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Speaker 1: Mother Knows Death starring Nicole and Jemmy and Maria qk.

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Speaker 2: Hi, Everyone, Welcome The Mother Knows Death. On today's episode,

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Speaker 2: we have a special guest joining us for some news stories,

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Speaker 2: Amy Lochran, better known as The Good Nurse. We have

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Speaker 2: had Amy on Mother Knows Death back in November of

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Speaker 2: twenty twenty three, but for those of you who are unfamiliar,

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Speaker 2: Amy is a former registered nurse turned whistleblower and is

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Speaker 2: best known for her role in exposing one of the

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Speaker 2: most prolific serial killers in American medical history, Charles Cullen.

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Speaker 2: While working alongside him in the ICU, Amy discovered that

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Speaker 2: he was killing their patients. Then she teamed up with

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Speaker 2: law enforcement to help and his killing spray. Her story

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Speaker 2: was later portrayed in the Netflix film The Good Nurse,

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Speaker 2: where she was played by Jessica Chestain. I highly suggest

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Speaker 2: you going back to that episode and listening to her story. Hi, Amy,

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Speaker 2: Welcome The Mother Knows Death.

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Speaker 1: My favorite podcast is my favorite podcast. I listen to

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Speaker 1: you guy, Oh my God, like I just laugh and

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Speaker 1: laugh and laugh, and I know that it's gallows humor.

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Speaker 1: But you know, with my story, I have to have

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Speaker 1: gallows humor.

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Speaker 2: I know, I love you. We couldn't wait to have

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Speaker 2: you back either. Yeah, I know we were thinking that.

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Speaker 2: I was just like, we're gonna try to keep We

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Speaker 2: always try to find stories, especially if we have guests

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Speaker 2: that are a little bit lighter, because we do love

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Speaker 2: to laugh on this show. But unfortunately, it's just there's

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Speaker 2: a lot of other stuff going on too, So yeah,

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Speaker 2: you're going to keep it light though.

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Speaker 1: Yeah, I definitely like creepy shit, but it's like you

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Speaker 1: have to laugh at the absurdities that are happening out there. Yeah.

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Speaker 2: Really, And this first case that we're going to talk

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Speaker 2: about it, I mean, you have kids, so you could

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Speaker 2: totally relate to this story. So let's have Maria talk

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Speaker 2: about it and then we'll get into it.

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Speaker 3: All right, So to introduce this first case, this twelve

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Speaker 3: year old had snuck out of her house in the

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Speaker 3: middle of the night to meet her thirteen year old

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Speaker 3: friend to go subway surfing. And the most outrageous part

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Speaker 3: of this story is the next morning, her mother is

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Speaker 3: cooking or cooking breakfast, I guess, with her younger sister

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Speaker 3: and they're watching news coverage about two teens that had

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Speaker 3: died while subway surfing, and in the news coverage, the

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Speaker 3: other daughter sees her sister's purse and her skateboard and

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Speaker 3: says to her mother, I think that's my sister's and

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Speaker 3: they couldn't believe it at first, but it ended up

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Speaker 3: being coverage of the daughter's death.

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Speaker 2: What is happening now? I guess kids have done this

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Speaker 2: because Gabe tells me that this that he used to say,

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Speaker 2: sneak out in the after his mom went to bed.

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Speaker 2: It's just nothing I ever did.

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Speaker 1: Well at twelve years old. I mean, there has to

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Speaker 1: be something else going on, you know, when you're sneaking

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Speaker 1: out at twelve. I mean, my gosh, at twelve years old.

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Speaker 1: I you know, I had a very dysfunctional upbringing. I

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Speaker 1: didn't have to sneak out at twelve because I had

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Speaker 1: very little parenting. But my gut reaction when I read

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Speaker 1: this was, if she's doing this at twelve, there's some there.

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Speaker 1: There were already some serious things happening in her life.

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Speaker 3: That's an interesting take because I was thinking about that too,

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Speaker 3: And the mom waking up and not I don't know

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Speaker 3: something about it to me, doesn't seem right that she

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Speaker 3: woke up and didn't realize she wasn't in the house.

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Speaker 2: I thought she was staying at her dad's. No. Oh,

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Speaker 2: I don't think I read that.

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Speaker 1: I didn't. I didn't read that either, And just the

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Speaker 1: way that even if she was staying at the dad's house,

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Speaker 1: it's still like why, I don't know it just there

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Speaker 1: was something so off about the parenting aspect of it.

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Speaker 1: And the the other challenge I when I was reading

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Speaker 1: this was they they tried to make this out to

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Speaker 1: be something that was like a TikTok challenge or a

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Speaker 1: you know, something that they're seeing on social media. Definitely

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Speaker 1: not on TikTok. Like I actually tried to search it

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Speaker 1: after I read about it, and it won't even come up.

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Speaker 1: Like a subway saying we're train surfing, it won't come up.

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Speaker 1: It just comes up with a warning. So I don't

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Speaker 1: know if it was because of this case that that

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Speaker 1: has has happened, but subway surfing has always kind of

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Speaker 1: been a thing. It just seems to have gained popularity recently.

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Speaker 1: But with someone so young, you know, a twelve and

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Speaker 1: thirteen year old going out at three o'clock in the

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Speaker 1: morning to get on a subway, to get on top

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Speaker 1: of a train. Because what they do is climb up

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Speaker 1: and get on top of the train. Usually they would

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Speaker 1: be lying down and just looking at things passing by.

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Speaker 1: But the way that I read this is they actually

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Speaker 1: fell off and both of them had serious head injuries

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Speaker 1: that it was lenthorse drama to and so they had

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Speaker 1: irreversible brain damage. And that's that's really how how they died,

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Speaker 1: is that they must have been going so fast on

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Speaker 1: the train and they both slid off.

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Speaker 3: That was gonna be my question too, is how are

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Speaker 3: they even getting up there without other people noticing them?

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Speaker 3: Because we've been I feel like recently we covered a

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Speaker 3: story about adults that had done something like this, but to.

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Speaker 2: See more people died from it this year. Yeah, and

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Speaker 2: that's just people that other people aside from this, Like

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Speaker 2: it's a problem. Do you think it has to because

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Speaker 2: I feel like there's just this habitual one upping, you

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Speaker 2: know that kids just have to. Gabe and I always

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Speaker 2: talk about this, like when we were teenagers, it was

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Speaker 2: just like the most punk rock person like had all

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Speaker 2: these studs on their jacket and they had a mohawk

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Speaker 2: that was blue, and and now it's just like that

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Speaker 2: that's that's like normal now, right, so it's just like

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Speaker 2: pushing it and one upping it. So when Gabe was

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Speaker 2: a teenager in skateboard and it would be like, oh,

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Speaker 2: they would go into a park that was banned, that

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Speaker 2: was like the bad thing they were doing, you know.

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Speaker 2: And now it's like that's you're allowed to skateboard everywhere now,

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Speaker 2: so like this is the next one up thing, right.

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Speaker 3: Oh yeah, totally. I mean I was thinking, I guess

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Speaker 3: I can't believe this, but I was in high school

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Speaker 3: fifteen years ago at this point.

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Speaker 1: But even back.

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Speaker 3: Back in my day, like our our franks were planking,

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Speaker 3: which is you would just lay as straight as you

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Speaker 3: could on any surface, or you would go to McDonald's

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Speaker 3: and grab the ice cream cone upside down. You weren't

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Speaker 3: doing these weird dangerous things. I was.

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Speaker 1: Yeah, like I said, I had, I had a I

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Speaker 1: definitely had like kind of I mean, my parents probably

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Speaker 1: would have noticed that I snuck out, but I didn't

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Speaker 1: really have to. I was like already drinking alcohol at

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Speaker 1: like twelve and thirteen, so we were going out. I

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Speaker 1: can't even remember. I must have had some kind of

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Speaker 1: a curfew, but I don't remember having to worry about

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Speaker 1: it that much. So we were drinking at that time,

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Speaker 1: and I wonder, you know, toxicologically, what's going to come

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Speaker 1: through in the autopsy.

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Speaker 2: Yeah, that's actually an interesting point too. One other thing

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Speaker 2: is is that I believe that this girl that she

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Speaker 2: met up with she met on social media, so it

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Speaker 2: wasn't even a friend in real life. It was yeah,

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Speaker 2: which you're also just like, this is the thing, like

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Speaker 2: my twelve year old couldn't even at least I mean

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Speaker 2: she could totally leave in the middle of the night,

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Speaker 2: I suppose, but she couldn't even communicate with anyone overnight

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Speaker 2: because I have her iPad turns off at nine o'clock.

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Speaker 2: So because there's no reason that a child needs to

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Speaker 2: be on social media or a in the middle of

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Speaker 2: the night talking to people. It's just nothing good's gonna

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Speaker 2: come of it, right, So that's a huge part of it.

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Speaker 2: Is just like kids are on social media too young

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Speaker 2: and stuff too. But I mean this could have happened

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Speaker 2: with a real life friend too, I guess, yeah.

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Speaker 1: And I we always kind of go back to when

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Speaker 1: kids are this young, the parenting. I you know, I wonder,

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Speaker 1: just even from the way that the sister made this

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Speaker 1: statement that she's probably better off with God. God needed

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Speaker 1: her more. I don't remember how she phrased it, but

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Speaker 1: it was very much like something like, oh, well, you know,

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Speaker 1: this was kind of meant to be. And so I

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Speaker 1: just wonder, like culturally what was going on in the house,

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Speaker 1: and maybe they just we're not literate in what kids

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Speaker 1: are capable of accets saying on these devices. I you know,

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Speaker 1: it seemed like there may have been at least a

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Speaker 1: little bit of just them being very naive about what

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Speaker 1: a twelve year old can get into.

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Speaker 2: Yeah, and that's I mean, I see that because my

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Speaker 2: kid is literally twelve years old right now. So it's

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Speaker 2: you know, I see everything that's going on, and the kids,

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Speaker 2: some kids have a lot of access to everything. Ohthers,

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Speaker 2: don't you know. It's just and each kid could handle

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Speaker 2: everything different. And I don't really care what people do

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Speaker 2: with their kids. I'm just not doing it with mine.

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Speaker 1: You know.

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Speaker 2: It's just sad because kids just don't They just don't

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Speaker 2: realize it, and you think that. I mean a lot

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Speaker 2: of times kids want to do things, even if they

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Speaker 2: are dangerous, just to be the one that because I'm

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Speaker 2: sure lots of people do subbly surfing and survive it.

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Speaker 2: And they you know, that's what's risky about it. It's

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Speaker 2: like riding a motorcycle without your helmet or any you know.

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Speaker 2: There's just like all things that people do to be

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Speaker 2: a little bit on the edge.

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Speaker 1: Like that drugs, you know.

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Speaker 2: Yeah, I mean exactly, because.

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Speaker 1: You can die from these drugs. So I guess it's

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Speaker 1: their way of having that intense high of being scared.

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Speaker 1: And yeah, it's it's like a drug, all right.

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Speaker 2: So let's get get let's get into this next case

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Speaker 2: because this is such a cool story.

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Speaker 3: It is, and I have a lot of questions too,

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Speaker 3: So okay, Back in December of nineteen eighty nine, a

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Speaker 3: woman was found stabbed to death in an Arizona desert.

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Speaker 3: Two days later, these two baby girls, who were two

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Speaker 3: months old and fourteen months old, were found abandoned in

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Speaker 3: a park bathroom and put in foster care. So I

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Speaker 3: guess for years they had no idea these two cases

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Speaker 3: were connected at all. They weren't sure of the identity

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Speaker 3: of the woman for a while, they weren't sure of

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Speaker 3: the identity of the children. They kind of just showed up.

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Speaker 3: So all these years later, they ended up identifying the

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Speaker 3: mother through fingerprints because she had an arrest for shoplifting

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Speaker 3: back in the eighties, and then they ran further DNA

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Speaker 3: and ended up connecting the two because they found that

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Speaker 3: these two girls put in foster care were her daughters.

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Speaker 2: It's it's really it's really amazing. I guess my first

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Speaker 2: question was why did it take until twenty twenty two

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Speaker 2: for them to identify her fingerprint went with an arrest

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Speaker 2: from nineteen eighty nine, because that technology has been around

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Speaker 2: for a while. But that was a case that I

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Speaker 2: actually had. I wanted to ask one of my friends

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Speaker 2: in law enforcement, like, if there's a cold case, is

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Speaker 2: there any kind of system that's just constantly running prints

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Speaker 2: through to see if there's matches or does someone physically

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Speaker 2: have to go in the case and run it Because obviously,

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Speaker 2: if you have a bunch of cold cases, like you'll

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Speaker 2: work on it for a while and then you'll say, okay,

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Speaker 2: we have no additional leads, and then you put it

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Speaker 2: away for years and then pull it back out, whereas

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Speaker 2: if there was a system that was constantly trying to

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Speaker 2: make matches, that might be more helpful.

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Speaker 3: Well, remember at the Cheryl's Winding crime on Friday, when

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Speaker 3: Joe was going over his lecture. He was saying there

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Speaker 3: just in more recent years combining databases. I think it

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Speaker 3: was they ran it in a more local database and

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Speaker 3: now over time they've kind of become wider nets. So

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Speaker 3: if they rerun it, they are more likely to have

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Speaker 3: a hit.

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Speaker 1: And is it like on television where someone ends up

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Speaker 1: being part of a task force of looking into these

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Speaker 1: cold cases, Like they just kind of sit on a

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Speaker 1: shelf and then the cold case comes up on someone's

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Speaker 1: desk that is part of that that task force. So,

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Speaker 1: you know, it's it's hard to say because there's so

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Speaker 1: many active investigations that going back to a cold case,

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Speaker 1: I think that it would have to be distinctly someone

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Speaker 1: being assigned to it.

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Speaker 2: Yeah, And I mean, this is happening all over the place,

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Speaker 2: and this is what was really cool about. So we

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Speaker 2: went to this lecture last weekend and it was our

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Speaker 2: friend Cheryl McCollums. She actually has a cold case institute

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Speaker 2: that she works on cold cases all the time. There

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Speaker 2: and Cool Joe, who we love him and he's going

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Speaker 2: to be on our show soon within the next couple

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Speaker 2: of weeks. He was talking about how he had this

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Speaker 2: really cool lecture of how regular people who aren't in

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Speaker 2: law enforcement could help solve cold cases and just giving

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Speaker 2: advice about like how you could I mean, and sometimes

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Speaker 2: if you have a lot like an old school library

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Speaker 2: that doesn't have all of their files uploaded to a website,

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Speaker 2: like you actually have to go to the library, which

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Speaker 2: is just crazy to think that you have, you know

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Speaker 2: what I mean, like old school when we were kids,

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Speaker 2: even you Maria, when you were a kid, right, Like,

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Speaker 2: there wasn't there wasn't a ton of stuff online. So

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Speaker 2: but it's really interesting how this stuff all comes out.

