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Chief in Tech S1E29 - Claire Hough : Transforming Access to Quality Healthcare Through Technology · Chief in Tech Deep Dive Shortcast

• 0:00
  Imagine taking the exact software architecture
• 0:03
  used to teach someone how to code online, right?
• 0:06
  And then just using that exact same tech to run
• 0:09
  a mass vaccination site at Dodger Stadium. In
• 0:12
  the middle of a global pandemic, Nellis? Yeah,
• 0:14
  in the middle of the pandemic. I mean, it sounds
• 0:16
  completely absurd. You wouldn't think an e -learning
• 0:19
  platform has like anything to do with emergency
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  medical triage. No, you really wouldn't. Yeah.
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  But the traditional You know, the analog tools
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  were just failing so spectacularly at that point.
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  Right. So a radically different technological
• 0:34
  approach was really the only way forward. It
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  forces this total reevaluation of what a medical
• 0:40
  network actually is. Because when you step into
• 0:42
  the wider world of the U .S. health care system,
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  you're immediately confronted with this, well,
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  the structural landscape that is just totally
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  siloed. Oh, absolutely siloed, frustrating. And
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  I mean, for millions of people, fundamentally
• 0:54
  inaccessible. Right. It operates way. more like
• 0:57
  a collection of walled gardens rather than an
• 1:00
  actual cohesive network designed to keep you
• 1:02
  healthy. So welcome to today's Deep Dive. We
• 1:04
  are so thrilled you're here with us. Today, we
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  are looking at how technology is being leveraged
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  to essentially tear down those walled gardens,
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  right? To re -engineer this broken US health
• 1:16
  care system and make it accessible and equitable
• 1:18
  for you. Yeah, and we're pulling our insights
• 1:21
  today. directly from presentations and detailed
• 1:25
  transcripts featuring Claire Hoff. Who is the
• 1:27
  chief technology officer at Carbon Health, right?
• 1:29
  Correct. And our mission today is to extract
• 1:32
  the actual blueprint of a tech -driven health
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  care future. We're moving entirely beyond those
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  Silicon Valley buzzwords to examine the actual
• 1:41
  operational mechanics. We want to see exactly
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  how data interoperability and proprietary applications
• 1:46
  are practically saving lives. And rescuing doctors
• 1:49
  from administrative collapse on the ground. Which
• 1:52
  is huge. Okay, let's unpack this. Because I am
• 1:54
  just intensely curious about how you even begin
• 1:56
  to tackle a systemic problem of this massive
• 1:59
  scale. It's a massive undertaking, yeah. I mean...
• 2:01
  Re -engineering U .S. health care while it is
• 2:04
  actively functioning, or you know, malfunctioning
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  it sounds like, trying to swap out the engine
• 2:09
  of an airplane while it is actively falling out
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  of the sky. That is a perfect analogy. What's
• 2:14
  fascinating here is the foundational mindset
• 2:17
  that actually initiated this specific operational
• 2:21
  approach. Right, because historically health
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  care in the U .S. has struggled deeply with equity.
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  Yes, and the quality of care is just wildly inconsistent
• 2:31
  depending on your zip code But carbon health
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  approach this by applying an e -learning mindset
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  to the medical field an e -learning mindset Yeah,
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  and that specific architectural philosophy is
• 2:42
  what fundamentally changes the game here Okay,
• 2:44
  let's dig into what that actually means technically
• 2:46
  because that framework comes directly from their
• 2:48
  founder Aaron Bali, right? Exactly. So Aaron
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  Bali founded carbon health back in 2016 But prior
• 2:55
  to stepping into the medical arena, he was actually
• 2:57
  the founder of Udemy. Oh, wow. The e -learning
• 3:00
  marketplace. Right. It's massive. They serve
• 3:01
  over 50 million students globally. I mean, Udemy
• 3:04
  is ubiquitous. But what makes an e -learning
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  platform scale to 50 million users isn't just
• 3:10
  like video hosting. No, not at all. It's the
• 3:13
  backend database architecture. Right. Right?
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  Tracking millions of individual progress states
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  seamlessly. It's modular. It's asynchronous.
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  It's designed to be universally accessible to
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  anyone with a browser. And that is the crucial
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  connection. The entire mission of Udemy was taking
• 3:28
  high quality education digitizing the delivery
• 3:31
  mechanism and making it available instantly.
