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Silicon Valley Is Turning Nurses Into Gig Workers w/ Katie J. Wells

56m 48s

Silicon Valley Is Turning Nurses Into Gig Workers w/ Katie J. Wells

The conversation explores how technology, particularly AI and gig economy platforms, is transforming healthcare, focusing on staffing and labor. The host highlights that AI is being sold to healthcare with dual narratives: one promising innovation for wealthy institutions, another offering cost-cutting band-aids for struggling facilities. Real-world harms include AI scribes generating inaccurate or offensive patient notes, and virtual companion devices that may replace human care, creating liability concerns for nurses who override AI decisions. The discussion traces nursing staffing history from collective private duty registries to hospital employment, then to travel agencies, and now to app-based gig work, which speeds up temporary staffing and undermines worker stability. Workers face poor scheduling control, variable hours, and isolation from decision-making, while companies like Palantir introduce scheduling apps that further separate frontline staff from care planning. This gig model, inspired by Uber, threatens care quality, worker safety, and collective bargaining power. The episode emphasizes the need for awareness and resistance to prevent these trends from degrading healthcare systems, especially where public models exist. It urges listeners to consider the broader implications of tech-driven efficiency and profit motives, advocating for critical engagement and pushback against such developments.

Transcription

10366 Words, 56545 Characters

English
Wall Street is coming in with private equity and buying out a lot of these facilities. I mean, there is no question in my mind that the corporatization of medicine in the US and abroad is deeply tied to Silicon Valley's ability to come in and offer this healthcare staffing app. Hello and welcome to Tech Want Save Us, made a partnership with the Nation magazine. I'm your host, Maris Marks, and this week my guest is Katie J. Wells. Katie is a senior fellow at the AI Now Institute and a co-author of Disrupting DC. Now, before we get into the topic of this week's episode, just a reminder that I am doing a survey right now to get your insights on where Tech Want Save Us should be going from here, the type of guests that I should be having on the show, and anything else that you want to tell me about what you think about the show and so I can get your thoughts, opinions, and so that can inform where the show goes from here. So if you do want to take a few minutes to fill that out, you can find the link in the show notes. I would of course really appreciate it if you could. Thank you so much. Now, this week's episode is about AI and technology in healthcare. Obviously, you will probably be aware that I did an episode a couple weeks ago on what we're seeing with AI and generative AI in particular rolling out in K to 12 schools in the United States specifically, but I think a lot of that applies to schools in a lot of places. Maybe it's not as far advanced as in the United States, but it's still applicable. And so I figured it was probably good to have a conversation about healthcare as well. And part of this is about generative AI and the way that we're seeing digital tools move into healthcare and how that is probably not being done in the most thoughtful way, thinking more about how to try to drive efficiency and for companies to try to make profit off of using these technologies and rolling them out rather than what it means for patients and employees and whatnot. But another part of this is also what it means for labor. And so this interview is sort of about AI, but it's really, I would say, about something a bit more than that, right? About the movement of kind of a gig economy model into healthcare staffing. Again, right now, primarily in the United States, where we are already seeing inklings of this in other countries around the world, including countries with public healthcare systems. And so I think that this is something that we really need to be aware of, whether it's in the United States, where this is more common, but there are still a lot of places where it's not so common, but also in places outside the United States where we can look and see, hey, this is happening here, we should be aware of it. And we should really start preparing to try to ensure it doesn't start to take hold in our own healthcare systems and really start to degrade the quality of care that's provided by changing the way that staffing is done. And so this is a really worrying development that I wanted to talk to Katie about where we're seeing kind of the uber model move into healthcare with people who are getting jobs through apps who are maybe only at a healthcare facility for a shift and they don't have the proper training on how the facility works, the different protocols at that facility where different supplies are located, they don't know their colleagues, all these sorts of things that actually have a real impact on the quality of care that can be provided to patients. And obviously that has a big effect on workers as well. What it means to work in this kind of way, what it means for their own safety, what it means for their ability to provide good care, what it means for their pay or their ability to kind of collectively push back, you know, against their employers, if they're doing something that is not that great toward the employees, they're a whole range of consequences here that I dig into with Katie as we try to understand what this model is, how it's being implemented, and whether there is an ability to push back against it to try to stop it from being rolled out and what we can really learn from the experience with uber that helps to inform us how this is happening, but also how it can potentially be stopped. So I think this is a really important conversation to have on an issue that maybe, you know, not enough people know about, but is already starting to move into healthcare systems with severe consequences. So if you do enjoy this conversation, make sure to leave a five star view on your podcast platform of choice. You can share the show on social media or with any friends or colleagues who you think would learn from it. And if you do want to support the work that goes into making tech won't save us every single week. So we can keep having these critical in-depth conversations to inform you on how generative AI is rolling out into different parts of society, but more broadly about how tech companies are affecting the society that we live in in, you know, sometimes positive, but more often very negative ways that we need to be aware of and pushing back against. And all those conversations are possible because of the support of listeners like you. So if you do want to support the show, you can join supporters like Ferris in New York City and Jacqueline in Victoria, British Columbia by going to patreon.com/techwontsaveus where you can become a supporter as well. Thanks so much and enjoy this week's conversation. Katie, welcome to Tech Won't Save Us. Thanks, Paris. I'm really happy to have you on the show. Obviously we've been in touch for a long time. You know, you've been working on Uber and the gay economy for ages and what's happening in transportation. Of course, that's been a focus and an interest of mine for a very long time as well. So I've been paying close attention to your work over those years. But unfortunately the gay economy is not just the, you know, in the transportation space, it's increasingly moving beyond that as well. And you have a fantastic new report with a couple co-authors digging into aspects of that. And before we get into the specifics, I wanted to ask a more general question, right? Because obviously since 2022, generative AI has been one of the key things that we have been talking about. And the industry has been trying to justify pushing its technologies into many different aspects of society. And certainly healthcare is one of those, right? And we have seen more implementation of generative AI in healthcare over the past couple of years. And so, you know, broadly, I guess, what are