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Speaker 2: And the girls knew that they were abandoned in a

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Speaker 2: rest stop or what was it a National Park restroom

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Speaker 2: or something.

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Speaker 3: It was that park bathroom. But I guess my question

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Speaker 3: here is that after this woman was identified her family,

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Speaker 3: the way the news article made a team was like

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Speaker 3: her family said, oh, yeah, she was last seen with

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Speaker 3: the two girls. For the two girls and this woman

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Speaker 3: not reported missing, and they did it put two and

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Speaker 3: two together. I'm just kind of confused about that part

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Speaker 3: about it.

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Speaker 1: I was so confused as well, like Okay, yes, this

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Speaker 1: one man goes missing, but the woman goes missing with

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Speaker 1: two children, Like yeah, why wasn't and yes, there wasn't

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Speaker 1: a connection between the two cases. However, where was this

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Speaker 1: woman's family, like this woman's friends, Like, why don't they

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Speaker 1: have been saying to law enforcement or saying, I don't know,

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Speaker 1: maybe she was just one of those people that would

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Speaker 1: just up and leave or but it's still was super

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Speaker 1: sketch thinking exactly this woman was missing for all this

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Speaker 1: time and then kind of like people said, oh, yeah,

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Speaker 1: she had two girls, like almost like it was an

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Speaker 1: off mean comment, Oh yeah, she did have two girls.

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Speaker 2: Well that's happened sometimes, Like even that recent case of

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Speaker 2: that Emmanuel Harrow baby that was missing. I don't know

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Speaker 2: if you heard about that, Remember the mom said that

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Speaker 2: it was kidnapped. Suck. You're just like then they're saying, oh,

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Speaker 2: the baby might have been killed months ago, and you're

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Speaker 2: just you think about your own life and you're just like,

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Speaker 2: you know, all these people that have babies and children,

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Speaker 2: who who is associating with people that are not reporting

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Speaker 2: if a child isn't seen for a long period of time,

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Speaker 2: it's just so it's just really something I can't even fathom,

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Speaker 2: just because of my particular life.

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Speaker 3: I guess when you just reel it back, you look

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Speaker 3: at this case as two missing babies were found thirty

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Speaker 3: six years later alive. This is a story we don't

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Speaker 3: ever hear. But were they ever really reported missing?

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Speaker 2: So technically they're missing, right, But are they considering missing persons?

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Speaker 2: I don't think so because it looks like they found

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Speaker 2: the kids, but they they didn't say that it was

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Speaker 2: connected to a missing children case. They just I mean,

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Speaker 2: this is nineteen eighty six in Arizona desert. Might have

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Speaker 2: been a little bit different as far as they're just like, well,

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Speaker 2: we found them for them, so you know they're good.

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Speaker 3: Well I assumed it was gonna be a Maricopa county

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Speaker 3: where seemingly all the weirdest crimes happened in the country,

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Speaker 3: but I don't think it was there. But anything we

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Speaker 3: cover in Arizona is typically the most bizarre crime we've

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Speaker 3: ever heard about.

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Speaker 2: Because I mean, that.

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Speaker 1: Says what about the women? Now they know they were

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Speaker 1: part of all of this. There wasn't like a follow

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Speaker 1: up about the actual women, like aren't they wanting to

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Speaker 1: talk about what? It just sort of like left off.

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Speaker 1: It was like, oh, yeah, we found out who they are.

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Speaker 1: I think it only gave that The one article I

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Speaker 1: read read about this, it only I think gave the

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Speaker 1: name of one of the women. But I just thought

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Speaker 1: perhaps that there would be more curiosity with this of

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Speaker 1: you know, how did they grow up? Who actually adopted them?

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Speaker 1: Like there it just felt like it just cut off like,

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Speaker 1: oh yeah, we found them.

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Speaker 3: Oh absolutely, and it just ended with basically, they grew

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Speaker 3: up in a loving home and their case is a

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Speaker 3: cold case too, like nobody wants to know where these

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Speaker 3: these two kids came from.

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Speaker 2: It's it's so bizarre.

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Speaker 1: They're happy, they're good, they have they have their.

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Speaker 3: Because it wasn't like they were dropped off at the

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Speaker 3: fire station, like don't ask, don't tell type of thing,

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Speaker 3: you know that we're always talking about. They were left

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Speaker 3: in a woman's bathroom in a park. And then as

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Speaker 3: far as the case goes with their mom that was murdered.

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Speaker 3: They said this was a huge advancement in the case,

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Speaker 3: but they still have no idea who killed her, so

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Speaker 3: they still need to find that too.

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Speaker 1: Yeah.

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Speaker 2: Yeah, only one.

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Speaker 3: Witness has come forward to say they saw the mother

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Speaker 3: with two men in the park. I guess the day

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Speaker 3: of her disappearance.

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Speaker 1: So I'm too. Wasn't there a woman too? Yeah? Okay, so.

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Speaker 3: That's interesting because I was gonna say the men intentionally

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Speaker 3: put the babies in a women's bathroom knowing they would

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Speaker 3: be found there eventually.

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Speaker 1: Yeah, I think there was that there was she was

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Speaker 1: seeing with a woman. Interesting.

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Speaker 2: Yeah, that's just it's it is just like such, it's

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Speaker 2: such a bizarre case because all of us are just like,

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Speaker 2: how did you girls grow up? They clearly witness the murder.

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Speaker 2: Likely there's a there's a murder in a town, and

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Speaker 2: so it's like dead lady two days later. Kids never

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Speaker 2: put two and two together that they could have been.

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Speaker 3: Like, that's what I'm wondering. I just feel like I'm

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Speaker 3: leaving this.

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Speaker 2: With a lot of questions, like the dead ladies, like whatever.

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Speaker 2: There's dead people all the time, but it's not very

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Speaker 2: common to find two children that just like came from nowhere, right.

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Speaker 1: It just seems like it would have been like this

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Speaker 1: huge story, like national story. Again, that's something that I

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Speaker 1: feel was and maybe because of where they found them

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Speaker 1: that it was kind of like, you know, all right, like.

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Speaker 3: This is every day here.

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Speaker 1: Yay.

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Speaker 2: Well I imagine that just being I mean, this is

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Speaker 2: back in the eighties, but just being closer to the border,

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Speaker 2: there are situations where they find dead bodies, right, and

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Speaker 2: just out there of people crossing over, so that's that's

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Speaker 2: not always. It's more common there, Like we're not used

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Speaker 2: to that in New Jersey, but like over there, that's

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Speaker 2: more common. So maybe they thought it was a situation

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Speaker 2: like that that just people put she was naked and

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Speaker 2: stabbed to death. No, not the woman was one thing.

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Speaker 2: I mean the children just in general.

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Speaker 1: Yeah, but there could have been someone who crossed the

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Speaker 1: border and then just left the children to have a

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Speaker 1: better light. Yeah, you know, that would make sense, That

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Speaker 1: would make sense. Mm hmm.

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Speaker 2: All right, let's let's lighten this up a little bit.

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Speaker 2: Although this has to do with the death a woman

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Speaker 2: this moment, with another death in this field. In this field,

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Speaker 2: you have to be able to laugh a lot. I

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Speaker 2: mean we we just talk about horrible stuff. We never

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Speaker 2: talk about good stuff like ever. I mean this, I

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Speaker 2: guess the story we just talked about has Oh cool.

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Speaker 2: At last these girls could finally figure out what happened

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Speaker 2: to them. Although it's terrible for them to know that

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Speaker 2: their mother was murdered and they were somehow like missing

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Speaker 2: for two days. Who knows what happened to them during

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Speaker 2: those two days, But we just never talk about like

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Speaker 2: good stuff, so you have to you have to laugh

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Speaker 2: a little bit sometimes.

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Speaker 3: All Right, about ten years ago, this girl from Poland

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Speaker 3: moved to the UK for college and there she learned

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Speaker 3: all about yoga, veganism and clean eating. So she took

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Speaker 3: it to the extreme and adopted an all fruit diet,

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Speaker 3: which ultimately led to her death a couple of years later.

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Speaker 3: It's kind of cool that she was able to live

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Speaker 3: a couple of years on like aas Yeah, I mean

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Speaker 3: she probably had severe she did have severe malnutrition.

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Speaker 1: Yeah, she also had a severe eating disorder prior to this,

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Speaker 1: so I think, you know, the idea of the malnutrition

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Speaker 1: piece of it is interesting because if you look up

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Speaker 1: fruititarians and people who are on these diets, and I

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Speaker 1: actually met a fruititarian when I was in Costa Rica

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Speaker 1: one year, and she was posting all of the time

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Speaker 1: on Instagram what she ate in a day. And it

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Speaker 1: isn't what we think about as just fruit. It's anything

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Speaker 1: with a seed, so tomatoes and avocados and squash and

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Speaker 1: like there's other things that they do eat. So it's

402
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Speaker 1: more like an extreme veganism. But some of these these

403
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Speaker 1: fruititarians are extremely healthy However, every fruititarian that I was

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Speaker 1: able to find on TikTok and on Instagram, all of

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Speaker 1: them had some type of like uh, energy work or

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Speaker 1: uh they were yoga instructors. There were you know, like

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Speaker 1: it's in that extreme and some health space. Uh. There

408
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Speaker 1: aren't like people out there advertising advertising that they're fruitarians

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Speaker 1: that are just like you know, regular people like you

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Speaker 1: and me, like I, you know, I'm a vegetarian. I've

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Speaker 1: been a vegan before. It wasn't for losing weight. And

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Speaker 1: I think with this particular case, she already had an

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Speaker 1: eating disorder, so she wasn't looking to be healthy bear.

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Speaker 1: I think that it was really associated with her severe anorexia,

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Speaker 1: and she just used that as a way to have

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Speaker 1: just more control over her body and weight loss. And

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Speaker 1: people do use the fruitarian diet for weight loss, but

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Speaker 1: it was Yeah, she was able to live on it

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Speaker 1: for I think a couple of years, right. Yeah.

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Speaker 3: It seems like she started it when she was nineteen

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Speaker 3: and she died at twenty seven, so she was only

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Speaker 3: eating this way. I think eight.

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Speaker 2: Years is crazy long. Yeah, Yeah, she wasn't doing it.

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Speaker 2: It's important to note though, that she wasn't doing it right, right,

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Speaker 2: like you were saying, you can get protein from certain

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Speaker 2: vegetables and fruits, and especially if you're doing seeds and

427
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Speaker 2: nuts even you can that work. But she was eating

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Speaker 2: I mean, if you eat watermelon every day, you're not

429
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Speaker 2: getting anywhere near that. You're getting no protein, you're not

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Speaker 2: getting any of the nutrition you need, and high sugar

431
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Speaker 2: diet on top of that. Yeah, I do think that

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Speaker 2: you're right and you're onto something though, because in my

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Speaker 2: personal life, I feel like throughout my life, I've met

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Speaker 2: a couple people that put these severe restrictions on what

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Speaker 2: they eat, to the point where I'm just like, this

436
00:27:31,280 --> 00:27:35,560
Speaker 2: is this is a sickness rather than a lifestyle, because

437
00:27:35,560 --> 00:27:38,720
Speaker 2: you're just like I can't eat anything. Yeah, there's nothing

438
00:27:38,760 --> 00:27:42,720
Speaker 2: you could eat. So then you know, it's it almost

439
00:27:42,800 --> 00:27:44,800
Speaker 2: feels that way to me sometimes with people.

440
00:27:46,119 --> 00:27:51,280
Speaker 1: Yeah, and I wonder in this particular, like in the

441
00:27:51,320 --> 00:27:55,600
Speaker 1: health space, like a lot of veguns that are very

442
00:27:55,680 --> 00:28:02,360
Speaker 1: much into fitness and yogun, like they or they're selling

443
00:28:02,440 --> 00:28:10,600
Speaker 1: supplements or like a lot of these these influencers, these

444
00:28:11,160 --> 00:28:15,120
Speaker 1: now frugitarians that I was looking up, all of them

445
00:28:15,320 --> 00:28:20,480
Speaker 1: have like this. I don't know, like there is it's

446
00:28:20,600 --> 00:28:25,800
Speaker 1: definitely a type. Let's just say, like, and it's very stereotypical.

447
00:28:26,080 --> 00:28:30,639
Speaker 1: You know, they're all very very thin, they you know, dreadlocks.

448
00:28:32,480 --> 00:28:39,160
Speaker 1: They're like, yeah, they're They're quintessential in the health space

449
00:28:39,320 --> 00:28:43,719
Speaker 1: that you would think about. And I think she was

450
00:28:43,960 --> 00:28:49,120
Speaker 1: in that space and used that as a way to

451
00:28:49,240 --> 00:28:51,600
Speaker 1: just be anorexic.

452
00:28:53,200 --> 00:28:58,880
Speaker 2: She's an influencer though, correct she was. I mean, that's

453
00:28:59,080 --> 00:29:01,680
Speaker 2: that's an important discussion to have. I mean, I guess

454
00:29:01,680 --> 00:29:04,800
Speaker 2: it could be same again set about the subway story

455
00:29:04,840 --> 00:29:08,200
Speaker 2: too of people. I mean, the subway thing is a

456
00:29:08,240 --> 00:29:13,760
Speaker 2: little bit more obviously dangerous. But there's one girl, you know,

457
00:29:13,800 --> 00:29:15,840
Speaker 2: that one girl that we talked about once. I feel

458
00:29:15,880 --> 00:29:17,440
Speaker 2: like her name's Eugene.

459
00:29:17,040 --> 00:29:18,800
Speaker 3: Something, Eugenia Clooney.

460
00:29:19,080 --> 00:29:25,560
Speaker 2: Yeah, she's visibly anorexic and and has gotten blocked off

461
00:29:25,600 --> 00:29:28,880
Speaker 2: social media, but now she's back on. I just saw

462
00:29:29,160 --> 00:29:34,280
Speaker 2: video of her recently, and a lot of people are

463
00:29:34,400 --> 00:29:37,120
Speaker 2: not okay with it because she's got a lot of

464
00:29:37,160 --> 00:29:41,640
Speaker 2: followers and people look at that and want to emulate that.

465
00:29:41,760 --> 00:29:41,920
Speaker 2: You know.

466
00:29:42,680 --> 00:29:45,240
Speaker 3: Yeah, I guess when we talked about her case before, though,

467
00:29:45,280 --> 00:29:47,040
Speaker 3: it was a lot of people were bringing up the

468
00:29:47,200 --> 00:29:51,200
Speaker 3: argument that how are you going to ban her? On

469
00:29:51,280 --> 00:29:56,160
Speaker 3: social media for showing unhealthy lifestyle when you're also like,

470
00:29:56,280 --> 00:29:59,560
Speaker 3: if you're gonna do that, you should also ban morbidly

471
00:29:59,600 --> 00:30:04,720
Speaker 3: obese people were also promoting an unhealthy lifestyle, and obviously,

472
00:30:04,880 --> 00:30:06,720
Speaker 3: like I don't care about what your body looks like

473
00:30:06,800 --> 00:30:08,760
Speaker 3: or whatever, but that was a big point people were

474
00:30:08,760 --> 00:30:10,440
Speaker 3: bringing up because it's like, where do you draw the

475
00:30:10,440 --> 00:30:13,200
Speaker 3: line at what you define as unhealthy?

