How cognitive biases impact healthcare decisions

https://www.linkedin.com/pulse/how-cognitive-biases-impact-healthcare-decisions-robert-pearl-m-d–ti5qc/?trackingId=eQnZ0um3TKSzV0NYFyrKXw%3D%3D

Day one of the healthcare strategy course I teach in the Stanford Graduate School of Business begins with this question: “Who here receives excellent medical care?”

Most of the students raise their hands confidently. I look around the room at some of the most brilliant young minds in business, finance and investing—all of them accustomed to making quick yet informed decisions. They can calculate billion-dollar deals to the second decimal point in their heads. They pride themselves on being data driven and discerning.

Then I ask, “How do you know you receive excellent care?”

The hands slowly come down and room falls silent. In that moment, it’s clear these future business leaders have reached a conclusion without a shred of reliable data or evidence.

Not one of them knows how often their doctors make diagnostic or technical errors. They can’t say whether their health system’s rate of infection or medical error is high, average or low.

What’s happening is that they’re conflating service with clinical quality. They assume a doctor’s bedside manner correlates with excellent outcomes.

These often false assumptions are part of a multi-millennia-long relationship wherein patients are reluctant to ask doctors uncomfortable but important questions: “How many times have you performed this procedure over the past year and how many patients experienced complications?” “What’s the worst outcome a patient of yours had during and after surgery?”

The answers are objective predictors of clinical excellence. Without them, patients are likely to become a victim of the halo effect—a cognitive bias where positive traits in one area (like friendliness) are assumed to carry over to another (medical expertise).

This is just one example of the many subconscious biases that distort our perceptions and decision-making.

From the waiting room to the operating table, these biases impact both patients and healthcare professionals with negative consequences. Acknowledging these biases isn’t just an academic exercise. It’s a crucial step toward improving healthcare outcomes.

Here are four more cognitive errors that cause harm in healthcare today, along with my thoughts on what can be done to mitigate their effects:

Availability bias

You’ve probably heard of the “hot hand” in Vegas—a lucky streak at the craps table that draws big cheers from onlookers. But luck is an illusion, a product of our natural tendency to see patterns where none exist. Nothing about the dice changes based on the last throw or the individual shaking them.

This mental error, first described as “availability bias” by psychologists Amos Tversky and Daniel Kahneman, was part of groundbreaking research in the 1970s and ‘80s in the field of behavioral economics and cognitive psychology. The duo challenged the prevailing assumption that humans make rational choices.

Availability bias, despite being identified nearly 50 years ago, still plagues human decision making today, even in what should be the most scientific of places: the doctor’s office.

Physicians frequently recommend a treatment plan based on the last patient they saw, rather than considering the overall probability that it will work. If a medication has a 10% complication rate, it means that 1 in 10 people will experience an adverse event. Yet, if a doctor’s most recent patient had a negative reaction, the physician is less likely to prescribe that medication to the next patient, even when it is the best option, statistically.

Confirmation bias

Have you ever had a “gut feeling” and stuck with it, even when confronted with evidence it was wrong? That’s confirmation bias. It skews our perceptions and interpretations, leading us to embrace information that aligns with our initial beliefs—and causing us to discount all indications to the contrary.

This tendency is heightened in a medical system where physicians face intense time pressures. Studies indicate that doctors, on average, interrupt patients within the first 11 seconds of being asked “What brings you here today?” With scant information to go on, doctors quickly form a hypothesis, using additional questions, diagnostic testing and medical-record information to support their first impression.

Doctors are well trained, and their assumptions prove more accurate than incorrect overall. Nevertheless, hasty decisions can be dangerous. Each year in the United States, an estimated 371,000 patients die from misdiagnoses.

Patients aren’t immune to confirmation bias, either. People with a serious medical problem commonly seek a benign explanation and find evidence to justify it. When this happens, heart attacks are dismissed as indigestion, leading to delays in diagnosis and treatment.

Framing effect

In 1981, Tversky and Kahneman asked subjects to help the nation prepare for a hypothetical viral outbreak. They explained that if the disease was left untreated, it would kill 600 people. Participants in one group were told that an available treatment, although risky, would save 200 lives. The other group was told that, despite the treatment, 400 people would die. Although both descriptions lead to the same outcome—200 people surviving and 400 dying—the first group favored the treatment, whereas the second group largely opposed it.

The study illustrates how differently people can react to identical scenarios based on how the information is framed. Researchers have discovered that the human mind magnifies and experiences loss far more powerfully than positive gains. So, patients will consent to a chemotherapy regiment that has a 20% chance of cure but decline the same treatment when told it has 80% likelihood of failure.

Self-serving bias

The best parts about being a doctor are saving and improving lives. But there are other perks, as well.

Pharmaceutical and medical-device companies aggressively reward physicians who prescribe and recommend their products. Whether it’s a sponsored dinner at a Michelin restaurant or even a pizza delivered to the office staff, the intention of the reward is always the same: to sway the decisions of doctors.

And yet, physicians swear that no meal or gift will influence their prescribing habits. And they believe it because of “self-serving bias.”

In the end, it’s patients who pay the price. Rather than receiving a generic prescription for a fraction of the cost, patients end up paying more for a brand-name drug because their doctor—at a subconscious level—doesn’t want to lose out on the perks.

Thanks to the “Sunshine Act,” patients can check sites like ProPublica’s Dollars for Docs to find out whether their healthcare professional is receiving drug- or device-company money (and how much).

Reducing subconscious bias

These cognitive biases may not be the reason U.S. life expectancy has stagnated for the past 20 years, but they stand in the way of positive change. And they contribute to the medical errors that harm patients.

A study published this month in JAMA Internal Medicine found that 1 in 4 hospital patients who either died or were transferred to the ICU had been affected by a diagnostic mistake. Knowing this, you might think cognitive biases would be a leading subject at annual medical conferences and a topic of grave concern among healthcare professionals. You’d be wrong. Inside the culture of medicine, these failures are commonly ignored.

The recent story of an economics professor offers one possible solution. Upon experiencing abdominal pain, he went to a highly respected university hospital. After laboratory testing and observation, his attending doctor concluded the problem wasn’t serious—a gallstone at worst. He told the patient to go home and return for outpatient workup.

The professor wasn’t convinced. Fearing that the medical problem was severe, the professor logged onto ChatGPT (a generative AI technology) and entered his symptoms. The application concluded that there was a 40% chance of a ruptured appendix. The doctor reluctantly ordered an MRI, which confirmed ChatGPT’s diagnosis.

Future generations of generative AI, pretrained with data from people’s electronic health records and fed with information about cognitive biases, will be able to spot these types of errors when they occur.

Deviation from standard practice will result in alerts, bringing cognitive errors to consciousness, thus reducing the likelihood of misdiagnosis and medical error. Rather than resisting this kind of objective second opinion, I hope clinicians will embrace it. The opportunity to prevent harm would constitute a major advance in medical care.

Healthcare CFOs explore M&A, automation and service line cuts in 2024

Companies grappling with liquidity concerns are looking to cut costs and streamline operations, according to a new survey.

Dive Brief:

  • Over three-quarters of healthcare chief financial officers expect to see profitability increases in 2024, according to a recent survey from advisory firm BDO USA. However, to become profitable, many organizations say they will have to reduce investments in underperforming service lines, or pursue mergers and acquisitions.
  • More than 40% of respondents said they will decrease investments in primary care and behavioral health services in 2024, citing disruptions from retail players. They will shift funds to home care, ambulatory services and telehealth that provide higher returns, according to the report.
  • Nearly three-quarters of healthcare CFOs plan to pursue some type of M&A deal in the year ahead, despite possible regulatory threats.