we seeing in the healthcare sector when it comes to the implementation of generative AI and the effects that that is having? Yeah, that's great. And Paris, I will say, I'm very lucky to have been in conversation with your work and learned from you over the last more than a decade. And I think that you're thinking about AVs or AI precursor to AI pre-automation, let's say, has really helped me think about what AI firms are doing in healthcare and how they might be selling a lot of the same pool aid. At this point, what I think we're seeing, and this will be, I think, familiar to tech critics in general, right? Is we're seeing two different often conflicting narratives about what AI can do to healthcare. On one hand, for very high-end, well-resourced facilities like Mayo Clinic, Think Mount Sinai, Think University of Pittsburgh System, what we're hearing is that AI is going to be their route to new, more efficient, and fabulously successful, personalized healthcare. That AI is going to be, you know, the thing that pushes us over the edge and takes us to the moon. At the same time, we also see a slew of AI firms selling their tools to a very different set of facilities. These are the under-resourced facilities. These are facilities in places like where I'm from in Ohio, places that are scraping by, and AI is being sold to them as a very cheap band-aid to try to make ends meet. And so one of the things I'm grappling with is, and I welcome your thoughts on this, is how do we make sense of these two conflicting narratives? And how much do those two narratives really echo the narratives that other gig economy companies made a decade ago about what, you know, new technology would do? I think that makes a lot of sense, right? You know, thinking about how they are positioning the technology differently based on different contexts. You know, they see that one group of healthcare providers have a lot of cash, and so that means a different kind of way of tailoring the message versus these more under-resourced, you know, areas. And I feel like when I think about the implementations of AI and healthcare, obviously there can be many, and your report touches on part of it. But one of the first things that comes to mind to me is actually a report that we had here in Canada just last month, I believe, from the auditor general in Ontario, which is our biggest province looking at these AI scribes, or like, you know, the use of AI tools to transcribe conversations between doctors and patients, and the high degree of errors and wrong information, wrong drugs that were recorded, and even how the AI tools inserted, like basically healthcare recommendations, we can put it that way, I guess, into these transcripts that were never made by the doctor, but were to use an industry term hallucinated by the tools. And so like, I don't know, the problems and the risks here are just so huge, right? They are. And I would love to also share another anecdote about sort of AI scribes. They are one of many tools, and I'll mention a few other ones. So I'd love to sort of make sure we sort of understand, which I'm still struggling with how many different iterations there are of AI in the healthcare system. So for instance, and nurse, I was speaking with told me a story about a different AI scribe. I don't think it was a proprietary one of that report, but the problem was the same, which is that there was a gay man who was a gardener who would come in and had a rash on his arm. And the AI scribe that was doing the note-taking created a narrative about not the prime needles that had given this man a rash but rather intravenous drug use and promiscuous sex because gay, man, needle created an association and that created a report that was deeply offensive, right, wrong and to some extent dangerous. I feel like I shouldn't be shocked but I still am to to hear you do something like that, right? Right and so we have that iterate. We have these LLL we have these AI scribes, these note-taking dictation machines that are doing smart phrases and things like that but we also have and I think for me this is the hardest thing to hear about are the replacement nurses. These are they come in two flavors, right? We have the remote nurses, the remote admin nurse. You know I have a friend whose family member recently had a psychiatric emergency and as if it were the 1990s and the person was in a you know public school like a TV being wheeled into the classroom instead of the hospital system a laptop was wheeled in and there was a remote doctor and a remote nurse that were brought in. So we have these remote nurses but then we have a even further devolution where we have virtual admin nurses we have virtual discharge nurses and we have virtual companions. It's a little plug-in box and it has a camera on top and it's often monitored by remote certified nursing assistance. They can be called tele-citters and they've been deployed in US hospitals and nursing care facilities a lot over the last two years and it you know in some ways hospital staffer understandably welcoming this extra support because they want their patients to be taken care of. They don't want them to fall out of bed things like that. On the other hand you know is it cutting back on unionized jobs? Is it degrading the quality of care? Is it making the nurse become like a babysitter for a technology? You know the extent to which nurses talk about how much they are becoming more like a machine is hard to hear but the thing that really echoes in a lot of the listening sessions I've been doing with nurses on the front lines is hearing this one anxiety which is not about becoming a machine although there's that. The anxiety is what happens when I say no to the AI? What happens to my license? What happens when the AI is wrong and I know it's wrong but I override it? Yeah it does does the risk then go on the nurse? The some degree of liability or something go on the nurse because you've now contradicted the system there's a real issue there right? And I feel like you're laying out a bunch of problems. Some of them are directly you know related to AI and some are about kind of the broader implementations of these digital technologies into the healthcare system and the questions that presents and as I'm hearing you talk about it I feel like it's bringing other things you know to mine for me right? Which I can just say very quickly which is you also have you know you're talking a lot about nurses in the delivery of care but on the back end you're having the kind of digitization of the kind of administrative tasks I guess of running the hospital or running the system or keeping account of patients. I've spoken to people here in Canada and in Norway and other countries about how these systems are all out they're often very flawed but in the process a lot of the workers who have that kind of you know knowledge of of you know how this administrative functions you know work end up getting laid off in the process because they're not needed anymore because now there's there's a new system to do it and obviously the the healthcare system in the United States is a heavily privatized one but in Canada what we have been seeing and I wonder if this is the case in in other countries that have you know a predominantly public healthcare systems is that while you know GPs and and family doctors and you know the healthcare system was traditionally something public as technology has moved in they have found ways around those rules to make it so you can now pay to see a family doctor in a way that you wouldn't in the Canadian system have been able to in the past because now as you're saying you connect to you know a phone or something like that to speak to the doctor instead of visiting in person and that allows you know getting around all the regulations that were designed to keep the privatization out but now the tech companies are finding ways to to bring it in right and there's some AI that's involved in that but it's also just the use of digital technology or broadly absolutely and I think the way I think about this and my colleagues are thinking about is that AI in a lot of cases or technology in general right it often is a symptom or it is a signal of what is a miss that it can