Artificial intelligence could revolutionize health care. It could also perpetuate and exacerbate generations of racial inequities. In Part 2 of our special series on racial bias in health care AI, we dig into what the Biden administration is doing to keep biased algorithms from getting to the bedside.

Doctors, data scientists and hospital executives believe artificial intelligence may help solve what until now have been intractable problems. Hospitals are already using AI to help clinicians diagnose breast cancer, read X-rays and predict which patients need more care. But as excitement grows, there’s also a risk: These powerful new tools can perpetuate long-standing racial inequities in how care is delivered.

“If you mess this up, you can really, really harm people by entrenching systemic racism further into the health system,” said Mark Sendak, a lead data scientist at the Duke Institute for Health Innovation.

These new health care tools are often built using machine learning, a subset of AI where algorithms are trained to find patterns in large data sets like billing information and test results. Those patterns can predict future outcomes, like the chance a patient develops sepsis. These algorithms can constantly monitor every patient in a hospital at once, alerting clinicians to potential risks that overworked staff might otherwise miss.

The data these algorithms are built on, however, often reflect inequities and bias that have long plagued U.S. health care. Research shows clinicians often provide different care to white patients and patients of color. Those differences in how patients are treated get immortalized in data, which are then used to train algorithms. People of color are also often underrepresented in those training data sets.

“When you learn from the past, you replicate the past. You further entrench the past,” Sendak said. “Because you take existing inequities and you treat them as the aspiration for how health care should be delivered.”

A landmark 2019 study published in the journal Science found that an algorithm used to predict health care needs for more than 100 million people was biased against Black patients. The algorithm relied on health care spending to predict future health needs. But with less access to care historically, Black patients often spent less. As a result, Black patients had to be much sicker to be recommended for extra care under the algorithm.

“You’re essentially walking where there’s land mines,” Sendak said of trying to build clinical AI tools using data that may contain bias, “and [if you’re not careful] your stuff’s going to blow up and it’s going to hurt people.”

The challenge of rooting out racial bias

In the fall of 2019, Sendak teamed up with pediatric emergency medicine physician Emily Sterrett to develop an algorithm to help predict childhood sepsis in Duke’s emergency department.

Sepsis occurs when the body overreacts to an infection and attacks its own organs. While rare in children — roughly 75,000 annual cases in the U.S. — this preventable condition is fatal for nearly 10% of kids. If caught quickly, antibiotics effectively treat sepsis. But diagnosis is challenging because typical early symptoms — fever, high heart rate and high white blood cell count — mimic other illnesses including the common cold.

An algorithm that could predict the threat of sepsis in kids would be a gamechanger for physicians across the country. “When it’s a child’s life on the line, having a backup system that AI could offer to bolster some of that human fallibility is really, really important,” Sterrett said.

But the groundbreaking study in Science about bias reinforced to Sendak and Sterrett they wanted to be careful in their design. The team spent a month teaching the algorithm to identify sepsis based on vital signs and lab tests instead of easily accessible but often incomplete billing data. Any tweak to the program over the first 18 months of development triggered quality control tests to ensure the algorithm found sepsis equally well regardless of race or ethnicity.

But nearly three years into their intentional and methodical effort, the team discovered possible bias still managed to slip in. Ganga Moorthy, a global health fellow with Duke’s pediatric infectious diseases program, showed the developers research that doctors at Duke took longer to order blood tests for Hispanic kids eventually diagnosed with sepsis than white kids.

“One of my major hypotheses was that physicians were taking illnesses in white children perhaps more seriously than those of Hispanic children,” Moorthy said. She also wondered if the need for interpreters slowed down the process.

“I probably had my hands on my face within seconds, just like, oh my God,” Sendak said. “We totally missed all of these subtle things that if any one of these was consistently true could introduce bias into the algorithm.”

Sendak said the team had overlooked this delay, potentially teaching their AI inaccurately that Hispanic kids develop sepsis slower than other kids, a time difference that could be fatal.

