AI Won’t Save Your Clinic (But This Might)

WEBVTT

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how tall are you six four yeah i'm

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not i'm not that tall although my

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brother's six three i'm five nine and we

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look vastly different in terms of height

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which is what you get on a live

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stream so we're live all right uh let's

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do this live from apta csm technology

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isn't coming to health care people it's

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already here we're doing a live stream

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from a conference of course technology is

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already here

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Our next guest lives at the intersection

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of physical therapy, data,

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and digital health,

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helping clinicians use technology without

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losing the human side of care.

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Welcome back to the show, Todd Norwood.

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Todd, welcome back to the show.

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Thanks for having me.

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Nice to meet you in person and be

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here together in person.

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We're also matching our shirts.

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We did seafoam green it up.

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You were from California.

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Are you still in California?

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I'm from California.

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Still in California.

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I live a mile from where I grew

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up.

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So you were on the show a few

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weeks ago.

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We did like a CSM prep episode because

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you're speaking tomorrow.

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Yep.

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There will be candy tomorrow morning.

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That was my first question.

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All right.

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If you're going to do a presentation on

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or near Valentine's Day,

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you have to have a little bit of

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the bribery.

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Give people who might be watching the live

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stream here,

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because we have had people walking around

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listening.

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It's still crazy.

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Technology.

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It's like watching the game while you're

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at the game.

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I don't know.

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We do it.

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Just give people just a quick reminder of

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what you're doing tomorrow.

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Yeah.

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We're going to be talking about how your

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skills as a PT translate readily to

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becoming a leader and how you can leverage

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those skills to lead and have a larger

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impact.

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You did that, like you said,

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you were in, like,

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give like your sort of like,

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how'd you go from a physical therapist to

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what you do now?

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The origin story, right?

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The superhero origin story.

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Right, so I was in a clinic,

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I was practicing orthopedic outpatient

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physical therapy.

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I met a patient,

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I work in Silicon Valley,

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so I met a patient who was in

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technology.

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You're bumping into tech people.

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That's right,

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you can't throw a stone and hit one,

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right?

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So I was out there,

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I met a patient,

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and we started talking about technology.

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He was quizzing me one night,

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he'd come in during his visit, like, hey,

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how'd you use technology differently?

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I was up for it.

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I love technology.

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So we started talking and that was Phizera

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ultimately.

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And then we built that up,

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grew a nationwide network of PTs doing

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telehealth before COVID,

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which is crazy when I think about it.

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And we got acquired by Omada back in

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twenty twenty and I'm still with Omada,

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still doing the same thing and and a

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few more things with what Omada does in

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the chronic disease space.

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I feel like every like third booth here

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now has some sort of technology component

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in it.

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But I want to talk to something more

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basic and emotion.

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When you were now leaving the clinic and

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going to this tech thing,

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did you feel impostery or like, hey,

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maybe I don't, do I belong here?

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Like, did you feel that at all?

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Oh my goodness, yes.

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And then, all right,

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so how'd you get over it?

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Or did you?

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How'd you get past it?

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Different question.

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It's so interesting because I think

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there's still moments today where I'll be

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in a room or I'll be in a

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venue and it's like, oh,

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do I belong here?

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Do I have the right to be here?

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And I think the thing about imposter

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syndrome is it comes and goes throughout

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your career.

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You have moments where you're like, yes,

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I earn my right to be here, right?

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I am the expert in the room and

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I own that.

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And sometimes as you grow, right,

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as you develop as a professional,

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you're going to be in rooms where you

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may question things

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Do I belong here?

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And I think for me,

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it's been an opportunity to look back on

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past experience.

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And I had a lot of experience as

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an athlete growing up and going through

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different ranks.

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And each time you got to that new

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level, it's like, oh, I was pretty good,

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right?

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I was a big fish in a small

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pond before,

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but this is a much bigger pond.

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And now am I a small fish here?

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And so I had that background.

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And I think that helped me a lot

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going through this experience.

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You're talking about that sort of at a

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macro level.

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You're describing a day for me.

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Like, nine a.m., I am a badass.

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By eleven, I'm like,

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I don't know what I'm doing.

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An hour later, I'm pretty okay.

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By three o'clock, I'm all right again.

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And hearing people say that makes me at

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least feel like I'm not the only crazy

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guy in the room.

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No, I mean, I think that's true.

