Visualizing Data

Just what the world needs — another blog.

Well, when it comes to the sharing the best practices for displaying healthcare data visually and finding and telling the story buried in your data that is EXACTLY what the world needs — a blog that delivers the information and help you've just got to have, but don't have easy access to.

And as much as I love the sound of my own voice (and I do, ask anyone) I encourage you to contribute your thoughts, questions and examples (HIPAA compliant please — I don't look good in stripes).

Let the blogging begin.

And Around We Go… Again

As I mature (and boy, is aging a high price to pay for maturity), I find I have very little need or even desire to win an (never mind every) argument, or to prove that I’m right about something.

I suppose that’s true in part because I understand that we all see the world in different ways, and in part because it seems to take a very long time for even solid, compelling evidence about anything to persuade people to change their firmly held beliefs. (And I admit that sometimes I count myself among those folks.)

It’s also why I’ve written very rarely (even though I’m occasionally tempted to say something) on “why I’m a card-carrying member of the ‘Better not use pie charts’ club.”

There are many expert voices, and there is plenty of evidence, on this topic.

The data-visualization pioneer Edward Tufte said that “pie charts should never be used”; William Cleveland referred to pie charts as “pop charts” because they are commonly found in pop culture media rather than in science and technology writing. Data-visualization expert Stephen Few wrote the widely-read and frequently-referenced essay “Save the Pies for Dessert.” All the same, I feel the need to add my voice to the chorus in the hope of improving healthcare data visualizations.

What pushed me over the edge?

A free e-book from a software vendor (that should have been my first clue) which, in spite of well-established expert opinions and evidence about why pie charts are not as effective as other display devices, presents advice about the misuse of pie charts – that is, it explains how to use pie charts correctly. And around we go again – oh, my aching head!

Let’s walk through what is suggested and why those suggestions constitute bad advice; and then let’s turn to the part left out: how to display data better with nary a pie chart in sight.

Here are some excerpts (I’m paraphrasing):

Example 1

The book says…

“Don’t squeeze too much information into a pie chart: the slivers get too thin, and the audience confused.”

I say…

Use a bar chart like the one in Example 1, below. We humans find it very difficult to judge the size of the angle in a pie chart. With a bar chart, we can immediately tell the size of the data being encoded by the length of each bar. It’s then easy to directly compare the lengths of the bars, and determine which values are larger or smaller.

We can also add a comparison or target line if we need to, which we can’t do on a pie chart.

We can label each value being displayed directly rather than making our viewers match a color-coded key to each slice of the pie, all while trying to hold the information in short-term memory as they look back and forth from the chart to the key. (Try it, and you’ll see what I mean!)

EXAMPLE 1

around-we-go-example-1

(click to enlarge)

Example 2

The book says…

“Order your slices from largest to smallest for easiest comparison.”

I say…

Okay, this is just silly!! Simply use a ranked bar chart like the one in the Example 2, below.

EXAMPLE 2

around-we-go-example-2

(click to enlarge)

Example 3

The book says…

“Avoid using pie charts side by side – it’s an awkward way to compare data.”

I say…

Yep, you guessed it: use bar charts. And if you need to encode additional comparison data, try a bullet graph (a modified form of the bar chart). In addition to being a better way to display data, a bar chart allows additional context for visualizations.

In the example below, by using a bar chart and leveraging the fact that my viewers read from left to right, I label the data once and accomplish all of the following. I can

  • show the number of cases eligible for the measure (the denominator);
  • display compliance compared to target;
  • note the difference between the current quarter performance and the target; and
  • record how each clinician has performed over the last four quarters.

You simply can’t do all this – quickly, clearly, and in a modest display space – with a pie chart. Look at the results in Example 3, below.

EXAMPLE 3

around-we-go-example-3

(click to enlarge)

Here’s the bottom line – pretty much anything you can do with a pie chart, you can do better with a bar chart. This is especially true for the types of displays we create in healthcare.

Bar charts make it easy to:

  • directly compare the sizes of data groups displayed.
  • directly label the data.
  • easily rank the data.
  • include comparison or target data.
  • include additional contextual data.

As is clear from this last example above, bar charts are also far superior when used on a dashboard. They take up less space than pie charts and (as previously noted), make it possible to display much additional contextual data, such as performance over time.

Every so often I come across a forum where people still rant on about how maligned pie charts are. I admit I find them – both the people and the pie charts – infuriatingly amusing. Yes, the charts can be fun on an infographic, or useful for teaching young children the concept of part-to-whole, but for me and the work I do the evidence is in – forever – and pie charts are out.

