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The Hole Truth - Blogs about the Outside-In PDF Print E-mail

 

CURRENT:

 

It doesn't have to be so hard

 

 

... prior posts:

 

(16 OCT 2012) Interlude: musing about the nature of demand data.  

(22 AUG 2012) What Doctors Want.III  Making sense of all those statements

(12 JUL 2012)  Tales from the Clinical Village   Pain and delight through stories

(15 JUN 2012)  What Doctors Want.II  A few details

(18 MAY 2012)  What Doctors Want     And its not what you want 

 


 

It doesn't have to be so hard

 

I continue to be amazed at all the sites and services offering insights, lessons, how-to’s, tools, tricks&traps, and “all sorts of color 8x10 color glossy photographs” for innovating.  What I find amazing is that they all circle around the truth without explaining that it’s all driven by a very very easy principle:

innovation means “new value”, not “new products”, and customers innately understand this while corporations balk at it. 

So the easy answer to how to get an organization to innovate is simply to get the organization to want to deliver new value, not new products. 

To do that, of course, the organizational emphasis has to change, it has to understand what its customers value on their own terms not in the terms of Product Offerings, and the executives have to be willing to go out on some limbs in search of the fruit (because “new value” means “new value”, ie no one has been there before).

OK, so the principle’s easy but making it happen is not.  But it’s a lot easier when you understand what you are actually trying to accomplish.  When you do, everything falls into place, whether you are pursuing new value in the form of new value-chains, brand messagings, technology platforms, new offerings, new whatevers.

Remember, they don’t want ¼” drill-bits (even if that’s what they are asking for), they want ¼” holes.

 

 

Interlude: Musing about the nature of demand data

 

It has been a busy summer and I am now back on the blog roll.  An article about “Dr.” Watson caused me to flash on the fact that Watson operates on verbal data.  I got into an interesting discussion just last night with a software guy working on commercializing neuroscience EEG technology.  When I told him that not all data was numerical, he got confused.  I pointed out that he and I had just exchanged an enormous amount of data without a single number involved, in fact he was still “transmitting” in body-language, that the data I work with are Points of Pain/Delight and Points of Goal.  He kinda got it. 

It’s this type of qualitative data that is the key to demand landscaping.  What people say is saturated with meaning.  The challenge is to extract that meaning in ways that can be analyzed and shared or published with others so that the knowledge can be made useful.  One of the attributes of demand landscaping that I find so appealing as an innovator is that it has exactly this property of forming data-bases of meaning.  With these data-bases, you can share how customers think, what they value, how they live their lives and why they live their lives the way they do.  Certainly can’t do that with conventional, numerical data.

 

 

What Doctors Want .III 

Making sense of all those statements

 

In What Doctors Want .II, I described some methods for extracting points of qualitative data from interviews.   It is a skill of active (or passive) listening, translating what gets said in to what was meant.  Which can take some practice.

To get some practice, I recommend a wonderful website full of first-person stories, patientvoices.org.uk, as a source of statements of pain and motivation.  Great for patient data, but also has some provider voices too.  Here are links to some of them:

Stories from doctors and medical students: http://www.patientvoices.org.uk/lssc.htm

Stories from nurses: http://www.patientvoices.org.uk/rcnpdf.htm

Stories from care givers: http://www.patientvoices.org.uk/sheffcc.htm

Stories from patients: http://www.patientvoices.org.uk/dc.htm

Now, go forth and Listen …..

cliniciansexpressing.xsm

…..  a dozen interviews (or video stories) later,  you will have roughly hundreds of statements to try to make sense of.  Daunting challenge at first glance.  In fact, one of the reasons why people tend to avoid doing qualitative work is the problem of making sense of the data. 

And for that I recommend two of the Seven New Tools of Management (Seven Management and Planning Tools): affinity diagrams and hierarchical trees.   

The affinity diagram and the hierarchical tree are sepcially useful in sense-making.    First use the affinity diagram (sometimes called a cluster diagram) to cluster the statements that seem to belong together.  Then use the hierarchical tree to model the relationships.

Affinity diagramming uses a large number of Post-It notes, one for each statement, which as a team you cluster in to related themes.  Getting everyone on the team to work together requires that they pool their knowledge, this is not a consensus exercise.  To pool knowledge, you may only explain your reasoning based on what you know, not based on how the other guy(s) are wrong.  Ie, you may not “put down” what some other person has contributed, only what you know to be true. 

