Doctors are getting on board with genAI, survey shows | Healthcare IT News

In a swift reversal since Open.ai was released in late September 2023 surveys of physicians reveal more acceptance of its use, while patients are less confident.

In an online survey of 100 practicing physicians who work in a large U.S. hospital or health system and use clinical decision support tools, four in five providers – 81% – agreed that generative artificial intelligence can improve care team interactions with patients.

The doctors surveyed by Wolter Kluwer also indicated high standards for selecting genAI tools – with 89% reporting they need vendors to be transparent about the sources of CDS data and want to be sure it comes from practicing medical experts before they use it for their clinical decisions. 

However, they overestimated American health consumers' openness to AI-supported medical advice compared to a previous genAI in healthcare survey of those consumers that the company conducted in November. 

The gap between physician and patient readiness for the role of artificial intelligence in care is noteworthy, Wolters Kluwer said in a statement.

Wolters Kluwer survey: Over two-thirds of U.S. physicians have changed their mind, now viewing GenAI as beneficial in healthcare.

Forty percent ready to use GenAI this year at point of care but 89% of doctors need content source transparency for confident adoption. 

A new Wolters Kluwer Health survey¹ released today finds that 40% of U.S. physicians are ready to use generative AI (GenAI) this year when interacting with patients at the point-of-care. The findings reflect a rapid acceptance of the new technology more broadly, with 68% saying they have changed their views over the last year, and are now more likely to think that GenAI would be beneficial to healthcare.

Physicians, however, are wary of which GenAI tools they would be comfortable using, with 91% of respondents saying they need to know the GenAI sourced materials were created by doctors and medical experts before using it in clinical decisions. Similarly, 89% report they need vendors to be transparent about where information came from, who created it, and how it was sourced.

Transformative tech: GenAI viewed as helping to save time and optimize care teams 

With healthcare facing challenges with staffing shortages and burnout, physicians see many benefits to applying GenAI in the care continuum. When asked how GenAI could support decision making or improve interactions at the point-of-care:

• Four in five (81%) physicians say GenAI will improve care team interaction with patients.
• Over half believe GenAI will save them 20% or more time. 
• Over two-thirds (68%) say it can save time by quickly searching medical literature.
• Three in five (59%) say it can save time by summarizing data about patients from the electronic health record (EHR).
• Only 3% do not believe GenAI will improve interactions with patients.

Doctors and patients diverge on GenAI in care 

Comparing results of this survey with a Wolters Kluwer survey of U.S. consumers conducted in late 2023 shows that consumers have different views on the integration of GenAI into the physician/patient interaction. Two-thirds of physicians say that patients would be confident in GenAI results to make clinical decisions while just over half of patients report they would be confident. When physicians were asked if they believe patients would be concerned about the use of GenAI in a diagnosis, only one out of five physicians said yes. Conversely, when asked directly, four out of five Americans reported they would be concerned, suggesting a wide gap in perceptions about GenAI readiness among health consumers.

Doctors set high transparency and content source standards for GenAI guidelines

Physicians’ responses reflect a landscape that is still developing clear guidance or policies on using GenAI. Over a third (37%) say there are currently no guidelines in place at their organizations about using GenAI while almost half (46%) say they don’t know of any guidelines. 

Still, physicians have concerns about the source of content and want transparency. For the majority of physicians (58%), the number one most important factor when selecting a GenAI tool is knowing the content it is trained on was created by medical professionals.

Nine out 10 (89%) report they would be more likely to use GenAI in clinical decisions if the vendor was transparent about where the information came from, who created it, and how it was resourced. Knowing that the technology is from a well-known, trusted company was also a priority: 76% would be more comfortable using GenAI knowing it came from established vendors in the healthcare sector.

A responsible approach to Clinical GenAI

Wolters Kluwer Health recently expanded the beta of AI Labs, its collaborative solution to explore the experimental use of Clinical GenAI, to 100 U.S. hospitals. AI Labs has access to the complete set of UpToDate® evidence-based clinical content and graded recommendations across more than 25 medical specialties. It is the only large language model (LLM) exclusively powered by UpToDate trusted content. UpToDate is used by more than two million users at more than 44,000 healthcare organizations in over 190 countries. Watch this video to learn more about Wolters Kluwer’s mission for Clinical GenAI.

Hospitals report shortened stays when  Uptodate using the beta of AI Labs was used for patient care.

Doctors are getting on board with genAI, survey shows | Healthcare IT News

How much time should a person spend exercising?

When it comes to exercise, we should only concern ourselves with duration insofar as it influences what we really care about: results. Exercise is not a goal in itself – rather, it is a means of achieving good cardiorespiratory fitness, strength, and metabolic health, and the ultimate indicators of sufficient exercise are therefore a good VO2 max and adequate muscle mass.

Why do we care about VO2 max and muscle mass?