476
00:30:13,720 --> 00:30:14,080
Speaker 1: Yes?

477
00:30:15,600 --> 00:30:18,360
Speaker 2: Well yeah, and who who makes that decision is more

478
00:30:18,400 --> 00:30:24,160
Speaker 2: important because obviously that would be whoever owns social media,

479
00:30:24,240 --> 00:30:27,080
Speaker 2: and then that's not fair for them to be the gate.

480
00:30:27,240 --> 00:30:29,600
Speaker 2: I mean, that could be said about all everything that's

481
00:30:29,640 --> 00:30:32,720
Speaker 2: talked about, I guess on there, but certainly as far

482
00:30:32,760 --> 00:30:35,280
Speaker 2: as health goes, I mean, there's just lots of different

483
00:30:35,280 --> 00:30:38,480
Speaker 2: things that people We talk about this at least once

484
00:30:38,520 --> 00:30:42,240
Speaker 2: a month of TikTok, challenges of the choking game, and

485
00:30:42,280 --> 00:30:45,560
Speaker 2: this and that, and just it's just kind of amazing

486
00:30:45,600 --> 00:30:48,640
Speaker 2: to me that there that their technology and algorithm can't

487
00:30:48,680 --> 00:30:50,080
Speaker 2: pick up any of this stuff.

488
00:30:50,960 --> 00:30:54,200
Speaker 1: You know, well, it is very much, very much linked

489
00:30:54,240 --> 00:30:57,760
Speaker 1: to body dys worthy because if you talk about people

490
00:30:57,800 --> 00:31:00,880
Speaker 1: that are more bid the obese, it's you talk about

491
00:31:00,920 --> 00:31:06,760
Speaker 1: people that are to the other extreme of anorexia. It's

492
00:31:06,800 --> 00:31:13,280
Speaker 1: all about body dysmorphia. I know that I have. I

493
00:31:13,400 --> 00:31:21,520
Speaker 1: have definitely realized I have somebody dysmorphia. And really it's

494
00:31:21,560 --> 00:31:26,360
Speaker 1: been recent I I recently gained a lot of weight,

495
00:31:27,200 --> 00:31:31,680
Speaker 1: and I was looking back at pictures of me, like

496
00:31:31,840 --> 00:31:35,480
Speaker 1: from when I was on press tour and hanging out

497
00:31:35,520 --> 00:31:41,200
Speaker 1: with teeny tiny Jessica Chastain, who is you know, all

498
00:31:41,240 --> 00:31:45,720
Speaker 1: of like one hundred pounds and like two and here

499
00:31:45,800 --> 00:31:49,520
Speaker 1: I am, you know, in this this bigger body. But

500
00:31:49,600 --> 00:31:54,000
Speaker 1: I look back at that and I was like, holy shit,

501
00:31:54,200 --> 00:31:57,600
Speaker 1: Like I thought that I was really happy, Like I

502
00:31:57,720 --> 00:32:03,760
Speaker 1: genuinely thought that I was is like embarrassingly heavy, and

503
00:32:04,400 --> 00:32:08,920
Speaker 1: I was. I was really really down on myself, and

504
00:32:09,080 --> 00:32:13,520
Speaker 1: like you know, I would change my outfit like twenty times,

505
00:32:13,560 --> 00:32:18,040
Speaker 1: like just because I felt I felt so big. And

506
00:32:18,560 --> 00:32:20,560
Speaker 1: I look back and I'm like, oh my god, like

507
00:32:20,720 --> 00:32:26,560
Speaker 1: I looked amazing, Like I looked amazing, but in my mind.

508
00:32:26,320 --> 00:32:28,040
Speaker 2: She still look amazing. Stop it.

509
00:32:29,760 --> 00:32:34,520
Speaker 1: It's so sweet. But in my mind this body dysmorphia,

510
00:32:34,680 --> 00:32:38,640
Speaker 1: Like I had no comprehension. And I look at this

511
00:32:38,800 --> 00:32:43,000
Speaker 1: frugitarian and she even you know, obviously she was making

512
00:32:43,080 --> 00:32:48,120
Speaker 1: videos she had to have seen, but what she actually

513
00:32:48,160 --> 00:32:54,920
Speaker 1: looked like, she just had no conception that she was that.

514
00:32:55,360 --> 00:33:00,120
Speaker 1: I'm sure that she felt like she was looking healthy.

515
00:33:00,400 --> 00:33:02,840
Speaker 3: Yeah, And I think it's interesting because her family really

516
00:33:02,880 --> 00:33:05,600
Speaker 3: brought up the point that after she learned about this

517
00:33:05,720 --> 00:33:08,560
Speaker 3: culture and then going to social media to look for

518
00:33:08,640 --> 00:33:12,360
Speaker 3: it more, it just really brainwashed and implemented the idea

519
00:33:12,360 --> 00:33:14,760
Speaker 3: in her brain that this was okay because you're seeing

520
00:33:15,240 --> 00:33:17,600
Speaker 3: a whole pocket of the internet doing it too, so

521
00:33:17,640 --> 00:33:19,000
Speaker 3: you're able to justify it.

522
00:33:19,480 --> 00:33:20,040
Speaker 1: Yes, you know.

523
00:33:20,080 --> 00:33:22,520
Speaker 2: It was really cool when I went to doctor Miami

524
00:33:22,600 --> 00:33:25,240
Speaker 2: and got my tummy tuck done like five years ago.

525
00:33:25,280 --> 00:33:28,040
Speaker 1: Now you'm tuk I want one.

526
00:33:28,720 --> 00:33:30,440
Speaker 2: Oh yeah, you have to go to him. It was

527
00:33:30,640 --> 00:33:33,440
Speaker 2: like eight years ago. No, it was twenty nineteen.

528
00:33:33,960 --> 00:33:36,640
Speaker 1: Oh my god, I want to do that. It's so bad.

529
00:33:36,840 --> 00:33:38,880
Speaker 2: No, go to him that. I love them there, They're

530
00:33:38,960 --> 00:33:42,720
Speaker 2: so good. It was so such a good experience. But

531
00:33:43,040 --> 00:33:45,320
Speaker 2: so I went and right before I went in for

532
00:33:45,400 --> 00:33:48,360
Speaker 2: the surgery, they gave me this piece of paper with

533
00:33:48,560 --> 00:33:51,760
Speaker 2: like ten different body types and it said what do

534
00:33:51,800 --> 00:33:55,640
Speaker 2: you think your body looks like? Circle one? And then

535
00:33:55,720 --> 00:33:58,080
Speaker 2: underneath it said what do you want your body to

536
00:33:58,120 --> 00:34:00,960
Speaker 2: look like circle one, and I thought that that was

537
00:34:01,000 --> 00:34:03,640
Speaker 2: so interesting because I'm wondering. I didn't really get to

538
00:34:03,680 --> 00:34:05,760
Speaker 2: talk to him about it, but I'm like, I'm wondering

539
00:34:06,520 --> 00:34:10,560
Speaker 2: if if I pick that, I'm like this supid, morbid,

540
00:34:10,640 --> 00:34:14,359
Speaker 2: obese person. I'm just like, like over, you know, if

541
00:34:14,400 --> 00:34:19,320
Speaker 2: you pick something outrageous and then you have like unrealistic expectations,

542
00:34:20,200 --> 00:34:23,400
Speaker 2: I wonder how they go about counseling for that, because.

543
00:34:23,280 --> 00:34:27,800
Speaker 1: It's interesting, Yes, yes, yeah.

544
00:34:27,680 --> 00:34:30,319
Speaker 2: Because like what you're looking at might not be what

545
00:34:30,360 --> 00:34:32,120
Speaker 2: you think you're looking at, you know.

546
00:34:32,040 --> 00:34:34,760
Speaker 1: What I mean, it just worth it is real.

547
00:34:35,800 --> 00:34:39,000
Speaker 3: And this girl was forty nine pounds when she died.

548
00:34:39,000 --> 00:34:39,760
Speaker 1: Oh my god.

549
00:34:40,680 --> 00:34:42,759
Speaker 3: She was staying in a hotel in Bali. They said

550
00:34:42,800 --> 00:34:45,520
Speaker 3: she wasn't even able to walk to her room half

551
00:34:45,560 --> 00:34:49,400
Speaker 3: the time. She couldn't turn over in bed herself. I

552
00:34:49,440 --> 00:34:53,080
Speaker 3: can't imagine feeling this way. My daughter Lilian is one

553
00:34:53,160 --> 00:34:57,759
Speaker 3: hundred pounds and she is very thin. She's twelve, so

554
00:34:58,040 --> 00:35:01,200
Speaker 3: I mean that's fine for her, but she's thin, she's

555
00:35:01,239 --> 00:35:03,920
Speaker 3: on the thinner side. She doesn't she doesn't eat it

556
00:35:04,080 --> 00:35:07,880
Speaker 3: like my other one's more like of a normal like

557
00:35:07,920 --> 00:35:10,120
Speaker 3: a more normal build, like a thicker a little bit

558
00:35:10,120 --> 00:35:13,480
Speaker 3: of a thicker build, but she's when I look at

559
00:35:13,520 --> 00:35:17,320
Speaker 3: her and say she's one hundred pounds at five to

560
00:35:17,320 --> 00:35:22,040
Speaker 3: two or whatever she is, I'm like, she's she's so thin.

561
00:35:22,239 --> 00:35:24,560
Speaker 3: What is it like for a person of her height

562
00:35:24,600 --> 00:35:27,440
Speaker 3: to be half of that weight. It's it's so disturbing,

563
00:35:28,239 --> 00:35:30,600
Speaker 3: very very very disturbing.

564
00:35:38,640 --> 00:35:41,960
Speaker 2: This episode is brought to you by the Gross Room guys.

565
00:35:42,000 --> 00:35:45,799
Speaker 2: So we have another great high profile death disseection this

566
00:35:45,880 --> 00:35:48,640
Speaker 2: week on Halloween that we've been talking about that is

567
00:35:48,760 --> 00:35:51,520
Speaker 2: kind of setting the mood for our Halloween themed death

568
00:35:51,560 --> 00:35:56,799
Speaker 2: dissections this month. We also have a bunch of obviously

569
00:35:56,840 --> 00:35:58,759
Speaker 2: every week we have some kind of posts of a

570
00:35:58,800 --> 00:36:01,480
Speaker 2: person that swallowed un to weird stuff, and every single

571
00:36:01,520 --> 00:36:04,719
Speaker 2: time it's something different, so that's really interesting. And we

572
00:36:04,800 --> 00:36:08,239
Speaker 2: also have this really crazy video that someone set me

573
00:36:08,400 --> 00:36:11,480
Speaker 2: of someone that is vomiting all of this thick blood,

574
00:36:11,520 --> 00:36:13,560
Speaker 2: so we go through all of the different things that

575
00:36:13,600 --> 00:36:16,080
Speaker 2: could be happening in that case. So it's pretty cool.

576
00:36:16,120 --> 00:36:18,640
Speaker 2: So check that out for only five ninety nine a month.

577
00:36:19,040 --> 00:36:21,520
Speaker 3: Head over to the Grossroom dot com now to sign up.

578
00:36:23,440 --> 00:36:26,319
Speaker 2: So the next two stories that we're going to talk

579
00:36:26,360 --> 00:36:29,600
Speaker 2: about for those of you that didn't, like I said

580
00:36:29,600 --> 00:36:32,279
Speaker 2: in the beginning of the intro, for them to go

581
00:36:32,400 --> 00:36:34,520
Speaker 2: back and listen to your interview that we did a

582
00:36:34,520 --> 00:36:36,560
Speaker 2: couple of years. It's god, it's almost been two years

583
00:36:36,560 --> 00:36:36,960
Speaker 2: now and.

584
00:36:37,040 --> 00:36:37,840
Speaker 1: I never believe that.

585
00:36:38,480 --> 00:36:41,359
Speaker 2: I know, right, We've been friends for such a long time.

586
00:36:41,400 --> 00:36:44,400
Speaker 3: It's one of our very first episodes. I know you

587
00:36:44,440 --> 00:36:45,760
Speaker 3: were episode number four.

588
00:36:45,960 --> 00:36:48,680
Speaker 2: Yeah, and they need yeah, well, they really need to

589
00:36:48,680 --> 00:36:51,959
Speaker 2: listen to it though, because that was you'll go into

590
00:36:52,000 --> 00:36:54,120
Speaker 2: your whole story. I did a little bit in the intro,

591
00:36:54,280 --> 00:36:58,960
Speaker 2: but it's it's really good. But you also talked about

592
00:36:59,120 --> 00:37:03,839
Speaker 2: being an emergency room nurse, and so these two stories

593
00:37:04,200 --> 00:37:07,440
Speaker 2: are I thought would be interesting to talk with you about.

594
00:37:08,719 --> 00:37:11,640
Speaker 2: All right, So do the first one ray, all right?

595
00:37:11,680 --> 00:37:13,880
Speaker 3: A British woman was shocked when she thought she had

596
00:37:13,920 --> 00:37:17,000
Speaker 3: appendicitis and ended up delivering a baby boy in the

597
00:37:17,040 --> 00:37:20,839
Speaker 3: back of an ambulance. So as two mothers, can you

598
00:37:21,080 --> 00:37:23,319
Speaker 3: I just can never imagine when we covered these, like,

599
00:37:23,360 --> 00:37:25,800
Speaker 3: I didn't know it was pregnant stories. If this happens

600
00:37:25,840 --> 00:37:28,960
Speaker 3: to peopul No didn't you both Did you both know

601
00:37:29,040 --> 00:37:31,720
Speaker 3: you were pregnant? I just feel like it's so obvious.

602
00:37:32,080 --> 00:37:36,680
Speaker 1: Well, a cryptic pregnancy is are a real thing. I

603
00:37:36,920 --> 00:37:43,960
Speaker 1: don't understand how the body works and these cryptic pregnancies

604
00:37:44,040 --> 00:37:48,520
Speaker 1: that they are, and and I think because of social

605
00:37:48,600 --> 00:37:53,799
Speaker 1: media we're hearing about them more. They've always happened, but

606
00:37:54,120 --> 00:37:59,600
Speaker 1: I think it's it's something that we think is less

607
00:37:59,719 --> 00:38:05,680
Speaker 1: rare than it is. Usually it's young people. I used

608
00:38:05,719 --> 00:38:14,000
Speaker 1: to believe these cryptic pregnancies were like obese, like women

609
00:38:14,680 --> 00:38:19,160
Speaker 1: who did not see a change in their body. And

610
00:38:20,280 --> 00:38:25,400
Speaker 1: that's not what this is. These are younger women with

611
00:38:26,080 --> 00:38:32,680
Speaker 1: flat tummies, almost completely flat tummies, coming to the er

612
00:38:33,440 --> 00:38:39,120
Speaker 1: with abdominal pain, and they have a fully formed fetus

613
00:38:39,400 --> 00:38:42,880
Speaker 1: that they're giving birth to, and they will show and

614
00:38:43,320 --> 00:38:48,040
Speaker 1: you know, there's multiple people now on TikTok and Instagram

615
00:38:48,480 --> 00:38:53,920
Speaker 1: showing their cryptic pregnancies and you cannot tell, You cannot tell.