help us understand the financial pressures of these facilities it can help us understand what's going on with staffing I mean the scheduling thing is really an administrative work that you flagged is really coming up in a lot of conversations with nurses that I've been having especially because we have companies like Palantir that has been getting in bad with let's say the Cleveland Clinic to develop a scheduling app called Tim Pony and they you know some facilities if they don't have that scheduling app they might have remote scheduling staff so we can think a lot about even in let's say mining you know think back to mining or agriculture in the way in which the workforce got isolated over time it really separated out communication and decision-making and it left a lot of the frontline workers without a lot of understanding about how to fix the how do they enter a numbers in a way that gets the outcome they want which often is just better care that that's a really good point you mentioned Palantir obviously we see that in other countries to the UK comes to mind where Palantir has a massive contract with the NHS that is incredibly controversial controversial so we see these tech companies moving in in so many ways and I wanted to kind of start to pivot us toward the report that you put together really looking at how AI and how these digital technologies are moving in to transform the way that you know staffing and employment and the labor model of healthcare is is changing particularly in the United States and to a lesser degree we're seeing that in other places but I wonder before we talk about that specifically can you tell us how staffing is traditionally handled in the healthcare industry so we have that kind of baseline understanding to understand how things are then changing yeah absolutely and it's an interesting history when we talk about nursing staffing because nursing staffing for the first half of the 20th century was actually not in house employment that nurses banded together in something called private duty registries where they would then collectively bargain or we could think of it as sectoral bargaining where they would bargain for their hours and their wages and their staffing levels with different clients and by the 1950s we saw that begin to change and as hospitals brought nurses in as direct hires one of things that changes a lot of the power that nurses had to set their own working conditions and so we see a lot of continued struggles and there have been amazing efforts to unionize nurses under this new model that's emerged since 1950s but it's just something to keep in mind as we try to make sense of what are these new app based scheduling options and how crazy is it to think of a nurse is not working for the hospital directly that we have a good model in which there were decent working conditions but as hospitals brought nurses in one thing they did is they were incentivized to do a lean model and so what that means is that around the 1970s travel nursing agencies emerge in the US context and this is sort of the model of you know you have a staff who's going on leave and you're going to bring someone in who is employed or is in a long-term contract and they're gonna have protections and they're gonna work in that facility for three to six months they're gonna be a temporary employee we have that model that's kept today but what this big nursing stuff is appears it's just a really sped up version yeah and and I feel like we you know we're gonna dig into that a lot more it's interesting you bring up the travel nurses because that has actually become a really big scandal locally where I live you know kind of an over reliance on travel nurses because there's not enough being employed by the system and they're it's actually quite expensive to rely on them over just employing people to do the work right but I'm wondering before we talk about the the you know kind of uber model moving into healthcare what are you know for for people who work in the healthcare industry right now what are kind of pay and working conditions typically like for these kinds of workers is it you know a good stable job are there difficulties associated with it what is it what is it like to work in the healthcare industry yeah that's great and I am only gonna be one of many sort of voices here so I can tell you what I've seen but certainly I'm sure that some of your listeners are far better experts than I am on this one of the biggest complaints that I hear and that folks in labor studies and in healthcare have voice is the lack of scheduling control that while the pay may be fine for some people the lack of scheduling control is not and so that means that sometimes hours are variable where you don't have a guaranteed number of hours but also just use long shifts and the varied shifts per week make it really difficult especially in the U.S. where we don't have much of a safety net or social support for raising a family or for taking care of elderly folks And so one of the things I often talk to about nurses about why they take on extra work or why they turn to the gig economy is because they say they can't get their dad to dialisist. They can't get their kid to their IEP meeting. They, you know, with the existing constraints of their jobs. And so I think about the rise of Uber for Nursing as really a symptom of a failed labor market for healthcare workers in the US context. And I often play that game, Paris, of like what conditions need to be in place so that Uber for Nursing is not the solution. And part of that is just better working conditions and more utilization and more representation for these workers on the front lines of the care economy. It makes a ton of sense, right? And I feel like when we talk about how these technologies, these exploitative technologies move into so many different sectors, it is often taking advantage of those problems that already exist, right? You know, there are these fractures. There are ways that say the education. And they know well problems, right? Totally. And part of why Uber had so much power is that because it addressed a problem with real purchase. And I don't doubt for a second that these nursing platforms aren't doing the same. Yeah, I completely agree with you, right? And I was just talking to somebody recently about the education system. And they were saying something similar, right? Part of the reason these tech companies can so easily move in and try to change things or pretend that they're doing something better is because of existing flaws that have developed in that model too. Just a final question on the staffing. Is it still the case that healthcare staffing would still be primarily kind of an industry that is mainly employing women or is it more balanced these days? How does that look? My understanding is that it is still primarily women is increasingly women of color. And there's a huge number of migrant workers in the US or at least there was. Gotcha. That makes a lot of sense. And so, you know, you've started to set it up for us. You know, obviously people know what we're talking about. But what is this new model? When do we start to see these, you know, gig platforms, these Uber platforms for nursing really start to emerge? And how are they different from the model that exists now? Yeah. So these have just emerged in the last 10 years. And what these gig nursing companies are, are they're an app that you can download on your phone? And you can sign up to work at a facility by uploading some documentation waiting for a background check. And then many cases you will bid on a shift. And this is what is really the hardest for me to swallow about this is that we have nurses bidding against each other for the lowest wage. These workers are treated as independent contractors. They are walking into facilities without orientation. Many cases, they're walking into facilities, either long-term care facilities, rehab facilities, acute and emergency hospital. Without a map of where the supply closet is, they don't know how to log into the patient portal to figure out whether a patient needs help with feeding or what allergies they might have. This is a model where nurses come in or nursing assistants for six hours, eight