Do the clustering in three rounds focusing on assembling the statements according to what you each understand the individual statements to mean.  If you have photos, artifacts, or direct quotes, attach them as explanations for why certain statements are placed where they are placed.  Do the rounds silently, each person moving Post-Its around to join the clusters they feel the statements belong in.  After about 5 minutes the hub-bub will die down and the moderator needs to halt the round.  Take a break, reinforce the project’s focus, and then do another silent round, this time working to shuffle statements from clusters you feel they don’t belong in, into clusters to which they do belong.   This takes longer to settle down, give them 10 minutes, then halt the round.  The third round is done as a group, with the moderator working the group to define names for each of the clusters that have formed.  Some of the statements really won’t fit, so either move them to clusters where all agree they belong, or set them aside in a “parking lot.”

Here’s a sample of a clustering exercise end result:

clustering-sm

 The second tool to apply is the forming of hierarchical trees.  This is done with the cluster titles and team input (remember about pooling knowledge).  Some of those titles may be low-level, some may be quite high-level concepts.  It helps to think in terms of “sibling”, “child” and “parent” concept relationships.  The only hard and fast rule is that if there is one “child” concept then there must be at least one other “child” concept or the “family” is not viable.  Ie, a branching with only one branch isn’t a branch.  Sort the concepts you have accordingly, and capture their relationships on a whiteboard as you go.  There will certainly be missing concepts, so brainstorm about what those are.   Aim for at least three layers but not more than five.  Five is quite complex, three is a bare minimum. 

tree

The final step is to review the tree to make sure that it is conherent, ie each “cousin” makes sense in comparison to the others, that there are no missing “families” and no “orphans..”  Are there overlaps of meaning, or differing interpreations amons the team?  Look hard at your parking lot (remember the parking lot?) and see if any of the ideas there fit in the whole picture. And as a final validation, you might arrange to validate the tree with some live customers to make sure it makes sense to them, that nothing is missing or overstated. This takes time, at least a third of the whole project, to get the tree right.

Congratulations, you have built a customer demand landscape! 

 

 

 

Tales from the Clinical Village

Pain and delight through stories

 

 

I am taking a break from Methodology today to tell stories from the clinical village.

Three surgeons walk in to a bar. . . .

Well, maybe not those kind of stories.  What I had in mind are personal tales from deep inside the clinical village.  Anecdotes, myths, perplexities, interpretations.  These are the basis for building a thorough understanding of the tribe.  Anthropologists use stories all the time in study of any tribe.

I’ll start with one from Atul Gawande ‘s first book, “Complications.”  He tells of one of his first experiences in the ER as a surgical resident.  A young man was brought in with a gun-shot wound to the right buttock.  The staff did a rectal exam and found blood.  They did a urinary flush and found blood.  The conclusion was clear, the bullet had done major internal damage and they had to operate stat to deal with it.  Once inside, the surgeons found nothing.  They looked for hours.   No damage, not to the bladder, the intestine, they couldn’t even find the bullet.  So they sewed him up and send him to the PACU.  Days later the bullet was found on chest x-ray lodged up against the underside of his diaphragm. No one was ever able to explain the blood findings.

Dr Gawande tells the story to make that point that medicine is imprecise and unpredictable.  I recount his story to make that point that doctors (and patients) want medicine to be precise and predictable, the fact that it isn’t bothers us all.  If I were try to extract some points of pain from this story I might start with:

               I get frustrated by being surprised

               I get anxious when I don’t understand what is really happening with my patient

               I am stymied by inexact and irrelevant clinical tests

               I hate it when I can't explain the findings

What sources of stories do you have?  What have you heard/seen in your own exposure to the clinical village?  Imagine what you might have as a trove of pain and delight if you were to compile them all.

Next week I will recount a story from NHS and the English public health situation, take the analysis to the next step.  In the UK, where doctors are all salaried, I saw a highly respected attending surgeon take a resident off at the knees for transferring a patient in to the surgeon’s service.  Mind boggling ..

 

Royce Johnson

 

12 JUL 2012

 

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What Doctors Want.II

 

So, what did they say and what did they mean, in detail, in the story from the surgical clinic?

Here are a few samples of detailed statements made in that clinic anecdote:
     "Little-town-not-far-away has fine hospitals and surgeons"
     "She probably drove up in a Mercedes"
     "The only reason she would have come up here was that she has no insurance"
     "Let's send her to Hospital X where Dr. Z is better at treating her condition"
       (yes, they really did say these things)

That's what they said; what did they mean?  Enter The Method of Five Why's.

A technique popularized by Toyota, The  Method of Five Whys is a method of laddering down to the root of the issue with iterative, interactive questioning of each subsquent reponse for up to five 'rounds', by which point the root of the issue is usually revealed.  For each answer to a question, the interviewer then probes for the next underlying 'why'.  For example, with the first statement above, the interviewer might respond with "oh really, you see that Little-town has good care available, why is that an issue in your response to this patient?", and then the same kind of query after the doctor responds to this next question, repeating this digging until the root issue is clear (or everyone runs out of patience!)  Each response reveals valuable insight about the doctors' frustrations and motivations.  