Posted in Design Basics, Graphs, Newsletters | 1 Comment

Best Available Incomplete Information (BAII)

When I was a teenager, I had one terrible habit that drove my mother over-the-edge crazy. (OK: I had more than one. But hey, “driving your mother crazy” is part of the official job description for “teen-age girl.” I looked it up.)

My particular expertise was in the fine craft of strategically omitting information that would’ve assuredly had a negative impact on my desired outcome.

For example, I would ask if I could go to my best friend Tracy’s house for the night, but I would leave out the fact that we would be stopping by bad-boy Tom’s house for a “my parents are away” party. This fact would of course have resulted in my having to stay home – that is, in my view, in the worst outcome imaginable. (Yes, I did consider law school early in life.)

In my defense, there were times when I didn’t know bad-boy Tom was having a party until after I’d received permission to go to Tracy’s house. On these occasions I asked for my Mom’s consent based on the best available incomplete information. Of course, as is the way with all mothers, she eventually found out where I’d been (even on the occasions when no police were involved). As a result, each of my subsequent requests for permission to go out elicited an ever more rigorous line of inquiry from her.

My (now) fond memory of these mother-daughter tussles was prompted by a recent article I read in the New York Times: “The Experts Were Wrong About the Best Places for Better and Cheaper Health Care.” Let me tell you why.

Until recently, the largest and best data-set available for the analysis and study of healthcare delivery in the U.S. was that based on Medicare claim data. Private-insurance statistics have long been almost entirely inaccessible for the same type of analysis and scrutiny, as they are held and managed by private companies that are not required to make them public.

This situation has left us scant choice but to make assumptions and decisions about how our healthcare system does and should deliver care using what I have come to term “best available incomplete information (BAII).”

As highlighted in the article I’ve cited above, Medicare data have revealed enormous amounts of information about regional differences in Medicare spending, which are driven mostly by the amount of healthcare patients receive, not the price per service.

Even more important, Medicare data reveal that places delivering lots of medical services to patients often do not have any better health outcomes than those locations delivering less medical care at lower cost.

These findings based on Medicare data have, by and large, been reduced to one simple message: if all healthcare systems could deliver care in the same way these low-cost ones do, the country’s notoriously high medical costs could be controlled, and might even decline.

On the face of it, this makes perfect sense. What’s missing, however, is how these systems are performing on the delivery of care to their non-Medicare patients. Are the results observed in one cohort of patients (Medicare) also the results for all other non-Medicare cohorts (private insurance, self-pay, etc.)? Data newly available from the Health Care Cost Institute (HCCI) about a large number of private insurance plans offer new hope that we may begin to answer these and other important questions more fully.

As a first high-level analysis described by the Times article reveals, places in the U.S. that have been heralded for low-cost, high-quality care delivered to Medicare patients are not necessarily performing in the same way for their private-insurance patients.

You can see these findings displayed in the side-by-side choropleth maps below.

(click to enlarge)

Displaying the data like this reveals that (for example) although Alaska’s per capita Medicare spending is average as compared to all other areas in the U.S., its per capita private-insurance spending is above average. The data reveal a similar pattern for several other areas in the states of Idaho, Michigan, and New Hampshire (for example), where Medicare costs are either average or below average, but private-insurance spending is above average.

This isn’t the only observable difference. Interestingly, in places like my home state of Massachusetts, the opposite of the above is true: Medicare spending is above average, while spending on private insurance is average across the state.

The Times article displays this information on the maps above and also in this simple but effective graphic (click here to check a place near you).

(click to enlarge)

I find these new data wildly interesting, am certain they will result in new findings, and devoutly hope that they will also lead to greater transparency in and other improvements to our healthcare system.

But this new information also serves as a serious and important reminder that we are all making decisions using the best available incomplete information currently available to us, and only that. As a result, we have to try to get better at understanding what it can and cannot enlighten us about, and how we will act when new information becomes available to us.

After I read the Times article and thought about its title, I found myself annoyed at what seemed a rather negative headline: “The Experts Were Wrong…” In fact, the experts were right about what the BAII they had at the time revealed. Was it the full story? Absolutely not. Do we know that full story yet? We do not: even this new analysis is missing data on patients insured by Blue Cross & Blue Shield and Medicaid, as well as on the under- and the un-insured. To put it another way: “We still don’t know what we don’t know.”

