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Welcome to our blog. Here you will find announcements about upcoming events and recaps of where we have been, workshops we have taught, and people we have met.

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2017 Summer Reading List for the Health and Healthcare Data Geek

I sincerely wish I could avoid typing the following: it is July 2017, which means it is time again for my annual book recommendations, as well as a swing in the hammock with a watermelon mojito (no regrets there).

Seriously? I’m going to hire a search and rescue team to find my life and bring it back to me (the edited version, of course), because I have no idea where it has zoomed off to! I know I was just celebrating my 21st birthday a few moments ago… denial is more than a river in Egypt (or so I’m told).

Here is some of what I’ve been reading. I think you’ll enjoy these titles, too.

Truman

I know: you’re probably wondering why this book is on the list. Although Ron Chernow’s Alexander Hamilton is all the rage these days (and for good reason – I read the book and loved it; helped to pass the time waiting for the next millennium to land tickets to the Broadway show), there is a wealth of other great books about the history of our country, including a special-interest subset recounting the lives and work of champions of universal healthcare for U.S. citizens. Until I read David McCullough’s book, I thought Lyndon Johnson had been the original promoter and architect of the Medicare program: I had not fully understood the huge role that Truman played.

Additionally, Truman’s famous defeat of Dewey in the 1948 election was extraordinarily fascinating in its demonstration of how pollsters can get it so wrong (yes, history repeats itself). The significant political repercussions highlighted the pitfalls of survey samples and results, and the need for rigor in this type of work.

Willful Ignorance – The Mismeasure of Uncertainty

The author, Causalytics founder Herbert I. Weisberg, Ph.D., weaves engaging stories about important thinkers, and how the problems they worked to solve using statistical methods helped propel scientific research. But this book is more than just a historical view of these efforts: it’s also a cautionary tale about the mountains of simplified studies and statistics that result in frequent reversals of scientific findings and recommendations.

As the title suggests, the fallacy of regarding probability as the full measure of our uncertainty is contributing to an oncoming crisis. Weisberg says, about clinical research and care, that our current methodological orthodoxy plays a major role in deepening the division between scientific researchers and clinical practitioners. Prior to the Industrial Age, research and practice were better integrated. Investigation was generally more directly driven by practical problems, and conducted by those directly involved in solving them. But then as scientific research became more specialized and professionalized, the perspectives of researchers and clinicians began to diverge. In particular, their respective relationships to data and knowledge have resembled each other less and less.

If you work with statistical methods, especially probabilities, or you have to understand them well enough to explain them (and their limitations), then this is a really top-notch book that you should seriously consider taking the time to read.

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

I state my disclaimer right up front: my HealthDataViz team and I contributed to this book. That is of course what makes this title a particularly great addition to your health|health care reference shelf (no brag; just fact)!

Each chapter follows a standard layout that I like a lot. Each begins with a summary of the big picture that the dashboard addresses, followed by the specific metrics displayed and related scenarios illustrated. “How People Use This Dashboard” is next, supported by different visuals on the dashboard as examples. A “Why This Works” section rounds out the chapter.

The authors also include a data visualization best practices summary by displaying what NOT to do – very useful! No red, yellow, green pies, donuts, bubbles, or word clouds, please!

A Civil Action

If you have never read this book, this is the summer to do so. I absolutely love it, and any time the movie based on it is on television, I drop everything and watch (just ask my husband, Bret – there is no dissuading me). A Civil Action is the true story of the quest by a somewhat idealistic young lawyer to collect damages from two corporate giants, Beatrice Foods and W.R. Grace, for allegedly polluting the water in Woburn, Massachusetts, a Boston suburb, with carcinogens. The case considers a cluster of leukemia victims in Woburn (the disease claimed the lives of at least six children), and the tremendous challenge of reconciling a preponderance of experiential or circumstantial evidence with scientific results. How do you prove causation in the courtroom? Is it possible or even correct to try and do so?

I love, love, love this book and am certain it will find its way into my summer bag yet again this year – a perfect read for a few hours in the hammock.

Coming Soon!! Tableau for Health and Healthcare, v. 3

Many of you have been asking, and the answer is a resounding YES! The HealthDataViz team has been hard at work updating our Tableau manual (a very special thanks to Janet, Ann, Marnie, Dan, Jim, Peter, and our very own GrammarLady, Anne). Data sources and examples have been updated to reflect what we’ve learned while training folks to successfully use Tableau, and there are some new tips and tricks. It’s due out in the coming weeks, so stay tuned for our announcement.