hours, 12 hours, not knowing any colleagues or any norms often on the Facebook groups or even on these platforms themselves. I see nurses asking each other, what color scrubs should I have? Do they have clean linens? Do I need to bring my own stethoscope? The point is that these apps have really lowered the barriers for bringing workers into facilities and in doing so really broken up the notion of sort of continuity of care. Even just hearing you describe that, like there are a lot of potential or very real issues that immediately come to mind to me as someone who's not even an expert, immediate red flags. If you're not very familiar with a facility, with the people who are there, you know, with the patients that are there, what does that mean for the type of care that is delivered, but then on the flip side of that, even for the worker who is doing that work, you know, if I'm thinking of someone going into one of these facilities as an independent contractor, you know, if something happens on the job, you know, obviously we know that there can be a high degree of kind of assaults in a healthcare workplace, you know, when you're dealing with patients, and they can be in different states of mind and things like that, you know, do you have protections if something happens to you while you're on that job? Yes, so talking about that. Care is you don't, and this is what's the wild, right? You don't have, you don't have protections. We have talked to nurses who have been injured on the job and to add insult to literal injury. One of these workers was on the ground, unable to move, having a hernia and could not get permission from the app or the facility to leave. So when the paramedics came, she stayed because she was afraid that she would be deactivated from the app. In fact, she was not paid for that shift because she quote left before the end of her shift. She is no sick leave, she has no workers come. Many of these workers don't even have health insurance. It's, it's just, it's just shocking to hear and I wonder, can you, so I want to break it down, right, versus the effect on the workers versus the effect on the patients, okay? And I start, I want to start by focusing more on the workers. Obviously you're talking about, you know, the kind of injury and the protections that they have. But can you talk more broadly about what it actually means to go to work in a context like this? You know, what it means when you go from being someone who is employed by the hospital or who is at least employed by some kind of travel nursing agency that brings you in versus just being someone who gets a shift on an app. It's very short term. You don't know where you're going. Like, you know, what is the effect of making that change? Yeah, there's a lot of effects. There's a personal risks, stress and anxiety around it. When sometimes nurses get to a parking lot and the doors are locked and they don't know how to get in, there's an anxiety of being a bad worker and not being able to show up on time. There's the anxiety of your Wi-Fi goes out and you're not going to get credit for having been there because you log in and log out on your phone. There's anxiety about, you know, these bad ratings. Are you going to be deactivated and not, you know, access to that? There's anxiety related to how much you're being paid, or you've been paid a fair amount compared to what the other folks are. You know, we don't know the extent to which workers are paid different amounts for the very same job. But there's more serious risks once a nurse enters a facility. Some nurses are worried they're going to lose their license because they were operating in these conditions and they didn't report it because they again didn't want to be deactivated. There's also no protection for discrimination. When you are an independent contractor, there's no rate to minimum wage, there's no right to be sick leave, things like that. And for many of these workers, the workplace solidarity disappears. One nurse said to me, and I, this is stayed with me, she said, I just feel like I'm on an island by myself a lot. And so you're doing this work, and you're often assigned the worst work, the worst hallway, the hardest jobs, and you're doing it without the supportive colleagues. For workers that are employed in contacts, there's a lot of anger and stress associated with someone coming from outside who might be paid higher for that day's work. Even though once we consider taxes and expenses, it might be lower, but there's a lot of opportunity for dissension and agonism amongst the working staff. I'm sure there's also the kind of fear that if you start to have some of these workers who are coming from the gig apps, then that could mean that more and more of these kind of more permanent positions where you are likely unionized, where there are protections there are going to get kind of eaten away at and replace more and more with contract gig workers. Right. And so I do want to stress something, Paris, and we don't know this to be true, but it is a working hypothesis, which is that we actually are not seeing these gig nursing apps showing up in a lot of unionized facilities. And I don't think that's an accident. When I mentioned that AI is sort of often being used as a cheap mandate for places that are under-resourced, it seems as if a lot of these gig nursing apps are really showing up in places that are under-resourced and non-unionized. And so it appears and might be really bifurcatedly on scatable health care that this is sort of being layered on top of. Yeah, you can you can certainly understand that, right? It makes perfect sense. Right. And one question I always ask these gigners is when I talk to them is like, hey, so of any of the facilities and would you've worked as a gigners, would you take a loved one if they were sick or would you go yourself? And almost always the answer is no. And that to me is really evocative. A lot of the gigners and gigners' assistance are quite upset about the quality of care that is being offered in places that use these apps to make their staffing pull us. Which I'm sure must be even more difficult, right? Because you're talking about the difficulties of doing this work of not having the support of colleagues and all this kind of stuff. But even as someone, I imagine most of the people going into a field like health care, they're caring people, right? They want to provide care. It's a difficult job. They're not getting paid the greatest amount of money for doing this work of caring for other people. So there obviously is kind of like a compassionate piece of doing this. And if you're going into these facilities where the care is not being great and you don't necessarily have the resource is to provide the care that you expect. That must be tough on its own. Let alone all of these other pieces that we're talking about. It sounds really rough. And I imagine one of the good things on a corporate level of shifting to something like one of these gig apps is that as you're talking about, it feels like the worker is then lose that ability to exert collective power to demand better pay. All of these sorts of things are broken down. I mean, there's a facility in DC where I continually see upwards of 13 gig workers per shift per day. Are there any existing employees there? I don't know. That's wild to even think about. Right? And do we know what the kind of prevalence of these workers are? So we have no stats on that. This is like back in the Uber. This is Uber then. This is Uber now. How many Uber drivers? You don't know how much is it happening? You don't know. I mean. And I guess with these apps too, like at least Uber was this public brand that was trying to get the public on board that was making advertising campaigns that was saying download our app. In this case, it's something happening much more behind the scenes that a lot of people would not be aware is probably even happening at all, right? Which makes it even harder to not so much to get people to care about, but like to actually get it on the agenda of an issue that. Or the answer? Basic questions. Paris, we are both empiricists. Like, I just want the information. Is this really a niche thing? Is it really happening? But here's a