Dive Insight:

Though inflationary pressures have eased since the height of the COVID-19 pandemic, healthcare CFOs remain cognizant of managing costs amid liquidity concerns, according to the report.

The firm polled 100 healthcare CFOs serving hospitals, medical groups, outpatient services, academic centers and home health providers with revenues from $250 million to $3 billion or more in October 2023.

Just over a third of organizations surveyed carried more than 60 days of cash on hand. In comparison, a recent analysis from KFF found that financially strong health systems carried at least 150 days of cash on hand in 2022.

Liquidity is a concern for CFOs given high rates of bond and loan covenant violations over the past year. More than half of organizations violated such agreements in 2023, while 41% are concerned they will in 2024, according to the report. 

To remain solvent, 44% of CFOs expect to have more strategic conversations about their economic resiliency in 2024, exploring external partnerships, options for service line adjustments and investments in workforce and technology optimization.

The majority of CFOs surveyed are interested in pursuing external partnerships, despite increased regulatory roadblocks, including recent merger guidance that increased oversight into nontraditional tie-ups. Last week, the FTC filed its first healthcare suit of the year to block the acquisition of two North Carolina-based Community Health Systems hospitals by Novant Health, warning the deal could reduce competition in the region.

Healthcare CFOs explore tie-ups in 2024

Types of deals that CFOs are exploring, as of Oct. 2023.

https://datawrapper.dwcdn.net/aiFBJ/1

Most organizations are interested in exploring sales, according to the report. Financially struggling organizations are among the most likely to consider deals. Nearly one in three organizations that violated their bond or loan covenants in 2023 are planning a carve-out or divestiture this year. Organizations with less than 30 days of cash on hand are also likely to consider carve-outs.

Organizations will also turn to automation to cut costs. Ninety-eight percent of organizations surveyed had piloted generative AI tools in a bid to alleviate resource and cost constraints, according to the consultancy. 

Healthcare leaders believe AI will be essential to helping clinicians operate at the top of their licenses, focusing their time on patient care and interaction over administrative or repetitive tasks,” authors wrote. Nearly one in three CFOs plan to leverage automation and AI in the next 12 months.

However, CFOs are keeping an eye on the risks. As more data flows through their organizations, they are increasingly concerned about cybersecurity. More than half of executives surveyed said data breaches are a bigger risk in 2024 compared to 2023.

JPM 2024 just wrapped. Here are the key insights

https://www.advisory.com/daily-briefing/2024/01/23/jpm-takeaways-ec#accordion-718cb981ab-item-4ec6d1b6a3

Earlier this month, leaders from more than 400 organizations descended on San Francisco for J.P. Morgan‘s 42nd annual healthcare conference to discuss some of the biggest issues in healthcare today. Here’s how Advisory Board experts are thinking about Modern Healthcare’s 10 biggest takeaways — and our top resources for each insight.

How we’re thinking about the top 10 takeaways from JPM’s annual healthcare conference 

Following the conference, Modern Healthcare  provided a breakdown of the top-of-mind issues attendees discussed.  

Here’s how our experts are thinking about the top 10 takeaways from the conference — and the resources they recommend for each insight.  

1. Ambulatory care provides a growth opportunity for some health systems

By Elizabeth Orr, Vidal Seegobin, and Paul Trigonoplos

At the conference, many health system leaders said they are evaluating growth opportunities for outpatient services. 

However, results from our Strategic Planner’s Survey suggest only the biggest systems are investing in building new ambulatory facilities. That data, alongside the high cost of borrowing and the trifurcation of credit that Fitch is predicting, suggests that only a select group of health systems are currently poised to leverage ambulatory care as a growth opportunity.  

Systems with limited capital will be well served by considering other ways to reach patients outside the hospital through virtual care, a better digital front door, and partnerships. The efficiency of outpatient operations and how they connect through the care continuum will affect the ROI on ambulatory investments. Buying or building ambulatory facilities does not guarantee dramatic revenue growth, and gaining ambulatory market share does not always yield improved margins.

While physician groups, together with management service organizations, are very good at optimizing care environments to generate margins (and thereby profit), most health systems use ambulatory surgery center development as a defensive market share tactic to keep patients within their system.  

This approach leaves margins on the table and doesn’t solve the growth problem in the long term. Each of these ambulatory investments would do well to be evaluated on both their individual profitability and share of wallet. 

On January 24 and 25, Advisory Board will convene experts from across the healthcare ecosystem to inventory the predominant growth strategies pursued by major players, explore considerations for specialty care and ambulatory network development, understand volume and site-of-care shifts, and more. Register here to join us for the Redefining Growth Virtual Summit.  

Also, check out our resources to help you plan for shifts in patient utilization:  

2. Rebounding patient volumes further strain capacity

By Jordan Peterson, Eliza Dailey, and Allyson Paiewonsky 

Many health system leaders noted that both inpatient and outpatient volumes have surpassed pre-pandemic levels, placing further strain on workforces.  

The rebound in patient volumes, coupled with an overstretched workforce, underscores the need to invest in technology to extend clinician reach, while at the same time doubling down on operational efficiency to help with things like patient access and scheduling. 

For leaders looking to leverage technology and boost operational efficiency, we have a number of resources that can help:  

3. Health systems aren’t specific on AI strategies

By Paul Trigonoplos and John League

According to Modern Healthcare, nearly all health systems discussed artificial intelligence (AI) at the conference, but few offered detailed implementation plans and expectations.

Over the past year, a big part of the work for Advisory Board’s digital health and health systems research teams has been to help members reframe the fear of missing out (FOMO) that many care delivery organizations have about AI.  

We think AI can and will solve problems in healthcare. Every organization should at least be observing AI innovations. But we don’t believe that “the lack of detail on healthcare AI applications may signal that health systems aren’t ready to embrace the relatively untested and unregulated technology,” as Modern Healthcare reported. 

The real challenge for many care delivery organizations is dealing with the pace of change — not readiness to embrace or accept it. They aren’t used to having to react to anything as fast-moving as AI’s recent evolution. If their focus for now is on low-hanging fruit, that’s completely understandable. It’s also much more important for these organizations to spend time now linking AI to their strategic goals and building out their governance structures than it is to be first in line with new applications.  

Check out our top resources for health systems working to implement AI: 

4. Digital health companies tout AI capabilities

By Ty Aderhold and John League

Digital health companies like TeladocR1 RCMVeradigm, and Talkspace all spoke out about their use of generative AI. 

This does not surprise us at all. In fact, we would be more surprised if digital health companies were not touting their AI capabilities. Generative AI’s flexibility and ease of use make it an accessible addition to nearly any technology solution.  

However, that alone does not necessarily make the solution more valuable or useful. In fact, many organizations would do well to consider how they want to apply new AI solutions and compare those solutions to the ones that they would have used in October 2022 — before ChatGPT’s newest incarnation was unveiled. It may be that other forms of AI, predictive analytics, or robotic process automation are as effective at a better cost.  

Again, we believe that AI can and will solve problems in healthcare. We just don’t think it will solve every problem in healthcare, or that every solution benefits from its inclusion.  

Check out our top resources on generative AI: 

5. Health systems speak out on denials

By Mallory Kirby

During the conference, providers criticized insurers for the rate of denials, Modern Healthcare reports. 

Denials — along with other utilization management techniques like prior authorization — continue to build tension between payers and providers, with payers emphasizing their importance for ensuring cost effective, appropriate care and providers overwhelmed by both the administrative burden and the impact of denials on their finances. 

  Many health plans have announced major moves to reduce prior authorizations and CMS recently announced plans to move forward with regulations to streamline the prior authorization process. However, these efforts haven’t significantly impacted providers yet.  

In fact, most providers report no decrease in denials or overall administrative burden. A new report found that claims denials increased by 11.99% in the first three quarters of 2023, following similar double digit increases in 2021 and 2022. 