It seems to me that the only sensible path to improving our healthcare system is to commit ourselves to continually seeking new data, information, and knowledge to support better-informed decisions, and to seek the courage to adjust our sails and lead change by following – even when that path may be disappointing, confusing, or difficult – where the data lead.

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Really Big Goals

Like a lot of people I am a big goal-setter. I especially love BHAG’s [pronounced “Be- hags”?]: Big, Hairy, Audacious Goals.

You know: the ones based on no logic or well-developed plan whatsoever, but rather conjured up by the sheer and (sometimes) delusional belief that “Somehow, I will find a way.”

Exhibit A: starting a business with a kid headed off to college and a big-ass mortgage (a technical term in my household). To be fair, I also set a lot of smaller and saner goals for myself: the amount of money I wanted to save for retirement each year; the number of trips to the gym each week.

As 2015 ends, I find myself going back over the year to consider what I accomplished compared to the goals I set, and visualize what I hope to accomplish in 2016. As is often the case, my mind wanders to different data visualization techniques and how I might display actual vs. desired progress using graphs.

Yes, I hear you: “Memo to self: add ‘Get out more!’ to my 2016 goal list.”

A graph I often use to show how well a group performs compared to a goal or benchmark is a deviation graph.

(If you are a regular subscriber to this newsletter, you’ll recall that I have written more than one article about these types of graphs; you can check out those articles by clicking here.)

I especially like them on monitoring dashboards, because the absolute value of a changing goal or benchmark is not displayed – only the difference or deviation of actual performance from it is shown, as in the following example.

really-big-goals-deviation-graph1

(click to enlarge)

Displaying the information like this allows the viewer to quickly and easily answer questions such as “Are we over or under budget on revenue or expenses?” or to evaluate medication reconciliation versus a target without worrying about the actual goal or performance values, as they often change over time or are different for a group or category of similar metrics (department budgets, for one).

Such a display lets them know if performance is above or below goal, and by how much.

Sometimes, a goal is set for a longer time frame, and we wish to display its actual value compared to performance. Most often a line graph like the one shown here is used for this type of display.

really-big-goals-line-graph

(click to enlarge)

While this is a perfectly acceptable way to show the data, it doesn’t clarify how far from target we are.

This is where a deviation graph – one that displays the actual target value and the actual performance difference or deviation, such as in the one below – can help.

really-big-goals-deviation-graph2

(click to enlarge)

This data display makes clear that the target is 90% on medication reconciliation, and how far below (orange bars) or above (blue bars) monthly results are. It’s also possible to see actual performance by comparing the ends of the bars to the Y-axis.

All three of these displays work – as long as they respond to these key criteria:

  • are target values fixed or variable?
  • is it enough to simply monitor deviation, or must actual values be displayed?

As I write this, and consider my options for displaying my own performance compared to my 2015 goals, I am beginning to get a little spooked. I may after all be awash in orange (below target!!) for some time.

But then I remember a famous comment by American author, salesman, and motivational speaker Zig Ziglar: “If you aim at nothing, you will hit your target every time.” Back to that BHAG list – and onward.

Posted in Design Basics, Graphs, Newsletters | 1 Comment

Postcard from New Zealand

It has been almost a month since my return to the States, following a truly gratifying professional engagement with the Canterbury Health District and Health Informatics New Zealand (HINZ).

If you’ve ever had the pleasure of traveling to New Zealand, you know all the accolades about it are true… true and oh so very, very true! The landscape is spectacular, the people are lovely and yes, of course, there are far more sheep in New Zealand than there are people. Lots and lots of sheep, like this darling little lamb at the Walter Peak High Country Farm in Queenstown (which, of course, they only let me hold after they had served me his brother for lunch – clearly a brilliant strategy to reduce the number of requests for vegan meals.)

kathy-holding-sheep

Given all the sheep and lambs we saw (it is spring in New Zealand now so there are even more lambs than usual!), it is no surprise that I started to think yet again about the great utility of small multiples to display our healthcare data.

If you are up on your data-visualization terms, you know that it was Edward Tufte, a statistician and Yale University professor, and a pioneer in the field of information design and data visualization, who coined the term “small multiples.” (You may be familiar with other names for this type of display: Trellis Chart, Lattice Chart, Grid Chart or Panel Chart.)