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That can happen within a day, for sure.

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For me as well.

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Rollercoaster.

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Yeah,

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I'll have those days where that first

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meeting, like, I am the man.

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I am the guy.

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And then later in the day...

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You get knocked out a few pegs by

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yourself.

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Am I the guy?

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Am I the guy?

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Should I even be on the team anymore?

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Let's get into some tech then.

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Why is good data foundational for modern

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healthcare?

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But before you answer that,

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explain to me what the difference is

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between good and bad data is.

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You gotta be able to trust your data.

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So for me,

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that's the difference between good and

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bad.

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Is it reliable?

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So I think,

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let's take something in PT that's common.

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Range of motion.

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We measure range of motion.

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many of us eyeball it and then what's

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the plus or minus even if we're doing

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it right and if we do with a

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goniometer and we write it down do we

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do we write if we did a goniometer

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do we write down to the degree or

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do we do round to five or ten

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this is where you go from having

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potentially good and precise data right

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having it to the degree or if you

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were in a biomechanics lab right it's to

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the degree it's the tenth of the degree

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even two in the clinic

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It's one-thirty-five, I think.

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Ish?

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Maybe.

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You can't really put ish into a

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spreadsheet.

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No,

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and AI is not going to like ish.

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Yeah, that's true.

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If AI is processing the data,

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it's not going to do okay with ish.

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It needs to be precise.

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Well,

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it'll handle the ish and you'll get

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garbage on the output.

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Garbage in, garbage out, right?

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That's right.

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That's the idea.

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That's right.

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So why is that so important?

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So if we're moving into this world,

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maybe as PTs we're moving into this world,

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the world's moved there already with AI

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and big data.

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If the data's not accurate,

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then the interpretation and the outputs

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are not gonna be accurate.

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And I think it's interesting from a

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patient and a care perspective is like

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either you get an ineffective

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intervention,

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an ineffective recommendation,

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or potentially a harmful recommendation if

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you don't have sound foundational data.

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And that's my fear.

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We have a lot of data in PT.

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one we may not all document uniformly and

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so that's a whole data cleansing issue we

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got to make sure that it's all uniform

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and then do we all agree that one

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thirty five is one thirty five or is

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your you know like your one thirty five

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is one thirty two mine's one forty the

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next person is one thirty

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That makes it challenging.

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It makes it really challenging.

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Especially when you get to start,

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when you start to use,

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when things get larger, when, you know,

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more and more clinicians and larger and

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larger.

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That's right.

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So can you imagine if you took data

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from clinician, let's go five,

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fifty outpatient clinics in fifty

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different states with fifty different

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clinicians.

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It's a lot.

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It's a lot of variability, potentially.

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Yeah.

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So what would you say to people?

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There's still a few people who, not brag,

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but say,

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I have not touched artificial intelligence

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yet.

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What would you say to that person?

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This isn't going,

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it's not going to infect your brain.

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It's not going to change how you do

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things.

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What would you say to someone who's still

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hesitant about leaning into,

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maybe not even just AI,

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but technology in a clinical setting?

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Well, I mean, do you have an iPhone?

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Because you've touched... Correct.

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If you have a mobile device,

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a smart device now,

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you've interacted with AI.

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It may not be the chat GPT or

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the perplexity or clot or the newer models

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that are out there,

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but you've certainly interacted with AI in

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some way, shape, or form.

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So I think there's that.

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I think there's this...

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threat around AI.

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I don't know,

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there's this belief that we're gonna get

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the Terminator,

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and AI's gonna rule the world.

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Okay, that's scary.

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I think that's a low probability,

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at least in the near term,

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of that happening.

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And then there's the,

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I think it feels comfortable AI,

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like our phones.

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We use these devices.

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Whether or not you've used them in the

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clinic yet,

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I don't know if we're there where I'm

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thinking, gosh,

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you're really behind if you haven't.

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I think we have some progress.

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I think all the adopters are there,

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but the middle of the bell curve isn't.

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Sure.

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Maybe.

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And I think that's maybe where we were

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with telehealth, right?

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Like COVID was a fire starter for that.

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It was like pouring fuel on the fire

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on telehealth.

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So I'm not concerned if you're like,

00:08:02.125 --> 00:08:02.845
I haven't used it.

00:08:02.944 --> 00:08:04.326
I'm concerned if you're saying...