Finally, let me take a moment to thank all my faithful subscribers and our clients. You are the best and biggest reason for what we do, and we are deeply grateful for your support. We look forward to presenting engaging new ideas and fresh approaches, and collaborating on innovative projects with you.

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Scope of Responsibility Changes the View

I’ve been teaching a lot of data visualization workshops lately. Inevitably, when I reach the part of the day when I ask participants how they gather requirements to build a monitoring dashboard, I always get the same rote, data-analyst-centric response: “I ask my customers what questions they want answered.”

My job (or cross to bear; you decide) is to then firmly nudge them toward a new approach, one that requires them to ask instead, “What is your role and scope of responsibility? As you work in that role, what decisions must you make to achieve your goals and objectives?”

Dashboards exist to help people visually monitor – at a glance – the data and information they need to achieve one or more goals and objectives quickly and easily. This is considerably different from analyzing data to answer a specific question or to uncover potentially interesting relationships in that data.

With this construct about the purpose of a dashboard in mind, let’s consider examples of two different prototype Emergency Department (ED) dashboards designed using the same source data. We’ll ask end users to describe their role (position) in the ED, the scope of their responsibility there, and what summary information they need in deciding how to meet their goals and objectives. We’ll call this the RSD [Role, Scope, Decisions] approach.

Example A: Emergency Department Operations Manager

Here, the ED Operations Manager’s role and scope of responsibility are to ensure that patients arriving at the ED receive timely and appropriate care, and that the ED doesn’t become overloaded, thereby causing unduly long patient wait times or diversion to another facility.

Given these parameters, the chronological frame for the dashboard below is present|real time, and is focused on where and for how long actual ED patients are in the queue to receive care.

Click to expand

In the upper left-hand section of this dashboard is a summary ED Overload Score (70), overlaid on a scale of No to Extreme Overload. Under this summary are elements of the score: ED Triage (10 points), Seen by MD|Waiting for Specialty (10 points), Specialty Patients Waiting (20 points) and Waiting for In-Patient Bed (30 points). This summary provides a mechanism for the manager to monitor both the risk of overload and the key factors driving the score higher.

Additional information on the dashboard helps the Manager analyze (across census categories) the patient census, and see how many cubicles are currently in use vs. available for examination and treatment. Average wait times in minutes and by patient triage level in eight (8) categories such as Arrival to MD Evaluation (compared to a hospital goal), and ED Length of Stay (LOS) are displayed using bar graphs in the middle section. The lower left-hand display projects when additional cubicles will be available (blue signals available cubicles; orange, a shortage); the lower right-hand one shows information on patient wait times by sub-specialty.

All of this dashboard’s metrics are designed to help the ED Operations Manager identify active and potential bottlenecks, and to act to meet the objectives: delivering timely and appropriate care, and avoiding ED overload.

Example B: Emergency Department Executive Director

Here, the Executive Director’s role and scope of responsibility are to ensure that not only is the ED team providing timely and appropriate care, but that reimbursement is not forfeited because pay-for-performance (annual|contractual|third-party|value-based-purchasing) goals are missed.

In response to this role’s needs, display time frames include both Month to Date and Current and Previous YTD performance, allowing the Director to stack current performance against agreed-upon targets for metrics tied to third-party reimbursement, as well as potential opportunities for improvement.

Click to expand

At the top of the dashboard is a table of summary performance metrics (number of patients seen and treated; time in diversion due to ED Overload) for the current month vs. current and previous years, and change over time. The middle bar charts provide the Director with the current month and YTD performance compared to targets for the metrics often tied to third-party, value- based (pay-for-performance) reimbursement. The deviation graphs at the bottom of the dashboard provide context for monthly performance compared to targets trended over time.

In this dashboard, summary metrics help the ED Executive Director monitor overall performance, identify areas for improvement in delivering timely care and avoiding ED Overload, and ensure that reimbursement is not lost.

Shifting from asking your customers what questions they need to answer to asking them to describe scope, role, and decisions may seem like a distinction without a difference. It isn’t. Framing inquiries this way stimulates everyone to step back and examine what is required to support a universal, shared goal: acquiring the right information – at a glance – to work toward goals and objectives, and hit those targets, quickly, confidently, and well.

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Stop Hunting Unicorns and Start Building Teams

Guests in our home are often very generous with their compliments on my cooking skills. While I sincerely wish those compliments were deserved, the sad (and, okay, shocking) truth is that they are not.