“I was angry with myself. How could we not see this?”

Regulators are taking notice

Over the last several years, hospitals and researchers have formed national coalitions to share best practices and develop “playbooks” to combat bias. But signs suggest few hospitals are reckoning with the equity threat this new technology poses.

Researcher Paige Nong interviewed officials at 13 academic medical centers last year, and only four said they considered racial bias when developing or vetting machine learning algorithms.

“If a particular leader at a hospital or a health system happened to be personally concerned about racial inequity, then that would inform how they thought about AI,” Nong said. “But there was nothing structural, there was nothing at the regulatory or policy level that was requiring them to think or act that way.”

Several experts say the lack of regulation leaves this corner of AI feeling a bit like the “wild west.” Separate 2021 investigations found the Food and Drug Administration’s policies on racial bias in AI uneven, with only a fraction of algorithms even including racial information in public applications.

The Biden administration over the last 10 months has released a flurry of proposals to design guardrails for this emerging technology. The FDA says it now asks developers to outline any steps taken to mitigate bias and the source of data underpinning new algorithms.

The Office of the National Coordinator for Health Information Technology proposed new regulations in April that would require developers to share with clinicians a fuller picture of what data were used to build algorithms. Kathryn Marchesini, the agency’s chief privacy officer, described the new regulations as a “nutrition label” that helps doctors know “the ingredients used to make the algorithm.” The hope is more transparency will help providers determine if an algorithm is unbiased enough to safely use on patients.

The Office for Civil Rights at the U.S. Department of Health and Human Services last summer proposed updated regulations that explicitly forbid clinicians, hospitals and insurers from discriminating “through the use of clinical algorithms in [their] decision-making.” The agency’s director, Melanie Fontes Rainer, said while federal anti-discrimination laws already prohibit this activity, her office wanted “to make sure that [providers and insurers are] aware that this isn’t just buy a product off the shelf, close your eyes and use it.”

Industry welcoming — and wary — of new regulation

Many experts in AI and bias welcome this new attention, but there are concerns. Several academics and industry leaders said they want to see the FDA spell out in public guidelines exactly what developers must do to prove their AI tools are unbiased. Others want ONC to require developers to share their algorithm “ingredient list” publicly, allowing independent researchers to evaluate code for problems.

Some hospitals and academics worry these proposals — especially OCR’s explicit prohibition on using discriminatory AI — could backfire. “What we don’t want is for the rule to be so scary that physicians say, okay, I just won’t use any AI in my practice. I just don’t want to run the risk,” said Carmel Shachar, executive director of the Petrie-Flom Center at Harvard Law School. Shachar and several industry leaders said that without clear guidance, hospitals with fewer resources may struggle to stay on the right side of the law.

Duke’s Mark Sendak welcomes new regulations to eliminate bias from algorithms, “but what we’re not hearing regulators say is, ‘We understand the resources that it takes to identify these things, to monitor for these things. And we’re going to make investments to make sure that we address this problem.’”

The federal government invested $35 billion to entice and help doctors and hospitals adopt electronic health records earlier this century. None of the regulatory proposals around AI and bias include financial incentives or support.

‘You have to look in the mirror’

A lack of additional funding and clear regulatory guidance leaves AI developers to troubleshoot their own problems for now.

At Duke, the team had feared the delay in treating Hispanic patients would be baked into their algorithm for predicting sepsis among children. But after weeks of additional rigorous testing, they determined the algorithm predicted sepsis at the same speed for all patients.

Sendak’s best guess is that Duke has treated too few cases of sepsis among children for the algorithm to learn the human bias. He said the conclusion was more sobering than a relief.

“I don’t find it comforting that in one specific rare case, we didn’t have to intervene to prevent bias,” he said. “Every time you become aware of a potential flaw, there’s that responsibility of [asking], ‘Where else is this happening?’”

Sendak plans to build a more diverse team, with anthropologists, sociologists, community members and patients working together to root out bias in Duke’s algorithms. But for this new class of tools to do more good than harm, Sendak believes the entire health care sector must address its underlying racial inequity.