00:08:05.322 --> 00:08:06.762
I'm not ever interested in using it.

00:08:06.802 --> 00:08:07.103
I won't.

00:08:07.322 --> 00:08:08.783
I won't use this technology.

00:08:09.485 --> 00:08:11.565
I think the clinic owners, of course,

00:08:11.646 --> 00:08:13.067
were the first people who were approached

00:08:13.108 --> 00:08:16.750
by everybody who was selling a scribe two,

00:08:16.769 --> 00:08:17.531
three years ago.

00:08:17.971 --> 00:08:19.791
And the smart ones were being good

00:08:19.812 --> 00:08:21.072
clinicians and saying, like, show me,

00:08:21.213 --> 00:08:22.033
show me.

00:08:22.113 --> 00:08:22.713
Don't give me the,

00:08:23.435 --> 00:08:24.274
I don't want the brochure.

00:08:24.615 --> 00:08:25.435
I want you to show me on your

00:08:25.475 --> 00:08:26.216
screen right now.

00:08:26.576 --> 00:08:27.997
And then show me ten people who've done

00:08:28.017 --> 00:08:28.298
it already.

00:08:28.358 --> 00:08:30.059
And if you were selling that three years

00:08:30.079 --> 00:08:31.120
ago, you're like, I don't have ten people.

00:08:31.379 --> 00:08:33.302
You're the first people I'm calling.

00:08:33.562 --> 00:08:34.883
But now we're sort of past that.

00:08:35.679 --> 00:08:35.980
Yep.

00:08:36.039 --> 00:08:38.202
And I think as a business owner,

00:08:38.602 --> 00:08:39.384
what I want to see is,

00:08:39.563 --> 00:08:40.063
does it work?

00:08:40.104 --> 00:08:42.145
So can I do a mock case right

00:08:42.187 --> 00:08:42.366
now?

00:08:43.368 --> 00:08:44.369
Don't show me the fancy demo.

00:08:44.989 --> 00:08:45.389
That's cool.

00:08:45.429 --> 00:08:46.270
We can polish the demo.

00:08:46.650 --> 00:08:49.614
But let's do a mock case right now.

00:08:50.375 --> 00:08:51.975
And let me take your subjective and your

00:08:52.015 --> 00:08:53.498
history on a condition.

00:08:53.778 --> 00:08:54.879
And let's see what happens.

00:08:55.440 --> 00:08:56.760
And does it do what I expect it

00:08:56.841 --> 00:08:57.181
to do?

00:08:57.280 --> 00:08:57.501
Right.

00:08:57.741 --> 00:08:59.123
I was working for a small tech company.

00:08:59.884 --> 00:09:01.065
as sort of a consultant for a little

00:09:01.085 --> 00:09:03.206
while, and I was gonna shock you.

00:09:03.547 --> 00:09:04.768
When you go to conferences,

00:09:04.788 --> 00:09:05.489
all of a sudden you wind up in

00:09:05.528 --> 00:09:07.230
an Uber with three people you don't know

00:09:07.269 --> 00:09:08.010
and two people you do.

00:09:08.471 --> 00:09:09.432
And someone was like,

00:09:09.511 --> 00:09:10.513
who are you and what do you do?

00:09:10.533 --> 00:09:10.832
I'm like,

00:09:10.852 --> 00:09:12.114
I'm doing this thing with a tech company.

00:09:12.614 --> 00:09:13.115
And they're like, well,

00:09:13.134 --> 00:09:13.855
you should give me a demo.

00:09:13.934 --> 00:09:14.956
And I was like, great,

00:09:15.395 --> 00:09:16.096
let's do it right now.

00:09:16.177 --> 00:09:16.697
And they're like, well,

00:09:16.717 --> 00:09:18.099
there's a lot of loud noise in the

00:09:18.178 --> 00:09:18.379
Uber.

00:09:18.418 --> 00:09:19.058
I go, great,

00:09:19.078 --> 00:09:20.220
there's a lot of loud noise in your

00:09:20.240 --> 00:09:20.701
clinic.

00:09:20.821 --> 00:09:21.861
If I can't do it at the back

00:09:21.902 --> 00:09:23.863
of the Uber with my knees in my

00:09:23.923 --> 00:09:24.663
chest, and we did.