 

Caveat: repeatedly challenging an interviewee with "why?  Why? Why?" can be quite agravating to them.  Imagine the same from a three-year-old child.  Interviewers must use creativity and sensitivity to dig without aggravating.  Training in Active Listening helps a lot.

 

These data will accumulate over the course of multiple interviews to represent a large database of qualitiatve data.  That large databse can then be consolidated to form a comprehensive, 360 model of what the customers want.  

But I am getting ahead of myself.

When we did that probing on the exmple statements above, we got the following:

     "Little-town-not-far-away has fine hospitals and surgeons"
          But we are very good at what we do
          Want you to understand that our care is not the reason she came here
          Slightly envious that those guys are probably paid more than we are
          I don't feel fully compensated for how my work 
     "She probably drove up in a Mercedes"
          Make it clear that she has money, just not insurance
          Frustrated by the patient's selfishness for hitting us up to cover what she could probably pay for herself
          I want to take care of people really need my help
     "The only reason she would have come up here was that she has no insurance"
          Frustrated by people taking advantage of the System
          Frustrated by having to use up resources that are truely needed by others
          I want to provide care for people who really need the help and can't get it any other way
     "Let's send her to Hospital X where Dr. Z is better at treating her condition"
           I want to get rid of her but not by preventing her from getting care 
          Would be great to get her care that's not using up our scarce resources
          I like to promote my colleagues as professional partners regardless of where they work          

Those four statements generated 13 statements of goal/pain.  What's more, the statements of pain (eg "Iam frustrated by ..") can be turned in to statements of goal by recognizing how the stated frustration represents what they want but cannot achieve.  And that was just a portion of one interaction.  You can see that the data builds up quickly.  

A typical 1.5 hr interview can generate a fifty detailed statments of goal/pain.  Do that with a dozen interviewees and now there is a mountain of qualitative data to try to make sense of.  Next month I will look at how to condense all that qualitative data into insightful themes.  These themes form the foundation of the models of what your customers want.       

 

Royce Johnson

15 JUN 2012

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What Doctors Want

  

To begin, let's begin with doctors.  Most of us think that doctors drive the decisions in clinical settings (they think so too), so let's start there.  I will slowly unpeel this onion and the data that it's built on.  The end result will hopefully be new insights for  you in how to understand your own customers.

As 'homework', I will recommend that you go watch Mel Gibson's "What Women Want".  I saw it again after many years and was amazed at what they were showing of the methods for really doing consumer research.  The film shows Gibson literally getting into the shoes of women, listening carefuly to their points of pain, what they mean and not what they say.  And they show very elegantly the ethical conflicts this kind of work sets up.  Admittedly, the movie uses a magical mind-reading tool that none of us have, but the message is still loud and clear - get inside their minds, not just what they say.

What  I want to do with this blog, then, is the same thing, but with clinicians.  No, I can't read minds, but I have spent many many years working in and among clinicians, I am married to one!  And they, like the rest of us, mean a lot more than what they say.

One of my favorite stories comes from a week I spent embedded with a surgical trauma team in a major metropolitan county-funded Level-1 hospital.  I spent one morning shadowing them during their surgical clinic session.  A clinic session is a block of time set aside for seeing patients in exam rooms, making initial or follow-up assessments, making plans with the patients for actual care.  In the case of surgeons, that's generally a path to surgery.  

They were working off a large whiteboard in a sort of bull-pen where all the Residents and Attendings were gathered to take assignments, patient by patient, as the exam rooms were set up by the nursing staff.  After many patients, the board was cleared down to a patient who was listed as having come from Little-town-not-far-away.  The lead surgeon running the show rolled his eyes as he announced this and the whole room full of surgeons groaned, moaned, or had other disparaging reactions.  What was wrong with Little-town-not-far-away?  I had a chance later to ask the lead surgeon what was up with that reaction to Little-town-not-far-away.  He explained:  the town is an affluent small community about a hundred miles away.  They have fine hospitals, good surgeons, etc. but none of them are publically funded.  The only reason anyone would drive all the way up to the big city hospital was because they didn't  have insurance.  Ie the patient in question had no insurance and had come in to be seen and treated for free.  Which of course isn't free to the hospital.

Before I start analyzing that story for what they meant, not what they said, a little introduction in to how to analyze this kind of data is in order.  

It is data.  It may be qualitative, but it is data.  And specific 'atoms' of data we want are Points of Pain (or Delight) and specific statements of Goal.  By that, I mean what it is that they seek/desire/strive-for.  It's best if you can get it straight from a customer's mouth, but it's fair-game to infer these also, but only if you are confident of understanding what was really meant.  

Next month, I will go through this story and pull out a few of those atoms.  Please email me with any you see, and I will fold them in to the next post.

 

Royce Johnson

18 MAY 2012

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