“You have to look in the mirror,” he said. “It requires you to ask hard questions of yourself, of the people you work with, the organizations you’re a part of. Because if you’re actually looking for bias in algorithms, the root cause of a lot of the bias is inequities in [our] care.”

Tradeoffs coverage on diagnostic excellence is supported in part by the Gordon and Betty Moore Foundation.

Episode Transcript and Resources

Episode Transcript

Dan Gorenstein: Doctors, data scientists and hospital executives believe *artificial intelligence may help solve what until now have been intractable problems.

Emily Sterrett: When it’s a child’s life on the line, having a backup system that AI could offer is really, really important.

DG: But with these new possibilities come new risks, namely the threat of perpetuating racial bias.

Mark Sendak: When you learn from the past, you replicate the past. You further entrench the past.

DG: In Part 1 of our special two-part series on racial bias in AI, we met a team at Duke Health that built a computer program to help diagnose kids with sepsis, but was blindsided when they realized all that hard work could delay care for Hispanic children. 

MS: I was angry with myself because I know that it is easy to put something out there that can cause massive harm.

DG: Today, in Part 2, Duke’s efforts to scrub bias from their program, and what the Biden administration is doing to push the health care world to diagnose and reduce racial bias in AI.

From the studio at the Leonard Davis Institute at the University of Pennsylvania, I’m Dan Gorenstein. This is Tradeoffs.

****

DG: If you missed Part 1, go back and listen to that first. It should be right below this episode in your feed. 

Today, I’m joined by Tradeoffs producer Ryan Levi to talk about the federal government’s response to racial bias in health care AI. Ryan, how you doing, man?

Ryan Levi: Doing well, Dan.

DG: So, look, before we get going, can you just spell out for us what artificial intelligence actually is?

RL: Sure, so AI broadly means a computer program that’s trained to perform tasks normally done by humans. And we’re mostly going to be talking about a subset of AI called “machine learning” where those computer programs are trained to find patterns in huge amounts of data like test results and patients’ bills.

DG: Got it, great. So you’ve spent the last several months talking with federal officials, legal experts, data scientists and hospital folks to get a handle on how they’re all trying to keep racial bias from the bedside. What have you learned?

RL: Well, there’s this telling phrase that keeps coming up, Dan.

Carmel Shachar: Wild west.
Mark Sendak: The wild, wild west.
Paige Nong: It’s the wild west.

RL: I know you hate a cliche.

DG: True, true.

RL: But this one feels pretty on the mark. Using artificial intelligence to do everything from streamlining billing to helping diagnose cancer is a new frontier. And like the wild west, there’s this lawless feeling to it right now. There are few rules especially when it comes to preventing developers from baking racial inequity into their algorithms.

DG: But you’ve been talking with federal regulators who are hoping to change that. I’d like to know what these agencies are doing to make it easier to diagnose racial bias in health care algorithms. So, one of my favorite questions as you know: Who is doing what?

RL: Well, there are three agencies tackling different pieces of this puzzle — three sheriffs, if you will, trying to impose some order in this AI wild west.

DG: Dear lord, Ryan. Okay, fine, I will go along with it. So there are three sheriffs.

RL: That’s right, and each is responsible for tackling a different part of the AI bias problem. We’ve got the Food and Drug Administration or FDA. They regulate the developers who actually make these algorithms — big companies like GE and MedTronic. Then there’s the Office of the National Coordinator for Health Information Technology.

DG: A brutal name for a super important agency.

RL: Absolutely. They go by ONC for short. Their jurisdiction is the electronic health record, which is really integral to how these AI tools operate a lot of times. And finally, we’ve got the Department of Health and Human Services’ Office for Civil Rights, known as OCR. Their job is to make sure that clinicians, health systems and insurers aren’t discriminating against patients.

DG: Right, three regulators, excuse me, sheriffs: FDA, ONC and the Office for Civil Rights. Who should we start with? 