clue. Okay. The amount of money, the amount of venture capital and private equity money that is backing these entities makes me feel quite strongly that this is not a niche thing. And the amount of lobbying work that is associated with legitimating these things and carving them out of existing state supplemental healthcare staffing laws suggest, okay, there's this is not a cutesy little thing that's happening in the corner. Yeah. And I guess that's a mix of like what's already out there and also the opportunity that they see to grow this even further if they can win, you know, these regulatory changes and things, right? I know I mentioned I wanted to move on to the patients, but before we talk about that, I feel like there's another piece that can't be ignored here, right? And that is the like hospital administrators or whatnot that are actually taking advantage of these applications, you know, of this way of, you know, changing the labor model of hospitals. Have you spoken to people who explain why exactly they are going to these sorts of apps versus hiring more employees or even taking advantage of travel nurses or something like that? What is the incentive to use something like this on the administration level? So from my conversations with administrators, they are tired. It is hard. After the COVID pandemic in particular, they are struggling to hire nurses at the rates that they're offering them jobs. And so honestly, this new tech coming in and offering to handle a lot of the work, it's quite attractive. And I'm sympathetic to these administrators who feel as if they can't manage all these demands and they are having trouble getting staff. Again, though, it's at the rates that they're willing to offer it. And I do want to flag one thing for you, Paris, because I know you've followed a lot of the real page drama around algorithmic rent setting. There are some indicators that raise a red flag for me around the extent to which there is some algorithmic price fixing that could be happening with these systems. When we have so many different facilities and their administrators using the same software to set wages and to hire in the same metro areas. That makes a lot of sense that that would be an issue. And on the side of the people who are doing this work, I know obviously one of the issues has been how the platforms themselves are designed to try to encourage people to kind of push down the rates of pay that they're going to get. I imagine that we're seeing that in the way that the apps are designed as well. Is that right? That's what we're trying to figure out. I would imagine it's the case, but yeah, I take what you're saying. And so if we look then at the the patient side of things, right, you're already saying that, you know, there's an issue with care and a lot of the facilities where these things are already being used. But what risks is that to the use of these kind of temporary workers who are only going to be there for a shift and don't have this kind of connection to the hospital? What risks does that present for the patient? Well, I'll tell you a story that I read recently in one of an inspection report about a facility that used one of these apps called Shift Key. The inspection report was going over the unexpected death of one of the patients. And in the report, the government health inspectors were noting that there was a gigners present at the time that this patient had a cardiac event and died, and that they were not trained in the facility's cardiac rescue operations. And I'm using probably the rail for its care. But the point was that this report went into detail. And then the administrator of that facility agreed in the report that yeah, like temporary staff, gignerses are not trained in those things. One thing that gignerses often report about patient care is just how little they are prepared to deal with the nuances of that facility or to address the needs in a way that feels good and sufficient and safe and hopeful. And so we have a lot of concern about the continuity of care. I can tell you my dad had a stroke in Ohio two years ago and we were so lucky with the care he received at a rehab facility. But you better believe that on Saturday when a new worker came in, a new nurse in a assistant came in. She tried to apply his eye drops to his ears. Okay, these are routine mistakes that happen. And these happen even in the best of situations. Right. And so the risks are only accelerated when you have someone with less familiarity about the workplace. Yeah, and I'm sure like almost every listener has a story like that where small mistakes can happen. But then if you don't have the people who are trained and you know have that knowledge in that relationship with the patients, then it increases the risk that you have much more of those those mess ups, right? You know those things. And when you mess up, you go to your colleague and oh, help me. I made this mistake. How do I fix it? Right? Can you imagine like had the nurse that was helping my dad like not known who the other folks on, you know, on staff were to call and get help. And so I think for a lot of these folks at devolution of a workforce of a team of having colleagues, you know, we say often that like in the healthcare space, AI has eaten the managers. But it's also, you know, eaten your peers. Absolutely. And you know, we're talking a lot about nursing. Are we seeing this move into these apps move into other areas of healthcare staffing as well? We are. We're seeing it in dental offices. We're seeing it. Other kinds of facilities. OTPT. Presenting similar risks in those places as well, I imagine. I mean, I have not studied it yet, but my goodness, Paris, it doesn't seem hard to amast it, right? Exactly. Exactly. And so obviously we're talking about the issues with these platforms, you know, how that changes what's happening in these healthcare facilities and the risks that presents on many different levels, right? Not just for patients, but to many people who are in the facilities themselves. So how does something like this actually happen? Like we know with Uber, they rolled out and they just kind of rolled over regulations in the United States and then worked hard to change regulations and make themselves legal in other parts of the world. What are we seeing with these apps? Did they just roll out and hope they're not going to have something enforced on them? Are they kind of being proactive in going to state governments in the US and trying to get the law changed? What are we seeing in the kind of legal landscape here? Okay, Paris, this is especially for you because I think no one else will understand this as much as you will. Paris, it like my heart was beating fast because it was such dayjavu to Uber 2012. Paris, I swear, it was like, I've seen this game before. I know how this movie ends. What we are seeing is exactly as you describe these companies come in, they operate, but I would argue illegally and then trying to buy, you know, legitimation or going state-by-state and telling legislators, "Hey, our entity is not a healthcare staffing entity. We need a new business category. We need a new business category and we need some new regulation and don't worry, we'll help you with all of it." Paris, we've seen this, this was Uber, right? Uber created the entity called a transportation network company and I'm sure you're listening. You guys all know this already, okay? But guess what? The new version of the healthcare staffing agency is called healthcare worker platform, or healthcare platform, healthcare technology platform. We're seeing this definition pop up in all these state bills. We can call it "definitional arbitrage," right? Sort of like if we are thinking through the parallels and other industries, but it's a really familiar story and it does make my heartbeat faster because I worry that they will win. Like picking up on what you're saying, how have there been no lessons learned? How can then this happened all over again. - I know. Well, I mean, part of it, Paris, and you know this, right? Part of it is that Uber won the dams. Uber won the left and the right. Uber is more partisan than beer. And so it is no surprise that healthcare, supplemental healthcare staffing, you know, firms