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  Right. So when Bali exited Udemy and analyzed
• 3:37
  the U .S. health care system, he saw the polar
• 3:39
  opposite of that. He saw the walled gardens.
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  Exactly. Highly localized physical bottlenecks
• 3:45
  where underserved communities are just consistently
• 3:46
  being left out. So he realized that if you could
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  build a software stack to track the educational
• 3:52
  health of 50 million students, you could theoretically
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  build a system to track the physical health of
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  a population with that same seamless borderless
• 3:59
  efficiency. That is an incredibly ambitious vision
• 4:02
  to have back in 2016. But I mean, any theoretical
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  software model eventually has to meet reality.
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  It does. And for the entire global medical system,
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  that reality check was obviously the COVID -19
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  pandemic. Right. And before we can even grasp
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  the mechanics of carbon health's technology today,
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  we have to look at what happened to their infrastructure
• 4:23
  during this period. Because it was the ultimate
• 4:25
  test. Yeah. At the very start of the pandemic,
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  Carbon Health only operated six physical clinics.
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  just sex nationwide. Which is a remarkably small
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  physical footprint when you consider the tidal
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  wave that was approaching. Unbelievably small.
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  But unfazed by that lack of physical infrastructure,
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  they leaned immediately into their e -learning
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  DNA, right? Yes. Organize the information first
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  and make it universally accessible. So their
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  very first major move wasn't even clinical, it
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  was informational. They built a comprehensive
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  nationwide digital index of all the COVID -19
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  testing sites. And the utility I mean, it cannot
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  be overstated. In those chaotic early days, if
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  you typed COVID testing near me into Google,
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  you were almost certainly looking at their proprietary
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  index. Wait, really? That was them? Yeah. Google's
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  algorithms recognized the structured, highly
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  organized data. that Carbon Health had compiled
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  and just ranked it as the top result. Wow. So
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  they basically solved the information bottleneck
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  before they even touched the clinical bottleneck.
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  Exactly. But they did eventually pivot to the
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  clinical side, and they specifically targeted
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  what we'd call health care deserts. Right. Places
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  like East Oakland and the Bayview District of
• 5:39
  San Francisco. Areas that historically lacked
• 5:41
  adequate medical facilities. And they just started
• 5:44
  rapidly deploying these pop -up clinics. Yeah.
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  And as the months progressed into June of 2020,
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  their operational capacity really grew. Businesses,
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  government agencies, school districts, they were
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  all desperate for safe reopening strategies.
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  Sure. Everybody was trying to figure out how
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  to get back to normal. Right. Carbon Health responded
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  by engineering these mobile testing teams that
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  could deploy on site to these organizations,
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  effectively creating decentralized clinics wherever
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  they were needed. But the narrative takes a massive
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  leap in December of 2020. I mean, this is the
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  moment that forces a complete stress test of
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  their software. The Los Angeles situation. Yes.
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  The city of Los Angeles reaches out. LA is facing
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  a catastrophic crisis at this point and during
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  roughly 1 ,500 COVID deaths a week. It was devastating.
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  It was. And the city asks Carbon Health to help
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  roll out vaccines at scale. Within just two weeks
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  of that initial request, Carbon Health transforms
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  Dodger Stadium into a massive vaccination hub.
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  Two weeks. Transforming a sprawling baseball
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  stadium into a highly regulated medical facility
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  in 14 days requires like a logistical synchronization
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  that most municipal governments struggle to achieve
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  in a year. Let's talk about the mechanics of
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  that scale, because going from running a handful
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  of clinics to managing Dodger Stadium overnight.
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  It's unheard of. In traditional IT terms, that's
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  not like an indie band suddenly playing a stadium.
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  That is like a small local website suddenly getting
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  hit with Super Bowl level server traffic. Right.
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  It's a massive concurrent data spike. Yeah. If
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  you just throw 500 iPads at volunteers and tell
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  them to start registering patients, a legacy
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  database is going to lock up entirely when hundreds
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  of people hit submit at the exact same second.
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  Oh, it would crash instantly. So how did their
• 7:29
  entire IT structure not just buckle and crash
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  under that weight? Well, if we connect this to
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  the bigger picture, the pandemic acted as a tragic
• 7:37
  but absolutely flawless proving ground for the
• 7:41
  software architecture they had been quietly building
• 7:43
  since 2016. So they were ready for the traffic.
