How to train employees to have difficult conversations | Tamekia MizLadi Smith

113,627 views

2018-08-20 ・ TED


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How to train employees to have difficult conversations | Tamekia MizLadi Smith

113,627 views ・ 2018-08-20

TED


Please double-click on the English subtitles below to play the video.

00:12
We live in a world where the collection of data
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is happening 24 hours a day, seven days a week,
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365 days a year.
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This data is usually collected by what we call a front-desk specialist now.
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These are the retail clerks at your favorite department stores,
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the cashiers at the grocery stores,
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the registration specialists at the hospital
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and even the person that sold you your last movie ticket.
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They ask discreet questions, like: "May I please have your zip code?"
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Or, "Would you like to use your savings card today?"
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All of which gives us data.
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However, the conversation becomes a little bit more complex
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when the more difficult questions need to be asked.
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Let me tell you a story, see.
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Once upon a time, there was a woman named Miss Margaret.
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Miss Margaret had been a front-desk specialist
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for almost 20 years.
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And in all that time, she has never, and I do mean never,
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had to ask a patient their gender, race or ethnicity.
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Because, see, now Miss Margaret has the ability to just look at you.
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Uh-huh.
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And she can tell if you are a boy or a girl,
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black or white, American or non-American.
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And in her mind, those were the only categories.
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So imagine that grave day,
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when her sassy supervisor invited her to this "change everything" meeting
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and told her that would have to ask each and every last one of her patients
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to self-identify.
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She gave her six genders, eight races and over 100 ethnicities.
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Well, now, Miss Margaret was appalled.
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I mean, highly offended.
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So much so that she marched down to that human-resource department
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to see if she was eligible for an early retirement.
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And she ended her rant by saying
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that her sassy supervisor invited her to this "change everything" meeting
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and didn't, didn't, even, even
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bring, bring food, food, food, food.
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(Laughter)
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(Applause) (Cheers)
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You know you've got to bring food to these meetings.
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(Laughter)
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Anyway.
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(Laughter)
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Now, that was an example of a healthcare setting,
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but of course, all businesses collect some form of data.
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True story: I was going to wire some money.
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And the customer service representative asked me
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if I was born in the United States.
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Now, I hesitated to answer her question,
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and before she even realized why I hesitated,
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she began to throw the company she worked for under the bus.
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She said, "Girl, I know it's stupid, but they makin' us ask this question."
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(Laughter)
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Because of the way she presented it to me,
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I was like, "Girl, why?
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Why they makin' you ask this question?
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Is they deportin' people?"
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(Laughter)
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But then I had to turn on the other side of me,
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the more professional speaker-poet side of me.
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The one that understood that there were little Miss Margarets all over the place.
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People who were good people, maybe even good employees,
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but lacked the ability to ask their questions properly
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and unfortunately, that made her look bad,
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but the worst, that made the business look even worse
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than how she was looking.
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Because she had no idea who I was.
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I mean, I literally could have been a woman who was scheduled to do a TED Talk
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and would use her as an example.
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Imagine that.
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(Applause)
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And unfortunately,
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what happens is people would decline to answer the questions,
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because they feel like you would use the information
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to discriminate against them,
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all because of how you presented the information.
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And at that point, we get bad data.
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And everybody knows what bad data does.
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Bad data costs you time, it costs you money
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and it costs you resources.
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Unfortunately, when you have bad data,
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it also costs you a lot more,
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because we have health disparities,
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and we have social determinants of health,
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and we have the infant mortality,
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all of which depends on the data that we collect,
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and if we have bad data, than we have those issues still.
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And we have underprivileged populations
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that remain unfortunate and underprivileged,
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because the data that we're using is either outdated,
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or is not good at all or we don't have anything at all.
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Now, wouldn't it be amazing if people like Miss Margaret
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and the customer-service representative at the wiring place
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were graced to collect data with compassionate care?
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Can I explain to you what I mean by "graced?"
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I wrote an acrostic poem.
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G: Getting the front desk specialist involved and letting them know
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R: the Relevance of their role as they become
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A: Accountable for the accuracy of data while implementing
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C: Compassionate care within all encounters by becoming
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E: Equipped with the education needed to inform people
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of why data collection is so important.
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(Applause)
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Now, I'm an artist.
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And so what happens with me
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is that when I create something artistically,
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the trainer in me is awakened as well.
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So what I did was, I began to develop that acrostic poem into a full training
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entitled "I'm G.R.A.C.E.D."
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Because I remember, being the front-desk specialist,
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and when I went to the office of equity to start working,
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I was like, "Is that why they asked us to ask that question?"
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It all became a bright light to me,
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and I realized that I asked people and I told people about --
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I called them by the wrong gender, I called them by the wrong race,
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I called them by the wrong ethnicity,
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and the environment became hostile,
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people was offended and I was frustrated because I was not graced.
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I remember my computerized training,
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and unfortunately, that training did not prepare me to deescalate a situation.
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It did not prepare me to have teachable moments when I had questions
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about asking the questions.
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I would look at the computer and say, "So, what do I do when this happens?"
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And the computer would say ...
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nothing, because a computer cannot talk back to you.
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(Laughter)
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So that's the importance of having someone there
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who was trained to teach you and tell you what you do
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in situations like that.
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So, when I created the "I'm G.R.A.C.E.D" training,
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I created it with that experience that I had in mind,
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but also that conviction that I had in mind.
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Because I wanted the instructional design of it
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to be a safe space for open dialogue for people.
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I wanted to talk about biases,
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the unconscious ones and the conscious ones,
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and what we do.
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Because now I know that when you engage people in the why,
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it challenges their perspective, and it changes their attitudes.
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Now I know that data that we have at the front desk
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translates into research that eliminates disparities and finds cures.
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Now I know that teaching people transitional change
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instead of shocking them into change
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is always a better way of implementing change.
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See, now I know people are more likely to share information
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when they are treated with respect by knowledgeable staff members.
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Now I know that you don't have to be a statistician
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to understand the power and the purpose of data,
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but you do have to treat people with respect and have compassionate care.
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Now I know that when you've been graced,
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it is your responsibility to empower somebody else.
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But most importantly, now I know
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that when teaching human beings
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to communicate with other human beings,
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it should be delivered by a human being.
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(Applause)
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So when y'all go to work
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and y'all schedule that "change everything" meeting --
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(Laughter)
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remember Miss Margaret.
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And don't forget the food, the food, the food, the food.
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Thank you.
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(Applause) (Cheers)
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Thank you.
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(Applause)
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