  Our team is actively researching the root cause of this discrepancy and reasons for the noted increase in denials. Stay tuned for more on improving denials performance — and the broader payer-provider relationship — in upcoming 2024 Advisory Board research. 

For now, check out this case study to see how Baptist Health achieved a 0.65% denial write-off rate.  

6. Insurers are prioritizing Star Ratings and risk adjustment changes

By Mallory Kirby

Various insurers and providers spoke about “the fallout from star ratings and risk adjustment changes.”

2023 presented organizations focused on MA with significant headwinds. While many insurers prioritized MA growth in recent years, leaders have increased their emphasis on quality and operational excellence to ensure financial sustainability.

  With an eye on these headwinds, it makes sense that insurers are upping their game to manage Star Ratings and risk adjustment. While MA growth felt like the priority in years past, this focus on operational excellence to ensure financial sustainability has become a priority.   

We’ve already seen litigation from health plans contesting the regulatory changes that impact the bottom line for many MA plans. But with more changes on the horizon — including the introduction of the Health Equity Index as a reward factor for Stars and phasing in of the new Risk Adjustment Data Validation model — plans must prioritize long-term sustainability.  

Check out our latest MA research for strategies on MA coding accuracy and Star Ratings:  

7. PBMs brace for policy changes

By Chloe Bakst and Rachael Peroutky 

Pharmacy benefit manager (PBM) leaders discussed the ways they are preparing for potential congressional action, including “updating their pricing models and diversifying their revenue streams.”

Healthcare leaders should be prepared for Congress to move forward with PBM regulation in 2024. A final bill will likely include federal reporting requirements, spread pricing bans, and preferred pricing restrictions for PBMs with their own specialty pharmacy. In the short term, these regulations will likely apply to Medicare and Medicaid population benefits only, and not the commercial market. 

Congress isn’t the only entity calling for change. Several states passed bills in the last year targeting PBM transparency and pricing structures. The Federal Trade Commission‘s ongoing investigation into select PBMs looks at some of the same practices Congress aims to regulate. PBM commercial clients are also applying pressure. In 2023, Blue Cross Blue Shield of California‘s (BSC) decided to outsource tasks historically performed by their PBM partner. A statement from BSC indicated the change was in part due to a desire for less complexity and more transparency. 

Here’s what this means for PBMs: 

Transparency is a must

The level of scrutiny on transparency will force the hand of PBMs. They will have to comply with federal and state policy change and likely give something to their commercial partners to stay competitive. We’re already seeing this unfold across some of the largest PBMs. Recently, CVS Caremarkand Express Scripts launched transparent reimbursement and pricing models for participating in-network pharmacies and plan sponsors. 

While transparency requirements will be a headache for larger PBMs, they might be a real threat to smaller companies. Some small PBMs highlight transparency as their main value add. As the larger PBMs focus more on transparency, smaller PBMs who rely on transparent offerings to differentiate themselves in a crowded market may lose their main competitive edge. 

PBMs will have to try new strategies to boost revenue

PBM practice of guiding prescriptions to their own specialty pharmacy or those providing more competitive pricing is a key strategy for revenue. Stricter regulations on spread pricing and patient steerage will prompt PBMs to look for additional revenue levers.   

PBMs are already getting started — with Express Scripts reporting they will cut reimbursement for wholesale brand name drugs by about 10% in 2024. Other PBMs are trying to diversify their business opportunities. For example, CVS Caremark’s has offered a new TrueCost model to their clients for an additional fee. The model determines drug prices based on the net cost of drugs and clearly defined fee structures. We’re also watching growing interest in cross-benefit utilization management programs for specialty drugs.  These offerings look across both medical and pharmacy benefits to ensure that the most cost-effective drug is prescribed for patients. 

Check out some of our top resources on PBMs:  

To learn more about some of the recent industry disruptions, check out:   

8. Healthcare disruptors forge on

 By John League

At the conference, retailers such as CVS, Walgreens, and Amazon doubled down on their healthcare services strategies.

Typically, disruptors do not get into care delivery because they think it will be easy. Disruptors get into care delivery because they look at what is currently available and it looks so hard — hard to access, hard to understand, and hard to pay for.  

Many established players still view so-called disruptors as problematic, but we believe that most tech companies that move into healthcare are doing what they usually do — they look at incumbent approaches that make it hard for customers and stakeholders to access, understand, and pay for care, and see opportunities to use technology and innovative business models in an attempt to target these pain points.

CVS, Walgreens, and Amazon are pursuing strategies that are intended to make it more convenient for specific populations to get care. If those efforts aren’t clearly profitable, that does not mean that they will fail or that they won’t pressure legacy players to make changes to their own strategies. Other organizations don’t have to copy these disruptors (which is good because most can’t), but they must acknowledge why patient-consumers are attracted to these offerings.  

For more information on how disruptors are impacting healthcare, check out these resources:  

9. Financial pressures remain for many health systems

By Vidal Seegobin and Marisa Nives

Health systems are recovering from the worst financial year in recent history. While most large health systems presenting at the conference saw their finances improve in 2023, labor challenges and reimbursement pressures remain.  

We would be remiss to say that hospitals aren’t working hard to improve their finances. In fact, operating margins in November 2023 broke 2%. But margins below 3% remain a challenge for long-term financial sustainability.  

One of the more concerning trends is that margin growth is not tracking with a large rebound in volumes. There are number of culprits: elevated cost structures, increased patient complexity, and a reimbursement structure shifting towards government payers.  

For many systems, this means they need to return to mastering the basics: Managing costs, workforce retention, and improving quality of care. While these efforts will help bridge the margin gap, the decoupling of volumes and margins means that growth for health systems can’t center on simply getting bigger to expand volumes.

Maximizing efficiency, improving access, and bending the cost curve will be the main pillars for growth and sustainability in 2024.  

 To learn more about what health system strategists are prioritizing in 2024, read our recent survey findings.  

Also, check out our resources on external partnerships and cost-saving strategies:  

10. MA utilization is still high

By Max Hakanson and Mallory Kirby  

During the conference, MA insurers reported seeing a spike in utilization driven by increased doctor’s visits and elective surgeries.  

These increased medical expenses are putting more pressure on MA insurers’ margins, which are already facing headwinds due to CMS changes in MA risk-adjustment and Star Ratings calculations. 

However, this increased utilization isn’t all bad news for insurers. Part of the increased utilization among seniors can be attributed to more preventive care, such as an uptick in RSV vaccinations.  

In UnitedHealth Group‘s* Q4 earnings call, CFO John Rex noted that, “Interest in getting the shot, especially among the senior population, got some people into the doctor’s office when they hadn’t visited in a while,” which led to primary care physicians addressing other care needs. As seniors are referred to specialty care to address these needs, plans need to have strategies in place to better manage their specialist spend.   

To learn how organizations are bringing better value to specialist care in MA, check out our market insight on three strategies to align specialists to value in MA. (Kacik et al., Modern Healthcare, 1/12)

*Advisory Board is a subsidiary of UnitedHealth Group. All Advisory Board research, expert perspectives, and recommendations remain independent. 

3 huge healthcare battles being fought in 2024

https://www.linkedin.com/pulse/3-huge-healthcare-battles-being-fought-2024-robert-pearl-m-d–aguvc/?trackingId=z4TxTDG7TKq%2BJqfF6Tieug%3D%3D

Three critical healthcare struggles will define the year to come with cutthroat competition and intense disputes being played out in public:

1. A Nation Divided Over Abortion Rights

2. The Generative AI Revolution In Medicine

3. The Tug-Of-War Over Healthcare Pricing American healthcare, much like any battlefield, is fraught with conflict and turmoil. As we navigate 2024, the wars ahead seem destined to intensify before any semblance of peace can be attained. Let me know your thoughts once you read mine.