00:09:24.783 --> 00:09:26.144
We did a subjective evaluation right

00:09:26.164 --> 00:09:26.785
there, and I said,

00:09:27.446 --> 00:09:28.586
I even threw in some curveballs.

00:09:28.626 --> 00:09:28.886
I was like,

00:09:28.927 --> 00:09:30.648
what was your last movie you saw or

00:09:30.687 --> 00:09:32.528
last concert or like how old are your

00:09:32.548 --> 00:09:33.068
kids?

00:09:33.109 --> 00:09:33.629
And he was like,

00:09:33.708 --> 00:09:34.509
why are you asking me this?

00:09:34.830 --> 00:09:35.750
And when I showed him the output,

00:09:35.791 --> 00:09:36.831
he was like, I said,

00:09:37.370 --> 00:09:38.611
where's the thing about the Garth Brooks

00:09:38.631 --> 00:09:40.153
concert and your nine year old in the

00:09:40.192 --> 00:09:40.452
movie?

00:09:40.472 --> 00:09:41.253
And he's like, oh, it's not there.

00:09:41.274 --> 00:09:41.974
I go, right.

00:09:41.994 --> 00:09:42.573
That's right.

00:09:42.614 --> 00:09:43.774
Because it has to filter that out.

00:09:44.034 --> 00:09:44.955
It's not clinically relevant.

00:09:45.176 --> 00:09:45.535
Correct.

00:09:45.635 --> 00:09:46.475
And it understood that.

00:09:46.515 --> 00:09:48.216
But their clinic owners are right for

00:09:48.317 --> 00:09:48.657
showing.

00:09:48.717 --> 00:09:49.118
Show me.

00:09:49.337 --> 00:09:50.057
I want you to be.

00:09:50.437 --> 00:09:51.359
I need to beat on this thing.

00:09:51.519 --> 00:09:52.278
Yep, absolutely.

00:09:52.339 --> 00:09:53.639
I think that's true of any technology.

00:09:54.000 --> 00:09:55.580
Any new technology that you're going to

00:09:55.620 --> 00:09:56.340
adopt in your clinic,

00:09:56.360 --> 00:09:57.799
whether it's AI based or anything else,

00:09:58.341 --> 00:09:59.100
you've got to test it.

00:09:59.140 --> 00:10:00.520
You've got to push through its paces.

00:10:00.660 --> 00:10:01.181
Absolutely.

00:10:01.520 --> 00:10:02.581
Yeah.

00:10:02.721 --> 00:10:04.062
What are some of the things that get

00:10:04.101 --> 00:10:08.043
people past that skeptical phase?

00:10:08.302 --> 00:10:08.482
I mean,

00:10:08.503 --> 00:10:09.682
this would be the million dollar question.

00:10:09.702 --> 00:10:10.243
Just how many?

00:10:10.302 --> 00:10:11.663
Because I'm going to get past that as

00:10:11.683 --> 00:10:13.024
fast as possible if I'm selling some of

00:10:13.043 --> 00:10:13.403
that stuff.

00:10:13.629 --> 00:10:13.989
For sure.

00:10:14.490 --> 00:10:15.429
I think you got to see it work.

00:10:15.590 --> 00:10:16.551
That's a big piece of it.

00:10:16.791 --> 00:10:18.172
And you've got to see it work in

00:10:18.211 --> 00:10:20.433
that case where you as a skeptic don't

00:10:20.453 --> 00:10:21.313
believe it's going to work.

00:10:21.955 --> 00:10:22.115
Right.

00:10:22.154 --> 00:10:23.296
So if you're like, oh, well,

00:10:23.355 --> 00:10:24.777
I can't do it in a noisy clinic.

00:10:24.976 --> 00:10:27.097
And then you show me in the middle

00:10:27.138 --> 00:10:27.798
of the exhibit hall.

00:10:27.839 --> 00:10:28.318
It's noisy.

00:10:28.339 --> 00:10:29.379
I'm like, OK.

00:10:29.559 --> 00:10:30.500
All right.

00:10:31.120 --> 00:10:32.221
Or, you know,

00:10:32.261 --> 00:10:33.302
it's not going to filter out if we

00:10:33.322 --> 00:10:35.543
have chit chat about the baseball game

00:10:35.783 --> 00:10:36.244
last week.

00:10:36.345 --> 00:10:36.565
Right.

00:10:37.205 --> 00:10:38.086
Oh, okay.