RL: I’m going to say FDA. They’re the old timers of this group or at least as much of an old-timer as you can have in AI. They approved their first AI-powered device back in 1995. 

DG: Wow. I had no idea AI had been kicking around in health care for that long, Ryan. I thought it was just more recent tech from this ChatGPT era.

RL: Yeah, in its capacity to review and approve medical devices, the FDA has actually greenlit more than 500 AI devices over the past 25-plus years, the vast majority of them in radiology, Dan. And it’s the FDA’s job to make sure the algorithms it reviews are safe and effective, just like the agency does for new prescription drugs and other medical devices.

DG: Okay, so what does that look like, Ryan, when it comes to racial bias?

RL: The agency told me that when an algorithm comes to them for review, they take a close look at the data used to build it. And this is where bias can come in. Remember, machine learning algorithms are fed historical data, like test results and diagnoses, and that helps the algorithms predict future outcomes. Because people of color are often underrepresented in those data sets, that can cause problems. So the FDA looks to make sure the data underpinning an algorithm is diverse enough, and they also ask developers to explain the steps they took to mitigate bias. The agency also says it’s working with academic researchers to come up with better ways to deal with this.

DG: I hate to ruin your metaphor, Ryan, but that doesn’t sound very wild west-y. It sounds like the FDA is pretty focused on this.

RL: Fair point, fair point. But when you talk to the researchers and clinicians working on health care AI, they say that the FDA has a lot more work to do.

Minerva Tantoco: Regulations have always lagged behind technology advances.

RL: Minerva Tantoco is the Chief AI Officer at the New York University McSilver Institute for Poverty Policy and Research. She, along with developers and academics who study this space, say they’d like the FDA to establish public guidelines that spell out what developers must do to prove their AI tools are unbiased.

MT: By setting the standard, the FDA can signal that this needs to be part of your design. The folks who are developing these tools, they’re going as quickly as possible. They’re taking the data they can access. But unless the FDA specifically says we are going to test for racial and ethnic bias, they won’t necessarily account for that.

RL: Minerva and other experts I spoke to told me they’re glad — really glad — the FDA is stepping up its efforts on bias, but they feel like the FDA is missing stuff right now. And setting this kind of public standard — becoming a stricter sheriff if you will — would help crack down on the lawless feeling out there.

DG: Is there any proof, Ryan, that the FDA is actually missing bias in these tools?

RL: Well, several sources told me hospitals have algorithms up and running right now, today,  that did not go through any FDA review, much less one for bias. And as for the tools that are reviewed by the FDA, an investigation by STAT News and a study by researchers at Stanford, both in 2021, found that the FDA’s approach to looking at race in AI-devices was really uneven — sometimes asking for information, but often not. And that really captured the concerns I heard, Dan, around the FDA not having these public requirements for what developers have to do or have to show to prove their algorithms are unbiased.

DG: So while the FDA is taking direct steps, it sounds like there are still blind spots and a hunger for more regulation, more safeguards to beef up trust in these AI tools?

RL: Exactly.

DG: So that’s FDA. Let’s move onto our second sheriff in the AI bias wild west, the Office of the National Coordinator for Health IT or ONC. What part of the AI bias world are they focused on?

RL: ONC is also regulating developers like FDA, but for ONC, it’s only when it comes to the electronic health record where a lot of these clinical algorithms end up.

DG: And since just about every hospital and doctor’s office in the country uses electronic health records at this point, ONC has jurisdiction over pretty much the whole health care system. 

RL: Absolutely. And their strategy comes down to this:

Kathryn Marchesini: Transparency
Jordan Everson: Transparency
Jeff Smith: Transparency

RL: That was Kathryn Marchesini, Jordan Everson and Jeff Smith, who are all top officials at ONC and helped draft some new regulations the agency just put out. And ONC is betting transparency is a first step to rooting out bias. You know, these algorithms are often a black box, Dan. They’re technically complex and developers fiercely guard their intellectual property, which makes it really hard for hospitals to know whether the AI they’re buying is biased. So you know how the FDA, at least sometimes, evaluates the data developers use to build their algorithms?