I’m not a great cook: rather, I am an excellent assembler of food that other people have created. I know where to shop, and the way to put together terrific dishes, and I know how to pour a generous glass of wine (or three). These skills appear to convince people that I know how to cook.

Here’s another thing I’m great at assembling: fun, smart, wildly talented, highly collaborative, and productive professional teams. What’s my secret? I know that unicorns aren’t real.

Unfortunately many health and healthcare organizations, rather than working to assemble these types of teams, persist in hunting unicorns. They assume that one person can posses every skill required to create compelling and clear analysis and reporting.

These organizations need to stop the fairy-tale hunt, and start building data-analytics and communications teams. The idea that any one analyst or staff person will ever have every single bit of knowledge and skill in health and healthcare, technical applications, and data visualization and design required to deliver beautiful and compelling dashboards, reports, and infographics is just – well, sheer lunacy.

3 Tips for Building Data-Analytics and Reporting Teams

Tip 1: Search For Characteristics & Core Competencies

To build a great team, you need to understand what characteristics and core competencies are required to complete the work. Here’s where to begin:

  • Curiosity. When teams are curious they, question, probe, and inquire. Curiosity is a crucial impetus for uncovering interesting and important stories in our health and healthcare data. Above all else, you need a team of curious people! (Read my previous post about this here.)
  • Health & Healthcare Subject-Matter Expertise. Team members with front-line, boots-on-the-ground, clinical, operational, policy, financial, and research experience and expertise are essential for identifying the questions of interest and the decisions or needs of the stakeholders for and to whom data is being analyzed and communicated.
  • Data Analysis and Reporting. Without exception, at least one member of your team must have math, statistics, and data-analysis skills. Experience with data modeling is a plus if you can find it; at a minimum, some familiarity with the concept of modeling is very helpful. The ability to use data-analysis, reporting, and display tools and applications is also highly desirable, but another more technically trained IT team member may be able to bring this ability to the table if necessary.
  • Technical: IT & Database Expertise. Often, groups will confuse this skill area with data-analysis and reporting competence. Data and database architecture and administration require an entirely different set of skills from those needed for data analysis, so it’s important not to conflate the two. You’ll need team members who know how to extract, load, and transform (ETL) and architect data for analysts to use. And while you may sometimes find candidates who have both skill-sets, don’t assume that the presence of one means a lock on the other.
  • Data Visualization & Visual Intelligence. Knowledge of best practices and awareness of current research is required to create clear, useful, and compelling dashboards, reports and infographics. But remember, these skills are not intuitive; they must be learned and honed over time. And although it is not necessary for every team member to become an expert in this field, each should have some awareness of it to avoid working at cross-purposes with team members employing those best practices. (That is, everyone should know better than to ask for 3D red, yellow, and green pie charts.)
  • Project Management. A project manager with deep analytic, dashboard, and report-creation experience is ideal – and like the mythical unicorn nearly impossible to find. But don’t let that discourage you. Often a team member can take on a management role in addition to other responsibilities, or someone can be hired who, even without deep analytics experience, can keep your projects on track and moving forward.

Tip 2: Be Prepared to Invest in Training and/or External Resources

  • Why? Because they don’t teach this stuff in school.

At present, formal education at institutions of higher learning about the best practices of data visualization, and state-of-the-art visualization and reporting software applications is scarce, and competition to hire qualified data analysts is fierce. As a result, you must be prepared to invest in training the most appropriate team members in many of these new skills, and/or working with qualified external resources.

Tip 3: Have A Compass. Set a Course. Communicate It Often.

  • The primary challenge for your team is not to simply and boldly wade into the data and find something interesting. Rather, team efforts should be aligned with the organization’s goals. This means that you must establish and communicate clear direction and objectives for everyone to deliver on from Day One. Having a compass and setting a well-defined course also help keep your teams from getting caught up in working on secondary or tertiary problems that are interesting, but unlikely to have significant impact on the main goal.

I do wish that data-analysis and reporting unicorns were real! Life would be so much simpler. But they aren’t and never will be, so I let go of that fantasy long ago. You should, too.

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Mental Models

Whenever I teach my “data visualization best practices” courses, I always include an introductory overview about mental models – an explanation of a person’s thought process about how something works in the real world. I do this because understanding mental models can help us construct an effective approach to solving problems and accomplishing tasks.

First, I ask course participants to think about, then describe, how they read a printed book.

The responses always include such observations as, “I look at the Table of Contents; then I turn the pages from right to left. I read the words on the pages from left to right and top to bottom. If a passage holds particular interest, I often underline it; if I come across an unfamiliar word, I sometimes look it up in a dictionary.”

