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.


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.

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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.

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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.


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.


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.

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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.

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“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.

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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.


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…


Twitter Me This

Time for a confession: I’ve been a Twitter skeptic from day one.

Even though I understand how it works (140-character electronic updates – “Tweets” – that people post for their followers – friends, family, political junkies – and that fill the gaps between other types of communications, such as e-mail and blog postings), I’ve still wondered, “Why would I want to do that?”

It’s only after experiencing Twitter over time that I’ve come to understand its value. And these real-world experiences have made me care about Twitter in a way that neutral facts or statistics never could. 140 characters cleverly arranged are much more than friendly updates. In some cases, they have enormous influence – good, bad, and occasionally ugly (you know it’s true). Tweets can be powerful.

In reflecting on my skepticism about Twitter, I also realized that I had been a bit of a hypocrite (a Twittercrite?): almost daily, I use display devices such as Sparklines (to name only one) that condense lots of data into one concise display – a sort of “Twitter for data visualizations.”

And as happens with Twitter, once I began using them regularly, it became clear that, deployed in a clever and correct way, this “condensing and concentrating” type of display tool could empower me to deliver far more information on my dashboards and reports than could other methods.

Edward Tufte coined the term “Sparkline” in his book Beautiful Evidence: “These little data lines, because of their active quality over time, are named sparklines – small, high-resolution graphics usually embedded in a full context of words, numbers, images. Sparklines are datawords: data-intense, design-simple, word-sized graphics” (47).

Typically displayed without axes or coordinates, Sparklines present trends and variations associated with some measurement of frequent “sparks” of data in a simple, compact way. They can be small enough to insert into a line of text, or several Sparklines may be grouped as elements of a Small-Multiple chart. Here are a few examples.

Example 1: Patient Vital Signs

Here, 24-hour Patient Vital Signs (blood pressure, heart-rate, etc.) are displayed in the blue Sparkline, along with the normal range of values, displayed in the shaded bar behind them. To the right of the Sparkline is a simple table that shows the median, minimum, and maximum values recorded in the same 24-hour time-frame.

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This basic display delivers a lot of valuable information to care-givers monitoring patients, making it clear that during the same period around the middle of each day, all of the patients’ vital signs fall outside normal ranges.

Example 2: Deviation from Clinic Budget

In this second example, we used a deviation Sparkline to show whether use of available surgical-center hours at three different locations is above or below budget. We added two colors to the Sparkline to make clear the difference between the two values (blue for “above”; orange for “below”) within a rolling 12-month time-frame.

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Example 3: Deviation From Hospital Budget

Here we created a deviation Sparkline to show the departure from hospital budget numbers across several metrics (“Average Daily Census” and “Outpatient Visits,” for two examples), but instead of using brighter colors to indicate where the performance falls, as we did in Example # 2, we have chosen a pale gray shade to indicate when daily real performance drops below projected targets.

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Please note as well that in each of these three examples, we have embedded the Sparklines into the display and provided context through the use of words, numbers, and icons. We do this because most of the time Sparklines cannot stand on their own; rather, they require some additional framework to convey information and signal value to the viewer.

Finally: although I have been a Twitter skeptic, HDV does have a Twitter account at @vizhealth and Tweets occasionally about things that interest us, or what the company is up to. Take a look!