I think of small multiples as displays of data that use the same basic graphic (a line or bar graph) to display different parts of a data set. The beauty of small multiples is that they can show rich, multi-dimensional data without attempting to jam all the information into one, highly complex chart like this one:

count-by-department

Now take a look at the same data displayed in a chart of small multiples:

count-by-category-and-department

What problems does a small-multiples chart help solve?

  1. Multiple Variables. Trying to display three or more variables in a single chart is challenging. Small multiples enable you to display a lot of variables, with less risk of confusing or even losing your viewers.
  1. Confusion. A chart crammed with data is just plain confusing. Small multiples empower a viewer to quickly grasp the meaning of an individual chart and then apply that knowledge to all the charts that follow.
  1. Difficult Comparisons. Small multiples also make it much easier to compare constant values across variables and reveal the range of potential patterns in the charts.

Now, before you construct a small-multiples data display, here are a few additional pointers:

  1. Arrangement. The arrangement of small-multiples charts should reflect some logical measurement or organizing principle, such as time, geography, or another interconnecting sequence.
  1. Scale. Icons and other images in small-multiple displays should share the same measure, scale, size, and shape. Changing even one of these factors undermines the viewers’ ability to apply the understanding gained from the first chart to subsequent charts or display structures.
  1. Simplicity. As with most things in life, simplicity in the small-multiples chart is crucial. Users should be able to easily process information across many charts, and see and understand the story in the data.

I still go a little soft when I think of holding that darling lamb and patting its ears as it fell asleep in my arms. And while it is highly likely that this sweet memory will fade and I may eventually eat lamb once again, I will always remember seeing pasture after pasture of these gentle creatures and will continue to relate them to small multiples to display data!

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My Bin or Yours?

I am writing this newsletter from my coach seat (28F, window) on United flight UA1292 from Boston to San Francisco. Funny how inspiration for a newsletter about healthcare data visualization and histogram bins can show up in the tightest spots.

This particular snug niche was the few cubic inches I desperately sought in an overhead bin, so I could stow my carry-on. As I did so, I was aggravated by the way luggage and other belongings had been shoved into places where it was clear they didn’t have a snowball’s chance in hell of fitting. Hello? If your things hang out of the bin and the door won’t shut, there’s a problem!

Then there’s the whole armrest fiasco. Is it mine, or is it the territory of the person next to me? Where does my boundary begin and his|hers end – when am I in the right space, and when have I illegally crossed the armrest border?

All of this got me thinking about the intervals, or “bins,” on histograms – the charts used to show the distribution of numerical data and to estimate the probability distribution of a continuous (quantitative) variable. Histograms are really useful, but – as with airplane bins – you need to be careful not to fall into “your bin or mine?” confusion.

A histogram is a type of graph most commonly used to show frequency distributions, or how often each different value in a set of data occurs. It looks much like a bar chart, but there are either no, or minimal, spaces between its bars, a feature which helps remind the viewer that the variables are continuous.

As a result, bins are usually specified as “consecutive, non-overlapping intervals” of a variable. The bins (intervals) must be adjacent, and are usually of equal size.

Histograms are very useful when you need to:

  • Display the distribution of continuous data (ages, days, time, etc.).
  • See if the data is distributed relatively evenly, is skewed (unbalanced), or is some other interesting shape as in some of the following examples:

normal-distribution

In a Normal Distribution, data tends to be around a central value with no bias left or right (often referred to as a bell curve because its shape is similar to that of a bell).

right-skewed-distribution

Skewed Distributions commonly have one tail of the distribution considerably longer or drawn out relative to the other. A “skewed right” distribution has a tail on the right side, a “skewed left” one, on the left. The above histogram shows a distribution skewed right.

Clearly, histograms are a great choice when you wish to display and communicate data distribution quickly and easily – but again, don’t fall into that “my bin or yours?” trap. Often I see data displayed in a histogram like this one, which I created using data from the National Vital Statistics Reports, v. 64, No. 1, January 15, 2015*:

histogram1

Histogram (1) displays the percentage of low-risk cesarean deliveries (C-Sections) by maternal age in the U.S. in 2013. Note that the X axis has divided maternal ages into bins; if you look closely, you’ll catch the “my bin or yours?” trap. If a woman is 30 years old when she has a C-Section, does she belong in the third bin (25-30 years) or the fourth (30-35)?

