David Dyke is the Chief Product Officer of Relatient, which is one of the biggest players in the up and coming area of direct patient scheduling. As anyone who has been stuck in a phone tree or tried to reach a live human just to get an appointment at a doctor’s office knows, scheduling in health care is way behind the eight ball compared to booking a restaurant, massage, or basically anything else online. Why is it so hard? David explained that and then demos how Relatient allows provider organizations to let both new and returning patients self-schedule. There is a ton of complexity behind this including what David says is an average of infinity minus one API calls to the practice management system and EMR of all of its clients. But speaking as someone who has literally left a message and hoped that someone called me back “within 4 business days” for my last specialty appointment, I’m glad to see one company at least is taking on this challenge–Matthew Holt
Software Living in an Enterprise World: Why Digital Behavioral Health Can’t Gain Traction
By TREVOR VAN MIERLO
Let’s face it: for the past 25 years, digital behavioral health has struggled. Yet, we keep reinventing (and funding) the same models over and over again.
How It All Started
In the beginning (mid-1990s), a handful of developers, researchers, and investors envisioned high reach, lower-cost, highly tailored, anonymous interventions reaching millions of people with limited healthcare access.
The initial focus was never healthcare providers and insurers. These organizations were seen as too slow to adopt new technologies, and there was a general distrust of integrated care and insurers. Many digital health companies feared these organizations (and pharma) would leverage their power to learn from smaller companies, and then redevelop interventions internally.
Instead, the focus was on partnerships and B2C sales. Funding was easier to obtain from granting agencies, and there was ample development support flowing from sources like the tobacco Master Settlement Agreement (MSA). The primary concern was 1) whether the population could access these revolutionary tools and, 2) who would pay for them.
The Digital Divide
Back then, funders were often short-sightedly obsessed with the digital divide – the gap between people who had access to digital technology (mostly educated, higher-income earners in large cities) and everyone else. The argument was, “Why should we fund digital tools that will only benefit those who already have access to healthcare?”
Data was available, so academics armed themselves with ANOVA and relentlessly examined variables such as hardware costs, processing speed, age, gender, race, ethnicity, geography, income, and education. If you check Google Scholar, you can see the prevailing sentiment was that it would take decades for the digital divide to narrow, and new policy was desperately required to fix the problem (see: here, here, here, and here).
No More Excuses
Fast forward to 2024. According to a recent article in Forbes, there are 5.4 billion internet users worldwide (66% of the global population). In the U.S., 94.6% of Americans have internet access. Most US households have multiple devices, and according to Pew Research Center Research, 97% own a cellphone, of which 90% are smartphones.
As a Gen X’er who used a typewriter in college before upgrading to a Compaq Deskpro 286 from Future Shop (for about $400), my adult life has been a witness to the rapid progression of digital. Now, my 9-year-old daughter is teaching me how to play Fortnite (Epic Games), my 11-year-old is the only kid on his hockey team without a smartphone (this won’t last), and STARLINK allows me to chat face-to-face with my parents in rural Northern Ontario.
All aspects of technology are pervasive and accessible – but if you search Google or Bing for immediate, evidence-based behavioral help, you can’t get it. If you can find access it’s behind a paywall: through your employer (contact HR), health plan (call to see if you’re covered), or subscription ($19.99 per month).
That’s not meeting the original vision – and we have the technology. So, what’s the problem?
Continue reading…Moving the bar(rier) forward: the benefits of de-risking cytokine release syndrome
By SAMANTHA McCLENAHAN
Every breakthrough in cancer treatment brings hope, but it also comes with a staggering price, raising a critical question: how do we balance groundbreaking advances with the financial reality that could limit access for many patients?
Developing new cancer medications involves extensive research, clinical trials, and regulatory approvals; a lengthy process that requires substantial financial investment. Within clinical trials, this includes maintaining stringent safety protocols and managing a variety of adverse events, from mild reactions requiring little to no care to extremely severe events with hefty hospital stays and life-saving medical intervention. Take Cytokine Release Syndrome (CRS), for example. CRS is a common adverse event associated with chimeric antigen receptor (CAR) T cell therapy and other immunotherapies that presents across this spectrum with flu-like symptoms in mild cases of CRS to organ damage, and even death, in severe cases. The median cost of treating CRS following cancer-target immunotherapy is over half a million dollars in the United States. Tackling that large price tag – in addition to another $500,000 for CAR-T cell therapies – and reducing associated risks are necessary to break down barriers to care for many patients – especially those who are uninsured or with limited resources hindering the ability to travel, miss work, or secure a caregiver.