616
00:38:54,360 --> 00:39:01,000
Speaker 1: And everyone that is saying, oh, she knew, she absolutely new.

617
00:39:01,680 --> 00:39:05,080
Speaker 1: They are no possible way she could have known. There's

618
00:39:05,400 --> 00:39:11,080
Speaker 1: just there. They continued with their periods that they they

619
00:39:11,480 --> 00:39:15,439
Speaker 1: continue to you know, they didn't have any symptoms. There

620
00:39:15,600 --> 00:39:22,320
Speaker 1: wasn't any nausea. For some reason, the hormones did. It's

621
00:39:21,960 --> 00:39:28,200
Speaker 1: it is so bizarre, but the female body is so

622
00:39:29,600 --> 00:39:37,120
Speaker 1: just amazing and miraculous that it could hide a pregnancy,

623
00:39:37,480 --> 00:39:40,480
Speaker 1: Like talk about knowing that I was pregnant, Are you

624
00:39:40,560 --> 00:39:44,120
Speaker 1: kidding me? I look like the cow that swallowed can't.

625
00:39:44,600 --> 00:39:50,560
Speaker 1: Like my belis so big, like there's no freaking way

626
00:39:50,640 --> 00:39:55,640
Speaker 1: of course. But I mean, really, I I really did

627
00:39:55,680 --> 00:40:00,760
Speaker 1: not understand cryptic pregnancies, and until I saw one on

628
00:40:00,760 --> 00:40:04,160
Speaker 1: on social media, I did deal with a crypto pregnant

629
00:40:04,280 --> 00:40:08,200
Speaker 1: pregnancy and the er and I didn't believe it. Like

630
00:40:08,480 --> 00:40:12,480
Speaker 1: I I absolutely did not believe it. And she was

631
00:40:12,680 --> 00:40:16,600
Speaker 1: she was heavy, and I was like, no, she was

632
00:40:16,800 --> 00:40:19,640
Speaker 1: just you know, she just didn't want anyone to know.

633
00:40:20,000 --> 00:40:22,600
Speaker 1: But now I look back, I was like, oh my god,

634
00:40:22,600 --> 00:40:26,720
Speaker 1: it was so judgy. I was so judgy. I feel

635
00:40:26,800 --> 00:40:28,040
Speaker 1: really bad woman.

636
00:40:28,280 --> 00:40:30,520
Speaker 2: Yeah, I mean to your defense though, it's like when

637
00:40:30,560 --> 00:40:33,040
Speaker 2: you're a mom that's gone through pregnancy, you're just like,

638
00:40:34,200 --> 00:40:36,839
Speaker 2: this is why Maria is in that, Like I don't

639
00:40:36,880 --> 00:40:40,120
Speaker 2: see it's possible, because you know, she just has seen

640
00:40:40,400 --> 00:40:43,000
Speaker 2: she saw me be pregnant, right, and it's just like

641
00:40:43,239 --> 00:40:44,280
Speaker 2: I just can't imagine.

642
00:40:44,320 --> 00:40:46,799
Speaker 3: I just can't wrap my head around because I saw

643
00:40:46,880 --> 00:40:49,600
Speaker 3: you and a couple of my friends that they just

644
00:40:49,960 --> 00:40:52,720
Speaker 3: had so many symptoms that it was just so obvious.

645
00:40:52,840 --> 00:40:57,320
Speaker 3: I can't wrap my head around your body not giving

646
00:40:57,360 --> 00:41:01,160
Speaker 3: you any signs. Should we quote quote Bryce Haer's nice quote?

647
00:41:01,160 --> 00:41:04,600
Speaker 3: This morning, one of our star baseball players on the

648
00:41:04,600 --> 00:41:07,240
Speaker 3: Phillies did a press conference. He just had a baby

649
00:41:07,320 --> 00:41:09,200
Speaker 3: last week, and he said about.

650
00:41:08,920 --> 00:41:10,120
Speaker 2: His wife, what a breed.

651
00:41:10,640 --> 00:41:13,360
Speaker 3: Really nice way to explain your wife giving birth.

652
00:41:14,800 --> 00:41:15,920
Speaker 1: Are you shitting man?

653
00:41:16,080 --> 00:41:17,960
Speaker 2: He also referred to his baby as an it.

654
00:41:18,480 --> 00:41:22,240
Speaker 3: No wait, I'm gonna read the exact or something. Shit,

655
00:41:23,239 --> 00:41:26,400
Speaker 3: it was so unbelievable. He said, I've got an incredible wife.

656
00:41:26,440 --> 00:41:26,640
Speaker 1: Man.

657
00:41:26,719 --> 00:41:29,800
Speaker 3: She pushed that thing out in three pushes in thirty seconds.

658
00:41:29,840 --> 00:41:33,160
Speaker 3: She's an absolute monster doing it women. Man, what a breed?

659
00:41:33,320 --> 00:41:35,520
Speaker 1: All right? Who hin's this? Dip shit?

660
00:41:37,600 --> 00:41:39,280
Speaker 3: He's our star baseball player.

661
00:41:39,560 --> 00:41:42,080
Speaker 2: Don't attack him until like three days from now when

662
00:41:42,160 --> 00:41:43,480
Speaker 2: we know that they won the playoffs.

663
00:41:43,520 --> 00:41:45,800
Speaker 3: Oh, he's listen, he's fine, but he's kind of a

664
00:41:45,880 --> 00:41:48,160
Speaker 3: douche And like, I just feel like that's sealed the deal.

665
00:41:48,400 --> 00:41:53,439
Speaker 2: Yes, but listen, I think as Amy does. I think

666
00:41:53,480 --> 00:41:57,640
Speaker 2: that it's possible too, not just because and those videos

667
00:41:57,680 --> 00:42:01,320
Speaker 2: that you're talking about on Instagram and TikTok they're very

668
00:42:01,880 --> 00:42:06,240
Speaker 2: they're very striking. I can't believe it. Did you see them? Maria,

669
00:42:06,640 --> 00:42:08,960
Speaker 2: Like a woman will say she's like nine months pregnant

670
00:42:08,960 --> 00:42:10,759
Speaker 2: and you look at her and her belly looks like

671
00:42:11,000 --> 00:42:14,480
Speaker 2: mine after a tomy talk like you know what I mean,

672
00:42:14,560 --> 00:42:17,880
Speaker 2: Like I like, in my family, we just have genetics that,

673
00:42:18,000 --> 00:42:22,680
Speaker 2: like our bellies stick out. It's just my Grandma, my mom, Maria,

674
00:42:22,800 --> 00:42:27,880
Speaker 2: my little ones. And then there's other I've seen, you know,

675
00:42:27,960 --> 00:42:31,839
Speaker 2: I've been in multiple pelvic cavities throughout my career, and

676
00:42:31,880 --> 00:42:36,800
Speaker 2: there's some people that they're just so tucked back that

677
00:42:38,280 --> 00:42:41,080
Speaker 2: they could grow a whole pregnancy and you wouldn't see it.

678
00:42:41,080 --> 00:42:43,319
Speaker 2: It's just the way that their pelvis is formed. It's

679
00:42:43,360 --> 00:42:43,920
Speaker 2: really cool.

680
00:42:44,760 --> 00:42:46,680
Speaker 3: Did you think it was weird that they brought her

681
00:42:46,719 --> 00:42:49,680
Speaker 3: to a hospital, she starts gushing blood and the doctors

682
00:42:49,719 --> 00:42:52,360
Speaker 3: then realize she's giving birth, So then at that moment

683
00:42:52,440 --> 00:42:55,960
Speaker 3: they decide to transfer her to another hospital, and that's

684
00:42:56,000 --> 00:42:57,960
Speaker 3: when she ended up giving birth in the back of

685
00:42:58,000 --> 00:42:58,600
Speaker 3: an ambulance.

686
00:42:58,640 --> 00:42:58,799
Speaker 1: Yeah.

687
00:42:58,800 --> 00:42:59,680
Speaker 2: I thought that was weird.

688
00:43:00,680 --> 00:43:05,520
Speaker 1: Yeah, there was also like something weird that she said,

689
00:43:06,440 --> 00:43:10,840
Speaker 1: all of a sudden, there were like seventeen doctors around her.

690
00:43:11,200 --> 00:43:16,799
Speaker 1: And I'm thinking, babe, if you think there are seventeen

691
00:43:16,960 --> 00:43:23,040
Speaker 1: doctors in a hospital at one time, like.

692
00:43:22,520 --> 00:43:26,720
Speaker 4: In the whole hospital, that was one doctor, and especially

693
00:43:26,800 --> 00:43:29,839
Speaker 4: if that was like a smaller hospital, that was one

694
00:43:30,000 --> 00:43:33,680
Speaker 4: doctor and all those other people were nurses.

695
00:43:34,080 --> 00:43:39,799
Speaker 1: Like you know, there's I'm just I that always gets me, like,

696
00:43:40,040 --> 00:43:45,880
Speaker 1: oh yeah, when when people are are sick and in

697
00:43:45,920 --> 00:43:49,400
Speaker 1: the hospital, they'll be like, uh, get a doctor, you know,

698
00:43:49,440 --> 00:43:52,040
Speaker 1: their friend, you know, if something's going on, they yell

699
00:43:52,200 --> 00:43:55,840
Speaker 1: out into the hallway like call a doctor.

700
00:43:56,160 --> 00:43:59,279
Speaker 2: Oh do you actually that's the last person you want

701
00:43:59,320 --> 00:43:59,520
Speaker 2: to hear.

702
00:44:01,160 --> 00:44:05,359
Speaker 1: No, there's twenty nurses that you're becking call and we

703
00:44:05,440 --> 00:44:08,880
Speaker 1: are going to save your life. Like, yeah, better hope

704
00:44:09,320 --> 00:44:14,120
Speaker 1: that this first year resident doesn't show up. So probably

705
00:44:14,640 --> 00:44:18,280
Speaker 1: the worst, yes, yeah, it probably was this little community

706
00:44:18,360 --> 00:44:23,120
Speaker 1: hospital and they had no fucking clue how to deal

707
00:44:23,280 --> 00:44:28,720
Speaker 1: with premature birth, Like they just they get They were like, Okay,

708
00:44:28,760 --> 00:44:31,239
Speaker 1: you're going to die here, or you're going to die

709
00:44:31,280 --> 00:44:34,279
Speaker 1: on the way there, so let's put an ambulance. Get

710
00:44:34,280 --> 00:44:37,959
Speaker 1: her in an ambulance, hurry up so she doesn't die here.

711
00:44:39,600 --> 00:44:41,880
Speaker 2: You know what, I used to work at a very

712
00:44:42,640 --> 00:44:46,000
Speaker 2: a very very good hospital one of the top hospitals

713
00:44:46,000 --> 00:44:49,279
Speaker 2: I would say in the country. Definitely in Philadelphia, and

714
00:44:49,520 --> 00:44:52,680
Speaker 2: I always thought the surgeons were so awesome there because

715
00:44:52,719 --> 00:44:55,560
Speaker 2: they would come down to pathology and they would look

716
00:44:55,600 --> 00:44:57,719
Speaker 2: for the PAS before they would look for any of

717
00:44:57,760 --> 00:45:01,120
Speaker 2: the doctors. Always it's her dumb yeah, and I was like,

718
00:45:01,160 --> 00:45:03,560
Speaker 2: they know what's up. They know what's up, like they

719
00:45:03,600 --> 00:45:05,719
Speaker 2: know that we're the ones that look at it every day,

720
00:45:05,760 --> 00:45:08,759
Speaker 2: and they would be like, you know, and then we

721
00:45:08,760 --> 00:45:11,280
Speaker 2: would call the doctors into but they were like wanted

722
00:45:11,360 --> 00:45:13,480
Speaker 2: us to look at stuff first, and that was I

723
00:45:13,520 --> 00:45:18,480
Speaker 2: thought that that was cool. Yeah, So this particular case

724
00:45:18,560 --> 00:45:20,840
Speaker 2: is weird because they said that she was vomiting. She

725
00:45:20,920 --> 00:45:23,840
Speaker 2: said she was vomiting blood. I don't know if you

726
00:45:23,960 --> 00:45:26,720
Speaker 2: caught that, which I was just kind of like, that's weird.

727
00:45:27,800 --> 00:45:30,680
Speaker 2: And then she said that her and her son had

728
00:45:31,200 --> 00:45:35,640
Speaker 2: both had sepsis and then he was released. So there

729
00:45:35,719 --> 00:45:38,840
Speaker 2: is a picture of her on there sixteen days postpartum,

730
00:45:38,880 --> 00:45:41,600
Speaker 2: and she doesn't look sixteen days postpartum, so I believe

731
00:45:41,640 --> 00:45:43,080
Speaker 2: that her belly was not big.

732
00:45:43,239 --> 00:45:49,759
Speaker 1: Now she looks like she just went to a yoga weekend. Yeah, exactly.

733
00:45:50,880 --> 00:45:54,279
Speaker 2: So I'm curious what you think though, I mean, do

734
00:45:54,320 --> 00:45:59,080
Speaker 2: you think where did the sepsis come from? That both

735
00:45:59,280 --> 00:46:02,400
Speaker 2: her and the baby have it, so she had to

736
00:46:02,440 --> 00:46:06,279
Speaker 2: pass it to the baby at some point. I'm just curious, like.

737
00:46:07,280 --> 00:46:11,360
Speaker 1: Her water could have broken, like she she could easily

738
00:46:11,520 --> 00:46:15,759
Speaker 1: have been on the toilet. Water breaks, you just think

739
00:46:16,000 --> 00:46:19,560
Speaker 1: that it's a gush of pea, and especially when you

740
00:46:19,840 --> 00:46:25,880
Speaker 1: don't have any other symptom, your initial thought is, Okay,

741
00:46:26,040 --> 00:46:28,759
Speaker 1: I'm keeeing a lot. Wow, that was a lot. That

742
00:46:28,920 --> 00:46:29,560
Speaker 1: was crazy.

743
00:46:30,160 --> 00:46:34,279
Speaker 2: But it's you know, like whoa that I bust to

744
00:46:34,440 --> 00:46:35,040
Speaker 2: drink a lot?

745
00:46:35,200 --> 00:46:37,919
Speaker 1: Like yeah, yeah, yeah, like that was all.