are able to go in and say, hey, we're not, we're not a healthcare staffing from work tech company. We just connect people. We're a marketplace. And again, look, how does it happen? We have infrastructure that is right for investment, desperate for solutions, and we have Silicon Valley coming in. You know, one thing I haven't flagged, you know, that's also a foot, we think, is that Wall Street is coming in with private equity and buying out a lot of these facilities. - That makes a lot of sense, right? You know, you're transforming the way that the hospital works. They certainly want to cut costs when you have private equity and certainly want to disempower the workers who are in the facility delivering the care. And so the gig platform is a natural thing to help to do that, I would imagine. And I would guess private equity firms are also investing in these platforms too. - Correct. - And when, for everyone, except for us, right, the patient, the worker, I mean, these also the communities. I mean, this is where, you know, thinking about bargaining for the common good or labor organizing, I could imagine could be, you know, coming in here or in the same way that, you know, all your work around data centers, I wonder, could the healthcare facility be the next data center that ignites disparate interest to come together and articulate resistance against the future that Big Tech has on offer for us? Could enough people say, actually, I don't want a hospital like that. - I would hope so, right? And I feel like healthcare has already become a big point of organizing in the United States as hospitals have been, you know, coming on the ownership of private equity, some of them being shut down, people losing access to care, you know, and obviously that's, there's the whole, you know, private healthcare system that is obviously something that is always in the kind of political debate as well. But yeah, I would have to imagine that that kind of plays into the broader conversation, right? - I hope so, I hope there's something, I mean, I don't want there to be something dystopian, but I hope that this gigner scene edge case of nurses bidding against each other on shifts and not knowing how to get to the supply closet is enough of a story that folks understand what these healthcare apps are doing and what will happen if we let Big Tech run healthcare. - Yeah, and we know that they have already had that ambition for quite some time, right? To move more into healthcare, to make more money off of healthcare, to try to bring more of it into their systems. And this is just another, - Yeah, I mean, the list I have in front of me of how many different dozens of technologies, much of them, you know, AI and BC backed, how many of them there already are, and they've come in often without FDA oversight, any kind of federal government review, because they're software, they're not hardware. - Yeah, and so when we do look at the legislation that these companies are winning in these various state legislatures, what does that actually look like? Like how are they defining them? How are they kind of putting it together to make it legal and justifiable for these companies to operate? - Yeah, so they're doing a few things. One thing is that remember the travel nurses we talked about, they are positioning travel nurses as the vulture and themselves as a savior. Both, you know, travel nurses, you know, and travel agencies, they're charging you too much. They're, you know, making you pay if you're gonna hire, you know, with a binder's fee and things like that. So they're positioning themselves as a better version at the same time, and this is these contradictions, at the same time they argue, they're not a healthcare staffing agency, and they are not subject to existing rules around supplemental healthcare staffing. And some legislators are buying that and saying, "Oh, okay, what we do have though is the inkling of some resistance. We have insurance providers saying, "I'm not so sure how this is gonna work out for my facilities that I ensure." Because what happens to liability in these cases where we have a lot of workers that don't have protection, right? In case of something to go wrong and is a facility gonna be held liable? We also have heard from other folks that are concerned about the precedent the sets for other industries. What will happen to firefighters? Will they similarly just hop on and out? - That's wild. You get a push notification that there's a fire who is gonna take the gig to go put it out. You can imagine it, right? It's not beyond the realm of imagination. - The training, but all that training, all that camaraderie, all the tacit knowledge that gets exchanged in the course of ongoing relationships in the facility, out the window. And so add a lot of these hearings around gig nursing apps and their legislation. We're hearing a few different trends. On one hand, we're hearing this constant assertion that these are just part-time jobs. And that should echo familiar to you, Paris, right? About part of how Uber won is, they're like, "Oh, this is just supplemental income. Everybody has insurance, don't you worry." So we're hearing that a bit. And we're also hearing that like, "Look, there is a healthcare staffing problem." In reality, in the US, we have more trained and able-bodied nurses and nursing assistants than we ever have. And many more than all the jobs that are available. - It's just like, are you gonna pay them, right? Are you gonna give them the ability to actually get that work into survive doing that work and have a good living? - Well, in control, have some semblance of control. So we do have some, I do wanna highlight, we have to remember, and you know this, if the technology itself is not the problem, the idea of workers picking their own shifts isn't the problem, the problem is all the conditions that go around that. So there are some hospital systems that have developed their own in-house app to allow existing employed workers to pick up extra shifts and change their schedules. - Perfect, you know? Why not do more of that? - It's very comforting and a good reminder that this does not have to be predatory and exploitative, the use of technology and scheduling and in the deployment of sort of care. - Yeah, but it doesn't provide the ability to push down on the rights of workers that a gig app does, right? Am I right that New York has taken a slightly different approach to the regulatory aspect on this? How does that look a bit different? - So New York State has actually taken an affirmative position at this point. And when gig nursing apps there, we're trying to receive an exemption, to write themselves out of existing supplemental healthcare staffing laws. New York did an interesting thing. They said, and they pointed, right? This metaphorically pointed, said, "Hey you, over there with the app. We see you and we wanna let you know you're still a supplemental healthcare staffing agency." So like all our laws, they still apply to you. What it means is that they have to abide by the rules that have been set over decades. We as a society believe that there are rules that help make us function better in cities with strangers. And some of those rules have to do with how workers are compensated, have to do with how reporting exists. So one thing we do in the States, and many countries abroad do this, is they check in to see, what are your hours? How many, you know, accidents have you had? How many failed inspections have you had? What's happening in your facility? Is it safe for our community? And so part of that has to do with labor turnover. And so these gagnosing apps, you know, are required in the state of New York to follow existing rules that provide some semblance of a baseline, some standard of reporting, and also the provision of some insurance policies for those workers. - All right, so it doesn't have to just be a free for all where you hand everything to these apps. I guess the other approach would be just to say, you can't operate altogether, rather than trying to fit under existing regulations. But, you know, maybe we'll get there one day. - Or we have the third route, right? Of like, what can we do in New York to invest in our facilities so that they have