Unlocking Cost Efficiency in Clinical Trials with Digital Health Technologies
Integration of digital health technologies (DHTs) including telehealth, wearables such as smart watches, remote patient monitoring, and mobile applications in oncology care and clinical trials has shown immense value in improving patient outcomes, despite the slow uptake within the field. General benefits during clinical trials are captured through:
- Reducing clinical visits and shortening trial length – Remote patient monitoring and virtual consultations minimize the need for physical visits, accelerating trial timelines.
- Enhancing recruitment, diversity, and participant completion – Targeted outreach supported by big data analytics and machine learning algorithms helps to effectively identify and engage with eligible candidates, leading to faster recruitment and lower dropout rates. Digital technologies also overcome traditional barriers to participation, such as location, transportation, language barriers, and information access. for a broader representation of patient demographics and more generalized findings and improved healthcare equity.
- Increasing availability of evidentiary and safety requirements – Continuous data collection and monitoring in the setting most comfortable to patients – extending beyond clinical walls. This provides a pool of data to support clinical endpoints and enhances patient safety by enabling early detection of adverse events.
While the exact cost of these digital interventions varies by study, there is significant evidence that cost-saving measures are emerging.
Continue reading…Everything you ever want to know about birth control and much more — Sophia Yen, Pandia Health
Dr. Sophia Yen is the Chief Medical Officer (and Founder) of Pandia Health. She is about as expert as it comes on the topics contraception, emergency contraception, medication abortion, menopause and lots more. Her PR peeps asked if I’d interview her about Pandia Health, which is a fantastic online clinic & pharmacy for women at basically all ages. But I couldn’t have her on THCB without having her tell all about the world of contraception, menopause and of course reproductive health. I promise you that if you are a woman or somone who knows a woman, this is a fascinating interview. You will learn a lot, and there are lots of suggestions for how to manage many aspects of your health–Matthew Holt
The Fantastic Fungi — Biohybrid Bots Are Mushrooming
By KIM BELLARD
I hadn’t expected to write about a biology-related topic anytime soon after doing so last week, but, gosh darn it, then I saw a press release from Cornell about biohybrid robots – powered by mushrooms (aka fungi)! They had me at “biohybrid.”
The release talks about a new paper — Sensorimotor Control of Robots Mediated by Electrophysiological Measurements of Fungal Mycelia – from the Cornell’s Organic Robotics Lab, led by Professor Rob Shepherd. As the release describes the work:
By harnessing mycelia’s innate electrical signals, the researchers discovered a new way of controlling “biohybrid” robots that can potentially react to their environment better than their purely synthetic counterparts.
Or, in the researchers’ own words:
The paper highlights two key innovations: first, a vibration- and electromagnetic interference–shielded mycelium electrical interface that allows for stable, long-term electrophysiological bioelectric recordings during untethered, mobile operation; second, a control architecture for robots inspired by neural central pattern generators, incorporating rhythmic patterns of positive and negative spikes from the living mycelia.
Let’s simplify that: “This paper is the first of many that will use the fungal kingdom to provide environmental sensing and command signals to robots to improve their levels of autonomy,” Professor Shepherd said. “By growing mycelium into the electronics of a robot, we were able to allow the biohybrid machine to sense and respond to the environment.”
Lead author Anand Mishra, a research associate in the lab, explained: “If you think about a synthetic system – let’s say, any passive sensor – we just use it for one purpose. But living systems respond to touch, they respond to light, they respond to heat, they respond to even some unknowns, like signals. That’s why we think, OK, if you wanted to build future robots, how can they work in an unexpected environment? We can leverage these living systems, and any unknown input comes in, the robot will respond to that.”
The team build two robots: a soft one shaped like a spider, and a wheeled one. The researchers first used the natural spike in the mycelia to make them walk and roll, respectively, using the natural signals from the mycelia. Then researchers exposed them to ultraviolet light, which caused the mycelia to react and changed the robots’ gaits. Finally, the researchers were able to override the mycelia signals entirely.