746
00:46:37,960 --> 00:46:40,439
Speaker 2: It dribbles for me. It dribbled two times. I never

747
00:46:40,520 --> 00:46:42,160
Speaker 2: had the gush, so yeah.

748
00:46:42,480 --> 00:46:47,879
Speaker 1: And so if that membrane was broken, it's all bacteria

749
00:46:48,160 --> 00:46:49,640
Speaker 1: that goes up into.

750
00:46:49,400 --> 00:46:53,439
Speaker 2: There, so that so that would be like coreo amneitis

751
00:46:53,440 --> 00:46:56,759
Speaker 2: and premature rupture of membranes. Okay, yes, I was just

752
00:46:56,840 --> 00:46:59,160
Speaker 2: curious about that. And then I guess if you form

753
00:46:59,239 --> 00:47:04,719
Speaker 2: stepsish you can get ulcers and bleed or also in pregnancy.

754
00:47:04,880 --> 00:47:08,080
Speaker 2: I guess because I'm assuming that this didn't happen, that

755
00:47:08,120 --> 00:47:10,600
Speaker 2: she wasn't nauseous and wasn't throwing up a lot, because

756
00:47:10,640 --> 00:47:13,160
Speaker 2: sometimes if you throw up a lot over a period

757
00:47:13,160 --> 00:47:15,920
Speaker 2: of time, you could get these tears in youu esophagus.

758
00:47:16,160 --> 00:47:19,040
Speaker 1: I was just gonna say that I couldn't remember the

759
00:47:19,120 --> 00:47:20,640
Speaker 1: name of those terrors.

760
00:47:20,840 --> 00:47:22,280
Speaker 2: Oh, Mallory Whiste's terrs.

761
00:47:22,360 --> 00:47:27,680
Speaker 1: Yes, Mallorie Weiss, that was Yes, that is very, very possible.

762
00:47:28,360 --> 00:47:32,560
Speaker 2: But she, I mean, in theory, she wasn't vomiting like

763
00:47:32,640 --> 00:47:34,919
Speaker 2: that and not going and getting checked out to see

764
00:47:34,920 --> 00:47:37,120
Speaker 2: why she was vomiting so much, you know.

765
00:47:37,160 --> 00:47:40,200
Speaker 1: What I mean, Like I had, I was under the

766
00:47:40,200 --> 00:47:44,759
Speaker 1: impression when I read through that she was not on

767
00:47:44,800 --> 00:47:45,719
Speaker 1: the bright side.

768
00:47:46,400 --> 00:47:49,440
Speaker 2: Oh okay, so yeah, she was kind of ignoring stuff.

769
00:47:49,120 --> 00:47:51,640
Speaker 1: And yeah, yeah, yeah, absolutely.

770
00:47:52,160 --> 00:47:54,520
Speaker 2: I think that's a whole other thing with pregnancy too,

771
00:47:54,640 --> 00:47:58,160
Speaker 2: Like just aside from the anatomical part of it, a

772
00:47:58,200 --> 00:48:01,880
Speaker 2: lot of it is like when you're pregnant, you're just constantly,

773
00:48:01,920 --> 00:48:04,239
Speaker 2: like very in tune with your body to be like

774
00:48:04,400 --> 00:48:06,440
Speaker 2: did I feel a twitch? Did I feel it? Did I?

775
00:48:06,760 --> 00:48:06,920
Speaker 1: You know?

776
00:48:07,480 --> 00:48:10,719
Speaker 2: But like if you there's also times, like within the

777
00:48:10,760 --> 00:48:12,920
Speaker 2: past couple of years, like I'll be laying in bed

778
00:48:13,000 --> 00:48:15,600
Speaker 2: and I'll get this like gas bubble through my bows

779
00:48:15,600 --> 00:48:17,480
Speaker 2: and I'm like, oh, that felt like a baby moving.

780
00:48:17,880 --> 00:48:20,719
Speaker 2: You know, it's like that same kind of and I know,

781
00:48:21,239 --> 00:48:24,359
Speaker 2: so you're just like you could just ignore, like your

782
00:48:24,400 --> 00:48:27,600
Speaker 2: mind is really could do amazing things like that and

783
00:48:27,640 --> 00:48:31,239
Speaker 2: totally and and we've had that case that we've been

784
00:48:31,280 --> 00:48:36,000
Speaker 2: talking about with that Lake in Snelling girl who the cheerleader,

785
00:48:36,080 --> 00:48:40,319
Speaker 2: who she looked obviously pregnant to her friends and to

786
00:48:40,440 --> 00:48:43,200
Speaker 2: anybody looking at pictures of her, and then she gave

787
00:48:43,280 --> 00:48:47,080
Speaker 2: birth and killed the baby. But did she did she

788
00:48:47,400 --> 00:48:50,640
Speaker 2: like really not know that that she was pregnant the

789
00:48:50,640 --> 00:48:52,520
Speaker 2: whole time? You know, she might have known once she

790
00:48:52,560 --> 00:48:54,680
Speaker 2: was in labor, but like up until that point, was

791
00:48:54,719 --> 00:48:55,480
Speaker 2: she in denial?

792
00:48:56,760 --> 00:49:03,239
Speaker 1: Yeah? And like I'm kind of psychotic break that that

793
00:49:03,239 --> 00:49:08,760
Speaker 1: that type of denial is so it's just so strong,

794
00:49:09,360 --> 00:49:13,440
Speaker 1: and you know, if there was a type of psychosis

795
00:49:13,880 --> 00:49:17,400
Speaker 1: that went along with that, yeah, I could believe. I'd

796
00:49:17,480 --> 00:49:18,279
Speaker 1: like she didn't know.

797
00:49:20,600 --> 00:49:23,880
Speaker 2: That could be a dysmorphia thing too, like just looking

798
00:49:23,920 --> 00:49:26,719
Speaker 2: in the mirror and seeing your body change, but you're

799
00:49:28,200 --> 00:49:30,080
Speaker 2: you're just in denial about.

800
00:49:29,760 --> 00:49:31,200
Speaker 1: It, you know. Yeah?

801
00:49:31,880 --> 00:49:35,440
Speaker 2: Yes, all right, So wrap it up with this last

802
00:49:35,480 --> 00:49:37,960
Speaker 2: one because this is an interesting thing to talk about

803
00:49:38,000 --> 00:49:38,720
Speaker 2: to this case.

804
00:49:39,400 --> 00:49:42,400
Speaker 3: Okay, back in twenty twenty three, this college student was

805
00:49:42,440 --> 00:49:44,839
Speaker 3: going to a soccer game at Yankee Stadium. He told

806
00:49:44,840 --> 00:49:46,960
Speaker 3: his friends he wasn't feeling well and the next day

807
00:49:47,080 --> 00:49:49,560
Speaker 3: ends up going to the er with headache and chills,

808
00:49:49,560 --> 00:49:52,200
Speaker 3: but was sent home. The next day he was feeling

809
00:49:52,239 --> 00:49:55,400
Speaker 3: even worse. He went back again and again was discharged

810
00:49:55,640 --> 00:49:58,560
Speaker 3: in what they called a cute viral syndrome. But a

811
00:49:58,560 --> 00:50:00,600
Speaker 3: couple of days later he was found dead in his

812
00:50:00,600 --> 00:50:02,680
Speaker 3: dorm room.

813
00:50:03,520 --> 00:50:08,760
Speaker 2: Yes, so explain to people, Amy, what because I feel

814
00:50:08,760 --> 00:50:11,160
Speaker 2: like I'm explaining this to people in my life a lot,

815
00:50:11,440 --> 00:50:13,920
Speaker 2: like reasons to go to the emergency room. And then

816
00:50:13,920 --> 00:50:17,080
Speaker 2: when you go to the emergency room, what is their purpose?

817
00:50:18,120 --> 00:50:27,240
Speaker 1: The way people use the emergency room is is something

818
00:50:27,239 --> 00:50:30,200
Speaker 1: that you would normally go to, like an urgent care

819
00:50:30,520 --> 00:50:34,960
Speaker 1: instead of the er, but it's off hours, so you

820
00:50:35,000 --> 00:50:42,280
Speaker 1: go to the emergency room. Also, because of our extremely

821
00:50:42,360 --> 00:50:47,200
Speaker 1: fucked up healthcare system, so many people do not have

822
00:50:47,360 --> 00:50:55,040
Speaker 1: primary care coverage. They don't or they don't establish I

823
00:50:55,880 --> 00:51:01,640
Speaker 1: was he was he a citizen? I don't I know.

824
00:51:01,640 --> 00:51:04,960
Speaker 2: That you couldn't tell because he his parents were I

825
00:51:04,960 --> 00:51:09,200
Speaker 2: believe he was, but he was at school? Yeah, I think,

826
00:51:09,239 --> 00:51:11,440
Speaker 2: don't you have to have insurance when you're in college.

827
00:51:11,560 --> 00:51:14,680
Speaker 1: Wasn't that the thing you do? And especially if you're

828
00:51:14,719 --> 00:51:18,759
Speaker 1: there on visa, you can't come over without any type

829
00:51:18,760 --> 00:51:25,760
Speaker 1: of like you have to have insurance. So however, in eers,

830
00:51:26,280 --> 00:51:32,799
Speaker 1: people are coming in with cold, sniffles, stub my toe,

831
00:51:33,320 --> 00:51:39,719
Speaker 1: I have to fart, sunburns, and we can get a

832
00:51:40,000 --> 00:51:46,680
Speaker 1: full array from you know, literally a kid had vomited

833
00:51:46,840 --> 00:51:52,560
Speaker 1: once and now they're in the pediatric er or or

834
00:51:52,560 --> 00:51:56,760
Speaker 1: a gunshot, so you're like or you know, extreme trauma.

835
00:51:57,520 --> 00:52:03,080
Speaker 1: The challenge is that these docs, these er docs and

836
00:52:03,480 --> 00:52:09,680
Speaker 1: er nurses are really trained to focus on those people

837
00:52:10,239 --> 00:52:15,680
Speaker 1: who don't really have a chance, like they're actively having

838
00:52:15,920 --> 00:52:21,400
Speaker 1: a heart attack, they are actively in trauma, they actively

839
00:52:21,600 --> 00:52:26,319
Speaker 1: have a bleed or you know, their lung has collapsed,

840
00:52:26,440 --> 00:52:31,240
Speaker 1: or we are trained to focus on those things. However,

841
00:52:32,120 --> 00:52:37,840
Speaker 1: you become a clinic and those clinic patients are not

842
00:52:38,040 --> 00:52:42,680
Speaker 1: well cared for because you can push them into the back,

843
00:52:43,360 --> 00:52:47,799
Speaker 1: put them on a stretcher. This was Sam. His name

844
00:52:47,880 --> 00:52:54,359
Speaker 1: was Sam, right, They from the description they put him

845
00:52:54,560 --> 00:52:57,800
Speaker 1: in the back of the er on a stretcher, young,

846
00:52:57,880 --> 00:53:02,920
Speaker 1: strapping man. He's you know, he's probably got a cold,

847
00:53:03,120 --> 00:53:06,719
Speaker 1: he's not with mommy and daddy, and you're you know,

848
00:53:06,840 --> 00:53:14,080
Speaker 1: that judgment, that triage type judgment kicks in because they

849
00:53:14,280 --> 00:53:20,239
Speaker 1: aren't the primary patient in the er, and this happens

850
00:53:20,960 --> 00:53:26,920
Speaker 1: all the time. The er docs then are focused not

851
00:53:27,360 --> 00:53:31,680
Speaker 1: on treating these clinic patients, like the nurses are really

852
00:53:31,719 --> 00:53:36,120
Speaker 1: the ones treating these clinic patients, and then you say

853
00:53:36,200 --> 00:53:40,360
Speaker 1: to the doc, uh, this is what I'm ordering. So

854
00:53:41,520 --> 00:53:44,440
Speaker 1: when I was reading this, I was very much like,

855
00:53:44,960 --> 00:53:52,160
Speaker 1: where was the nurse. There was their resident, Yes, there

856
00:53:53,040 --> 00:53:57,560
Speaker 1: there was a resident that spoke to a doctor his

857
00:53:58,080 --> 00:54:03,120
Speaker 1: uh the er doctor, and then they also spoke with

858
00:54:05,160 --> 00:54:10,279
Speaker 1: i think a chief resident about this particular case. And

859
00:54:10,360 --> 00:54:14,000
Speaker 1: so he was just kind of overlooked even though he

860
00:54:14,080 --> 00:54:20,040
Speaker 1: had come in twice. He was sent home viral come

861
00:54:20,080 --> 00:54:26,040
Speaker 1: back in definitely showing symptoms as some type of sepsis.

862
00:54:26,480 --> 00:54:31,560
Speaker 1: But the lawsuit is really focused on why did the

863
00:54:31,600 --> 00:54:35,480
Speaker 1: doctor not do a chest X right because on autopsy

864
00:54:36,200 --> 00:54:45,000
Speaker 1: he had a lung filled with blood, his heart was enlarged,

865
00:54:45,080 --> 00:54:50,120
Speaker 1: his liver was enlarged. So this was something that happened

866
00:54:50,480 --> 00:54:53,280
Speaker 1: like as you know, you don't all of a sudden

867
00:54:53,400 --> 00:54:56,960
Speaker 1: have an enlarged liver like there was something else going

868
00:54:57,000 --> 00:55:00,319
Speaker 1: on with him for a while, and then he was

869
00:55:00,360 --> 00:55:05,200
Speaker 1: having symptoms. But it's really the nurses who are dealing

870
00:55:05,239 --> 00:55:08,080
Speaker 1: with those clinic patients, and they're focused so much on

871
00:55:08,120 --> 00:55:12,080
Speaker 1: the doctors. I would have been really focused on why

872
00:55:12,160 --> 00:55:19,120
Speaker 1: were the nurses not? Like we have n eers. We

873
00:55:19,800 --> 00:55:24,799
Speaker 1: have a checklist for sepsis. We have to do it

874
00:55:24,840 --> 00:55:28,719
Speaker 1: on people who have a fever where their heart rate

875
00:55:28,840 --> 00:55:31,640
Speaker 1: is high with the fever. It's a checklist that we do.

876
00:55:31,880 --> 00:55:39,920
Speaker 1: It's an algorithm and it then inspires a lab, a

877
00:55:40,040 --> 00:55:49,720
Speaker 1: chest x ray, an EKG, lactic acid labs, and blook

878
00:55:49,760 --> 00:55:55,000
Speaker 1: cultures and all of those things are are kicked in

879
00:55:55,600 --> 00:56:01,360
Speaker 1: once you recognize that this patient has some type of

880
00:56:01,360 --> 00:56:07,279
Speaker 1: of an infection that could be septic. And they it

881
00:56:07,600 --> 00:56:15,359
Speaker 1: did actually inspire those those labs and the x ray

882
00:56:15,440 --> 00:56:21,680
Speaker 1: to be to have been done, but no one did them.