Once we have gone through this exercise, I ask how they read a book on a Kindle or other electronic device. Their responses are almost identical to the first set. Turning pages and text exploration are faster and more effortless on an e-reader (if less tactilely satisfying) – but they are essentially the same processes.

Next, I ask them to weigh in on how successful they believe Amazon would have been had its designers created an e-reader that required people to process a book in an entirely new way – for example, by starting on the last page, turning pages from left to right, and reading from bottom to top. How many of you, I ask, would have even considered reading on a Kindle? Not a single hand is ever raised.

This simple, familiar example makes the point: it’s really difficult, if not impossible, to get people to change the way they think about doing something – especially when that way is familiar, and works.

As a result, the importance of uncovering and understanding the mental models of the viewers of our dashboard and reports – the way they use data and information to support their work – is essential to designing and building something of value. Quite simply, before we ever sit down to our design work at a computer screen, we must endeavor to learn as fully as possible the process by which our internal and external customers use data to make decisions about the work they do.

Let’s consider a simple example: post-discharge referrals to home health care providers by a local hospital.

How might a discharge or case manager think about – what is the mental model for – determining which patients to refer for services and where to refer them? It is highly likely (and has been confirmed based on previous work analyzing one such group’s mental model) that these managers think about and want to know the answers to such questions as:

  • are all patients who could benefit from home health care services – say, patients who might be at increased risk for readmission within 30 days – receiving referrals to them?
  • which providers are geographically closest to a patient’s home?
  • how well do different agencies perform by quality-of-care measures?
  • how do patients rate different agencies on satisfaction surveys?

Using the questions gleaned from our example discharge or case manager’s mental model as a guide, we created the following three interactive dashboards to display, highlight, and clarify data in alignment with these questions.

The first dashboard filters for a particular hospital and desired date. The top section displays summary metrics that drill down by hospital service line. The map pinpoints the ZIP code locations of home health agencies with referrals, while a bar graph quantifies referrals per agency. Each Provider Name is a hyperlink to the Home Health Agency Comparison dashboard.

(click to expand)

On the second dashboard, “At Risk by DRG,” is a summary narrative capturing statistics on missed opportunities – that is, concerning patients who may be at risk for readmission and for whom home health care may help reduce that risk; a visual trend line highlights these figures. Additionally, the data displays categories, and drills down to a specific DRG level. To the right is a payer heat map that uses color to identify those at highest risk.

(click to expand)

“Home Health Agency Comparison,” the third dashboard, shows – with an easy-to-use, side-by-side comparison tool – how HHA’s perform on publicly reported quality metrics.

(click to expand)

Far too often we blame ourselves when we fail to grasp how something new to us works, or can’t make any sense of the information we have been given in a dashboard or report. Most of the time, though, we are not to blame. Rather, the product designer or data analyst has failed to understand our mental model – the way we interact with or think about things in the real world. We end up looking for this:

And worse than banging our heads against the foolishness of paying for and being handed something we don’t want and won’t use is the inevitable result that we will simply revert to what we know: a book printed on paper, or an Excel spreadsheet – thereby missing the potential to do more and see better in a new and exciting way.

And wouldn’t that be a shame?

P.S. To view all three of these examples as interactive dashboards, click here.

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Red, Yellow, Green… Save It For The Christmas Tree

Listen up folks… it is time for a red, yellow, green color intervention of the most serious kind. The use of red, yellow, green to indicate performance on your reports and dashboards has reached a crisis level and can no longer be ignored.

It is time for some serious professional help.

Here is your choice: go into color rehab treatment and clean up your act or, risk losing your stakeholders attention and – even more damaging – risk obscuring important information they require to make informed decisions.

And just to be clear – you are absolutely risking these things by overusing and incorrectly using red, yellow, green color coding in your reports and dashboards. (And besides, red, yellow, green is SO last season.)

I can read your thoughts – “but that is what people ask for – they want to emulate a stoplight – they LIKE red, yellow, green.” And I liked cheap beer until I tasted the good stuff.

Let’s consider how the use of these colors is hurting and your reports and what you can do to fix it.