• 7:46
  Exactly. The underlying reason the system didn't
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  crash is because they bypassed traditional medical
• 7:52
  software entirely. They were utilizing their
• 7:55
  own proprietary provider and patient applications,
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  which were built on modern auto -scaling cloud
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  infrastructure. Oh, so literally the exact same
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  way an e -learning platform dynamically scales
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  server capacity when a million students log on
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  for a test at the same time. That is exactly
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  it. That explains a sheer volume they were able
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  to handle. I mean, scaling from 6 ,000 initial
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  appointments to processing 26 ,000 appointments
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  across seven different sites. It's too critical.
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  They vaccinated over 1 .5 million people in just
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  four to five months. And that volume is staggering.
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  They didn't achieve that by magically hiring
• 8:32
  thousands of administrative workers in two weeks.
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  They achieved it by deploying software designed
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  to ingest, process, and route millions of data
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  points simultaneously without the need for manual
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  human transcription at every single step. But
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  vaccinating a million people is an incredible
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  feat that inherently creates a massive data bottleneck.
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  Always. If those doctors and nurses had to chart
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  those 1 .5 million individual visits on a standard
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  legacy medical system... the administrative backlog
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  would have taken years to clear. Oh, decades
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  maybe. Which forces us to look closely at what
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  carbon health's technological stack actually
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  is. And what were they replacing? What is the
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  status quo of medical software? So the status
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  quo in traditional health care is heavily, heavily
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  fragmented. Hospital networks, private clinics,
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  independent labs. They generally use entirely
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  different database systems. And they don't talk
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  to each other. No, these systems are notoriously
• 9:29
  walled off. The data does not talk freely. And
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  at the absolute center of this fragmentation
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  is the Legacy Electronic Medical Record System,
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  or EMR. Ugh, the infamous EMR. This is the clunky
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  software the doctors perpetually clicking through
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  while they're sitting with you in the exam room,
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  right? The very one. Just looking at their laptop
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  instead of making eye contact with you. Yeah,
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  because legacy EMRs were fundamentally designed
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  as digital filing cabinets for billing and insurance
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  coding. Wait, they weren't built for patient
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  care? No, they were not built to optimize clinical
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  workflows or deliver highly coordinated patient
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  care. It was all about billing. That makes so
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  much sense. Right. So when Aaron Bali evaluated
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  these existing market options, he deemed them
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  structurally incapable of supporting a modern,
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  equitable health care model. So what did they
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  do? They made a highly unusual and expensive
• 10:21
  decision. They engineered their own proprietary
• 10:24
  EMR stack from the ground up. Meaning they built
• 10:26
  a closed loop system, a proprietary app for the
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  clinical providers seamlessly connected in real
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  time to an app for the patients. Yes. And the
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  sources note. Over one million downloads of this
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  patient app, boasting an 80 % usage rate among
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  their active patient base. 80%. Yeah, an 80 %
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  adoption rate for patient -facing medical software
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  is remarkably high. Most patient portals see
• 10:49
  a fraction of that engagement. OK, here's where
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  it gets really interesting, because I have to
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  push back on this adoption metric. OK, let's
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  hear it. Look, you and I both have a dozen health,
• 10:59
  wellness, or fitness apps sitting on our phones
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  right now just gathering digital dust. Guilty
• 11:03
  as charged. Yeah. App fatigue is a very real
• 11:06
  psychological barrier. Furthermore, getting patients
• 11:08
  to proactively input complex medical history
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  or fill out digital forms before they even arrive
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  for an appointment sounds like a massive UX hurdle.
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  It does. Why aren't patients simply ignoring
• 11:19
  the proprietary app and demanding to fill out
• 11:22
  paper forms at the front desk? Why does another
• 11:24
  app actually stick? It is a vital question. To
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  understand why patients actually adopt it, you
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  have to look at how the software actively removes
• 11:33
  friction for both the patient and the provider
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  simultaneously. Okay, how so? Well, the patient
• 11:38
  app is intrinsically tied to access. It allows
• 11:41
  you to secure same -day urgent care appointments.
• 11:45
  But the true magic happens with the pre -visit
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  questionnaires. Right, the sources claim these
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  digital questionnaires save five to ten minutes
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  in the clinic. But how exactly? Because of data
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  mapping. In a legacy system, You fill in a paper
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  clipboard. You hand it to a receptionist who
• 12:00
  manually types it in. Usually with one finger.
• 12:03
  Right. And then the doctor has to reread it and
• 12:05
  navigate 20 different drop -down menus just to
• 12:08
  log your symptoms. It's so inefficient. Very.