00:10:38.525 --> 00:10:39.927
Although I do think it's interesting as a

00:10:39.966 --> 00:10:40.466
clinician,

00:10:41.107 --> 00:10:43.028
some of those things I would always jot

00:10:43.068 --> 00:10:43.830
down somewhere, right?

00:10:43.850 --> 00:10:44.190
Like, oh,

00:10:44.230 --> 00:10:46.350
you have a nine-year-old daughter that

00:10:46.370 --> 00:10:46.932
likes softball.

00:10:46.971 --> 00:10:47.491
Ah, good point.

00:10:47.792 --> 00:10:49.452
Because those are good social interaction

00:10:49.493 --> 00:10:52.355
pieces for us to build that relationship

00:10:52.375 --> 00:10:53.035
with the patient.

00:10:53.456 --> 00:10:55.076
And so it's an interesting balance, right?

00:10:55.096 --> 00:10:57.018
If you're letting the AI do too much

00:10:57.057 --> 00:10:58.458
and it's filtering out too much of that.

00:10:58.499 --> 00:10:58.820
Yeah, yeah.

00:10:59.500 --> 00:11:01.100
Last thing I'll ask, roles.

00:11:01.120 --> 00:11:02.922
You stepped into a technology role,

00:11:03.201 --> 00:11:04.503
maybe not on purpose,

00:11:04.802 --> 00:11:06.043
but one sort of appeared.

00:11:06.724 --> 00:11:08.085
What would you tell, you said earlier,

00:11:08.245 --> 00:11:10.505
you probably as a clinician have the

00:11:10.566 --> 00:11:11.386
skills already.

00:11:11.746 --> 00:11:14.087
If someone, and I'm in Facebook groups,

00:11:14.347 --> 00:11:15.129
for better or for worse,

00:11:15.168 --> 00:11:16.529
the comments section of Facebook groups

00:11:16.570 --> 00:11:17.049
are interesting.

00:11:17.470 --> 00:11:19.331
But people are asking those questions,

00:11:19.350 --> 00:11:21.131
like, hey, I'm thinking about, or,

00:11:21.572 --> 00:11:23.153
and it is nice to see people who've

00:11:23.253 --> 00:11:24.572
stepped into those roles say,

00:11:24.732 --> 00:11:25.653
I will have a phone call with you.

00:11:26.073 --> 00:11:26.953
I will show you what I did,

00:11:26.995 --> 00:11:27.975
I don't know if that's gonna be relevant

00:11:27.995 --> 00:11:28.294
for you,

00:11:28.554 --> 00:11:31.096
but what would your advice or suggestion

00:11:31.135 --> 00:11:31.976
be for someone who's like,

00:11:32.376 --> 00:11:34.538
I do like this, this is fast-paced,

00:11:34.557 --> 00:11:36.178
and I can see working in technology,

00:11:36.219 --> 00:11:38.120
instead of helping one person at a time,

00:11:38.179 --> 00:11:39.799
I could help a thousand people at a

00:11:39.919 --> 00:11:40.061
time.

00:11:40.120 --> 00:11:42.221
What would your step one or two and

00:11:42.261 --> 00:11:42.642
a half be?

00:11:43.772 --> 00:11:44.852
I think you have to understand what the

00:11:44.952 --> 00:11:46.975
role is that you may want to pursue

00:11:47.615 --> 00:11:50.017
and try to understand what that looks

00:11:50.076 --> 00:11:50.256
like.

00:11:50.277 --> 00:11:51.498
Because I have a lot of folks reach

00:11:51.518 --> 00:11:52.038
out, it's like, oh,

00:11:52.078 --> 00:11:53.178
can I do customer success?

00:11:53.360 --> 00:11:54.581
Yeah, you could.

00:11:55.160 --> 00:11:56.761
Do you understand what customer success

00:11:57.123 --> 00:11:57.482
means?

00:11:57.503 --> 00:11:58.943
What's entailed in the day to day?

00:11:59.804 --> 00:12:00.784
Sounds like customer service to me.

00:12:00.804 --> 00:12:01.966
Well, yes and.

00:12:02.366 --> 00:12:04.008
So do your research,

00:12:04.048 --> 00:12:05.729
I think would be my first recommendation.

00:12:06.350 --> 00:12:08.030
Understand what it is that you're looking

00:12:08.270 --> 00:12:08.431
at.