DG: I’m with you.

RL: Well, ONC wants to make sure the end-users — hospitals, doctors, nurses — also know what that data looks like. In April, ONC proposed a rule that would require developers to let clinicians see two things: One, what kind of data was used to build an algorithm. And two, what steps the developer took to ensure the final product was unbiased. Kathryn Marchesini at ONC says they think of it like a nutrition label.

KM: What are the ingredients used to make the algorithm? Who was represented in the data that was being used? So the demographic type of information. Where did that data come from? What type of data set?

RL: ONC hopes making developers share an ingredient list will help hospitals and clinicians decide if an algorithm is unbiased enough — really, safe enough — to use. 

DG: I love that nutrition label metaphor, Ryan. It helps make ONC’s approach really clear. At the same time, it makes me wonder how effective it’s going to be. How many people in a hospital or doctor’s office know enough about all this stuff to evaluate whether an algorithm is biased or not?

RL: So funny you should say that. Carmel Shachar asked the same thing when I talked with her about this new rule. Carmel studies the regulation of digital health tools at the Petrie-Flom Center at Harvard Law School.

CS: Transparency is really good and important, but we can’t rely solely on transparency because clinicians might look at this and say, okay, that’s kind of cool and interesting. But again, I’m not a data scientist, so what am I supposed to do here?

RL: Carmel says what would be really helpful is having this nutrition label, this ingredient list out there publicly so everybody could kick the tires on these algorithms. 

DG: Everybody like who?

RL: Anyone, Dan. Data scientists, academics, independent researchers. Carmel says they should all be able to evaluate algorithms and root out hidden bias.

CS: The more people who are keeping an eye out, the more likely it is if there’s something problematic, somebody’s going to notice something and say something.

RL: ONC told me, Dan, that the agency is open to future regulation that would require developers to share this information publicly.

DG: Thanks, Ryan. When we come back, we hear about our last sheriff and learn how Duke tried to keep a last-minute bias out of their new childhood sepsis algorithm.

MIDROLL

DG: Welcome back. I’m here with Tradeoffs producer Ryan Levi, who is walking us through how three different federal agencies are trying to make it easier to diagnose and eliminate racial bias in clinical AI tools. Alright, Ryan, the sun’s going down here on your wild west which means it’s time for our last sheriff to make an appearance: the Office for Civil Rights at HHS.

RL: Right, OCR. They are legitimately a law enforcement agency, by the way, so the whole sheriff thing is a little less far fetched with them.

DG: Good, good to hear.

RL: Anyway, OCR enforces federal health care anti-discrimination laws, and OCR Director Melanie Fontes Rainer told me those laws already cover discrimination by algorithms.

Melanie Fontes Rainer: We have open investigations in this space right now looking at use of predictive analytics and possible violation of federal civil rights laws and discrimination.

RL: While these laws, Dan, broadly prohibit health care providers from discriminating, there was nothing that explicitly said, “it’s illegal to use an algorithm to discriminate.” Period. So in August 2022, Melanie says OCR proposed a rule that said hospitals, physician practices and insurers…

MFR: must not discriminate on the basis of race, color, national origin, sex, age or disability through the use of clinical algorithms and decision making.

DG: So that’s interesting, Ryan. OCR proposed a regulation to ban something that is already against the law? Why get more specific if they already have this broader authority?

RL: Melanie says the point is to send a clear and obvious signal to hospitals and doctors that if they use an algorithm that is biased against their patients, they are breaking the law and they are responsible.

MFR: To make sure that they’re aware that this isn’t just, you know, buy a product off the shelf, close your eyes and use it. It’s another tool we have to regulate to make sure that they’re not using it in some way that furthers disparities.