The rationale for emphasizing VO2 max and muscle mass is simple: these are the metrics with the greatest implications for healthspan and lifespan.

VO2 max, a measure of the body’s maximal ability to utilize oxygen during intense exercise, is indicative of overall cardiorespiratory fitness and, as discussed in detail in a recent premium newsletter, has been shown in multiple large-scale studies to be strongly and inversely associated with all-cause mortality risk across all adult age groups. Indeed, a low VO2 max is reported to be a far better predictor of mortality than diabetes, cancer, cardiovascular disease, smoking, or even age¹. The strength of this association can in large part be attributed to the fact that, in contrast to many other performance metrics, a high VO2 max requires consistency in training over relatively long spans of time and reflects not only aerobic fitness but body composition as well, underscoring the importance of maintaining this metric at a high level throughout life.

In addition to VO2 max, muscle mass – and to a greater extent, muscle strength – is also inversely correlated with mortality. As explained in AMA #27, low muscle mass (meeting clinical definitions of sarcopenia) has been reported to be associated with a 60% increase in risk of mortality relative to the absence of sarcopenia². Inadequate muscle leads to metabolic dysfunction and frailty, both of which can limit both quality and duration of life, but unfortunately, gradual losses of muscle mass and muscle strength are features of the aging process that cannot fully be avoided. Therefore, the most effective strategy for preventing sarcopenia in our latter decades of life is to build and maintain as much muscle as possible leading up to (and including) those decades – in other words, to ensure that we have enough muscle to buffer against the inevitable losses over time.

Focus on outputs over inputs

Many research studies claim to identify an “optimal” amount of time to devote to exercise each day or week in order to maximize health and lifespan benefits, but in the short video below, I explain why these results can be deceiving – and why focusing on outcomes of exercise, i.e., VO2 max and muscle mass, is a better strategy for setting goals around exercise and determining what is or isn’t “enough.”

If the goal for exercise is to improve health and extend lifespan, then the metrics that matter are those most closely related to health and lifespan – i.e., VO2 max and muscle strength. Exercise duration is another step removed from that aim and is only one of the various “inputs” – along with exercise intensity and consistency over time, for instance – that impact the more relevant “outputs” of cardiorespiratory fitness and muscle mass. While setting goals for exercise duration may be valuable for some as a means of keeping oneself accountable on a day-to-day level, it’s critical to keep in mind that such short-term “goals” are merely stepping stones in a longer path; they are not ends in themselves. And to keep our eyes on that ultimate end of a longer, healthier life, we must focus instead on metrics that are indicative of how exercise is affecting our overall fitness and strength.

Peter Attia M.D.

Terminology for using Artificial Intelligence

ARE YOU FRUSTRATED BY THE TERMINOLGY OF ARTIFICIAL INTELLIGENCE

WHAT DO YOU NEED TO KNOW?

Sure! Here are some common terminology related to AI (Artificial Intelligence):

1. Artificial Intelligence (AI): The field of computer science that focuses on creating intelligent machines that can mimic human cognitive functions.Sure! Here are some common terminology related to AI (Artificial Intelligence):

 

2. Machine Learning (ML): A subset of AI that involves developing algorithms and statistical models that enable computers to learn patterns and make predictions or decisions without being explicitly programmed.

3. Deep Learning: A subfield of machine learning that focuses on artificial neural networks, particularly deep neural networks with multiple layers. Deep learning has been successful in areas such as image recognition and natural language processing.

4. Neural Network: A computational model inspired by the structure and functioning of the human brain. It consists of interconnected nodes (neurons) organized in layers, and it processes information through a series of mathematical transformations.

5. Supervised Learning: A type of machine learning where the algorithm is trained on labeled data, meaning it is provided with input-output pairs to learn from. The goal is to learn a mapping between inputs and outputs so that it can make predictions on new, unseen data.

6. Unsupervised Learning: A type of machine learning where the algorithm learns patterns and structures in unlabeled data. It aims to discover underlying patterns, relationships, or clusters in the data without being given explicit output labels.

7. Reinforcement Learning: A type of machine learning where an agent learns to interact with an environment and takes actions to maximize a reward signal. The agent receives feedback in the form of rewards or penalties based on its actions, and it learns to optimize its behavior over time.

8. Natural Language Processing (NLP): The branch of AI that focuses on the interaction between humans and computers using natural language. NLP encompasses tasks such as text parsing, language translation, sentiment analysis, and question answering.

9. Computer Vision: The field of AI that deals with enabling computers to gain high-level understanding from digital images or videos. It involves tasks such as image recognition, object detection, image segmentation, and image generation.

10. Chatbot: An AI-powered program or application that can simulate conversation with human users through text or voice. Chatbots are often used in customer service, virtual assistants, and other interactive systems.