00:12:09.331 --> 00:12:11.793
mine was sort of a happened by chance

00:12:11.874 --> 00:12:14.275
and i had the right interaction that

00:12:14.316 --> 00:12:15.856
person was probably sizing you up though

00:12:16.158 --> 00:12:17.639
that's right that's right and i think it's

00:12:17.798 --> 00:12:18.919
it's so interesting that's the thing that

00:12:19.379 --> 00:12:21.841
in retrospect i recognized at first i was

00:12:21.861 --> 00:12:23.582
like oh gee i had i had ready

00:12:23.744 --> 00:12:25.605
i had great fortune to meet this person

00:12:25.644 --> 00:12:27.206
and like yes it's true there's some luck

00:12:27.265 --> 00:12:29.408
involved right but he wouldn't have called

00:12:29.447 --> 00:12:31.629
me back had i not answered the questions

00:12:31.649 --> 00:12:32.910
in the right way and put the pieces

00:12:32.951 --> 00:12:33.551
together so so

00:12:34.451 --> 00:12:35.572
Yes, luck and.

00:12:35.952 --> 00:12:37.033
You have to have the skill and the

00:12:37.133 --> 00:12:37.613
knowledge.

00:12:38.192 --> 00:12:38.773
Ask yourself,

00:12:38.913 --> 00:12:40.754
what are your competencies from a

00:12:40.833 --> 00:12:41.793
technical standpoint?

00:12:42.114 --> 00:12:42.835
What do you know?

00:12:42.855 --> 00:12:46.176
I did a prompt once yesterday.

00:12:46.895 --> 00:12:47.816
Okay, that's good.

00:12:48.535 --> 00:12:48.996
Keep digging.

00:12:49.017 --> 00:12:49.336
Good start.

00:12:49.677 --> 00:12:51.736
Also, AI is a fantastic tool.

00:12:51.917 --> 00:12:52.998
I do it all the time now.

00:12:53.658 --> 00:12:55.219
But ask it, right?

00:12:55.239 --> 00:12:57.399
I'm a physical therapist with five years

00:12:57.460 --> 00:12:58.961
of clinical experience in outpatient

00:12:59.000 --> 00:12:59.581
orthopedics.

00:12:59.740 --> 00:13:01.442
I'm looking to transition into a

00:13:01.481 --> 00:13:04.202
technology role in customer success.

00:13:04.783 --> 00:13:06.644
What skills should I develop?

00:13:07.083 --> 00:13:08.764
What competencies may I already have?

00:13:08.965 --> 00:13:09.865
I go one step ahead.

00:13:09.985 --> 00:13:11.586
I download my resume from LinkedIn.

00:13:11.947 --> 00:13:13.407
I download the job description and I go,

00:13:13.506 --> 00:13:14.087
match me,

00:13:14.368 --> 00:13:16.629
give me the five main skills a great

00:13:16.708 --> 00:13:18.429
this could have and rank me from zero

00:13:18.450 --> 00:13:19.250
to a hundred on those.

00:13:19.690 --> 00:13:20.730
And then help me understand if I should

00:13:20.750 --> 00:13:20.951
apply.

00:13:20.971 --> 00:13:21.130
Yeah.

00:13:22.178 --> 00:13:22.639
Use the tool.

00:13:22.980 --> 00:13:23.320
That's right.

00:13:24.179 --> 00:13:25.000
And that's the thing, I think.

00:13:25.160 --> 00:13:26.782
AI is an incredible tool.

00:13:27.923 --> 00:13:29.283
I think we're all still learning how to

00:13:29.383 --> 00:13:29.964
use it best.

00:13:30.004 --> 00:13:30.683
Pilot it, right.

00:13:31.345 --> 00:13:33.826
You're handed an infinite information

00:13:33.865 --> 00:13:34.186
machine.

00:13:35.006 --> 00:13:36.768
We're not all using the same inputs.

00:13:37.268 --> 00:13:37.908
And somehow you're like,

00:13:37.928 --> 00:13:38.869
how did you get that great output?

00:13:39.149 --> 00:13:39.850
Just the same as data.

00:13:40.029 --> 00:13:40.809
I gave it a better input.

00:13:40.889 --> 00:13:41.211
That's right.

00:13:41.350 --> 00:13:42.191
You gave it a better prompt.

00:13:42.270 --> 00:13:42.971
And sometimes...