• 12:11
  But in CarbonStack, when you tap my throat hurts
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  and I have a fever into the app from your couch,
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  that data maps directly into the provider's specific
• 12:20
  clinical templates. Oh, wow. The database auto
• 12:23
  -populates the medical chart before you even
• 12:25
  walk through the clinic doors. So the doctor
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  isn't starting from a blank screen, they're simply
• 12:30
  verifying the data you already submitted, which
• 12:32
  drastically cuts down their manual keystrokes.
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  And that leads to the most critical operational
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  shift in this entire deep dive. In traditional
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  systems, doctors spend literally hours after
• 12:44
  their shift doing what is universally referred
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  to as pajama time. Pajama time. That sounds cozy,
• 12:50
  but I'm guessing it's not. It's terrible. They
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  go home, eat dinner, and spend their evenings
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  doing administrative data entry into clunky EMRs.
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  It is the leading cause of severe physician burnout
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  nationwide. But because Carbon Health's Passion
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  app feeds directly into those optimized provider
• 13:05
  templates, the data entry is essentially happening
• 13:08
  currently with the visit. Yes. The result is
• 13:12
  that Carbon Health providers only spend an average
• 13:14
  of 20 minutes after hours charting. Total. Wait,
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  20 minutes total? Total. Going from hours of
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  pajama time to just 20 minutes is a staggering
• 13:23
  reduction in cognitive load. And this is the
• 13:26
  actual deduction here. This answers my pushback
• 13:28
  about app fatigue. How so? Because the app isn't
• 13:30
  just a flashy portal to view your lab results.
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  It functions as a digital shield against physician
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  burnout. Precisely. When the physician isn't
• 13:38
  exhausted by administrative bloat, when their
• 13:41
  evenings aren't consumed by data entry, the quality
• 13:44
  of care skyrockets. Because they're actually
• 13:47
  present. Yes. The doctor sitting across from
• 13:51
  you has the cognitive bandwidth to actually look
• 13:53
  at you, listen to you, and practice medicine
• 13:56
  rather than practicing data entry. Re -engineering
• 14:00
  the software stack clears the administrative
• 14:02
  debris so genuine human connection can happen
• 14:04
  in the exam room. Exactly. Fixing that in -clinic
• 14:08
  experience is a monumental step. But once you've
• 14:11
  freed up the doctor's time and optimized the
• 14:13
  physical visit, you run into the next logical
• 14:15
  frontier. Which is? How do you manage a patient's
• 14:19
  health when they aren't sitting in front of you?
• 14:21
  I mean, healthcare shouldn't just be restricted
• 14:23
  to a 15 -minute exam room visit twice a year.
• 14:25
  It really shouldn't. Which brings us to the concept
• 14:27
  of the Living Blueprint pushing data -driven
• 14:30
  care directly into the patient's home. Yeah.
• 14:32
  Carbon Health recognized that managing chronic
• 14:34
  conditions required a presence outside the clinic
• 14:37
  walls. So they launched home -based care programs
• 14:40
  overseen by primary care physicians targeting
• 14:43
  widespread issues like hypertension and diabetes.
• 14:47
  The mechanics of the diabetes program are particularly
• 14:49
  fascinating to me. Oh, it's brilliant. When a
• 14:52
  patient enrolls, they don't just get like...
• 14:54
  A generic pamphlet? Carbon Health sends a continuous
• 14:57
  glucose monitor, a CGM, directly to their house.
• 15:01
  Right. And the patient attaches this small hardware
• 15:04
  device, which tracks their blood sugar levels
• 15:06
  continuously, 24 -7. But the hardware is only
• 15:09
  half the equation, right? Exactly. That CGM transmits
• 15:12
  a continual stream of blood glucose data directly
• 15:15
  back into the Carbon Health app. Okay. And simultaneously,
• 15:18
  the patient is required to actively log their
• 15:21
  food intake, their meals, their specific snacks.
• 15:23
  as well as their daily exercise routines within
• 15:26
  that same app. So what does this all mean? The
• 15:29
  best way to visualize this mechanism is to compare
• 15:31
  it to a really robust financial budget tracking
• 15:34
  app. Okay, let's explore that framework. Think
• 15:37
  about the traditional healthcare model. It is
• 15:39
  very much like going to a strict accountant once
• 15:42
  a year. The accountant looks at your annual statements
• 15:45
  and yells at you for being in terrible debt.