Once you catch this, it seems easy enough to fix.

histogram2

In histogram (2), I changed the bin labels to eliminate this overlap – but in doing so, I may have created a new problem. If the data captures the exact age of women (i.e., years and months), and a woman is 24.5, 29.7, or another “in between” age when she has a C-Section, which bin is she in? We might just make an assumption and move on, but there’s a better way.

histogram3

In the final histogram, (3), the addition of the “greater than,” “less than,” and “equal to” symbols provides the clarity we need to avoid the trap about where data with this level of detail falls in the distribution.

Now we can see that if a woman is age 24.5 when she has a C-Section, she is in the second bin; if 25.7, she is in the third one. Trap avoided: we have clearly labeled each five-year bin, thereby eliminating confusion.

The devil is always in the details, isn’t he? And yes, details matter if you are serious about making the story in your data (and bins!) clear, and if you want to avoid the “whose bin?” trap.

As for my personal bin and armrest struggles, flying first class may be the only solution.

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Love, Time, Environment

As my daughter Annie’s tenth birthday approached, our friend Erik asked her what special gift she was hoping for. Without hesitation, Annie responded, “A crane.” We all turned and looked at her in astonishment. What would a ten-year-old girl want with a crane?

“I want a crane,” she calmly continued, “so I can lift up my piano and smash it to the ground.”

Stunned silence – followed by uproarious laughter – greeted her matter-of-fact but implacable pronouncement. In that moment, I saw my dream of vicariously becoming a concert pianist smashed (metaphorically) to smithereens.

This story has become legend among our family and friends because it says so much about Annie’s succinct style of expression, and creates an indelible image of how she really felt about learning to play the piano (as compared with my romantic fantasy about her learning to play).

But the anecdote has also stayed with me all these years because of the big life lesson it taught me. I believed that if I just gave her everything she needed – a beautiful instrument, lessons, encouragement – she would become a really great pianist.

What I failed to understand, however, was that the piano had not captured her imagination. She didn’t love it – clearly, she didn’t even like it – and she was never, ever going to move beyond playing a few simple (albeit charming) tunes.

Additionally, I came to realize that I didn’t really love the piano, either; as a result, my encouragement was cursory at best. I had no burning desire to create an environment that wholeheartedly nurtured and supported her learning to play, and love, the instrument. I had to face it: there was a real dearth of piano-playing passion in our lives.

I don’t regret having spent my time and money on any of it because I have faith (and evidence) that it raised Annie’s awareness and appreciation of music and beauty. But that’s pretty much the extent of my return on investment – except, of course, in the way that the piano episode has informed my professional work, particularly concerning the way people learn.

As you know, I have created curricula specific to health and healthcare professionals that teach the best practices of data visualization and the fundamentals of analysis and statistics.

Each time I conduct a workshop or training, I can pretty accurately predict which participants will love the material, and will continue to research and practice ways to improve their dashboards and reports, and which won’t.

Here are some clues to correctly identifying the successful ones:

  • The team lead, director, manager, or supervisor is in the course alongside the team, fully interested, engaged, and encouraging. Even if the leaders are unlikely ever to create a report or dashboard themselves, they are signaling their commitment to and support for the process.
  • I have the successful attendees’ full attention: phones out of sight; eyes on the examples I present; focused consideration of what they are seeing; articulate, involved communication with me about it; enthusiastic interest in the subject.
  • When it’s time to complete a group case study, they dig in and hang on. I see them opening books, talking to their colleagues, checking in with me, pro-actively putting pencil to paper to sketch out multiple strategies.
  • When I encourage them to think of a report they currently produce, and how they might improve it using what they have learned, they jump at the chance to re-imagine it, eagerly soliciting my feedback along the way.
  • After the course is over, they stay engaged and interested, sending me e-mails with reports or dashboards attached that they have re-designed and that have been, they tell me, effective and well received. And they continue to drop me notes on occasion, to ask advice or recommend a useful article.

Here’s the bottom line. Becoming good at something – creating powerful health and healthcare reports and dashboards, or just about anything in life – requires three things: [a] an interest in or love for the subject; [b] training bolstered by practice (10,000 hours of it, according to Malcolm Gladwell’s Outliers); and [c] a supportive and nurturing environment in which to develop and refine your knowledge and skill.

This last point is especially important for managers to understand. You can send me teams of people, and I can raise their awareness and with luck ignite the fire of their imaginations about the best practices of data visualization and healthcare analytics.

But if you don’t share in that interest, or neglect to arrange things so that those who do can encourage, inform, and cheer on their colleagues, no amount of training in isolation is going to improve your health and healthcare reports and dashboards. (I’m good, but I’m not that good.)