“This kind of project is not just about controlling a robot,” Dr. Mishra said. “It is also about creating a true connection with the living system. Because once you hear the signal, you also understand what’s going on. Maybe that signal is coming from some kind of stresses. So you’re seeing the physical response, because those signals we can’t visualize, but the robot is making a visualization.”
Dr. Shepherd believes that instead of using light as the signal, they will use chemical signals. For example: “The potential for future robots could be to sense soil chemistry in row crops and decide when to add more fertilizer, for example, perhaps mitigating downstream effects of agriculture like harmful algal blooms.”
It turns out that biohybrid robots in general and fungal computing in particular are a thing. In last week’s article I quoted Professor Andrew Adamatzky, of the University of the West of England about his preference for fungal computing. He not only is the Professor in Unconventional Computing there, and is the founder and Editor-in-Chief of the International Journal for Unconventional Computing, but also literally wrote the book about fungal computing. He’s been working on fungal computing since 2018 (and before that on slime mold computing).
Professor Adamatzky notes that fungi have a wide array of sensory inputs: “They sense light, chemicals, gases, gravity, and electric fields,” which opens the door to a wide variety of inputs (and outputs). Accordingly, Ugnius Bajarunas, a member of Professor Adamatzy’s team, told an audience last year: “Our goal is real-time dialog between natural and artificial systems.”
With fungal computing, TechHQ predicts: “The future of computing could turn out to be one where we care for our devices in a way that’s closer to looking after a houseplant than it is to plugging in and switching on a laptop.”
But how would we reboot them?
Continue reading…Biology to the Rescue?
By KIM BELLARD
I feel much about synthetic biology as I do AI: I don’t really understand it from a technical point of view, but I sure am excited about its potential. Sometimes they even overlap, as I’ll discuss later. But I’ll start with some recent developments with bioplastics, a topic I have somehow never really covered.
Let’s start with some work at Washington University (St. Louis) involving, of all things, purple bacteria. In case you didn’t know it – I certainly didn’t – purple bacteria “are a special group of aquatic microbes renowned for their adaptability and ability to create useful compounds from simple ingredients,” according to the press release. The researchers are turning the bacteria into bioplastic factories.
One study, led by graduate student Eric Connors, showed that two “obscure” species of purple bacteria can produce polyhydroxyalkanoates (PHAs), a natural polymer that can be purified to make plastics. Another study, led by research lab supervisor Tahina Ranaivoarisoa, took another “well studied but notoriously stubborn” species of purple bacteria to dramatically ramp up its production of PHAs, by inserting a gene that helped turn them into “relative PHA powerhouses.” The researchers are optimistic they could use other bacteria to produce even higher levels of bioplastics.
The work was done in the lab of associate professor Aripta Bose, who said: “There’s a huge global demand for bioplastics. They can be produced without adding CO2 to the atmosphere and are completely biodegradable. These two studies show the importance of taking multiple approaches to finding new ways to produce this valuable material.”
“It’s worth taking a look at bacteria that we haven’t looked at before,” Mr. Conners said. “We haven’t come close to realizing their potential.” Professor Bose agrees: “We hope these bioplastics will produce real solutions down the road.”
Meanwhile, researchers at Korea Advanced Institute of Science and Technology, led by Sang Yup Lee, have manipulated bacteria to produce polymers that contain “ring-like structures,” which apparently make the plastics more rigid and thermally stable. Normally those structures would be toxic to the bacteria, but the researchers managed to enable E. coli bacteria to both tolerate and produce them. The researchers believe that the polymer would be especially useful in biomedical applications, such as drug delivery.
As with the Washington University work, this research is not producing output at scale, but the researchers have good confidence that it can. “If we put more effort into increasing the yield, then this method might be able to be commercialized at a larger scale,” says Professor Lee. “We’re working to improve the efficiency of our production process as well as the recovery process, so that we can economically purify the polymers we produce.”
Because the polymer is produced using biological instead of chemical processes, and is biodegradable, the researchers believe it can be important for the environment. “I think biomanufacturing will be a key to the success of mitigating climate change and the global plastic crisis,” says Professor Lee. “We need to collaborate internationally to promote bio-based manufacturing so that we can ensure a better environment for our future.”