Modern medicine, for most of its history, has operated within a collegial environment—an industry of civility where physicians, hospitals, pharmaceutical companies and others stayed in their lanes and out of each other’s business.

It used to be that clinicians made patient-centric decisions, drugmakers and hospitals calculated care/treatment costs and added a modest profit, while insurers set rates based on those figures. Businesses and the government, hoping to save a little money, negotiated coverage rates but not at the expense of a favored doctor or hospital. Disputes, if any, were resolved quietly and behind the scenes.

Times have changed as healthcare has taken a 180-degree turn. This year will be characterized by cutthroat competition and intense disputes played out in public. And as the once harmonious world of healthcare braces for battle, three critical struggles take centerstage. Each one promises controversy and profound implications for the future of medicine:

1. A Nation Divided Over Abortion Rights

For nearly 50 years, from the landmark Roe v. Wade decision in 1973 to its overruling by the 2022 Dobbs case, abortion decisions were the province of women and their doctors. This dynamic has changed in nearly half the states.

This spring, the Supreme Court is set to hear another pivotal case, this one on mifepristone, an important drug for medical abortions. The ruling, expected in June, will significantly impact women’s rights and federal regulatory bodies like the FDA.

Traditionally, abortions were surgical procedures. Today, over half of all terminations are medically induced, primarily using a two-drug combination, including mifepristone. Since its approval in 2000, mifepristone has been prescribed to over 5 million women, and it boasts an excellent safety record. But anti-abortion groups, now challenging this method, have proposed stringent legal restrictions: reducing the administration window from 10 to seven weeks post-conception, banning distribution of the drug by mail, and mandating three in-person doctor visits, a burdensome requirement for many. While physicians could still prescribe misoprostol, the second drug in the regimen, its effectiveness alone pales in comparison to the two-drug combo.

Should the Supreme Court overrule and overturn the FDA’s clinical expertise on these matters, abortion activists fear the floodgates will open, inviting new challenges against other established medications like birth control.

In response, several states have fortified abortion rights through ballot initiatives, a trend expected to gain momentum in the November elections. This legislative action underscores a significant public-opinion divide from the Supreme Court’s stance. In fact, a survey published in Nature Human Behavior reveals that 60% of Americans support legal abortion.

Path to resolution: Uncertain. Traditionally, SCOTUS rulings have mirrored public opinion on key social issues, but its deviation on abortion rights has failed to shift public sentiment, setting the stage for an even fiercer clash in years to come. A Supreme Court ruling that renders abortion unconstitutional would contradict the principles outlined in the Dobbs decision, but not all states will enact protective measures. As a result, America’s divide on abortion rights is poised to deepen.

2. The Generative AI Revolution In Medicine

A year after ChatGPT’s release, an arms race in generative AI is reshaping industries from finance to healthcare. Organizations are investing billions to get a technological leg up on the competition, but this budding revolution has sparked widespread concern.

In Hollywood, screenwriters recently emerged victorious from a 150-day strike, partially focused on the threat of AI as a replacement for human workers. In the media realm, prominent organizations like The New York Times, along with a bevy of celebs and influencers, have initiated copyright infringement lawsuits against OpenAI, the developer of ChatGPT.

The healthcare sector faces its own unique battles. Insurers are leveraging AI to speed up and intensify claim denials, prompting providers to counter with AI-assisted appeals.

But beyond corporate skirmishes, the most profound conflict involves the doctor-patient relationship. Physicians, already vexed by patients who self-diagnose with “Dr. Google,” find themselves unsure whether generative AI will be friend or foe. Unlike traditional search engines, GenAI doesn’t just spit out information. It provides nuanced medical insights based on extensive, up-to-date research. Studies suggest that AI can already diagnose and recommend treatments with remarkable accuracy and empathy, surpassing human doctors in ever-more ways.

Path to resolution: Unfolding. While doctors are already taking advantage of AI’s administrative benefits (billing, notetaking and data entry), they’re apprehensive that ChatGPT will lead to errors if used for patient care. In this case, time will heal most concerns and eliminate most fears. Five years from now, with ChatGPT predicted to be 30 times more powerful, generative AI systems will become integral to medical care. Advanced tools, interfacing with wearables and electronic health records, will aid in disease management, diagnosis and chronic-condition monitoring, enhancing clinical outcomes and overall health.

3. The Tug-Of-War Over Healthcare Pricing

From routine doctor visits to complex hospital stays and drug prescriptions, every aspect of U.S. healthcare is getting more expensive. That’s not news to most Americans, half of whom say it is very or somewhat difficult to afford healthcare costs.

But people may be surprised to learn how the pricing wars will play out this year—and how the winners will affect the overall cost of healthcare.

Throughout U.S. healthcare, nurses are striking as doctors are unionizing. After a year of soaring inflation, healthcare supply-chain costs and wage expectations are through the roof. A notable example emerged in California, where a proposed $25 hourly minimum wage for healthcare workers was later retracted by Governor Newsom amid budget constraints.

Financial pressures are increasing. In response, thousands of doctors have sold their medical practices to private equity firms. This trend will continue in 2024 and likely drive up prices, as much as 30% higher for many specialties.

Meanwhile, drug spending will soar in 2024 as weight-loss drugs (costing roughly $12,000 a year) become increasingly available. A groundbreaking sickle cell disease treatment, which uses the controversial CRISPR technology, is projected to cost nearly $3 million upon release.

To help tame runaway prices, the Centers for Medicare & Medicaid Services will reduce out-of-pocket costs for dozens of Part B medications “by $1 to as much as $2,786 per average dose,” according to White House officials. However, the move, one of many price-busting measures under the Inflation Reduction Act, has ignited a series of legal challenges from the pharmaceutical industry.

Big Pharma seeks to delay or overturn legislation that would allow CMS to negotiate prices for 10 of the most expensive outpatient drugs starting in 2026.

Path to resolution: Up to voters. With national healthcare spending expected to leap from $4 trillion to $7 trillion by 2031, the pricing debate will only intensify. The upcoming election will be pivotal in steering the financial strategy for healthcare. A Republican surge could mean tighter controls on Medicare and Medicaid and relaxed insurance regulations, whereas a Democratic sweep could lead to increased taxes, especially on the wealthy. A divided government, however, would stall significant reforms, exacerbating the crisis of unaffordability into 2025.

Is Peace Possible?

American healthcare, much like any battlefield, is fraught with conflict and turmoil. As we navigate 2024, the wars ahead seem destined to intensify before any semblance of peace can be attained.

Yet, amidst the strife, hope glimmers: The rise of ChatGPT and other generative AI technologies holds promise for revolutionizing patient empowerment and systemic efficiency, making healthcare more accessible while mitigating the burden of chronic diseases. The debate over abortion rights, while deeply polarizing, might eventually find resolution in a legislative middle ground that echoes Roe’s protections with some restrictions on how late in pregnancy procedures can be performed.

Unfortunately, some problems need to get worse before they can get better. I predict the affordability of healthcare will be one of them this year. My New Year’s request is not to shoot the messenger.

Sam Altman’s wild year offers 3 critical lessons for healthcare leaders in 2024

https://www.linkedin.com/pulse/sam-altmans-wild-year-offers-3-critical-lessons-2024-pearl-m-d–sj1kc/?trackingId=G7JzFhoHSvuK7BRMyo4gcQ%3D%3D

What a wild end to the year it was for Sam Altman, CEO of OpenAI.

In the span of five white-knuckle days in November, the head of Silicon Valley’s most advanced generative AI company was fired by his board of directors, replaced by not one but two different candidates, hired to lead Microsoft’s AI-research efforts and, finally, rehired back into his CEO position at OpenAI with a new board.