By the way: if you know anyone who might be interested in a brand-new, barely-been-played, threatened-within-an-inch-of-its-life piano, drop me a line, won’t you?

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What’s Hue?

Recently someone sent me the bar chart below, and asked what I thought of it (as if I needed an invitation to comment!).

whats-hue-01

My immediate reaction was to ask why the author had used different shades – or “hues,” as they are called in graphic design – of black on the bars.

The heights of the bars clearly show that revenue has increased each month, so it’s redundant (and distracting) to use color hues to display the same increases. Of course it’s also true that hues often highlight variations in volumes, rates, and other measurement, but here they aren’t needed for that purpose, either.

The change in color is redundant (am I repeating myself?). Simply display the bar-graph data like this:

whats-hue-02

This is perhaps a good time to ask, “when, how, and why do hues work best in data visualization?” As I suggested above, we might want to show changes in volumes or rates with them, as on this choropleth map from the Dartmouth Atlas:

2010 Part D Medicare Enrollment Cohort
Percent Filing at Least One Prescription For a Dementia Medication

whats-hue-03

By using different hues of yellow to brown (from the lightest shade for the lowest percentages, deepening to dark brown for the highest), we can illustrate that in 2010 the percentage of Medicare beneficiaries enrolled in Part D (the prescription drug program) and who had filled at least one prescription for a dementia medication, was much higher in Southern California than in Northern California (for example).

The use of hues on this type of map helps us quickly and easily see and compare low and high values, and even to better grasp the full “what and why” behind the display. This use of color hues makes complete sense. (To see and learn more about this type of data display, take a look at The Dartmouth Atlas of Health Care.)

Hues also often work well to dynamically direct viewers’ attention to a metric that signals an urgent situation requiring immediate attention. This is particularly useful and important on dashboards or in summary reports; I’ve discussed these frequently in previous newsletters and posts.

In the issue of 15 May 2015, for example, I used a dot indicator in dark red to draw attention to those measures that fall furthest below the national comparison, then incrementally lightened the hue to match the diminishing differences between actual performance and the standard.

whats-hue-04

In another example – you can see it in our HDV website portfolio by clicking hereI used arrow-shaped indicators and a range of black tones to show changes (increases, decreases) in a hospital’s payor mix from one year to the next.

whats-hue-05

Note that the selection of a neutral color for icons (instead of more emotion-laden colors such as red or green) allows the viewer to quickly see changes in the data without conveying any judgment on the value of the change. This is especially important in a display presenting an element such as payor mix, where the same changes may be good in one situation and bad in another.

Further, avoiding colors such as red and green makes understanding the display easier for those with visual variations and inability to see certain shades accurately.

The example offered to me for comment (the first graphic, above) demonstrates a fundamental understanding of the use of color hues to show differences in volume. That’s a good thing.

But as with everything in data-viz (okay, and perhaps in life in general), true mastery resides in knowing precisely when and how to use (or not to use) a technique, so you can get your point across without distracting or losing your audience.

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The 2015 Hammock Chronicles

Greetings from Bustins Island, Maine — where accomplishing as little as humanly possible is the only measure of a successful day.

To that end, I am a total overachiever at underachieving, and damn proud of it. So far this week, I have managed to reclaim the title of champion hammock-swinger and cocktail balancer, with a few obligatory westward strolls to watch the sunset and make certain my legs still work (they do).

It was during one of these strolls that my eight-year-old nephew, Aidan, and his younger cousin, Chase and I came upon the sign in the photo below, surrounded by flags and a few stones:

please-be-considerate

As soon as they saw this arrangement, the boys’ youthful imaginations took off. This, they explained to me in hushed tones, was where an islander’s beloved dog was buried. It had died in some tragic accident and most likely would come back as an island ghost dog. (I’m making the ghost part up, but you get the picture.)

Of course, I cracked up laughing, and then pricked their bubble by advising them to “Read the sign, you silly boys! There’s no dead dog under the sign — it’s simply a message reminding people to clean up after their pets.” My nephews now fondly refer to me as “Auntie Killjoy,” because their story was far more intriguing than the truth.

I’ve walked by this sign several times this week — which started me thinking about how looking at only the rocks, flags, and dog drawing in the arrangement caused the boys to jump to an (incorrect) conclusion. The accompanying (and central) message, communicated with words, on the other hand, made the real purpose of the display clear.