Environmental impact is also very much on the minds of researchers at the University of Virginia. They are working on creating biodegradable bioplastics from food waste. “By creating cost-effective bioplastics that naturally decompose, we can reduce plastic pollution on land and in oceans and address significant issues such as greenhouse gas emissions and economic losses associated with food waste,” said lead researcher Zhiwu “Drew” Wang.
The team is developing microorganisms that convert food waste into fats, which are then processed into bioplastics. Those bioplastics then should easily be composed. “Our first step is to make single-layer film to see if it can be utilized as an actual product,” said Chenxi Cao, a senior in packaging and system design. “If it has good oxygen and water vapor barriers and other properties, we can move to the next step. We aim to replace traditional coated paper products with PHA. Current paper products are often coated with polyethylene or polyactic acid, which are not fully degradable. PHA is fully biodegradable in nature, even in a backyard environment.”
The approach is currently still in the pilot project stage.
If all that isn’t cool enough, our own bodies may become biofactories, such as to deliver drugs or vaccines. Earlier this year researchers at UT Southwestern reported on “in situ production and secretion of proteins,” which in this case targeted psoriasis and two types of cancer.
The researchers say: “Through this engineering approach, the body can be utilized as a bioreactor to produce and systemically secrete virtually any encodable protein that would otherwise be confined to the intracellular space of the transfected cell, thus opening up new therapeutic opportunities.”
“Instead of going to the hospital or outpatient clinic frequently for infusions, this technology may someday allow a patient to receive a treatment at a pharmacy or even at home once a month, which would be a significant boost to their quality of life,” said study leader Daniel Siegwart, Ph.D. Professor Siegwart believes this type of in situ production could eventually improve health and quality of life for patients with inflammatory diseases, cancers, clotting disorders, diabetes, and a range of genetic disorders.
I promised I’d touch on an example of synthetic biology and AI overlapping. Last year I wrote about how “organoid intelligence” was a new approach to biocomputing and AI. Earlier this year Swiss firm FinalSpark launched its Neuroplatform, which uses 16 human brain organoids as the computing platform, claiming it was: “The next evolutionary leap for AI.”
“Our principal goal is artificial intelligence for 100,000 times less energy,” FinalSpark co-founder Fred Jordan says.
Now FinalSpark is renting its biocomputers to AI researchers at several top universities…for only $500 a month. “As far as I know, we are the only ones in the world doing this” on a publicly rentable platform, Dr. Jordan told Scientific American. Reportedly, around 34 universities requested access, but FinalSpark so far has limited use to 9 institutions, including the University of Michigan, the Free University of Berlin, and the Lancaster University in Germany.
Scientific America reports related work at Spain’s National Center for Biotechnology, using cellular computing, and at the University of the West of England, using – I’m serious! – fungal networks. “Fungal computing offers several advantages over brain-organoid-based computing,” Andrew Adamatzky says, “particularly in terms of ethical simplicity, ease of cultivation, environmental resilience, cost-effectiveness and integration with existing technologies.”
Bioplastics, biofactories, biocomputing — pretty cool stuff all around. I’ll admit I don’t know where all of this is leading, but I can’t wait to see where it leads.
What will Harris mean for Health Care? – Not much
By MATTHEW HOLT
The Democratic convention wrapped with a fine speech from Kamala Harris, star power from the Obamas and Clintons, and a bunch of Republicans telling their ideological brethren that it was better to be a Democrat than a Trumper. More importantly no Beyonce/Taylor Swift duet–as we were promised by Mitt Romney.
There was a lot of talk about some aspects of health care. But overall if Harris wins, don’t expect much change to the current health care system.
Why not?
First there’s the pure politics. The Dems need to win back the House (probable but not certain) and hold the Senate to pass legislation. Right now they have a 51-49 edge in the Senate. Most likely that goes to 50-50 as the Republicans will definitely pick up Joe Manchin’s seat in West Virginia. There’s a series of seats the Dems currently hold in close races (Montana, Ohio, MIchigan, Nevada, Arizona) that they’ll need to keep to maintain it at 50-50, and it’s hard to see any pickups from Republicans (perhaps Florida or Texas if you squint really hard). The good news is that Manchin (WV) and Sinema (AZ) will soon both be gone, so the Dems that will be there won’t be as difficult to persuade to follow a Presidential agenda. But that will still leave Walz as VP to do what Harris did and pass a bunch of deciding votes under reconciliation, which massively limits what the legislation can do–it has to be “budget related.”
