So, this episode is… well, it’s a little personal. I thought I’d kick things off by sharing a bit from a letter I wrote to Carol—my own handwriting, or well, I guess these days it’s mostly me pecking at a keyboard with one decent hand, but it’s, it’s the real deal. I wrote, “I am writing it because I am having trouble talking. I love you Carol.” It’s a pretty simple line, but it says a lot. When your voice isn’t working—because of health stuff, surgeries, whatever—you realize just how fragile everyday connections can be. You don’t think about it until suddenly you, you can’t do it.That’s why, honestly, alternative ways to communicate end up meaning everything. I mean, historically, look at someone like Stephen Hawking, right? The guy was brilliant, but ALS took away his speech—and tech gave it back, kind of. The computer voice, the cheek muscle sensors—without all that, he’d never have been able to keep sharing his ideas. Nowadays, we’ve got predictive text, hell, even AI tools that sort of figure out what you’re trying to say before you say it. There was a time I would have rolled my eyes at autocorrect, but now? I rely on it almost daily.And on a personal note—I’m not saying this to get sappy—using those AI-powered voice-to-text systems, or even just a simple app that reads out messages aloud, it’s honestly kept me in the loop with the people I care about during some… yeah, some pretty dark stretches. Little things, but they let me say, “Hey Carol, I’m still here.” Where was I going with this? Oh, right—point is, when speech fails, tech steps in, and that matters more than people realize.Now, speaking of tech and connection, let’s talk data—and, alright, yes, this is probably my favorite part. I put this in my letter—“I literally lost 155 pounds, this is the documented.” I actually have spreadsheets, charts, all the stuff you’d expect from someone who’s been doing data profiling and analytics for thirty years. I’m not bragging—well, maybe just a little bit—but seeing that number? It’s a milestone, a checkpoint along the way to real recovery.Here's the thing: in healthcare, data isn’t just about numbers. It’s about tracking progress over time, finding those little signals in all the noise, like when you’re feeling better—or worse—and using that info to guide treatment. There are these case studies, right? Hospitals use everything from wearable devices to patient portals to monitor blood pressure, glucose, all of it. And yeah, I mean, you get automated reminders, trends plotted on dashboards, and suddenly you can spot, say, if recovery's stalling or if there’s a complication bubbling up under the hood.So what about questions like, “How does automated statistical analysis actually help?” Well, if you’re a patient—or a caregiver—you can pick up on patterns you might otherwise miss. Like, “Oh, there’s a weight plateau, do we need to change something?” Or, “Is this spike just a blip, or is it a thing?” I do kind of have to mention, though, you gotta be careful about privacy. Data profiling is insanely useful, but you want to be sure only the right folks are looking at those charts. You don’t want your health story showing up where it shouldn’t. It’s always that balance—the value of insight, but not at the cost of trust.Alright, let’s shift gears just a bit and get to the heart of things—no pun intended. We always talk about data for physical health, but what about how you’re actually feeling? AI these days isn’t just for counting steps or calories. You put your words into a text, an email, heck, maybe a letter like mine, and machines can now run sentiment analysis on it. Like, they pick out stuff—phrases like “I love you” or “stay in contact.” They flag those as positive, emotional signals.You see it in mental health work—text mining of journals, online forums, even therapy sessions that get, uh, transcribed and analyzed for themes: loneliness, gratitude, depression, all that. And it’s not just theory, this actually gives professionals another way to catch a dip in mood, or, honestly, to notice when things are trending up.But—and this is the stickler for me—can a machine really, like, “understand” what’s in a personal letter? I mean, I wrote to Carol, “I love you and always will… I’d love to stay in contact with you every day…In many ways you have saved me.” You can train an algorithm to spot the words, maybe even the sentiment, but does it get the story under the story? That’s the part I always puzzle over. AI’s pretty sharp, but real emotion? Context, history, love, regret—those don’t always fit into neat little data boxes. Maybe someday the models get closer, I don’t know. For now, at least, there’s still something kind of uniquely human about trusting another person with those feelings—way more than a dashboard or a sentiment score ever could do.

Episodes

Data, Love, and Letters