883
00:56:22,280 --> 00:56:25,600
Speaker 1: No one like they were trying to order them. The

884
00:56:25,640 --> 00:56:29,760
Speaker 1: resident didn't know how to order it because they wanted

885
00:56:29,800 --> 00:56:35,600
Speaker 1: to pull some things off of the protocol. They only

886
00:56:35,640 --> 00:56:38,840
Speaker 1: wanted certain things done, which you really should not be

887
00:56:38,960 --> 00:56:45,200
Speaker 1: doing in any case. But when you are so busy

888
00:56:46,040 --> 00:56:50,720
Speaker 1: and your er is filled with people that are probably dying,

889
00:56:51,600 --> 00:56:57,080
Speaker 1: or coding, or you've got six ambulances coming in. You

890
00:56:57,280 --> 00:56:59,680
Speaker 1: don't want them to be waiting for an X ray,

891
00:57:00,120 --> 00:57:04,319
Speaker 1: don't want them to be having a cat stand. You

892
00:57:04,600 --> 00:57:09,439
Speaker 1: want them to be a virus. So they are more

893
00:57:09,560 --> 00:57:13,600
Speaker 1: looking for a way to discharge you. That is their

894
00:57:13,640 --> 00:57:17,680
Speaker 1: primary goal. Their primary goal is not to treat you.

895
00:57:18,240 --> 00:57:22,680
Speaker 1: Their primary goal with these low level patients is to

896
00:57:22,880 --> 00:57:25,000
Speaker 1: get you the fuck out of the er so that

897
00:57:25,080 --> 00:57:29,240
Speaker 1: they can take care of the patients that are, you know,

898
00:57:29,440 --> 00:57:34,720
Speaker 1: actual emergencies. And that's the way every er works. Every

899
00:57:35,040 --> 00:57:40,320
Speaker 1: single er works this way. And his death this is

900
00:57:40,360 --> 00:57:44,040
Speaker 1: not a rare case. This is not rare. This is

901
00:57:44,080 --> 00:57:50,600
Speaker 1: something I am certain that happens all the time. We

902
00:57:50,760 --> 00:57:55,280
Speaker 1: just don't hear about it because people don't usually sue.

903
00:57:55,480 --> 00:57:58,560
Speaker 2: His y his parents, and his parents are the reason

904
00:57:58,640 --> 00:58:01,040
Speaker 2: that we're hearing about it because yes.

905
00:58:00,920 --> 00:58:03,960
Speaker 3: Well, in the research too, his dad said he found

906
00:58:03,960 --> 00:58:07,200
Speaker 3: that more than two hundred thousand people will die each

907
00:58:07,280 --> 00:58:10,400
Speaker 3: year from preventable medical errors. And for my purpose, they

908
00:58:10,440 --> 00:58:13,160
Speaker 3: had estimated it to the amount of at least one

909
00:58:13,200 --> 00:58:14,960
Speaker 3: fatal bowing crash a week.

910
00:58:15,320 --> 00:58:16,600
Speaker 2: Yes, think about that.

911
00:58:16,960 --> 00:58:22,000
Speaker 1: Yes, and that is that is exactly what is happening.

912
00:58:22,040 --> 00:58:26,040
Speaker 1: In our ers because we're not equipped to be clinics.

913
00:58:26,800 --> 00:58:32,680
Speaker 1: We're just not. And some ers are doing better by

914
00:58:32,760 --> 00:58:37,640
Speaker 1: having like an urgent care area so that they are

915
00:58:39,200 --> 00:58:44,880
Speaker 1: connected to a clinic and then the actual emergent cases

916
00:58:45,240 --> 00:58:48,360
Speaker 1: are in the ers. But for the most part, and

917
00:58:48,400 --> 00:58:50,920
Speaker 1: it's going to get worse. It's going to get much

918
00:58:50,960 --> 00:58:57,000
Speaker 1: worse because if we start losing medicage. People do not

919
00:58:57,280 --> 00:59:04,760
Speaker 1: understand that when people come to the er to receive

920
00:59:04,880 --> 00:59:09,960
Speaker 1: treatment and they do not have insurance, it's the hospital

921
00:59:10,120 --> 00:59:14,240
Speaker 1: reaching for those Medicaid dollars. It's not necessarily that that

922
00:59:14,480 --> 00:59:18,160
Speaker 1: patient is covered under medicaing. It's that there's a certain

923
00:59:18,160 --> 00:59:21,960
Speaker 1: amount of Medicaid dollars that the er can pull from

924
00:59:22,360 --> 00:59:25,560
Speaker 1: so that we can take care of the emergencies even

925
00:59:25,600 --> 00:59:29,360
Speaker 1: if they don't have insurance. Pretty soon, those Medicaid dollars

926
00:59:29,400 --> 00:59:33,040
Speaker 1: are not going to be there, and ers are the

927
00:59:33,120 --> 00:59:36,320
Speaker 1: place that they're going to suffer because there's going to

928
00:59:36,360 --> 00:59:40,960
Speaker 1: be more people without insurance, more people without primary care,

929
00:59:41,680 --> 00:59:47,400
Speaker 1: and the ers are already flooded. It's going to be

930
00:59:47,440 --> 00:59:48,240
Speaker 1: an epidemic.

931
00:59:49,360 --> 00:59:52,280
Speaker 2: See this is an interesting thing because I talk about

932
00:59:52,320 --> 00:59:55,120
Speaker 2: this with other things, just like you hear me on

933
00:59:55,200 --> 00:59:57,800
Speaker 2: every episode bitching about my car and just like all

934
00:59:57,800 --> 01:00:00,120
Speaker 2: this progress that we're having and I just don't think

935
01:00:00,160 --> 01:00:03,000
Speaker 2: things are getting better. And it's like I think about

936
01:00:03,000 --> 01:00:04,960
Speaker 2: when I was a kid. There was no such thing

937
01:00:05,000 --> 01:00:06,760
Speaker 2: as urgent care when I was a kid, so it

938
01:00:06,840 --> 01:00:08,640
Speaker 2: was like you either went to your doctor or you

939
01:00:08,720 --> 01:00:11,920
Speaker 2: went to the emergency room. Yes, so they have urgent

940
01:00:11,960 --> 01:00:15,760
Speaker 2: cares on every corner now, at least around here, And

941
01:00:16,040 --> 01:00:18,640
Speaker 2: You're like, why are the er so flooded still if

942
01:00:18,680 --> 01:00:22,520
Speaker 2: they have all this this like a triage centers that

943
01:00:22,520 --> 01:00:25,080
Speaker 2: are supposed to be taking care of all of these

944
01:00:25,160 --> 01:00:28,960
Speaker 2: low level emergencies, you know, like, why are it? Like,

945
01:00:29,040 --> 01:00:30,680
Speaker 2: what's happening? What's the problem?

946
01:00:30,960 --> 01:00:35,000
Speaker 3: Do you think obviously healthcare is the number one contributing

947
01:00:35,040 --> 01:00:37,600
Speaker 3: factor to this, But do you think people also having

948
01:00:37,640 --> 01:00:40,480
Speaker 3: access to the internet and googling their symptoms and going

949
01:00:40,520 --> 01:00:43,520
Speaker 3: on web md and possibly seeing something horrible that that's

950
01:00:43,520 --> 01:00:47,360
Speaker 3: triggering more people to go than normal to I.

951
01:00:47,360 --> 01:00:51,040
Speaker 1: Don't think so, because I mean I've been a nurse

952
01:00:51,160 --> 01:00:57,320
Speaker 1: for thirty seven years now, so I have seen the

953
01:00:57,600 --> 01:01:02,280
Speaker 1: full arc of it, you know, working trauma back in

954
01:01:02,320 --> 01:01:07,160
Speaker 1: the eighties and then working trauma in the twenty twenties.

955
01:01:09,360 --> 01:01:15,560
Speaker 1: It's I think that what it has done more than

956
01:01:15,680 --> 01:01:26,040
Speaker 1: anything is and bold and people to advocate incorrectly for

957
01:01:26,160 --> 01:01:34,360
Speaker 1: themselves and for their loved ones. I support advocacy for

958
01:01:34,400 --> 01:01:42,000
Speaker 1: your loved one, for yourself, however, it's it's also they're

959
01:01:42,040 --> 01:01:46,000
Speaker 1: advocating for things that are just not even like they're

960
01:01:46,720 --> 01:01:52,000
Speaker 1: you know, web MD is. I think it can be

961
01:01:52,080 --> 01:01:56,080
Speaker 1: a contributing factor for more people coming in because they

962
01:01:56,160 --> 01:01:59,520
Speaker 1: think they're dying rather than or they have cancer or

963
01:02:00,160 --> 01:02:05,720
Speaker 1: but I just think that the whole system is so

964
01:02:05,880 --> 01:02:14,440
Speaker 1: broken at this time that we see everything as an emergency,

965
01:02:14,520 --> 01:02:23,960
Speaker 1: and we also see that we have this immediate gratification

966
01:02:24,440 --> 01:02:29,960
Speaker 1: society that we don't want to wait to go to

967
01:02:30,720 --> 01:02:35,320
Speaker 1: our own primary care. We don't want to wait for

968
01:02:35,360 --> 01:02:40,080
Speaker 1: the urgent care to wait to open up tomorrow. So

969
01:02:40,760 --> 01:02:44,360
Speaker 1: I think people end up going to emergency. A lot

970
01:02:44,400 --> 01:02:46,680
Speaker 1: of people, I think also think that if they go

971
01:02:46,880 --> 01:02:50,800
Speaker 1: to the emergency room, then they're going to have access

972
01:02:50,960 --> 01:02:55,760
Speaker 1: to more high level of care like MRIs and cts.

973
01:02:55,800 --> 01:03:02,320
Speaker 1: And but I think it's a healthy point that yes,

974
01:03:02,520 --> 01:03:13,240
Speaker 1: they're all web mdying themselves, but the level of influence

975
01:03:13,320 --> 01:03:16,320
Speaker 1: that it has on people coming to the er I

976
01:03:16,360 --> 01:03:21,000
Speaker 1: think is smaller than you may think.

977
01:03:21,960 --> 01:03:24,840
Speaker 2: You know what else I think is a problem. For example,

978
01:03:25,360 --> 01:03:28,680
Speaker 2: like remember Ray you were having she was having like

979
01:03:28,760 --> 01:03:33,200
Speaker 2: some severe probably it was gastritis in the end a

980
01:03:33,240 --> 01:03:36,680
Speaker 2: couple of years ago. And it's like you call, you're

981
01:03:36,720 --> 01:03:39,600
Speaker 2: having and and it's it's not an emergency, but it's

982
01:03:39,680 --> 01:03:40,680
Speaker 2: highly uncomfortable.

983
01:03:40,960 --> 01:03:41,120
Speaker 1: Right.

984
01:03:41,720 --> 01:03:45,920
Speaker 3: Well, I had also gone to the doctor five times before,

985
01:03:46,960 --> 01:03:47,520
Speaker 3: but this is.

986
01:03:47,480 --> 01:03:51,040
Speaker 2: The family doctor. This is the family doctor, right, So

987
01:03:51,040 --> 01:03:52,920
Speaker 2: so then the next thing is, Okay, you're gonna have

988
01:03:52,960 --> 01:03:54,840
Speaker 2: to see a specialist. Will you make the appointment for

989
01:03:54,880 --> 01:03:58,320
Speaker 2: the gi doctor? And they I mean sometimes I call specialists,

990
01:03:58,400 --> 01:04:02,080
Speaker 2: especially from my daughter, Yeah that my little one, you know,

991
01:04:02,200 --> 01:04:05,280
Speaker 2: and they're like, oh, there's an appointment in eight months.

992
01:04:05,600 --> 01:04:08,920
Speaker 2: Yeah yeah, And just like people are like, okay, I'm

993
01:04:08,920 --> 01:04:10,520
Speaker 2: not going to feel like this for eight months, so

994
01:04:10,600 --> 01:04:12,280
Speaker 2: like I'm going to go to the emergency room and

995
01:04:12,320 --> 01:04:13,400
Speaker 2: maybe they'll give me something.

996
01:04:13,880 --> 01:04:14,080
Speaker 1: Yeah.

997
01:04:14,120 --> 01:04:15,920
Speaker 3: Well that was one of those I had gone to

998
01:04:15,960 --> 01:04:19,480
Speaker 3: my family I lost like thirty pounds in a month

999
01:04:19,600 --> 01:04:21,840
Speaker 3: or something. I was so sick. Anything I ate was

1000
01:04:21,840 --> 01:04:25,000
Speaker 3: making me sick. Couldn't figure it out. It hit very suddenly.

1001
01:04:25,600 --> 01:04:27,680
Speaker 3: I went to my primary care doctor. They were doing

1002
01:04:27,720 --> 01:04:30,160
Speaker 3: everything they could possibly do. They giving me all these

1003
01:04:30,160 --> 01:04:33,160
Speaker 3: different meds. Nothing was helping. And one night I was

1004
01:04:33,200 --> 01:04:36,400
Speaker 3: just throwing up so bad my stomach. I felt like

1005
01:04:36,400 --> 01:04:39,040
Speaker 3: like death right, And that was why I went. And

1006
01:04:39,080 --> 01:04:42,280
Speaker 3: it ended up long term being a glute anallergy that

1007
01:04:42,320 --> 01:04:43,360
Speaker 3: I was unaware of.

1008
01:04:44,600 --> 01:04:48,520
Speaker 2: But at the time and ask me sometimes like you can't.

1009
01:04:48,720 --> 01:04:50,880
Speaker 2: If you can't give yourself one like you got it,

1010
01:04:50,960 --> 01:04:52,960
Speaker 2: you gotta go to the hospital because.

1011
01:04:54,200 --> 01:04:56,720
Speaker 3: Yeah, and I couldn't get into the gi doctor like

1012
01:04:56,760 --> 01:05:00,240
Speaker 3: you're saying, for months. So that's why I went, because

1013
01:05:00,240 --> 01:05:03,800
Speaker 3: I'm like, I was convinced I had ulcers or stomach

1014
01:05:03,800 --> 01:05:04,840
Speaker 3: cancer or something that is.

1015
01:05:04,880 --> 01:05:06,040
Speaker 2: How badly it hurts.

1016
01:05:06,840 --> 01:05:10,720
Speaker 3: Yeah, And I have no interest in going. I hate

1017
01:05:10,760 --> 01:05:12,960
Speaker 3: the doctor. I hate being there. It makes me sweaty.

1018
01:05:13,240 --> 01:05:15,280
Speaker 3: I have no interest in going there. I only go

1019
01:05:15,320 --> 01:05:19,000
Speaker 3: to the hospital if it's like an absolute dire situation.