These are just a few examples of AI terminology. The field of AI is vast and continually evolving, so there are many more concepts and terms to explore.

More stuff

There it is.

 

The Rise and Fall of Paxlovid - HIV and ID Observations HIV and ID Observations

APRIL 8TH, 2024

The Rise and Fall of Paxlovid

It’s been quite the ride for our “preferred” outpatient therapy for COVID-19, nirmatrelvir with ritonavir — much better known as Paxlovid, so allow me the license to use the licensed name.

Let’s recap the astonishing success and now failure of this intervention (some dates approximate):

1. December 2021, the FDA issued an Emergency Use Authorization for Paxlovid:  Action is based on the efficacy shown in the EPIC-HR study of high-risk outpatients with COVID-19. Compared to those receiving placebo, Paxlovid-treated participants had an 89% reduction in risk of hospitalization or death. Exciting times.
2. Early 2022, that annoying rebound thing. No, it’s never been quite clear whether Paxlovid caused rebounds, or just didn’t prevent them, or whether it just happened in those people for whom COVID-19 illness lasted longer than the treatment’s 5 days (a large group!), but regardless — it was a major disincentive to clinicians and providers alike.
3. December 2022, the protocol for the EPIC-SR in “standard risk” outpatients was amended to increase the sample size. Though an interim analysis suggested such patients would benefit from treatment, the change in sample size signaled that such a benefit observed in this analysis might be small — or nonexistent.
4. August 2023, the negative results of EPIC-SR were posted on clinicaltrials.gov. Yes, these results have been in the public domain since last summer.
5. Last week, the disappointing EPIC-SR results appeared in the New England Journal of Medicine. This is the primary endpoint (from the Research Summary):

It wasn’t all bad news for treatment: Risks of severe outcomes (hospitalizations, ICU admissions, or deaths) were very low overall — hooray! — and numerically lower in the Paxlovid group; medical visits were significantly lower as well. However, as anyone who has taken Paxlovid can attest, the taste alone could have made study participants aware that they were already on treatment, hence discouraging them from seeking further evaluations.

What about rebounds? From these limited data, it appeared there was no difference:

By day 14, viral load rebound had occurred in 4.3% of the participants in the nirmatrelvir–ritonavir group and 4.1% of those in the placebo group; symptom rebound occurred in 11.4% and 16.1%, respectively, and symptom and viral load rebound together occurred in 1.2% and 0.5%, respectively.

So where does that leave us right now? There’s no doubt that the EPIC-SR data further confirm that this treatment has many limitations. Here’s a teaching slide I recently made:

But before we completely abandon Paxlovid for outpatient use, let’s remember that our options for outpatient treatment of COVID-19 remain highly limited. Furthermore, people at high risk for adverse outcomes still require hospitalization and still die from this infection — as they do from other viral respiratory illnesses. As a result, based on EPIC-HR, these EPIC-SR data, and the observational studies, I’d still recommend treatment for this very high-risk group.

The Rise and Fall of Paxlovid - HIV and ID Observations HIV and ID Observations

A summary of Digital Health Space, my other Blog

Title: Revolutionizing Healthcare: Exploring the Digital Health Space

Introduction:

In recent years, the healthcare industry has witnessed a dramatic transformation with the advent of digital health technologies. From wearable devices and telemedicine to health tracking apps and artificial intelligence, the digital health space is revolutionizing the way we approach healthcare. In this blog, we will delve into the exciting developments and key trends shaping the digital health landscape.

Wearable Devices and Remote Monitoring:

Wearable devices, such as fitness trackers, smartwatches, and biosensors, have become increasingly popular in the digital health space. These devices enable individuals to monitor their health and fitness levels in real-time, providing valuable insights into their well-being. Remote monitoring has also gained traction, allowing healthcare professionals to remotely track patients' vital signs, detect abnormalities, and intervene when necessary. We will explore the impact of wearable devices and remote monitoring on preventive care and personalized medicine.

Telemedicine and Virtual Care:

Telemedicine has emerged as a game-changer, especially in the wake of the COVID-19 pandemic. Virtual consultations and remote healthcare services have become the new norm, providing convenient and accessible care to patients. We will discuss the benefits and challenges of telemedicine, including improved access to healthcare, reduced costs, and potential limitations, such as the digital divide and privacy concerns.

Health Tracking Apps and Personalized Medicine:

Mobile applications dedicated to health tracking and wellness have gained immense popularity. These apps allow users to monitor their diet, exercise, sleep patterns, mental health, and more. Furthermore, they facilitate the collection of real-world data, which can be leveraged for research and development of personalized medicine. We will explore the potential of health tracking apps in promoting preventive care, disease management, and individualized treatment plans.

