeling).  So I opted for a middle road — a survey-type book that dived into specific analytical examples, challenges, and case studies — and “bookending” the overall chapter flow with a common-sense plan for how to execute these ideas.

What makes this book different?

There are so many informatics books already out there, and a lot of authors have exhaustively covered topics many people associate with analytics: business intelligence, dashboards, quality metric reporting, etc.  I knew going into this project that my content would be somewhat contrarian to the prevailing winds in informatics.  It’s not that I don’t think quality metrics are a good idea; they are fine.  I just don’t believe that the time, money, people, and attention being devoted to retrospective, descriptive statistics will produce the fundamental insights required to understand the delicate, complex balance between outcomes, costs, safety, and personalized medical decision making.  I think innovative results come from innovative approaches, so I wanted a book that argues for more innovation in how we use data.

Will you make much money from this book?

I doubt if any royalties I might someday see will cover much of the time I spent creating the book.  But I didn’t write it to make money.  It was a labor of love.  Maybe my next book will be something everyone (not just health professionals) might want to read, like a sci-fi epic.  Then again, I may have just written a sci-fi book; we’ll have to wait and see. :)

Will I write another one?

Maybe.  I really enjoyed the writing process — even the mundane stuff like editing — so I could see myself doing another one.  When I realized that I had something to say, the idea of writing this book became something worth doing.  So I suppose if/when I have more to say, that will be the sign to do the next one.

Where can I get a copy?

Health Analytics: Gaining the Insights to Transform Health Care is available at,,, Barnes &, and other retailers.  The ISBN codes are ISBN-10: 1118383044 and ISBN-13: 978-1118383049.  Let me know how you like it.