Which leads us to what we have been hearing from Harris and her campaign about health care? We’ve heard a lot about issues that have impacts on health, specifically creating affordable housing and fighting child poverty, but little that is directly related to health care itself. Really only two issues stand out. Abortion and reproductive rights, and drug prices.
Clearly Harris will take a swing at reversing Dobbs and passing a national right to abortion. This will need either a packing of the Supreme Court (my favorite) or ending the filibuster or both. Either of these will be incredibly tough to pull off constitutionally and politically and will take huge amounts of political oxygen. Of course the cynics would say, the Democrats are better off leaving this as an issue to use to beat up the Republicans on. But if it gets done, womens’ and reproductive rights will only be back where they were in 2022.
Regarding the cost of drugs, there will continue to be much justified bashing of big pharma, but the extension of insulin price controls is something that (eventually) the market via CivicaRX and others is getting to anyway. Meanwhile the IRA gave Medicare the right to negotiate drug prices and the results are not exactly earth shattering. For example, CMS says it’s negotiated the cost of blood thinner Eliquis from about $6,000 a year to under $3,000 This sounds good until you realize that the price is only that high because of patent games the manufacturer BMS plays in the US, and the price in the rest of the world is under $1,000. We’ll hear more about this as the price cuts come into effect, (although not till 2026!) and more drugs get negotiated, but overall this isn’t exactly an earth-shattering change.
Finally there’s already a guaranteed fight about extending the premium subsidies for ACA plans. These were first in the pandemic American Rescue Act, then extended in the IRA, but they currently are scheduled to end in 2025. It’s hard to imagine them not being extended further whatever the makeup of the Senate, assuming a Democratic House of Representatives. (A Marjorie Taylor Greene speakership does give me pause!). But again there’s nothing new here and the overall flavor of expensive premiums and high deductibles in the current ACA marketplace won’t change.
So what’s not going to happen? Virtually all the interesting stuff we were promised by Harris and for that matter Biden in 2020. You may have missed the one actual “policy-first” speech at the convention which came from Bernie Sanders. To be fair a lot of his agenda was already in the Biden legislation. That was no accident as Biden deliberately reached out to him in 2020 and 2021 and enacted a pretty radical agenda on infrastructure, climate, industrial policy and more. And when I say radical I mean milquetoast social democrat by European standards! But what wasn’t in that agenda? No Medicare for all, which Bernie ran on in 2019/20 and brought up again at the convention. Who else proposed that in 2019? Why, a certain Kamala Harris. That never made it into the Biden agenda. We didn’t even get legislation introduced about lowering the Medicare age to 60, which was a campaign promise. There’s been no conversation about any of this from Harris or from Biden before he withdrew. It’s just a bridge too far.
Which leads to the stuff that gets debated about in THCB and elsewhere as to how the system actually works. There’s been nothing about Medicaid expansion (or its continued contraction). No talk about reining in hospital consolidation. No mention even of insurers gaming Medicare Advantage or private equity buying up physician practices. Nothing about the expansion of value-based care.
What we can expect in a Harris administration is more of the same from CMS and potentially a slightly more aggressive FTC. That will mean continued efforts to veer slightly away from fee-for-service in Medicare, a few more constraints on the worst behavior in Medicare Advantage, and possibly some warning shots from the FTC about hospital monopolies. But the trends we’ve seen in recent years will largely continue. We’re not getting a primary-care based capitated system emerging from the wreckage of what we have now, and unlike the Clinton and even Obama administrations, there’s not even any rhetoric from Harris or Biden about how that would be a good idea.
So politically I don’t think the Harris administration will be very exciting for health care. And if the other guy wins, as Jeff Goldsmith wrote on THCB last month, expect even less.
Tiny Is Mighty
By KIM BELLARD
I am a fanboy for AI; I don’t really understand the technical aspects, but I sure am excited about its potential. I’m also a sucker for a catchy phrase. So when I (belatedly) learned about TinyAI, I was hooked.