1. Did you know that approximately 10% of all men and 1% of all women are colorblind? Yes, it is sad, but true. So, where most of us see this:

Our colorblind colleagues see this:

Which means, that when you publish a report that looks like this to the majority of us:

Medical Center Results 2010
Q1 Q2 Q3 Q4
Acute Myocardial Infarction (AMI)
Aspirin at Arrival 88% 83% 78% 83%
Aspirin Prescribed at Discharge 38% 86% 60% 86%
ACEI or ARB for LVSD 40% 70% 53% 83%
Adult Smoking Cessation Advice/Counseling 80% 80% 80% 80%
Beta-Blocker Prescribed at Discharge 89% 92% 89% 87%
Fibrinolytic Therapy Received Within 30 Minutes of Hosp Arrival 98% 98% 98% 97%
Primary PCI Received Within 90 Minutes of Hospital Arrival 86% 86% 86% 65%

There are about 10% of the men and 1% of women who will only see this:

Medical Center Results 2010
Q1 Q2 Q3 Q4
Acute Myocardial Infarction (AMI)
Aspirin at Arrival 88% 83% 78% 83%
Aspirin Prescribed at Discharge 38% 86% 60% 86%
ACEI or ARB for LVSD 40% 70% 53% 83%
Adult Smoking Cessation Advice/Counseling 80% 80% 80% 80%
Beta-Blocker Prescribed at Discharge 89% 92% 89% 87%
Fibrinolytic Therapy Received Within 30 Minutes of Hosp Arrival 98% 98% 98% 97%
Primary PCI Received Within 90 Minutes of Hospital Arrival 86% 86% 86% 65%

2. Additionally, without a column that indicates what the red, yellow and green thresholds mean (goal or benchmarking data) the viewer has no way of knowing when a measure rate changes. What is the rate that will change the color in this report to green? Or yellow? Or (oh horrors!) red?

And since when is red a “bad” color? It simply means stop on a traffic light – a very good thing for managing traffic. Red can symbolize fire, passion, heat and in many countries it is actually a symbol of good luck… but I digress.

Using all the red, yellow and green also breaks the big data display design rule – which is:

Increase the DATA INK and decrease the Non-Data INK

The data, data, data is what it is all about – not colors, gridlines and fanciful decoration.

So what can you to do without your stoplight colors in order to draw viewer’s attention to important data? Plenty…

You can eliminate all of the non-data ink and add data-ink to the areas of importance by:

  • Italicizing and bolding
  • Using soft hues of color to highlight data
  • Applying simple enclosures to denote the data as belonging to a group that needs attention paid.

You can do all of these things as I have below or just one or two depending on how much data you have in your table.

Medical Center Results 2010
Acute Myocardial Infarction (AMI) Q1 Q2 Q3 Q4 Target
Aspirin at Arrival 88% 83% 78% 83% 80%
Aspirin Prescribed at Discharge 38% 86% 60% 86% 80%
ACEI or ARB for LVSD 40% 70% 53% 83% 80%
Adult Smoking Cessation Advice/Counseling 80% 80% 80% 80% 80%
Beta-Blocker Prescribed at Discharge 89% 92% 89% 87% 85%
Fibrinolytic Therapy Received Within 30 Minutes of Hosp Arrival 98% 98% 98% 97% 95%
Primary PCI Received Within 90 Minutes of Hospital Arrival 86% 86% 86% 65% 85%

This method of displaying the data is much easier on the eyes and brain – it is far less jarring and allows the viewer to focus on the information that is important.

You could also simply sort and categorize the data to show where improvement is required versus where things are going well. Consider the following example report for Q3 results:

Medical Center Results 2010
Acute Myocardial Infarction (AMI) Q1 Q2 Q3 Target
Measures Requiring Improvement:
Aspirin at Arrival 88% 83% 78% 80%
Aspirin Prescribed at Discharge 38% 86% 60% 80%
ACEI or ARB for LVSD 40% 70% 53% 80%
Measures that Meet or Exceed Target:
Adult Smoking Cessation Advice/Counseling 80% 80% 80% 80%
Beta-Blocker Prescribed at Discharge 89% 92% 89% 85%
Fibrinolytic Therapy Received Within 30 Minutes of Hosp Arrival 98% 98% 98% 95%
Primary PCI Received Within 90 Minutes of Hospital Arrival 86% 86% 86% 85%

By arranging the report in this way I have eliminated the viewers need to hunt and peck and synthesize the measures that require improvement. They are at the top of the report and clearly and simply displayed.

Now go back and take a look at red, yellow, green table – check your pulse and note if your jaw is clenched. Look at the newly designed data tables – I bet you feel calmer already.

And if you were wondering how colorblind people manage to drive it is because of the order of the lights. They know that red is first, then yellow and green. If the lights are arranged horizontally though, all bets may be off and you should proceed with caution… lots and lots of caution…

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