The same thing is true about the tables, graphs, and dashboards we create to display health and healthcare data: they can’t stand on their own.

Rather, they always require clear, concise titles and labels, and in some cases well defined, clarifying X- and Y-axes as well as the occasional pithy note to help viewers correctly interpret what they are seeing. Every display must be labeled directly and closely, thereby eliminating the need for viewers to spend their mental energy matching keys or lists of data to a display.

Here’s a simple example of how leaving out some basic information can really confuse a reader:

wait-days-before

In this view, the bar chart makes it easy to see which person has the longest wait time for a new-patient visit — but it still leaves so much out. Are the providers physicians, nurse-practitioners, physical therapists, or some other group? What is the time-frame? Are the days listed the average? The median? With such patchy and incomplete information, it’s impossible to tell.

Now consider the use of just a few clarifying words in the title, and direct labeling of the bar chart:

wait-days-after

Thanks to the simple addition of “Average” and “Primary Care Physician,” along with the date of June 2015 and the addition of the number of days, the information in this display has been rendered 100% easier to comprehend.

Now, it’s easy to see that if a new patient called for an appointment in June to see Dr. Clark, [s]he would, on average, have to wait 75 days compared to 40 days for a new patient wishing to see Dr. Scott. Additionally, if I needed to include this chart on a dashboard, I have just freed up a whole lot of real estate by eliminating the data table.

These fixes might seem overly simplistic; but the display above is a real example of the kind I encounter regularly: with a bit of thought, such flawed displays can easily be improved.

Look at the entirety of the information you need to get across; then go over it carefully to see if you have condensed, combined, and labeled everything to convey as much and as meaningfully as possible.

Okay, I’m now losing the “how to be successful on Bustins Island in Maine” competition. I shall therefore return to my hammock to swing, and balance, and contemplate how very sweet island life can be.

Posted in Communicating Data to the Public, Graphs, Newsletters | 2 Comments

When was the Last Time You Asked “Why”?

Many moons ago, my husband, Bret, and I were running through a list of errands when we noticed that several of them intersected, prompting one of us to suggest, “Let’s kill two birds with one stone.”

Our (then) very young daughter immediately leveled her gaze upon us and demanded, “Why do we have to kill birds?” (Trust me, there’s nothing like the glare of a child who thinks you might be a cold-blooded avian-killer to stop you in your tracks.)

bird

Bret and I cracked up laughing because her question was so extraordinarily funny and touching–and perfectly justified. Why would we kill birds?

Here’s what I especially love so very, very much about her question: the way it showed her complete lack of ego and fear. She overheard something we said; it made no sense to her; and with complete spontaneity, she asked “why?” She didn’t stop to worry that she should know the answer, or that we would think she was stupid, or like or love her any less. She simply asked the obvious.

Now, here’s my question for you. When was the last time you let go of your ego and fear and simply asked, “why”? I know it can be hard to do something so basic, and we all have times when we decide (to paraphrase Mark Twain) that it’s better to keep our mouths shut and appear stupid than to open them and remove all doubt–but that attitude serves no one well. When we find the courage and evince the curiosity to ask “why” about things we don’t know or understand, everyone wins.

Asking questions

  • allows us to understand the reasons (to what purpose? toward what end?) we do something
  • gives us a chance to make predictions, consider alternative strategies, and change our thinking about or slant on a problem or task before actually testing methods or approaches
  • lets us make connections based on things we already know
  • enables us to understand unfamiliar terms, concepts, and acronyms
  • reminds us to review important information and build consensus

How we frame and ask our questions is just as important as (if not more so than) why we need to ask them. Consider these traps we fall into, and some ways to avoid them.

Leading The Witness. We ask questions that assume particular answers—that is, we think we already know the answers, and simply want people to confirm them.

Example: “Don’t you think this data is useless, and that we should ditch this project?”

We must instead ask questions that are objective, direct, and free of preconceived answers—that leave room for discussion, exploration, options.

Example: “What can you tell me about this data set? What can we do to test its validity and usefulness?”


Either/Or Scenarios. Instead of asking questions, we propose solutions disguised as questions.

Example: “Should we just scrap this dashboard and rework the entire thing, or should we send it out, and hope that no one notices that it doesn’t make any sense?”

Most people will choose one answer or the other–but what if there’s a better option that hasn’t been proposed?

Phrasing the question like this promotes exploration. “This dashboard has numerous flaws. What do you think we should do?”