1020
01:05:19,160 --> 01:05:23,680
Speaker 1: And that's the other challenge is you're coming in. You

1021
01:05:23,720 --> 01:05:30,720
Speaker 1: look like a young, healthy adult with abdominal paint. Abdominal

1022
01:05:30,800 --> 01:05:36,680
Speaker 1: pains are probably the number one reason people go to

1023
01:05:37,120 --> 01:05:45,200
Speaker 1: the er. So all ready there's a judgment in triage already,

1024
01:05:45,440 --> 01:05:50,880
Speaker 1: Oh geez, your belly hurts, probably have to poop, you

1025
01:05:50,920 --> 01:05:55,360
Speaker 1: probably have gas or take it pregnant like this, there's yeah,

1026
01:05:55,520 --> 01:05:58,960
Speaker 1: like you kind of like do the eye roll because

1027
01:05:59,360 --> 01:06:03,120
Speaker 1: you would us tree understand forty people with a bembo

1028
01:06:03,280 --> 01:06:09,920
Speaker 1: pain and that's I, you know, I it just becomes

1029
01:06:11,000 --> 01:06:19,760
Speaker 1: really dangerous because healthcare is so overwhelmed right now. Ers

1030
01:06:19,800 --> 01:06:26,800
Speaker 1: are so overwhelmed that they they're missing things because they

1031
01:06:26,960 --> 01:06:33,640
Speaker 1: have to do leap frog over these patients that look

1032
01:06:33,720 --> 01:06:34,560
Speaker 1: young and healthy.

1033
01:06:36,040 --> 01:06:38,800
Speaker 2: So in this particular case, like I don't know if

1034
01:06:38,840 --> 01:06:41,760
Speaker 2: you have any advice for this, but this kid was

1035
01:06:41,880 --> 01:06:46,240
Speaker 2: texting his family and saying, oh, I can't believe it's

1036
01:06:46,240 --> 01:06:49,200
Speaker 2: still a virus. I thought I was dying because that's

1037
01:06:49,200 --> 01:06:51,720
Speaker 2: how shitty he felt. And I can imagine that the

1038
01:06:51,800 --> 01:06:54,560
Speaker 2: amount of pulmonary hemorrhage that he had at autopsy, he

1039
01:06:54,640 --> 01:06:58,080
Speaker 2: did feel shitty. Like I'm telling you right now, I

1040
01:06:58,080 --> 01:06:59,080
Speaker 2: can't even relieve that.

1041
01:06:59,880 --> 01:07:05,040
Speaker 1: I'm telling you, if that patient had been mine in

1042
01:07:05,120 --> 01:07:10,720
Speaker 1: the er with the with the vital signs that he had,

1043
01:07:12,080 --> 01:07:15,800
Speaker 1: he would not have left. I would not have discharged him.

1044
01:07:16,160 --> 01:07:21,440
Speaker 1: And that's why I really talk about this about the nurses,

1045
01:07:22,000 --> 01:07:26,000
Speaker 1: because the doctors are not discharging him. The doctors are

1046
01:07:26,000 --> 01:07:30,800
Speaker 1: not doing these labs. The doctors are not figuring out

1047
01:07:30,880 --> 01:07:33,720
Speaker 1: whether they're going to go for a chest decks ray.

1048
01:07:34,280 --> 01:07:40,360
Speaker 1: This is a nurse going in assessing this patient and

1049
01:07:40,400 --> 01:07:45,200
Speaker 1: he's triaged by a nurse. His his vital signs were

1050
01:07:45,240 --> 01:07:52,440
Speaker 1: off and he definitely was showing sepsis. Most likely if

1051
01:07:52,480 --> 01:07:56,760
Speaker 1: they had done a chest des ray, his life would

1052
01:07:56,800 --> 01:07:59,480
Speaker 1: have been you know, and maybe he would have died anyway,

1053
01:07:59,560 --> 01:08:04,240
Speaker 1: because the autopsy, I think you read, they didn't really

1054
01:08:04,360 --> 01:08:08,680
Speaker 1: have a reason why his lung was filled with blood.

1055
01:08:09,280 --> 01:08:12,040
Speaker 2: So I think just from what I read from it,

1056
01:08:11,760 --> 01:08:16,400
Speaker 2: it's that he was showing some signs of sepsis, but

1057
01:08:17,400 --> 01:08:20,519
Speaker 2: all of the things weren't glowing up, which is why

1058
01:08:20,600 --> 01:08:22,519
Speaker 2: I think that they were like, we're going to try

1059
01:08:22,520 --> 01:08:25,080
Speaker 2: to bypass some of this stuff because his I think

1060
01:08:25,120 --> 01:08:30,440
Speaker 2: his blood cultures were negative, which even when they checked them. Yeah,

1061
01:08:30,800 --> 01:08:34,679
Speaker 2: so when they did the autopsy, they said his blood

1062
01:08:34,720 --> 01:08:39,240
Speaker 2: cultures on the second visit were negative. Took so they're saying,

1063
01:08:39,320 --> 01:08:44,320
Speaker 2: like he's he this is the thing, like he's a

1064
01:08:44,360 --> 01:08:48,800
Speaker 2: weird case. He's an atypical presentation of he probably has

1065
01:08:48,920 --> 01:08:52,840
Speaker 2: like evasculatus or a clotting disorder or something right that's

1066
01:08:52,960 --> 01:08:56,880
Speaker 2: causing him to bleed like that. But the bottom line

1067
01:08:56,920 --> 01:08:59,800
Speaker 2: is is that he had all of this stuff that

1068
01:09:00,040 --> 01:09:02,960
Speaker 2: should have been looked into. And if they did just

1069
01:09:03,040 --> 01:09:06,240
Speaker 2: simply do a chest X right, if his heart was

1070
01:09:06,360 --> 01:09:08,600
Speaker 2: enlarged at autopsy, it would have looked enlarged on the

1071
01:09:08,680 --> 01:09:12,400
Speaker 2: imaging at least like a basic X ray. And they

1072
01:09:12,439 --> 01:09:15,880
Speaker 2: also would have sold that he had consolidation in his lungs.

1073
01:09:16,200 --> 01:09:19,360
Speaker 2: And yes, I don't know how they didn't hear that.

1074
01:09:20,280 --> 01:09:21,519
Speaker 2: I don't know what that would sound like.

1075
01:09:21,920 --> 01:09:26,559
Speaker 1: They don't actually listen to their lungs. That's the other

1076
01:09:26,640 --> 01:09:30,519
Speaker 1: part of this is that the doctors come in, they

1077
01:09:30,560 --> 01:09:34,080
Speaker 1: do it once over. They read the nurse's note, they

1078
01:09:34,160 --> 01:09:37,920
Speaker 1: read the triage note, they look at the Bible signs,

1079
01:09:38,320 --> 01:09:42,200
Speaker 1: and should they do their little YadA YadA, I've seen you.

1080
01:09:43,760 --> 01:09:48,280
Speaker 1: Now I'm going back to my coupy uh to write

1081
01:09:48,280 --> 01:09:54,599
Speaker 1: on my computer. That's the problem that that right there.

1082
01:09:55,320 --> 01:09:58,559
Speaker 2: So when so I'm curious about this, So when the

1083
01:09:58,920 --> 01:10:01,800
Speaker 2: so when the doctor walked let's say this was your patient.

1084
01:10:01,840 --> 01:10:04,720
Speaker 2: If the doctor walked in and you said, yeah, he's

1085
01:10:04,760 --> 01:10:07,200
Speaker 2: got a virus, is that what you would do? You

1086
01:10:07,240 --> 01:10:09,080
Speaker 2: would be like, yeah, this is what I think it is,

1087
01:10:10,680 --> 01:10:12,280
Speaker 2: so yes.

1088
01:10:12,120 --> 01:10:19,400
Speaker 1: And no, I I it's it's really a keen eye

1089
01:10:20,000 --> 01:10:26,360
Speaker 1: and er nurses are amazing at picking up on these things.

1090
01:10:26,800 --> 01:10:31,519
Speaker 1: But when you're really really busy, like there's no you

1091
01:10:31,760 --> 01:10:37,160
Speaker 1: don't have a maximum assignment in the ER. So like

1092
01:10:37,400 --> 01:10:41,200
Speaker 1: if you're in the ICU, you know you max out

1093
01:10:41,280 --> 01:10:46,599
Speaker 1: on patients. You get three patients. Most mostly it's one

1094
01:10:46,720 --> 01:10:50,920
Speaker 1: nurse to three phacians if they're really sick, one to one.

1095
01:10:51,680 --> 01:10:56,680
Speaker 1: Uh usually one to two. But in the ear, I

1096
01:10:56,760 --> 01:11:01,599
Speaker 1: could already have three critical basis that I'm taking care of,

1097
01:11:02,240 --> 01:11:05,439
Speaker 1: and then I still have these clinic patients that I

1098
01:11:05,520 --> 01:11:09,320
Speaker 1: need to take care of. So where is the focus.

1099
01:11:09,439 --> 01:11:13,800
Speaker 1: The focus is going to be on these critical patients? Now,

1100
01:11:14,760 --> 01:11:23,000
Speaker 1: am I saying that nurses or doctors aren't doing their job? I? Yes,

1101
01:11:23,280 --> 01:11:27,200
Speaker 1: actually I am, because there are physicians that go in

1102
01:11:27,320 --> 01:11:32,759
Speaker 1: that don't actually do an assessment. They click, lungs are clear.

1103
01:11:33,439 --> 01:11:39,680
Speaker 1: He was complaining of shortness of breath at rest. He

1104
01:11:39,760 --> 01:11:42,719
Speaker 1: was complaining of shortness like he was having a hard

1105
01:11:42,760 --> 01:11:46,800
Speaker 1: time even getting over to the bathroom. He was really

1106
01:11:46,800 --> 01:11:50,880
Speaker 1: really short of breath. No one in that age group

1107
01:11:51,200 --> 01:11:54,840
Speaker 1: should have been short of breath, so that would have

1108
01:11:54,920 --> 01:11:58,920
Speaker 1: inspired me to say, get this kid in X ray,

1109
01:11:59,600 --> 01:12:03,800
Speaker 1: you know, definitely get this kid on some type of oxygen.

1110
01:12:04,320 --> 01:12:09,720
Speaker 1: I cannot believe that his oxygen saturation was was accurate

1111
01:12:09,920 --> 01:12:13,800
Speaker 1: because if his lung was actually filled with blood or

1112
01:12:13,920 --> 01:12:17,800
Speaker 1: he you know, it just doesn't make sense. It just

1113
01:12:17,840 --> 01:12:19,040
Speaker 1: doesn't make any sense.

1114
01:12:19,120 --> 01:12:20,800
Speaker 2: Well, I get mine. I mean, I know that I'm

1115
01:12:20,800 --> 01:12:23,800
Speaker 2: different because I wear acrylic nails, but they take mine

1116
01:12:23,840 --> 01:12:27,920
Speaker 2: all the time, and just like it doesn't work the socks.

1117
01:12:28,120 --> 01:12:29,519
Speaker 1: Yeah, and oh I got to.

1118
01:12:29,560 --> 01:12:31,960
Speaker 2: I gotta put in something, you know, and it's just like, well,

1119
01:12:32,439 --> 01:12:36,680
Speaker 2: that's not accurate because it doesn't work at all. Yeah,

1120
01:12:36,680 --> 01:12:42,920
Speaker 2: whenever you're putting it isn't isn't right. And I get that.

1121
01:12:43,000 --> 01:12:45,720
Speaker 2: I see that the check boxes are good to not

1122
01:12:45,800 --> 01:12:47,720
Speaker 2: have you guys miss stuff, but then that they would

1123
01:12:47,760 --> 01:12:50,479
Speaker 2: also hold you back if you're busy and you're just

1124
01:12:50,520 --> 01:12:51,599
Speaker 2: trying to get around it.

1125
01:12:51,720 --> 01:12:55,840
Speaker 1: No, you click those boxes. Just you're going down and

1126
01:12:55,880 --> 01:12:59,080
Speaker 1: clicking those boxes. Like, I think that was the worst

1127
01:12:59,120 --> 01:13:02,679
Speaker 1: thing that could have happen band to nursing. I think

1128
01:13:02,680 --> 01:13:04,000
Speaker 1: it's the worst thing.

1129
01:13:05,439 --> 01:13:08,880
Speaker 2: Well, yeah, because you're not thinking, no, you're just quick

1130
01:13:09,000 --> 01:13:12,720
Speaker 2: and that's yeah. And I was just curious because when

1131
01:13:12,720 --> 01:13:15,679
Speaker 2: we at least in the jobs I've worked at, either

1132
01:13:15,720 --> 01:13:18,920
Speaker 2: in surgical pathology or autopsy, the pas really are the

1133
01:13:18,920 --> 01:13:20,840
Speaker 2: ones that are like, this is what we think it is,

1134
01:13:20,920 --> 01:13:23,080
Speaker 2: and then you know, I'll do a whole autopsy this

1135
01:13:23,120 --> 01:13:25,920
Speaker 2: is what I think it is, and have the pathologist

1136
01:13:25,960 --> 01:13:27,640
Speaker 2: come in at the very end and be like I

1137
01:13:27,720 --> 01:13:31,840
Speaker 2: found this, this and this, and and like usually they

1138
01:13:31,920 --> 01:13:35,640
Speaker 2: agree with you. You know, yes, some of it's like

1139
01:13:35,680 --> 01:13:39,599
Speaker 2: a psychology thing, because we talk about that the case

1140
01:13:39,640 --> 01:13:42,160
Speaker 2: of Karen Reid. I don't know if you were following

1141
01:13:42,200 --> 01:13:45,679
Speaker 2: that trial, but like when you go in and tell

1142
01:13:45,800 --> 01:13:48,880
Speaker 2: somebody that this is a homicide or this is a

1143
01:13:49,040 --> 01:13:51,800
Speaker 2: this is an accident, and they go in with that

1144
01:13:51,960 --> 01:13:55,720
Speaker 2: preconceived notion in their head already when it sometimes it

1145
01:13:55,800 --> 01:13:59,040
Speaker 2: trips people out instead looking at it objectly.

1146
01:13:59,560 --> 01:14:02,400
Speaker 1: You know, it's I see what you're saying, and you're right,

1147
01:14:02,600 --> 01:14:06,840
Speaker 1: it's very very possible that the nurse who did the

1148
01:14:06,920 --> 01:14:11,479
Speaker 1: triage probably like did the rolling the eyes, saying like

1149
01:14:11,640 --> 01:14:15,200
Speaker 1: we've already seen him. He's just being a baby. He

1150
01:14:15,240 --> 01:14:17,559
Speaker 1: doesn't have mommy and daddy, Like I can see that

1151
01:14:17,720 --> 01:14:25,000
Speaker 1: happening and of course I'm being judgmental, but I was

1152
01:14:25,280 --> 01:14:29,280
Speaker 1: very concerned about the fact that they were only focused

1153
01:14:29,400 --> 01:14:33,920
Speaker 1: on the doctors for that suit. Not that I want

1154
01:14:34,000 --> 01:14:38,600
Speaker 1: any of my nurses to be sued, but the reality

1155
01:14:38,800 --> 01:14:43,560
Speaker 1: is those doctors see those patients for like thirty seconds,

1156
01:14:44,360 --> 01:14:50,240
Speaker 1: and there's not that it's right, but it's the nurses

1157
01:14:50,640 --> 01:14:55,519
Speaker 1: who are actually bedside. It's the nurses who are They

1158
01:14:55,600 --> 01:14:59,040
Speaker 1: are not supposed to be giving any kind of diagnosis,

1159
01:14:59,240 --> 01:15:01,040
Speaker 1: but A, of course we are.