Now, as it turns out, TinyAI (also know as Tiny AI) has been around for a few years, but with the general surge of interest in AI it is now getting more attention. There is also TinyML and Edge AI, the distinctions between which I won’t attempt to parse. The point is, AI doesn’t have to involve huge datasets run on massive servers somewhere in the cloud; it can happen on about as small a device as you care to imagine. And that’s pretty exciting.
What caught my eye was a overview in Cell by Farid Nakhle, a professor at Temple University, Japan Campus: Shrinking the Giants: Paving the Way for TinyAI. “Transitioning from the landscape of large artificial intelligence (AI) models to the realm of edge computing, which finds its niche in pocket-sized devices, heralds a remarkable evolution in technological capabilities,” Professor Nakhle begins.
AI’s many successes, he believes, “…are demanding a leap in its capabilities, calling for a paradigm shift in the research landscape, from centralized cloud computing architectures to decentralized and edge-centric frameworks, where data can be processed on edge devices near to where they are being generated.” The demands for real time processing, reduced latency, and enhanced privacy make TinyAI attractive.
Accordingly: “This necessitates TinyAI, here defined as the compression and acceleration of existing AI models or the design of novel, small, yet effective AI architectures and the development of dedicated AI-accelerating hardware to seamlessly ensure their efficient deployment and operation on edge devices.”
Professor Nakhle gives an overview of those compression and acceleration techniques, as well as architecture and hardware designs, all of which I’ll leave as an exercise for the interested reader.
If all this sounds futuristic, here are some current examples of TinyAI models:
- This summer Google launched Gemma 2 2B, a 2 billion parameter model that it claims outperforms OpenAI’s GPT 3.5 and Mistral AI’s Mixtral 8X7B. VentureBeat opined: “Gemma 2 2B’s success suggests that sophisticated training techniques, efficient architectures, and high-quality datasets can compensate for raw parameter count.”
- Also this summer OpenAI introduced GPT-4o mini, “our most cost-efficient small model.” It “supports text and vision in the API, with support for text, image, video and audio inputs and outputs coming in the future.”
- Salesforce recently introduced its xLAM-1B model, which it likes to call the “Tiny Giant.” It supposedly only has 1b parameters, yet Marc Benoff claims it outperforms modelx 7x its size and boldly says: “On-device agentic AI is here”
- This spring Microsoft launched Phi-3 Mini, a 3.8 billion parameter model, which is small enough for a smartphone. It claims to compare well to GPT 3.5 as well as Meta’s Llama 3.
- H2O.ai offers Danube 2, a 1.8 b parameter model that Alan Simon of Hackernoon calls the most accurate of the open source, tiny LLM models.
A few billion parameters may not sound so “tiny,” but keep in mind that other AI models may have trillions.
Continue reading…Phil Fasano, Recuro Health
Phil Fasano is CEO of Recuro Health. Phil was CIO at Kaiser Permanente in the glory years when it rolled out Epic/Health Connect, which was at the time the biggest roll out of an EMR and was instrumental in creating Kaiser’s system of virtual care. A decade+ later the concept of telehealth and virtual care has been battered around, notably in the stock price of Teladoc and others. However, Phil is now leading a smaller organization called Recuro Health which is delivering extensive primary hybrid care to small & medium employers, has more then 1 million lives on the system, and is profitable. Is this the future of digital health? Maybe, and it’s well worth listening to his approach–Matthew Holt
ChatGPT Vs. Magic 8 Ball: Who Can Solve “The HealthCare Crisis”?
By MICHAEL L. MILLENSON
Long before ChatGPT, whose question-answering choices still remain somewhat of a black box, there was an equally mysterious, question-answering black ball. I decided to ask them each of them how to solve the cost, quality and access issues labeled for more than half a century as “the healthcare crisis.”
The hard, plastic Magic 8 Ball was invented in 1946, two years before a landmark Supreme Court decision spurred a boom in employer-sponsored health insurance. It catapulted into kid-driven popularity in the 1970s, the same decade that rising healthcare costs propelled “healthcare crisis” into the public vocabulary.
The healthcare crisis is still with us, as is Magic 8 Ball, which, thanks to current owner Mattel, can now be consulted either in person (i.e., by holding and shaking it) or online. With a fiercely fought presidential election campaign underway, I decided that pitting the black box vs. the black ball to answer crucial health policy questions would likely provide just as much clarity as wading through weasel-worded white papers.