RL: The other sheriffs we’ve talked about, Dan — FDA and ONC — they’re focused on making sure developers have done everything they can to rid an algorithm of bias by the time it gets to the hospital. What OCR is saying is providers also have a role to play. Hospitals and clinicians have to be prepared to spend the time and money to make sure biased algorithms aren’t getting to the bedside, or they could face fines and be forced to make changes.

DG: So what will hospitals actually have to do if this rule is finalized?

RL: It’s a good question, and we don’t quite know yet. Melanie made it very clear that even without this rule, hospitals can not discriminate using algorithms. That’s against the law. But just like with the FDA, Dan, there’s little guidance here on what exactly hospitals have to do to stay on the right side of the law. And given the immense investment and expertise required to make sure algorithms are unbiased, Harvard’s Carmel Shachar is worried the OCR rule could have unintended consequences.

CS: What we don’t want is for the rule to be so scary that physicians say, okay, I just won’t use any AI in my practice. I just don’t want to run the risk.

RL: And, Dan, Carmel worries in particular about smaller, less resourced hospitals that don’t have a team of data scientists like they have at Duke to do this work. Melanie at the Office for Civil Rights downplayed this concern. She says her office isn’t knocking on every hospital door looking for biased algorithms. They’re really zeroed in on providers with a pattern of discrimination. And she says OCR’s goal is to help a hospital stop discriminating without having to issue fines or other penalties.

DG: Thanks for summing up how these three different regulatory sheriffs — FDA, ONC and OCR — are trying to bring some more order to this world, Ryan. I’m curious, at the end of day, did the developers, clinicians and researchers you talked with think these efforts will help get rid of bias in health care algorithms?

RL: It’s a little hard to say. On one hand, pretty much everyone I talked with including the hospitals want more regulation. They’re excited to see the sheriffs in town paying attention to this. But they also say they need more guidance — clear guidance — on how to keep bias out of clinical AI, and importantly, they say they need more money to actually do that. I thought Mark Sendak, who helps develop AI tools for Duke Health and who we heard from in Part 1 of the series, articulated this concern really well.

MS: We’re hearing very loud and clear that we have to eliminate bias from algorithms. But I think what we’re not hearing is a regulator say, we understand the resources that it takes to identify these things, to monitor for these things, and we’re going to make investments to make sure that we address this problem.

RL: What Mark’s talking about there, Dan, are federal investments. The last really big tech change in health care was the implementation of electronic health records, and the federal government spent $35 billion to entice and help providers adopt EHRs. Not one of the regulatory proposals I’ve seen around algorithms and bias mention anything about incentives or assistance like that.

DG: Tradeoffs producer Ryan Levi, thanks, as always, sir, for your reporting.

RL: Anytime, partner. 

DG: Oh my, okay. Off into the sunset he goes.

At the end of Part 1, we left Mark Sendak and the team at Duke a bit stunned. It was late 2022, and one of their colleagues had just told them that it often takes an hour or two longer for Hispanic kids at Duke to get checked out for sepsis compared to white kids — delays potentially caused by language barriers or provider bias. Mark realized they had unknowingly baked this delay into the data he and his team had so scrupulously tried to scrub clean.

MS: That would mean that the kid whose care was delayed would look like they have sepsis two hours later than the kid whose care wasn’t delayed.

DG: That kind of delay can be deadly with sepsis. Mark was disappointed with himself for missing something that now seemed so obvious like non-English speaking patients getting slower care. But Mark was also grateful for the chance to do something about it. He and his team dug in. They spent another two months trying to make sure their algorithm caught sepsis in Hispanic kids as quickly as all other kids.

MS: We did a ton of analysis and we looked at, okay, how would each of these items be represented in the data? How would we test what the impact of each of these is in the potential delays in care?

DG: Ultimately, the team determined that the algorithm was not predicting sepsis later for Hispanic patients. Mark says that’s likely because Duke treats a relatively small number of kids with sepsis, so there wasn’t enough data to tip the algorithm. Mark was relieved, and it cemented in his mind just how easy it is for bias to slip into AI. 