Rather than offering either|or choices, simply state the problem. Then ask, “What do you think?” Or, “what would you do? How should we handle this?” Then be still, and let people reflect–don’t rush to fill the silence.


Failure to Clarify. As previously noted, we are often afraid (and yes, sometimes too indifferent, tired, or–let’s be honest–lazy) to ask the questions that provide clarity. But we need to ask them, and doing so is not really very hard.

Example: “I don’t have much experience with this or how it works. How would you explain it to me?”

“That sounds good. Let me make sure I didn’t miss anything. Can you walk me through it one more time?”

“Here is what I understand; is that correct?”

The bottom line: don’t pretend you understand something when you don’t. Instead, learn to ask questions that will help you create better work, and provide the reports and dashboards that all stakeholders need to make informed health and healthcare decisions. (As an added bonus, I guarantee your job-satisfaction and performance evaluations will improve as well!)

P.S. Did you find yourself asking where the idiom “kill two birds with one stone” comes from? Here’s a story that holds a possible explanation. In Greek mythology, Icarus and his son Daedalus are being held in a high tower by King Minos of Crete. All they can see are high walls around and large birds overhead, circling as they await the death of their future dinner. Daedalus figures out how to throw stones at the birds–whose wings he hopes to use to enable the pair to fly out of their prison–then bring down a second with the ricochet, thus killing two birds with one stone. The rest is history.

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Is That a Zero In Your Data (or are you just glad to see me)?

I am highly suspicious of line graphs displaying the number zero (0).

I am in fact so suspicious of them that when I see one, I hear myself inquiring (in a most dubious tone of voice), “Is the value shown here truly zero (cross your heart and hope to die) or is there a gap (missing data) in the underlying data-set you’re using?”

The very substantial cause of my skepticism is the awareness that people sometimes mistakenly enter a value of zero on a spreadsheet or in a data table to stand for missing data. While many like to debate the point – “Don’t real numbers have to have value? Does nothingness actually count as a value?” – zero is a real number.

For example: zero patients responded “Agree” to a survey question; zero patients fell this month; zero new cases of pertussis were observed in the U.S. this year; zero payments were received on outstanding bills today. Knowing this, when I have been assured that the true result is zero, I am fine with a display like the following, and I cease my interrogation.

zero-in-data-01

If, however, data is missing for a particular period, I know there really is a gap that must be displayed as such. That is, I want to see a line graph like the one below, which makes it clear that June data is completely missing: there are no results in the data-set to report.

zero-in-data-02

Oh, but if it were always this straightforward, I just might be out of a job.

Many software applications – such as Excel in my examples – have been specifically designed to let users display this data correctly. But sadly – okay, infuriatingly – that is not the case with all of them.

Imagine my dismay when I recently learned from a colleague that in the application she was using, the software simply linked points in time on a line chart, completely obscuring the fact that data was missing. Instead of showing a gap for absent data, the software creates a display something like this:

zero-in-data-03

Oh, my aching head! As you can see, this display makes it appear as if there were known results for 2013, when in fact there are none. I later discovered that this flaw is a known problem that the vendor has yet to correct. (See Steve Few’s post about it here.)

What do you do if you encounter this issue?

Certainly, you can write the vendor – but that won’t solve your immediate problem. You can (and should) also note the misleading flaw in your report, drawing attention to the gap it causes in the data, and stating that the results for the time period (the year 2013 in the example above) are missing. Make that alert stand out, too! Something like

NO DATA AVAILABLE FOR 2013 – SOFTWARE AUTOMATICALLY LINKS YEARS

should be clear enough.

You could also delete 2013 from your data-set altogether, which would result in a graph that looks like this:

zero-in-data-04

This solution introduces a new problem, however: viewers will not easily pick up on the fact (if they do so at all) that 2013 is missing.

They will see a series and assume it is consistent, losing sight of the broken sequence at the end of the chart, where the years jump from 2012 to 2014. You will therefore – again – need to highlight this omission as you did with the warning above. And how annoying is that? Very.

The power of well-designed data visualization lies in how beautifully and simply lines, points, boxes, and bars can make the story in our data easy to see and understand.

The operative expression here, though, is “well-designed.” Only with good design will the charts and graphs you create using the best practices of data visualization be of any use. Unfortunately, not everyone has gotten the memo about how it all works – and Liquid Paper is no longer an option.

Posted in Communicating Data to the Public, Graphs, Newsletters | 1 Comment