A couple weeks later, TIME selected him “CEO of the Year.” Altman’s saga is more than a tale of tech-industry intrigue. His story provides three valuable lessons for not only aspiring and current healthcare leaders, but also everyone who works with and depends on them.

1. Agree On The Goal, Define It, Then Pursue It Tirelessly

OpenAI’s governance structure presented a unique case: a not-for-profit board, whose stated mission was to protect humanity, found itself overseeing an enterprise valued at more than $80 billion. Predictably, this setup invited conflict, as the company’s humanitarian mission began to clash with the commercial realities of a lucrative, for-profit entity.

But there’s little evidence the bruhaha resulted from Altman’s financial interests. According to IRS filings, the CEO’s salary was only $58,333 at the time of his firing, and he reportedly owns no stock.

While Altman clearly knows the company needs to raise money to fund the creation of ever-more-powerful AI tools, his primary goal doesn’t appear to revolve around maximizing shareholder value or his own wealth.

In fact, I believe Altman and the now-disbanded board shared a common mission: to save humanity. The problem was that the parties were 180 degrees apart when it came to defining how exactly to protect humanity.

Altman’s path to saving humanity involved racing forward as fast as possible. As CEO, he understands generative AI’s potential to radically enhance productivity and alleviate threats like world hunger and climate change.

By contrast, the board feared that breakneck AI development could spiral out of control, posing a threat to human existence. Rather than perceiving AGI (artificial general intelligence) as a savior, much of the board worried that a self-learning system might harm humanity.

This dichotomy pitted a CEO intent on changing the world against a board intent to progress at a cautious, incremental pace.

For Healthcare Leaders: Like OpenAI, American healthcare leaders share a common goal. Be they doctors, insurers or government health agencies, all tout the importance of “value-based care” (VBC), which in general terms, constitutes a financial and care-delivery model based on paying healthcare professionals for the quality of clinical outcomes they achieve rather than the quantity of services they provide. But despite agreeing on the target, leaders differ on what it means and how best to accomplish it. Some think of VBC as “pay for performance,” whereby doctors are paid small incentives based on metrics around prevention and patient satisfaction. These programs fail because clinicians ignore the metrics without incentives and total health suffers.

Other leaders believe VBC means paying insurers a set, annual, upfront fee to provide healthcare to a population of patients. This, too, fails since the insurers turn around and pay doctors and hospitals on a fee-for-service basis, and implement restrictive prior authorization requirements to keep costs down.

Instead of making minor financial tweaks that keep falling short of the goal, leaders who want to transform American medicine must play to win. This will require them to move quickly and completely away from fee-for-service payments (which rewards volume of care) to capitation at the delivery-system level (rewarding superior results by prepaying doctors and hospitals directly without insurers playing the part of middlemen).

Like OpenAI’s former board members, today’s healthcare leaders are playing “not to lose.” They avoid making big changes because they fear the backlash of risk-averse providers. But anything less than all in won’t make a dent given the magnitude of problems. To be effective, leaders must make hard decisions, accept the risks and be confident that once the changes are in place, no one will want to go back to the old ways of doing things.

2. Hire Visionary Leaders Who Inspire Boldly

Many tech-industry commentators have drawn comparisons between Altman and Steve Jobs. Both leaders possess(ed) the rare ability to foresee a better future and turn their visions into reality. And both demonstrate(d) passion for exceeding people’s wants and expectations—not for their own benefit but because they believe in a greater mission and purpose.

Altman and Jobs are what I call visionary leaders, who push their organizations and people to accomplish remarkable outcomes few could’ve believed possible. These types of leaders always challenge conservative boards.

When the OpenAI board realized it’s hard to constrain a CEO like Sam Altman, they fired him.

On day one of that decision, the board might have assumed their action would protect humanity and, therefore, earn the approval of OpenAI’s employees. But the story took a sharp turn when nearly all the company’s 770 workers signed a letter to the board in support of Altman, threatening to quit unless (a) their visionary leader was brought back immediately and (b) the board resigned.

Five days after the battle began, the board was facing a rebellion and had little choice but to back down.

For Healthcare Leaders: The American healthcare system is struggling. Half of Americans say they can’t afford their out-of-pocket expenses, which max out at $16,000 for an insured family. American health is languishing with average life expectancy virtually unchanged since the start of this century. Maternal and infant mortality rates in the U.S. are double what they are in other wealthy nations. And inside medicine, burnout runs rampant. Last year, 71,000 physicians left the profession.

Visionary leadership, often sidelined in favor of the status quo, is crucial for transformative change. In healthcare, boards typically prioritize hiring CEOs with the ability to consolidate market control and achieve positive financial results rather than the ability to drive excellence in clinical outcomes. The consequence for both the providers and recipients of care proves painful.

Like OpenAI’s employees, healthcare professionals want leaders who are genuine, who have the courage to abandon bureaucratic safety in favor of innovative solutions, and who can ignite their passion for medicine. For a growing number of clinicians, the practice of medicine has become a job, not a mission. Without that spark, the future of medicine will remain bleak.

3. Embrace Transformative Technology

OpenAI’s board simultaneously promoted and feared ChatGPT’s potential. In this era of advanced technology, the dilemma of embracing versus restraining innovation is increasingly common.

The board could have shut down OpenAI or done everything in its power to advance the AI. It couldn’t, however, do both. When organizations in highly competitive industries try to strike a safe “balance,” choosing the less-contentious middle ground, they almost always fail to accomplish their goals.

For Healthcare Leaders: Despite being data-driven scientists, healthcare professionals often hesitate to embrace information technologies. Having been burned by tools like electronic healthcare records, which were designed to maximize revenue and not to facilitate medical care, their skepticism is understandable.

But generative AI is different because it has the potential to simultaneously increase quality, accessibility and affordability. This is where technology and skilled leadership must combine forces. It’s not enough for leaders to embrace generative AI. They must also inspire clinicians to apply it in ways that promote collaboration and achieve day-to-day operational efficiency and effectiveness. Without both, any other operational improvements will be incremental and clinical advances minimal at best.

If the boards of directors and other similar decision-making bodies in healthcare want their organizations to lead the process of change, they’ll need to select and support leaders with the vision, courage, and skill to take radical and risky leaps forward. If not, as OpenAI’s narrative demonstrates, they and their organizations will become insignificant and be left behind.

ChatGPT will reduce clinician burnout, if doctors embrace it

Clinician burnout is a major problem. However, as I pointed out in a previous newsletter post, it is not a distinctly American problem.

A recent report from the Commonwealth Fund compared the satisfaction of primary care physicians in 10 high-income nations. Surprisingly, U.S. doctors ranked in the middle, reporting higher satisfaction rates than their counterparts in the U.K., Germany, Canada, Australia and New Zealand.

A Surprising Insight About Burnout

In self-reported surveys, American doctors link their dissatisfaction to problems unique to the U.S. healthcare system: excessive bureaucratic tasks, clunky computer systems and for-profit health insurance. These problems need to be solved, but to reduce clinician burnout we also need to address another factor that negatively impacts doctors around the globe.

Though national healthcare systems may vary greatly in their structure and financing, clinicians in wealthy nations all struggle to meet the ever-growing demand for medical services. And that’s due to the mounting prevalence and complications of chronic disease.

At the heart of the burnout crisis lies a fundamental imbalance between the volume and complexity of patient health problems (demand) and the amount of time that clinicians have to care for them (supply). This article offers a way to reverse both the surge in chronic illnesses and the ongoing clinician burnout crisis.

Supply vs. Demand: Reframing Burnout

When demand for healthcare exceeds doctors’ capacity to provide it, one might assume the easiest solution is to increase the supply of clinicians. But that outcome remains unlikely so long as the cost increases of U.S. medicine continue to outpace Americans’ ability to afford care.