MS: I don’t find it comforting that in one specific rare case, we didn’t have to intervene to prevent bias. Every time you become aware of a potential flaw there’s that responsibility of, where else is this happening? 

DG: Research shows Hispanic patients often wait longer for care in the emergency department. Mark says that means algorithms in use right now are diagnosing Hispanic patients for health conditions slower than other patients.

MS: And I can’t tell you what it is, but I’m sure it’s already causing harm to Spanish-speaking families.

DG: Duke’s pediatric sepsis algorithm is going through its final tests and hospital approvals, on track to switch on this summer. After that, Mark and physician Emily Sterrett hope to share it with other hospitals.

ES: The impact of us being able to figure this out and share it with other larger institutions is huge. If this works, it would truly revolutionize the job of a pediatric emergency medicine physician.

DG: That potential — to better diagnose this tricky condition and save kids’ lives — that’s why Emily and Mark and so many in health care are excited about AI. But to do more good than harm, Mark says, is going to take what often seems in short supply: devotion to addressing this racial bias in algorithms and the broader health care system.

MS: You have to look in the mirror. It’s very introspective, and it requires you to ask hard questions of yourself, of the people that you’re working with, of the organizations you’re a part of. Because if you’re actually looking for bias in algorithms, the root cause of a lot of the bias is inequities in care.

DG: Mark believes developers must work closely with front-line clinicians like Emily. And he sees now, he needs to build a more diverse team: anthropologists and sociologists, patients and community members — enough different folks to think through how bias could sneak into this groundbreaking work. It’s a lot, more than any of the proposed regulations would require. But necessary, Mark says, to help make sure these tools leave our biases behind. I’m Dan Gorenstein, this is Tradeoffs. 

Episode Resources

Selected Reporting, Research and Analysis on Regulation of AI and Racial Bias:

Selected Reporting on AI and Racial Bias:

Selected Research and Analysis on AI and Racial Bias:

Selected Best Practices to Mitigate Racial Bias in AI:

Episode Credits

Guests:

  • Emily Sterrett, MD, Associate Professor of Pediatrics, Director of Improvement Science, Duke University School of Medicine Department of Pediatrics
  • Mark Sendak, MD, MPP, Population Health & Data Science Lead, Duke Institute for Health Innovation
  • Minerva Tantoco, Chief AI Officer, New York University McSilver Institute for Poverty, Policy and Research
  • Carmel Shachar, JD, MPH, Executive Director, Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School
  • Kathryn Marchesini, JD, Chief Privacy Officer, Office of the National Coordinator for Health Information Technology
  • Melanie Fontes Rainer, JD, Director, HHS Office for Civil Rights
  • Ryan Levi, Reporter/Producer, Tradeoffs

The Tradeoffs theme song was composed by Ty Citerman, with additional music this episode from Blue Dot Sessions and Epidemic Sound.

This episode was reported by Ryan Levi, edited by Dan Gorenstein and Cate Cahan, and mixed by Andrew Parrella and Cedric Wilson.

Special thanks to: Suresh Balu, Jordan Everson, Sara Gerke and Jeff Smith.

Additional thanks to: Julia Adler-Milstein, Brett Beaulieu-Jones, Bennett Borden, David Dorr, Malika Fair, Marzyeh Ghassemi, Maia Hightower, John Halamka, Chris Hemphill, John Jackson, Jen King, Elaine Nsoesie, Ziad Obermeyer, Michael Pencina, Yolande Pengetnze, Deb Raji, Juan Rojas, Keo Shaw, Mona Siddiqui, Yusuf Talha Tamer, Ritwik Tewari, Danny Tobey, Alexandra Valladares, David Vidal, Steven Waldren, Anna Zink, James Zou, the Tradeoffs Advisory Board and our stellar staff!

Ryan is the managing editor for Tradeoffs, helping lead the newsroom’s editorial strategy and guide its coverage on its flagship podcast, digital articles, newsletters and live events. Ryan spent six...