• 15:48
  You need to eat better. Exactly. They tell you
• 15:50
  your A1C numbers are bad and you need to eat
• 15:52
  better. But you have absolutely no idea which
• 15:55
  specific purchases over the last 12 months actually
• 15:58
  ruined your budget. Right. You don't have the
• 16:00
  granular data. Was it the daily coffee? The car
• 16:03
  payment? The lack of data makes course correction
• 16:07
  impossible. It's an entirely retrospective, highly
• 16:10
  punitive model of care. But combining the CGM
• 16:12
  data with the app's food logging creates a real
• 16:16
  -time budget tracker. Right. The app pings you
• 16:19
  and says, hey, That specific $5 purchase you
• 16:22
  made at 2 p .m., that specific handful of pretzels
• 16:24
  you just ate, that is precisely what spiked your
• 16:27
  glucose and put you in the red today. And that
• 16:29
  provides an immediate visceral connection between
• 16:31
  your daily behavior and your internal biology.
• 16:34
  It allows for instant, actionable course correction.
• 16:38
  And that real -time feedback loop generates astounding
• 16:41
  clinical results. By showing patients exactly
• 16:45
  which variables spike their blood glucose, 80
• 16:48
  % of the patients participating in this diabetes
• 16:50
  program achieve normal blood glucose levels within
• 16:54
  roughly six weeks. 80%. 80%. In six weeks. Reversing
• 16:58
  those metrics for 80 % of a patient cohort in
• 17:01
  just six weeks is, I mean, that's life altering.
• 17:04
  It really is. But primary care and chronic management
• 17:06
  are still only pieces of the broader health care
• 17:09
  puzzle. Sure. Eventually, a patient's needs will
• 17:11
  exceed primary care. You might need a cardiologist
• 17:14
  or a dermatologist. And historically, securing
• 17:17
  a specialist referral is just a logistical nightmare
• 17:20
  of phone tag, lost faxes and endless waiting.
• 17:24
  It regularly takes two to three months just to
• 17:26
  get in the door of a specialist. Right. Carbon
• 17:29
  Health recognized this interoperability failure
• 17:32
  and launched Carbon Health Connect. They are
• 17:34
  basically building direct partnerships with external
• 17:37
  specialist networks. But how do they actually
• 17:39
  bypass the wait times? You can just ask a specialist
• 17:41
  to work faster. No, you can't. It comes back
• 17:44
  to the structured data. OK, explain that. Because
• 17:46
  Carbon Health has already compiled organized,
• 17:49
  high -quality, standardized clinical data via
• 17:53
  their proprietary EMR, they can package that
• 17:55
  patient profile using standardized APIs application
• 17:59
  programming interfaces. OK, so they're talking
• 18:01
  computer to computer. Exactly. They transmit
• 18:04
  a clean, instantly readable data package. directly
• 18:08
  to the specialist's system. The specialist doesn't
• 18:11
  have to spend weeks gathering records or requesting
• 18:13
  new preliminary labs. The pristine nature of
• 18:17
  the data transfer fast tracks the referral process,
• 18:20
  dramatically cutting down those massive wait
• 18:22
  times. This raises an important question, though.
• 18:24
  What's that? How does one company collecting
• 18:26
  all this highly structured data actually benefit
• 18:29
  the macro ecosystem? I mean, if carbon health
• 18:32
  is just building a better, faster silo for their
• 18:34
  own patients, it doesn't solve the national crisis.
• 18:37
  It's a crucial distinction to make. They are
• 18:39
  explicitly not hoarding this data. They are.
• 18:42
  No. They are applying advanced data science and
• 18:46
  artificial intelligence models to the aggregate
• 18:48
  anonymized patient inputs across their entire
• 18:51
  network. Okay, so they aren't scanning your personal
• 18:54
  private messages. They're looking at macro level
• 18:58
  symptom trends across geographical regions. Precisely.
• 19:01
  If thousands of patients across a specific zip
• 19:04
  code suddenly start logging low -grade fevers
• 19:06
  and dry coughs into the pre -visit questionnaires
• 19:09
  on the app. The AI sees it. The AI detects that
• 19:12
  regional symptoms spike instantly, often days
• 19:15
  before those patients even physically arrive
• 19:17
  at a clinic for a lab test. That is wild. Right.