Whenever healthcare costs exceed available funds, policymakers and healthcare commentators look to rationing. The Oregon Medicaid experiment of the 1990s offers a profound reminder of why this approach fails. Starting in 1989, a government taskforce brought patients and providers together to rank medical services by necessity. The plan was to provide only as many as funding would allow. When the plan rolled out, public backlash forced the state to retreat. They expanded the total services covered, driving costs back up without any improvement in health or any relief for clinicians.

Consumer Culture Can Drive Medical Culture

Ultimately, to reduce burnout, we will have to find a way to decrease clinical demand without raising costs or rationing care.

The best—and perhaps only viable—solution is to embrace technologies that empower patients with the ability to better manage their own medical problems.

American consumers today expect and demanded greater control over their lives and daily decisions. Time and again, technology has made this possible.

Take stock trading, for example. Once the sole domain of professional brokers and financial advisors, today’s online trading platforms give individual investors direct access to the market and a wealth of information to make prudent financial decisions. Likewise, technology transformed the travel industry. Sites like Airbnb and Expedia empowered consumers to book accommodations, flights and travel experiences directly, bypassing traditional travel agents.

Technology will soon democratize medical expertise, as well, giving patients unprecedented access to healthcare tools and knowledge. Within the next five to 10 years, as ChatGPT and other generative AI applications become significantly more powerful and reliable, patients will gain the ability to self-diagnose, understand their diseases and make informed clinical decisions.

Today, clinicians are justifiably skeptical of outsized AI promises. But as technology proves itself worthy, clinicians who embrace and promote patient empowerment will not only improve medical outcomes, but also increase their own professional satisfaction.

Here’s how it can happen:

Empowering Patients With Generative AI

In the United States, health systems (i.e., large hospitals and medical groups) that heavily prioritize preventive medicine and chronic-disease management are home to healthier patients and more satisfied clinicians.

In these settings, patients are 30% to 50% less likely to die from heart attack, stroke and colon cancer than patients in the rest of the nation. That’s because their healthcare organizations provide effective chronic-disease prevention programs and assist patients in managing their diabetes, hypertension, obesity and asthma. As a result, patients experience fewer complications like heart attacks, strokes, and cancer.

Most primary care physicians, however, don’t have the time to accomplish this by themselves. According to one study, physicians would need to work 26.7 hours per day to provide all the recommended preventive, chronic and acute care to a typical panel of 2,500 adult patients.

GenAI technologies like ChatGPT can help lessen the load. Soon, they’ll be able to offer patients more than just general advice about their chronic illnesses. They will give personalized health guidance. By connecting to electronic health records (EHR)—even when those systems are spread across different doctors’ offices—GenAI will be able to analyze a patient’s specific health data to provide tailored prevention recommendations. It will be able to remind patients when they need a health screening, and help schedule it, and even sort out transportation. That’s not something Google or any other online platform can currently do.

Moreover, with new tools (like doctor-designed plugins expected in future ChatGPT updates) and data from fitness trackers and home health monitors, GenAI will be capable of not just displaying patient health data, but also interpreting it in the context of each person’s health history and treatment plans. These tools will be able to provide daily updates to patients with chronic conditions, telling them how they’re doing based on their doctor’s plan.

When the patient’s health data show they’re on the right track, there won’t be a need for an office visit, saving time for everyone. But if something seems off—say, blood pressure readings remain excessively high after the start of anti-hypertensive drugs—clinicians will be able to quickly adjust medications, often without the patient needing to come in. And when in-person visits are necessary, GenAI will summarize patient health information so the doctor can quickly understand and act, rather than starting from scratch.

ChatGPT is already helping people make better lifestyle choices, suggesting diets tailored to individual health needs, complete with shopping lists and recipes. It also offers personalized exercise routines and advice on mental well-being.

Another way generative AI can help is by diagnosing and treating common, non-life-threatening medical problems (e.g., musculoskeletal, allergic or viral issues). ChatGPT and Med-PaLM 2 have already demonstrated the capability in diagnosing a range of clinical issues as effectively and safely as most clinicians. Looking ahead, GenAI’s will offer even greater diagnostic accuracy. When symptoms are worrisome, GenAI will alert patients, speeding up definitive treatment. Its ability to thoroughly analyze symptoms and ask detailed questions without the time pressure doctors feel today will eradicate many of our nation’s 400,000 annual deaths from misdiagnosis.

The outcomes—fewer chronic diseases, fewer heart attacks and strokes and more medical problems solved without an office visit—will decrease demand, giving doctors more time with the patients they see. As a result, clinicians will leave the office feeling more fulfilled and less exhausted at the end of the day.

The goal of enhanced technology use isn’t to eliminate doctors. It’s to give them the time they desperately need in their daily practice, without further increasing already unaffordable medical costs. And rather than eroding the physician-patient bond, the AI-empowered patient will strengthen it, since clinicians will have the time to dive deeper into complex issues when people come to the office.

A More Empowered Patient Is Key To Reducing Burnout

AI startups are working hard to create tools that assist physicians with all sorts of tasks: EHR data entry, organizing office duties and submitting prior authorization requests to insurance companies.

These function will help clinicians in the short run. But any tool that fails to solve the imbalance between supply (of clinician time) and demand (for medical services), will be nothing more than a temporary fix.

Our nation is caught in a vicious cycle of rising healthcare demand, leading to more patient visits per day per doctor, producing higher rates of burnout, poorer clinical outcomes and ever-higher demand. By empowering patients with GenAI, we can start a virtuous cycle in which technology reduces the strain on doctors, allowing them to spend more time with patients who need it most. This will lead to better health outcomes, less burnout for clinicians and further decreases in overall healthcare demand.

Physicians and medical societies have the opportunity to take the lead. They’ll have to educate the public on how to use this technology effectively, assist in connecting it to existing data sources and ensure that the recommendations it makes are reliable and safe. The time to start this process is now.

How generative AI will change the doctor-patient relationship

https://www.linkedin.com/pulse/how-generative-ai-change-doctor-patient-relationship-pearl-m-d-/?trackingId=sNn87WorSt%2BPg3F0SxKUIw%3D%3D

After decades of “doctor knows best,” the traditional physician-patient relationship is on the verge of a monumental shift. Generative AI tools like OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Bing are poised to give people significantly more power and control—not just over their personal lives and professional tasks, but over their own medical health, as well.

As these tools become exponentially smarter, safer and more reliable (an estimated 32 times more powerful in the next five years), everyday Americans will gain access to unparalleled medical expertise—doled out in easily understandable terms, at any time, from any place.

Already, Google’s Med-PaLM 2 has scored an expert-level 86.5% on the U.S. medical license exam while other AI tools have matched the skill and accuracy of average doctors in diagnosing complex medical diseases.

Soon, AI tools will be able to give patients detailed information about their specific medical problems by integrating with health monitors and electronic medical records (such EHR projects are already underway at Oracle/Cerner and Epic). In time, people will be able to self-diagnose and manage their own diseases as accurately and competently as today’s clinicians.

This newfound expertise will shake the very foundation of clinical practice.

Although public health experts have long touted the concept of clinicians and patients working together through shared decision-making, this rarely happens in practice. Generative AI will alter that reality.

Building on part one of this article, which explained why generative AI constitutes a quantum leap ahead of all the tech that came before it, part two provides a blueprint for strengthening the doctor-patient alliance in the era of generative AI.

Patients Today: Sick And Confused

To understand how generative AI will impact the practice of medicine, it’s best to look closer at the current doctor-patient dynamic.