the means and capacity to hire workers in a way that works better? - Absolutely. Is, you know, when we look at these apps, is this particularly related to the type of healthcare system that the United States has? You know, where it is a more privatized healthcare system? Or do you feel like it's very much independent from, you know, the broader way that the healthcare system works in the United States? - I don't know. I mean, I sometimes play that game out. I'm not sure where I land on that. And we do just to mention, we are seeing inclines of this trend abroad, where they don't have the egregious privatization and corporatization of medicine, which makes me think that, as these things pop up in other places, we can expect more pushback than we have in the US and part because of our size and part because it's so disperse. But I don't know. - I guess it also stands out to me that, you know, as you described that it looks like in the United States, these apps are more common in facilities that are non-union. And so it potentially feels like where the hospital is unionized, that it's much less likely for these companies to be able to move in. And if you're looking at a public health care system, where they would also be unionized, then maybe it makes it more difficult for them to move in there, though I'm sure that, you know, as you know, that pressure is already coming to other countries with public healthcare systems. It's not, you know, just in the United States, but it's just taken off quicker and earlier in the United States than in these other places. Yeah. And one thing also to keep in mind is that where we are seeing it pop up abroad, in other places, I'm thinking of Spain in particular, we are still seeing it with some employment protections. So we are not seeing it as dire as we are in the US. And how much money is going to be there, how much, how many billions they can burn. I don't know, and we'll see how long they show up. But we can think about sort of the evolution of Uber and where it exists today and where it doesn't, is a very uneven geography. And I would imagine we'll see something quite similar. But I wonder if it has less to do with a corporatization of medicine and more to do with the financialization of like all the money with AFRMS, but I'm not sure. Yeah, it's certainly possible, right? And just how so much of, I don't know, the economy has to be financialized in that way with how the economy is gone for several decades now, right? And it's slowly moving into even the sectors that were resistant to it for for so long. You know, obviously we're talking broadly about all this. And these are really worrying developments and trends that we're seeing. And so I wonder for you, you know, when you kind of zoom out and look at what you're seeing, where do you think that this is going? And do you see opportunities to push back on this and to rein this in? Oh, I hope so. I mean, the little bits we've seen in Belgium or Netherlands or Spain, we are not seeing a lot of traction. So that gives me tremendous hope. But I think that the way private capital is entering healthcare systems is not getting much resistance, which is sort of the bigger picture. This might just be a symptom of that larger acidization of healthcare. I'm really worried about, you know, how these gagnar scene platforms might be the very smallest issue we actually have to deal with a decade from now. Almost like an indicator of, you know, a broader problem that is forming that they are just one of the more visible aspects of. Yeah, absolutely. I mean, what's going to happen with recruitment and payment and career paths and employment trajectories when a lot of this, I mean, I can tell you the clipboard health app and now has janitors and chefs and teachers, right? It has already moved beyond just healthcare. That's again, I guess it's not surprising, but it's really worrying to hear it. And I feel like, you know, even the report that you've put together, it's almost like a signal on the one hand to states or facilities where this is less common, or even for those of us outside the United States where this is still less common or maybe hasn't tried to take take hold yet to be aware of what these companies are doing, be aware of what, you know, the tech industry and private equity firms and all this are trying to do because it's inevitable that they try to take it to other places as well. And we should be prepared to try to push back on that. Absolutely. And whether they take it to healthcare in Canada or they take it to teaching, I mean, we just think about the uberfights in Canada. I mean, this is a natural extension, I would argue. And I wonder if you buy that argument of uber's games. Oh, 100% right? You know, even from the very early days, they were already trying to move into, you know, this model was trying to find itself moving into other different sectors. And it's no surprise that now they're trying to move into, you know, these areas where you do have workers who are traditionally unionized, who do traditionally have more protections and where there has been a long term push to try to erode those things, right? So it's no surprise to see the gay economy model trying to move there as well. And maybe maybe the healthcare bit will be outrageous enough that we will find some resistance. I mean, I really do appreciate I've been watching a lot of the hearings and listening to testimony and reading it around these gignorcing apps in their legislation. And there are some wonderful questions that policymakers are asking to say, wait a second, don't we want the minimum wage to abide on healthcare workers? So I do have, you know, it's just a little bit of a hope that as more and more people understand what's on offer, that we will push back. That's good to hear, you know, as a starting point, right, that not everyone is just immediately buying into it and allowing it to be kind of rubber stamped and pushed through. But, you know, I think with more awareness also, yeah. People have hired policy makers or tired, they don't know. I mean, the one of these hearings even, it didn't even get one of the bills that like move forward in a committee, it didn't even get discussed in a hearing. I mean, when and this sort of goes back to sort of the larger sort of ethos, at least in the US context, when you have very, very low expectations for public government, Silicon Valley looks like an okay alternative. I couldn't agree with you more on that. I feel like that is one of the bigger problems, you know, one of the many problems that we face, but is that when you don't expect government to be able to do much, then if it doesn't do much, that's perfectly okay. And even when it tries to do something, maybe you're distrustful of it because, you know, you expect it to not do very much, which is a whole other problem in itself. Yeah, I mean, the technology in your pocket somehow looks a lot more useful, which is why I think when we see folks like Mamdani and we see folks talking and articulate in the idea of raising expectations for urban policymaking or for policymaking in general, right, it makes my heart flutter in a good way because I think that's how we stem a lot of this, which is by remembering that we can set common rules and we can create technology that works for us, that we don't have to see that control to Silicon Valley. Yeah, and I honestly think that is a great place to end our conversation. Katie, it's been fantastic to hear your insight on all of this to understand better how technology and in particular the gig economy is moving into healthcare and why we need to be very aware of that, whether we're in the United States or beyond. Thank you so much for taking the time. I really appreciate it. Care was an honor. Katie J. Wells is a senior fellow at the AI Now Institute. TechWon Sabas has made an partnership with the Nation Magazine and is hosted by me, Paris Marx. Production is by Kiley Hussin. TechWon Sabas are lies in the support of listeners like you to keep providing critical perspectives on the tech industry. You can join hundreds of other supporters by going to patreon.com/techwonsaybus and making a pledge of your own. Thanks for listening, and make sure to come back next week.