• 19:20
  Carbon Health utilizes this predictive modeling
• 19:22
  to identify early outbreaks or variant surges.
• 19:25
  They are effectively operating as an early warning
• 19:27
  radar system for the wider medical community.
• 19:30
  predicting where the strain on local hospital
• 19:32
  networks will occur before it even happens. Exactly.
• 19:35
  And the sources are very clear on their ultimate
• 19:37
  objective here. The CTO explicitly states their
• 19:40
  goal is to share these technological frameworks,
• 19:43
  these APIs and these predictive learnings with
• 19:45
  the broader health care industry. They want to
• 19:47
  upgrade the operating system of the entire U
• 19:49
  .S. medical landscape, not just dominate their
• 19:51
  own market share. It brings the narrative full
• 19:54
  circle back to that original e -learning mentality.
• 19:57
  How so? Well, you don't engineer a massive, scalable
• 20:00
  software platform just to educate one single
• 20:03
  classroom. You build the infrastructure to elevate
• 20:06
  the standard of learning universally. Carbon
• 20:09
  health is trying to elevate the standard of care
• 20:11
  universally by proving that the data architecture
• 20:14
  actually works. That's a great way to frame it.
• 20:17
  This has been a profoundly eye -opening look
• 20:19
  at what happens when technologists stop accepting
• 20:23
  a fragmented broken system as just the way things
• 20:26
  are. It really has. We have covered immense ground
• 20:29
  today, tracking the evolution from the high stakes
• 20:32
  logistical pressure cooker of Dodger Stadium
• 20:34
  to the quiet vital relief of a physician complete
• 20:38
  their administrative charting in just 20 minutes.
• 20:41
  And we always want to ensure we anchor these
• 20:43
  concepts back to you, the listener. Absolutely.
• 20:46
  Why does understanding the architectural decisions
• 20:48
  of a chief technology officer matter to your
• 20:51
  everyday life? Because the transcripts we unpack
• 20:53
  today highlight a fundamental tectonic shift
• 20:57
  in what your personal healthcare is going to
• 20:59
  look and feel like in the near future. The future
• 21:01
  of your well -being isn't about sitting reactively
• 21:04
  in a sterile waiting room, flipping through outdated
• 21:07
  magazines when you are already terribly sick.
• 21:10
  Right. It is shifting into an active continuous
• 21:12
  background process. Yes. Your health care is
• 21:15
  becoming a connected digital ecosystem designed
• 21:18
  to flag a slight blood sugar anomaly on a Tuesday
• 21:21
  afternoon, allowing you to correct it before
• 21:24
  it ever escalates into a catastrophic trip to
• 21:26
  the emergency room years later. It is about engineering
• 21:29
  a system that finally empowers you with your
• 21:32
  own real -time biological data. while simultaneously
• 21:36
  freeing your physician to focus their expertise
• 21:38
  entirely on healing you, rather than wrestling
• 21:41
  with outdated data entry software. Which leaves
• 21:43
  us with a fascinating and highly provocative
• 21:46
  final thought for you to consider. Yeah, we've
• 21:49
  established that this new blueprint can reduce
• 21:51
  a doctor's charting burden to 20 minutes. And
• 21:53
  we've seen that integrating continuous home -based
• 21:56
  data tracking can help 80 % of enrolled diabetes
• 21:59
  patients return to normal glucose levels in a
• 22:02
  mere six weeks. So if this proactive, decentralized,
• 22:07
  tech -driven care truly becomes the ubiquitous
• 22:10
  standard... What is the fate of the traditional
• 22:12
  hospital model over the next decade? It forces
• 22:15
  us to question the necessity of our current physical
• 22:17
  infrastructure. If the underlying software network
• 22:21
  becomes the primary arena where monitoring, diagnostics,
• 22:24
  and preventative healing actually happen... Will
• 22:26
  the massive, sprawling physical hospital complexes
• 22:29
  we rely on today become largely obsolete? We
• 22:33
  started today talking about how much we instinctively
• 22:35
  trust the clean physical visibility of an X -ray
• 22:38
  machine, but maybe the physical hospital itself
• 22:41
  is simply an outdated piece of machinery. That's
• 22:44
  a huge shift in perspective. Perhaps the most
• 22:46
  effective cure we have moving forward is the
• 22:48
  invisible, highly optimized digital network connecting
• 22:51
  us all. Think about it. Thanks for joining us,
• 22:54
  and we'll catch you on the next Deep Dive.