00:13:43.692 --> 00:13:45.774
you can ask it how to prompt for

00:13:45.815 --> 00:13:46.135
something.

00:13:46.176 --> 00:13:48.178
I think that's the hack that a lot

00:13:48.259 --> 00:13:51.043
of newcomers don't understand or don't

00:13:51.082 --> 00:13:51.344
see.

00:13:51.823 --> 00:13:53.447
It knows how to get the best out

00:13:53.466 --> 00:13:55.309
of itself, so ask it.

00:13:56.211 --> 00:13:57.472
I use that a lot for images.

00:13:58.250 --> 00:13:59.451
Because if I liked a way that I

00:13:59.572 --> 00:14:00.951
accidentally got to an output, I'm like,

00:14:00.991 --> 00:14:02.432
whoa, I like that.

00:14:02.692 --> 00:14:03.373
I want that more.

00:14:03.393 --> 00:14:04.474
But I don't want to have to sit

00:14:04.494 --> 00:14:05.193
here and explain it to you.

00:14:05.214 --> 00:14:05.653
So I'm like,

00:14:06.094 --> 00:14:08.034
give me the prompt that will get that

00:14:08.075 --> 00:14:09.316
type of image in the future.

00:14:09.556 --> 00:14:12.636
And I save that in Microsoft Notes.

00:14:13.076 --> 00:14:14.038
And then I come back to that and

00:14:14.057 --> 00:14:15.618
I have that as my image prompt.

00:14:15.758 --> 00:14:16.558
That's right.

00:14:16.578 --> 00:14:17.119
That's your default.

00:14:17.139 --> 00:14:18.538
And then you plug in the specifics of

00:14:18.559 --> 00:14:19.080
what you want.

00:14:19.519 --> 00:14:19.779
Yes.

00:14:19.980 --> 00:14:20.259
Perfect.

00:14:20.279 --> 00:14:21.740
All right.

00:14:21.780 --> 00:14:22.780
Last thing we do want to show is

00:14:22.801 --> 00:14:23.861
called the parting shot.

00:14:24.741 --> 00:14:26.123
Mic drop moment, soapbox statement.

00:14:26.143 --> 00:14:26.543
What do you got?

00:14:27.226 --> 00:14:27.427
You know,

00:14:28.106 --> 00:14:28.866
I think I told you on the last

00:14:28.886 --> 00:14:29.467
time I was here,

00:14:29.488 --> 00:14:30.508
I'm going to go with the same thing.

00:14:30.548 --> 00:14:32.327
I think it's so important and it's Warren

00:14:32.347 --> 00:14:33.369
Buffett quote, but he says,

00:14:33.408 --> 00:14:34.948
invest in yourself, right?

00:14:34.969 --> 00:14:36.889
Like if you invest in the stock market,

00:14:36.929 --> 00:14:38.009
as he did very successful,

00:14:38.570 --> 00:14:40.451
there's going to be some taxes you can

00:14:40.490 --> 00:14:40.770
lose.

00:14:41.091 --> 00:14:42.250
But if you invest in yourself,

00:14:42.530 --> 00:14:43.611
you take courses, you learn,

00:14:43.631 --> 00:14:44.412
you gain knowledge.

00:14:44.751 --> 00:14:46.912
There's no tax on that and you can't

00:14:46.951 --> 00:14:47.212
lose it.

00:14:47.352 --> 00:14:48.332
Nobody can take that away from you.

00:14:48.373 --> 00:14:48.712
So good.

00:14:49.052 --> 00:14:50.095
So invest in yourself,

00:14:50.134 --> 00:14:52.298
whether it's courses or reading,

00:14:52.318 --> 00:14:52.940
whatever it may be.

00:14:53.380 --> 00:14:53.761
Invest,

00:14:53.961 --> 00:14:55.404
make yourself better than you were

00:14:55.585 --> 00:14:55.946
yesterday.

00:14:56.105 --> 00:14:56.667
Todd, appreciate it.

00:14:56.988 --> 00:14:57.327
Absolutely.

00:14:57.347 --> 00:14:57.688
Thank you, Jimmy.

00:14:57.708 --> 00:14:58.090
Good luck tomorrow.

00:14:58.110 --> 00:14:59.692
Make sure you hand out the candy here

00:14:59.773 --> 00:15:00.774
live from CSM in