The relationship has undergone significant evolution. In the past century, patients and doctors held close, enduring relationships, built on trust and a deep understanding of the patient’s individual needs. These bonds were characterized by a strong sense of personal connection, as doctors had the time to listen to their patients’ concerns and provided not only medical treatment but also emotional support.

Today, the doctor-patient relationship remains vitally important, but it has undergone several meaningful changes. While medical advancements have greatly expanded the possibilities for diagnosis and treatment, the relationship itself has suffered from less trust and a more transactional focus. The average visit lasts just 15 minutes, barely enough time to address the patient’s current medical concerns. The doctor’s computer and electronic healthcare record systems sit, quite literally, between doctors and patients. The result is that patients feel rushed and find their medical care increasingly impersonal. Modern healthcare is characterized by time constraints, administrative burdens and a focus on efficiency. This can lead to a sense of impersonality and decreased communication between doctors and patients.

But throughout these changes, one thing has remained constant. The doctor-patient relationship, which dates back more than five millennia, has always existed on an uneven playing field, with patients forced to rely almost entirely on doctors to understand their diseases and what to do about them.

Though patients can and do access the internet for a list of possible diagnoses and treatment options, that’s not the same as possessing medical expertise. In fact, sorting through dozens of online sources—often with conflicting, inaccurate, outdated and self-serving information—proves more confusing than clarifying. Nowhere can web-surfers find personalized and credible advice based on their age, medical history, genetic makeup, current medications and laboratory results.

What’s needed now is modern doctor-patient relationship, one that is strong enough to meet the demands of medicine today and restore the vital, personal and emotional connections of the past.    

Patients Tomorrow: Self-Diagnosing And Confident

In the future, generative AI will alter the doctor-patient dynamic by leveling the playing field.

Already, consumer AI tools can equip users with not just knowledge, but expertise. They allow the average person to create artistic masterpieces, produce hit songs and write code with unimagined sophistication. Next generations will offer a similar ability for patients, even those without a background in science or medicine.

Like a digitized second opinion, generative AI will shrink the knowledge gap between doctors and patients in ways that search engines can’t. By accessing millions of medical texts, peer-reviewed journals and scientific articles, ChatGPT will deliver accurate and unbiased medical expertise in layman’s language. And unlike internet sources, generative AI tools don’t have built-in financial incentives or advertising models that might skew responses.

To help patients and doctors navigate the upcoming era of generative AI, here’s a model for the future of medical practice based on proven approaches in education:  

Introducing The ‘Flipped Healthcare’ Model

The “flipped classroom” can be traced back nearly four decades, but it became popularized in the United States in the early 2000s through the Khan Academy in Northern California.

Students begin the learning process by watching videos and engaging with interactive tools online rather than sitting through traditional lectures. This pre-class preparation (or “homework in advance”) allows people to learn at their own pace. Moreover, it enhances classroom discussions, letting teachers and students dive much deeper into topics than they ever could before. Indeed, students spend time in class applying knowledge and collaborating to solve problems—not merely listening and taking notes.  

The introduction of generative AI opens the door to a similar approach in healthcare. Here’s how that might work in practice:

  1. Pre-Consultation Learning: Before visiting a doctor, patients would use generative AI tools to understand their symptoms or medical conditions. This foundational knowledge would accelerate the diagnostic process and enhance patient understanding. Even in the absence of advanced diagnostic testing (X-rays or bloodwork), this pre-consultation phase allows the patient to understand the questions their clinicians will ask and the steps they will take.
  2. In-Depth Human Interactions: With the patient’s knowledge base already established, consultations will dive deep into proactive health strategies and/or long-term chronic-disease management solutions, rather than having to start at square one. This approach maximizes the time patients and clinicians spend together. It also addresses the reality that at least 50% of patients leave the doctor’s office unsure of what they’ve been told.
  3. Home Monitoring: For the 60% of American patients living with chronic diseases, generative AI combined with wearable monitors will provide real-time feedback, thereby optimizing clinical outcomes. These patients, instead of going in for periodic visits (every three to six months), will obtain daily medical analysis and insights. And in cases where generative AI spots trouble (e.g., health data deviates from the doctor’s expectations), the provider will be able to update medications immediately. And when the patient is doing well, physicians can cancel follow-up visits, eliminating wasted time for all.
  4. Hospital At Home: Inpatient (hospital) care accounts for 30% of all healthcare costs. By continuously monitoring patients with medical problems like mild pneumonia and controllable bacterial infections, generative AI (combined with home monitoring devices and telemedicine access) would allow individuals to be treated in the comfort of their home, safely and more affordably than today.
  5. Lifestyle Medicine: Generative AI would support preventive health measures and lifestyle changes, reducing the overall demand for in-person clinical care. Studies confirm that focusing on diet, exercise and recommended screenings can reduce the deadliest complications of chronic disease (heart attack, stroke, cancer) by 30% or more. Decreasing the need for intensive procedures is the best way to make healthcare affordable and address the projected shortage of doctors and nurses in the future.

The Future: Collaborative Care For Superior Outcomes

The U.S. healthcare model often leaves patients feeling frustrated and overwhelmed. Meanwhile, time constraints placed on doctors lead to rushed consultations and misdiagnoses, which cause an estimated 800,000 deaths and disabilities annually.

The “flipped” approach, inspired by the Khan Academy, leverages the patient expertise that generative AI will create. Following this model will free up clinician time to make the most of every visit. Implementing this blueprint will require improvements in AI technology and an evolution of medical culture, but it offers the opportunity to make the doctor-patient relationship more collaborative and create empowered patients who will improve their health.

Talk with educators at the Khan Academy, and they will tell you how their innovative model results in better-educated students. They’ll also tell you how much more satisfied teachers and students are compared to those working in the traditional educational system. The same can be true for American medicine.

The AI-empowered patient is coming. Are doctors ready?

https://www.linkedin.com/pulse/ai-empowered-patient-coming-doctors-ready-robert-pearl-m-d-/

Artificial intelligence (AI) has long been heralded as an emerging force in medicine. Since the early 2000s, promises of a technological transformation in healthcare have echoed through the halls of hospitals and at medical meetings.

But despite 20-plus years of hype, AI’s impact on medical practice and America’s health remains negligible (with minor exceptions in areas like radiological imaging and predictive analytics).

As such, it’s understandable that physicians and healthcare administrators are skeptical about the benefits that generative AI tools like ChatGPT will provide.

They shouldn’t be. This next generation of AI is unlike any technology that has come before. 

The launch of ChatGPT in late 2022 marked the dawn of a new era. This “large language model” developed by OpenAI first gained notoriety by helping users write better emails and term papers. Within months, a host of generative AI products sprang up from Google, Microsoft and Amazon and others. These tools are quickly becoming more than mere writing assistants.

In time, they will radically change healthcare, empower patients and redefine the doctor-patient relationship. To make sense of this bold vision for the future, this two-part article explores:

  1. The massive differences between generative AI and prior artificial intelligences
  2. How, for the first time in history, a technological innovation will democratize not just knowledge, but also clinical expertise, making medical prowess no longer the sole domain of healthcare professionals.

To understand why this time is different, it’s helpful to compare the limited power of the two earliest generations of AI against the near-limitless potential of the latest version.

Generation 1: Rules-Based Systems And The Dawn Of AI In Healthcare

The latter half of the 20th century ushered in the first generation of artificial intelligence, known as rule-based AI.

Programmed by computer engineers, this type of AI relies on a series of human-generated instructions (rules), enabling the technology to solve basic problems.

In many ways, the rule-based approach resembles a traditional medical-school pedagogy where medical students are taught hundreds of “algorithms” that help them translate a patient’s symptoms into a diagnosis.