Both ChatGPT (Cost to OpenAI: $400,000 per day to operate) and Magic 8 Ball (One-time cost: $14.99) were up for the challenge, though they acknowledged it wouldn’t be easy.
“Can you help me solve the healthcare crisis?” I asked. “Signs point to yes,” Magic 8 ball replied, in its typically pithy, understated manner. ChatGPT, on the other hand, took my question as an invitation to show off its artificial intelligence.
“Addressing the healthcare crisis is a complex and multifaceted challenge that requires a holistic approach,” ChatGPT began. Then, as if a Washington think tank had been crossed with an academic policy conference, the Large Language Model offered a very large helping of language. There were 8 “key strategies,” each of which contained three bullet points, and each of which, I was advised, “involves detailed planning, resource allocation, and collaboration among various stakeholders, including government, healthcare providers, insurers, and the public.”
Then there was this diss when I asked about its competitor. “It’s a fun toy,” sneered the chatbot (if chatbots could sneer), “but it doesn’t provide reliable or informed answers.”
I decided to home in on specifics.
“Is a government-run single payer system the right answer?” I asked. “My sources say ‘no,’” Magic 8 ball told me. ChatGPT was more positive, with caveats.
“A government-run single-payer healthcare system is one potential solution to the healthcare crisis, and it comes with its own set of advantages and challenges,” the chatbot replied. It added, “Whether it is the ‘right’ answer depends on various factors” – and then, once more, went on to provide a long list of relevant ones.
I decided to inquire about an approach with bipartisan support. “Is value-based healthcare the best way to control costs?”
“It is decidedly so,” said the Magic 8 Ball immediately. But ChatGPT, usually lightning quick, waited perhaps 20 seconds before not only responding positively, but presenting an overview and specific suggestions. There were 5 advantages and 5 challenges, plus 3 examples of possible strategies (accountable care organizations, bundled payments and patient-centered medical homes), all tied together with 5 considerations for implementation.
“Ultimately, VBHC can be a key component of a broader strategy to reform healthcare systems and achieve sustainable cost control,” ChatGPT concluded.
That pattern continued as I probed about the need for more effective financial incentives to reward high-quality, cost-effective care, a central component of VBHC. “It is certain,” Magic 8 Ball quickly agreed. ChatGPT, meanwhile, again paused for a lengthy period (by its standards) before responding “thoughtfully” (by human standards).
“Yes,” it said, “effective financial incentives are crucial for promoting high-quality, cost-effective care. Properly designed incentives can align the interests of healthcare providers, payers and patients, leading to better health outcomes and more efficient use of resources.”
The chatbot then listed 5 types of financial incentives, 5 key elements of effective incentive programs and three specific examples incorporating them.
Continuing the financial incentives theme, I asked whether health savings accounts could help. Magic 8 Ball simply replied, “Yes,” while ChatGPT carefully pointed out that while HSAs “offer some benefits, they are not a comprehensive solution to the broader health care crisis.”
Like politicians, both ChatGPT and Magic 8 Ball sometimes hedged. “Are hospital mergers good or bad for patients?” I asked. “Ask again later,” said Magic 8 Ball. “Hospital mergers can have both positive and negative impacts on patients,” responded ChatGPT, before presenting a long list of why either might be the case.
“Is private equity buying doctors’ practices good or bad for patients?” I inquired. “Concentrate and ask again,” evaded Magic 8 Ball, followed by an incomprehensible, “Most likely.” ChatGPT allowed that this was “a complex issue, with potential benefits and drawbacks for patients,” before going on to the kind of pro and con balancing act any politician might admire.
I decided it was time to cut to the heart of the matter.
“Will health care costs ever be effectively controlled in America?” I demanded.
Magic 8 Ball tried to spare my feelings – “Better not to tell you now”– while ChatGPT, in its elliptical way, pointed me towards the unpleasant truth. While the challenge was not “insurmountable,” answered ChatGPT, it would require a “multi-faceted approach” involving “strong political will, stakeholder collaboration, and continuous evaluation and adjustment of strategies.”
In other words, “No.”
Michael Millenson is President of Health Quality Advisors and a long time THCB regular, he’s also a Forbes columnist where this piece first appeared.