Podcast Summary

Key Points:

  1. The episode discusses the corporatization of healthcare and the role of technology, including generative AI and gig economy models, in reshaping the sector.
  2. AI in healthcare is marketed with conflicting narratives
  3. Examples of AI harms include AI scribes generating false or offensive patient notes, and remote "virtual companion" devices that may degrade care quality and shift liability to nurses.
  4. Traditional nursing staffing evolved from collective private duty registries to hospital employment, with travel nursing agencies emerging in the 1970s; app-based staffing is a sped-up, gig-style extension.
  5. Workers face issues like lack of scheduling control, variable hours, and long shifts, exacerbated by tech-driven staffing changes.
  6. Tech companies like Palantir are entering healthcare scheduling, raising concerns about worker isolation and reduced frontline decision-making.
  7. The gig model in healthcare risks lowering care quality, harming worker safety and pay, and undermining collective bargaining, echoing past Uber experiences.

Summary:

The conversation explores how technology, particularly AI and gig economy platforms, is transforming healthcare, focusing on staffing and labor. The host highlights that AI is being sold to healthcare with dual narratives: one promising innovation for wealthy institutions, another offering cost-cutting band-aids for struggling facilities. Real-world harms include AI scribes generating inaccurate or offensive patient notes, and virtual companion devices that may replace human care, creating liability concerns for nurses who override AI decisions.

The discussion traces nursing staffing history from collective private duty registries to hospital employment, then to travel agencies, and now to app-based gig work, which speeds up temporary staffing and undermines worker stability. Workers face poor scheduling control, variable hours, and isolation from decision-making, while companies like Palantir introduce scheduling apps that further separate frontline staff from care planning. This gig model, inspired by Uber, threatens care quality, worker safety, and collective bargaining power.

The episode emphasizes the need for awareness and resistance to prevent these trends from degrading healthcare systems, especially where public models exist. It urges listeners to consider the broader implications of tech-driven efficiency and profit motives, advocating for critical engagement and pushback against such developments.

FAQs

The main concern is that AI and digital tools are being implemented in healthcare primarily to drive efficiency and profit for companies, rather than thoughtfully considering impacts on patients and workers, potentially degrading care quality.

AI tools are marketed with conflicting narratives: to high-end facilities like Mayo Clinic as a route to personalized, efficient care, and to under-resourced facilities as a cheap band-aid to make ends meet.

Examples include AI scribes that transcribe doctor-patient conversations, remote nurses, virtual discharge nurses, and virtual companions monitored by remote certified nursing assistants, all deployed in hospitals and nursing care facilities.

AI scribes can produce errors, such as recording wrong drugs or hallucinating healthcare recommendations, and can create offensive or dangerous narratives, like associating a gay gardener with intravenous drug use based on biased associations.

The gig economy model, similar to Uber, is moving into healthcare through app-based scheduling where workers may only work a single shift at a facility, lacking proper training, familiarity with protocols, and relationships with colleagues, impacting care quality.

Nurses worry about what happens when they say no to AI, such as losing their license or facing liability if they override an AI decision that they know is wrong.

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