These decision-making algorithms resemble a tree, beginning with a trunk (the patient’s chief complaint) and branching out from there. For example, if a patient complains of a severe cough, the doctor first assesses whether fever is present. If yes, the doctor moves to one set of questions and, if not, to a different set. Assuming the patient has been febrile (with fever), the next question is whether the patient’s sputum is normal or discolored. And once again, this leads to the next subdivision. Ultimately each end branch contains only a single diagnosis, which can range from bacterial, fungal or viral pneumonia to cancer, heart failure or a dozen other pulmonary diseases.

This first generation of AI could rapidly process data, sorting quickly through the entire branching tree. And in circumstances where the algorithm could accurately account for all possible outcomes, rule-based AI proved more efficient than doctors.

But patient problems are rarely so easy to analyze and categorize. Often, it’s difficult to separate one set of diseases from another at each branch point. As a result, this earliest form of AI wasn’t as accurate as doctors who combined medical science with their own intuition and experience. And because of its limitations, rule-based AI was rarely used in clinical practice.

Generation 2: Narrow AI And The Rise Of Specialized Systems

As the 21st century dawned, the second era of AI began. The introduction of neural networks, mimicking the human brain’s structure, paved the way for deep learning.

Narrow AI functioned very differently than its predecessors. Rather than researchers providing pre-defined rules, the second-gen system feasted on massive data sets, using them to discern patterns that the human mind, alone, could not.

In one example, researchers gave a narrow AI system thousands of mammograms, half showing malignant cancer and half benign. The model was able to quickly identify dozens of differences in the shape, density and shade of the radiological images, assigning impact factors to each that reflected the probability of malignancy. Importantly, this kind of AI wasn’t relying on heuristics (a few rules of thumb) the way humans do, but instead subtle variations between the malignant and normal exams that neither the radiologists nor software designers knew existed.

In contrast to rule-based AI, these narrow AI tools proved superior to the doctor’s intuition in terms of diagnostic accuracy. Still, narrow AI showed serious limitations. For one, each application is task specific. Meaning, a system trained to read mammograms can’t interpret brain scans or chest X-rays.

But the biggest limitation of narrow AI is that the system is only as good as the data it’s trained on. A glaring example of that weakness emerged when United Healthcare relied on narrow AI to identify its sickest patients and give them additional healthcare services.

In filtering through the data, researchers later discovered the AI had made a fatal assumption. Patients who received less medical care were categorized as healthier than patients who received more. In doing so, the AI failed to recognize that less treatment is not always the result of better health. This can also be the result of implicit human bias.

Indeed, when researchers went back and reviewed the outcomes, they found Black patients were being significantly undertreated and were, therefore, underrepresented in the group selected for additional medical services.

Media headlines proclaimed, “Healthcare algorithm has racial bias,” but it wasn’t the algorithm that had discriminated against Black patients. It was the result of physicians providing Black patients with insufficient and inequitable treatment. In other words, the problem was the humans, not narrow AI.

Generation 3: The Future Is Generative

Throughout history, humankind has produced a few innovations (printing press, internet, iPhone) that transformed society by democratizing knowledge—making information easier to access for everyone, not just the wealthy elite.

Now, generative AI is poised to go one step further, giving every individual access to not only knowledge but, more importantly, expertise as well.

Already, the latest AI tools allow users to create a stunning work of art in the style of Rembrandt without ever having taken a painting class. With large language models, people can record a hit song, even if they’ve never played a musical instrument. Individuals can write computer code, producing sophisticated websites and apps, despite never having enrolled in an IT course.

Future generations of generative AI will do the same in medicine, allowing people who never attended medical school to diagnose diseases and create a treatment plan as well as any clinician.

Already, one generative AI tool (Google’s Med-PaLM 2) passed the physician licensing exam with an expert level score. Another generative AI toolset responded to patient questions with advice that bested doctors in both accuracy and empathy. These tools can now write medical notes that are indistinguishable from the entries that physicians create and match residents’ ability to make complex diagnoses on difficult cases.

Granted, current versions require physician oversight and are nowhere close to replacing doctors. But at their present rate of exponential growth, these applications are expected to become at least 30 times more powerful in the next five years. As a result, they will soon empower patients in ways that were unimaginable even a year ago.

Unlike their predecessors, these models are pre-trained on datasets that encompass the near-totality of publicly available information—pulling from medical textbooks, journal articles, open-source platforms and the internet. In the not-distant future, these tools will be securely connected to electronic health records in hospitals, as well as to patient monitoring devices in the home. As generative AI feeds on this wealth of data, its clinical acumen will skyrocket.

Within the next five to 10 years, medical expertise will no longer be the sole domain of trained clinicians. Future generations of ChatGPT and its peers will put medical expertise in the hands of all Americans, radically altering the relationship between doctors and patients.

Whether physicians embrace this development or resist is uncertain. What is clear is the opportunity for improvement in American medicine. Today, an estimated 400,000 people die annually from misdiagnoses, 250,000 from medical errors, and 1.7 million from mostly preventable chronic diseases and their complications.

In the next article, I’ll offer a blueprint for Americans as they grapple to redefine the doctor-patient relationship in the context of generative AI. To reverse the healthcare failures of today, the future of medicine will have to belong to the empowered patient and the tech-savvy physician. The combination will prove vastly superior to either alone.

Epic expanding its lead on EHR competitors

https://mailchi.mp/b7baaa789e52/the-weekly-gist-september-29-2023?e=d1e747d2d8

Two large nonprofit health systems made headlines earlier this month announcing that they plan to transition, enterprise-wide, from Oracle Cerner to Epic for their electronic health record (EHR) system.

Using data from KLAS Research, the graphic below shows how Epic has emerged in recent years as the leader in the hospital EHR market. From 2016 to 2022, Epic increased its acute care hospital market share from 26 percent to 36 percent, while its main rival, Oracle Cerner, held flat at 25 percent.

Moreover, Epic is gaining popularity among larger health systems, while Oracle Cerner lost almost 5K beds in 2022, despite gaining 22 hospitals, as it trades large systems for smaller hospitals. 

Epic’s ability to consolidate multiple archives into a single, more functional platform has made it popular with physicians, whose feedback was cited by Intermountain as a key reason behind the system’s decision to switch. 

With three quarters of Americans having an Epic record, the company is leveraging its pole position in aggregating healthcare data as healthcare approaches the cusp of a generative AI boom, recently announcing an expanded partnership with Microsoft focused on integrating AI tools into its EHR system. 

A health system’s guide to reducing bureaucratic clinician busywork

https://mailchi.mp/b7baaa789e52/the-weekly-gist-september-29-2023?e=d1e747d2d8

Published this week in the Harvard Business Review, this intriguing case study tells the story of how Hawaii Pacific Health, a four-hospital system based in Honolulu, worked with its providers to reduce the deluge of needless or low-value administrative tasks required each day by the system’s electronic health record (EHR) platform.

The system’s “Get Rid of Stupid Stuff” (GROSS) initiative created a simple, accessible submission form that allowed providers to flag EHR prompts and workflows ranging from inefficient (printing and scanning discharge papers patients had already signed electronically) to nonsensical (affirming adolescent patients had received proper care for their non-existent umbilical cords). Around 10 percent of suggestions submitted were for prompts that could be immediately eliminated, 15 percent caught gaps in communication and workflow, and the remaining 75 percent identified more complex opportunities for redesign. The GROSS initiative not only freed thousands of labor hours, but also boosted morale by engaging clinicians in the system’s efforts to improve operations. 

The Gist: While Hawaii Pacific Health is far from the only system to have successfully engaged its providers in the mission of reducing administrative busywork, this case study provides an example of how sometimes the simplest approaches can be the most effective. 

As systems now look to generative AI as the next frontier of bureaucratic efficiency, they will need to optimize workflow processes before automating them in order to avoid ingraining today’s inefficiencies.