1160
01:15:01,920 --> 01:15:07,080
Speaker 2: Of course we are, exactly. The whole thing is just

1161
01:15:08,640 --> 01:15:11,400
Speaker 2: I don't know, it's just it's really scary. From a

1162
01:15:11,439 --> 01:15:13,360
Speaker 2: patient's perspective.

1163
01:15:13,000 --> 01:15:17,080
Speaker 1: It is very, very scary. And I think that this

1164
01:15:17,280 --> 01:15:22,400
Speaker 1: kid was just too shy to be an advocate for himself,

1165
01:15:22,560 --> 01:15:26,719
Speaker 1: Like he could have easily put his foot down and said,

1166
01:15:27,360 --> 01:15:31,799
Speaker 1: I really want a chest sticks right, like I cannot breathe.

1167
01:15:31,920 --> 01:15:39,800
Speaker 1: I can't freaking breathe. And maybe if he had done that,

1168
01:15:40,720 --> 01:15:43,200
Speaker 1: we would be, you know, we wouldn't even be having

1169
01:15:43,240 --> 01:15:47,639
Speaker 1: this conversation. However, if he was already that far gone

1170
01:15:47,720 --> 01:15:56,000
Speaker 1: with some type of weird vascular disease or you know,

1171
01:15:56,520 --> 01:15:59,960
Speaker 1: it's hard to say. It's hard to say whether he'd

1172
01:16:00,080 --> 01:16:00,960
Speaker 1: still be alive or not.

1173
01:16:01,760 --> 01:16:05,719
Speaker 2: It's interesting because we really don't know the true number

1174
01:16:05,760 --> 01:16:08,200
Speaker 2: of how many times this happens, Like Maria was saying

1175
01:16:08,200 --> 01:16:11,840
Speaker 2: that crazy statistic, because these people didn't even know that

1176
01:16:11,880 --> 01:16:15,080
Speaker 2: this kid died until this lawsuit hit them. Yeah, and

1177
01:16:15,120 --> 01:16:19,120
Speaker 2: how many times. I mean as the nurse is really

1178
01:16:19,160 --> 01:16:21,479
Speaker 2: never going to the nurses or the doctors are never

1179
01:16:21,520 --> 01:16:23,439
Speaker 2: really going to learn because they're not getting a call

1180
01:16:23,479 --> 01:16:26,040
Speaker 2: the next day like yo, that guy that you released

1181
01:16:26,200 --> 01:16:29,680
Speaker 2: is dead. They how would they know? And most of

1182
01:16:29,720 --> 01:16:32,120
Speaker 2: the time they don't know because people aren't suing.

1183
01:16:33,160 --> 01:16:39,720
Speaker 1: We would only find out about those cases if they're

1184
01:16:40,160 --> 01:16:45,160
Speaker 1: you know, obviously in the newspaper, you know, back in

1185
01:16:45,200 --> 01:16:50,080
Speaker 1: the day where they made it to social media and

1186
01:16:50,160 --> 01:16:53,800
Speaker 1: someone actually saw the name and recognized it. But there's

1187
01:16:53,920 --> 01:16:58,679
Speaker 1: like thousands and thousands of patients that go through these ers,

1188
01:16:59,439 --> 01:17:04,600
Speaker 1: and you can't like there's you're you're not going to

1189
01:17:04,640 --> 01:17:07,479
Speaker 1: be able to be like, oh my gosh, that kid.

1190
01:17:07,600 --> 01:17:11,080
Speaker 1: I can't believe it unless someone truly points it out

1191
01:17:11,080 --> 01:17:16,200
Speaker 1: to you, and yeah, so I I it is not.

1192
01:17:16,760 --> 01:17:20,080
Speaker 1: I do not believe that it's a rare thing that

1193
01:17:20,200 --> 01:17:24,920
Speaker 1: people are being misdiagnosed in the er and sent home

1194
01:17:25,200 --> 01:17:30,519
Speaker 1: and they die. I think that it's way common as well.

1195
01:17:31,040 --> 01:17:35,160
Speaker 2: Thanks for that, Amy, because you really you're supposed to

1196
01:17:35,200 --> 01:17:38,840
Speaker 2: bring cheer and now you're like, everyone is going to die.

1197
01:17:39,120 --> 01:17:40,080
Speaker 2: I know, I can't.

1198
01:17:40,200 --> 01:17:42,680
Speaker 1: I feel so bad about saying too.

1199
01:17:42,840 --> 01:17:46,400
Speaker 2: Oh, it's it's true though, like, and that's it's important

1200
01:17:46,400 --> 01:17:50,479
Speaker 2: for us to talk about this, because I personally didn't know,

1201
01:17:50,560 --> 01:17:52,400
Speaker 2: like if they said to me, like, you're cool, you

1202
01:17:52,439 --> 01:17:54,960
Speaker 2: could go. I didn't really know that I could be like, no,

1203
01:17:55,360 --> 01:17:57,759
Speaker 2: I can't breathe and I really want a chest to excit.

1204
01:17:57,840 --> 01:17:59,840
Speaker 2: I ain't leave in this place until you X ray.

1205
01:17:59,760 --> 01:18:04,120
Speaker 1: My Oh yeah, yeah yeah. Advocate for yourself, Advocate for

1206
01:18:04,200 --> 01:18:09,720
Speaker 1: your left one. I remember when my oldest daughter, I

1207
01:18:09,760 --> 01:18:16,639
Speaker 1: think she was nineteen twenty at the time and twenty

1208
01:18:17,439 --> 01:18:20,600
Speaker 1: and I happened to be visiting her down here. She

1209
01:18:20,760 --> 01:18:24,479
Speaker 1: was already living in Florida and I happened to be

1210
01:18:24,600 --> 01:18:31,160
Speaker 1: visiting and she had such severe abdominal pain and her

1211
01:18:31,400 --> 01:18:36,840
Speaker 1: boyfriend now husband at the time said called me at

1212
01:18:36,880 --> 01:18:39,320
Speaker 1: like four o'clock in the morning and said that Alex

1213
01:18:39,600 --> 01:18:42,400
Speaker 1: is in severe bean I'm taking her to the er.

1214
01:18:43,000 --> 01:18:46,960
Speaker 1: And I was like, what, Like, Alex would never do

1215
01:18:47,000 --> 01:18:50,800
Speaker 1: that like what you would literally have to drag her

1216
01:18:51,560 --> 01:18:55,839
Speaker 1: because she passes out to even saying the word blood.

1217
01:18:57,200 --> 01:19:01,720
Speaker 1: We got we got to the er. I show up.

1218
01:19:02,840 --> 01:19:09,679
Speaker 1: The nurses are nasty, mean, just mean, And of course

1219
01:19:09,880 --> 01:19:13,679
Speaker 1: Mommy then shows up for this twenty year old and

1220
01:19:15,479 --> 01:19:19,040
Speaker 1: they like, they did an assessment on her. I did

1221
01:19:19,080 --> 01:19:23,520
Speaker 1: my own assessment on her, and I'm like, she has pencitis.

1222
01:19:23,720 --> 01:19:28,200
Speaker 1: She absolutely has a pendicitis. So they came in and

1223
01:19:28,200 --> 01:19:32,320
Speaker 1: they're like, we're gonna give her some you know, anti gas,

1224
01:19:32,439 --> 01:19:37,679
Speaker 1: and I'm like, she needs cat scan and she needs

1225
01:19:37,720 --> 01:19:42,799
Speaker 1: to be npo and we need a cat scan. And

1226
01:19:43,120 --> 01:19:47,679
Speaker 1: the doctor rolled his eyes and he's like, oh my god,

1227
01:19:48,160 --> 01:19:50,679
Speaker 1: you know, And I said, I'm an e R nurse.

1228
01:19:51,479 --> 01:19:56,320
Speaker 1: I I need her to have a cat scam. So

1229
01:19:56,880 --> 01:20:00,240
Speaker 1: he yells out from the room to the nurs is.

1230
01:20:00,840 --> 01:20:05,439
Speaker 1: Mom wants a cat scan, So go little girls getting

1231
01:20:05,439 --> 01:20:11,240
Speaker 1: a cat scan. Well, guess what her appendix had wrapped

1232
01:20:11,280 --> 01:20:16,840
Speaker 1: around her colon, was cutting off her colon and was

1233
01:20:16,880 --> 01:20:23,320
Speaker 1: about and had already started to leak out. So of

1234
01:20:23,439 --> 01:20:27,880
Speaker 1: course they like whisker away. No one ever apologized to me,

1235
01:20:28,720 --> 01:20:34,920
Speaker 1: but they whisker away into emergency surgery and luckily she

1236
01:20:35,400 --> 01:20:39,439
Speaker 1: you know, they got it in time. But that is

1237
01:20:39,600 --> 01:20:43,240
Speaker 1: the way that they treat people. And I'm I'm not

1238
01:20:43,360 --> 01:20:45,760
Speaker 1: afraid to be an advocate. I'm also not afraid to

1239
01:20:45,800 --> 01:20:49,839
Speaker 1: be wrong. I'm not afraid to be like, oh good, okay,

1240
01:20:50,000 --> 01:20:53,639
Speaker 1: we had the cat scan. It's fine, good, I'm glad.

1241
01:20:54,000 --> 01:20:56,439
Speaker 1: I don't I don't care whether I look like an idiot.

1242
01:20:56,920 --> 01:21:00,799
Speaker 1: But most people, I think, you know, they don't want

1243
01:21:00,840 --> 01:21:08,800
Speaker 1: to be that, like just assert it and they need to.

1244
01:21:11,200 --> 01:21:15,439
Speaker 2: Well, on that note, we're it's it's good that you're

1245
01:21:15,479 --> 01:21:18,040
Speaker 2: saying that though, for sure, But what do you are

1246
01:21:18,040 --> 01:21:21,200
Speaker 2: you working on anything else? Like, what's what's going on

1247
01:21:21,240 --> 01:21:23,280
Speaker 2: in Amy's life right now? I know that you were

1248
01:21:23,320 --> 01:21:27,080
Speaker 2: just on some ridiculous trip. Yes, it was so awesome.

1249
01:21:27,920 --> 01:21:31,439
Speaker 1: So I live in France part time now, Oh yeah,

1250
01:21:31,600 --> 01:21:36,479
Speaker 1: that's so. That is Yeah, that is just my life.

1251
01:21:37,000 --> 01:21:42,560
Speaker 1: And now I am kind of working on a project

1252
01:21:42,720 --> 01:21:47,439
Speaker 1: with Charles Graper, which we've been working on for a

1253
01:21:47,520 --> 01:21:51,400
Speaker 1: couple of years, but we both keep getting distracted. But

1254
01:21:51,560 --> 01:21:59,320
Speaker 1: for the most part, I am just pretty much retired,

1255
01:21:59,640 --> 01:22:04,799
Speaker 1: like doing not a lot of anything, like really hanging

1256
01:22:04,800 --> 01:22:10,639
Speaker 1: out with my granddaughters and reading and watching a lot

1257
01:22:10,640 --> 01:22:14,000
Speaker 1: of love is blind. I love that.

1258
01:22:14,360 --> 01:22:17,200
Speaker 2: I love that too, well. Thanks so much for being here.

1259
01:22:17,240 --> 01:22:21,880
Speaker 2: That's I mean, there's there's absolutely that's the best life, right,

1260
01:22:22,200 --> 01:22:25,960
Speaker 2: just enjoying life. You've been through a lot, guys. Listen

1261
01:22:26,040 --> 01:22:29,479
Speaker 2: to her last thing and you'll know why she just

1262
01:22:29,640 --> 01:22:32,800
Speaker 2: like should be chilling right now because you live through

1263
01:22:32,840 --> 01:22:36,680
Speaker 2: more than the normal person lives through. So thanks for

1264
01:22:36,720 --> 01:22:39,439
Speaker 2: being here, and thanks for all of your info. For

1265
01:22:39,520 --> 01:22:41,600
Speaker 2: the er visits. It's going to be helpful to a

1266
01:22:41,640 --> 01:22:42,240
Speaker 2: lot of people.

1267
01:22:42,680 --> 01:22:44,880
Speaker 1: It might piss off a few nurses.

1268
01:22:45,720 --> 01:22:47,120
Speaker 2: Oh, they know it's true.

1269
01:22:47,600 --> 01:22:54,680
Speaker 3: People want to get angry at anything. A sorry, thank

1270
01:22:54,760 --> 01:22:56,080
Speaker 3: you and come back.

1271
01:22:56,160 --> 01:23:04,200
Speaker 2: Please absolutely thank you for listening to Mother Knows Death.

1272
01:23:04,880 --> 01:23:08,640
Speaker 2: As a reminder, my training is as a pathologist's assistant.

1273
01:23:09,360 --> 01:23:12,840
Speaker 2: I have a master's level education and specialize in anatomy

1274
01:23:12,880 --> 01:23:16,479
Speaker 2: and pathology education. I am not a doctor, and I

1275
01:23:16,520 --> 01:23:20,320
Speaker 2: have not diagnosed or treated anyone dead or alive without

1276
01:23:20,360 --> 01:23:25,760
Speaker 2: the assistance of a licensed medical doctor. This show, my website,

1277
01:23:25,840 --> 01:23:29,200
Speaker 2: and social media accounts are designed to educate and inform

1278
01:23:29,280 --> 01:23:33,519
Speaker 2: people based on my experience working in pathology, so they

1279
01:23:33,560 --> 01:23:36,960
Speaker 2: can make healthier decisions regarding their life and well being.

1280
01:23:38,080 --> 01:23:40,960
Speaker 2: Always Remember that science is changing every day, and the

1281
01:23:41,000 --> 01:23:44,280
Speaker 2: opinions expressed in this episode are based on my knowledge

1282
01:23:44,320 --> 01:23:47,960
Speaker 2: of those subjects at the time of publication. If you

1283
01:23:48,000 --> 01:23:51,880
Speaker 2: are having a medical problem, have a medical question, or

1284
01:23:52,080 --> 01:23:56,320
Speaker 2: having a medical emergency, please contact your physician or visit

1285
01:23:56,360 --> 01:24:01,679
Speaker 2: an urgent care center, emergency room, or hospital. Please rate, review,

1286
01:24:01,800 --> 01:24:06,200
Speaker 2: and subscribe to Mother Knows Death on Apple, Spotify, YouTube,

1287
01:24:06,439 --> 01:24:16,160
Speaker 2: or anywhere you get podcasts. Thanks