Artificial Intelligence and Machine Learning:

Artificial intelligence (AI) and machine learning (ML) are driving significant advancements in the digital health space. AI algorithms can analyze vast amounts of medical data, identify patterns, and assist in diagnosis, prognosis, and treatment decisions. ML models can predict disease outcomes, identify high-risk patients, and optimize healthcare workflows. We will delve into the transformative potential of AI and ML in enhancing patient care, drug discovery, and population health management.

Data Security and Privacy:

With the proliferation of digital health technologies comes the need to address data security and privacy concerns. The collection and storage of sensitive health information raise ethical and legal implications. We will discuss the importance of robust data protection measures, compliance with regulations such as HIPAA, and the role of blockchain technology in ensuring secure sharing and access to health data.

Future Trends and Challenges:

Finally, we will explore emerging trends and challenges in the digital health space. This includes the integration of genomics and precision medicine, the rise of health-focused wearables, the role of big data analytics, the impact of Internet of Medical Things (IoMT), and the need for regulatory frameworks to keep pace with technological advancements.

Conclusion:

The digital health space is transforming healthcare delivery, empowering individuals, and improving patient outcomes. From wearable devices to AI-driven solutions, the possibilities are endless. In this blog, we have provided an overview of the key aspects of the digital health space, highlighting its potential to revolutionize the healthcare industry. As technology continues to evolve, it is crucial to navigate the ethical, legal, and practical challenges to harness the full potential of digital health for the benefit of individuals and society as a whole.

health train express

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Poe

Health Train Express is a popular healthcare blog that provides insightful articles, news, and analysis on various topics related to healthcare. The blog covers a wide range of subjects, including healthcare policy, medical technology, patient care, healthcare economics, and industry trends. It aims to keep healthcare professionals, policymakers, and the general public informed about the latest developments and issues in the healthcare field.

The blog's content is written by experienced healthcare professionals, researchers, and experts in the industry. They offer their expertise and perspectives on current healthcare issues, providing valuable insights and thought-provoking discussions.

Some common topics covered on Health Train Express may include:

Healthcare Policy and Reform: The blog explores the impact of healthcare policies and reforms on the industry and discusses the challenges and opportunities they present.

Medical Technology and Innovations: Health Train Express highlights the latest advancements in medical technology, such as telemedicine, robotics, artificial intelligence, and wearable devices, and examines their potential to improve patient care and outcomes.

Patient Care and Safety: The blog emphasizes the importance of patient-centered care, sharing best practices, and discussing strategies to enhance patient safety and quality of care.

Healthcare Economics and Finance: Health Train Express delves into the economic aspects of healthcare, including healthcare financing, reimbursement models, cost containment, and the impact of healthcare expenditures on the overall economy.

Healthcare Delivery and Organization: The blog explores different healthcare delivery models, such as accountable care organizations (ACOs) and patient-centered medical homes (PCMHs), and analyzes their effectiveness in improving care coordination and population health.

Healthcare Technology Adoption: Health Train Express examines the challenges and benefits of adopting new technologies in healthcare settings, addressing issues such as interoperability, data privacy, and the integration of electronic health records (EHRs).

Public Health and Health Promotion: The blog covers public health initiatives, preventive medicine, disease surveillance, and community health programs aimed at improving population health and reducing healthcare disparities.

Health Train Express strives to provide evidence-based information and foster constructive discussions on important healthcare topics. It serves as a valuable resource for healthcare professionals, policymakers, students, and anyone interested in staying informed about the latest developments in the healthcare industry.

Health Justice Monitor – Medicare Advantage: How Private Insurers Exploit Medicare

Medicare Advantage: How Private Insurers Exploit Medicare

An excellent interview with the former head of CMS, which oversees Medicare, lays out how Medicare Advantage insurers manipulate costs and quality, grabbing massive profits from public funds with no clinical benefits for patients. He wants its growth stopped and its problems fixed.

March 2, 2024

Obama CMS Chief: Medicare Advantage Plans Game the System
MedPage Today
March 1, 2024
by Cheryl Clark

(HJM bolding)

Donald Berwick, MD, MPP was the administrator of the Centers for Medicare & Medicaid Services (CMS) during the Obama administration and is president emeritus and senior fellow at the Institute for Healthcare Improvement.

MedPage Today interviewed him about his concerns that too many Medicare beneficiaries are being misled into enrolling into private Medicare Advantage plans, which he said should be slowed or stopped because the plans have gamed the system to receive billions more than what is spent for traditional Medicare beneficiaries’ care.

Cheryl Clark: Medicare Advantage, or MA, plan enrollment has been growing so fast; 52% of beneficiaries are now enrolled, with 60%, 70% projected in a few years. … I hear a traditional Medicare patient can’t find a primary care provider in some parts of the country because all the doctors are locked in MA.

Will every eligible beneficiary have no choice but Medicare Advantage? … And is that a good idea, given your concern about how MA plans have gamed the system to get more money?

Berwick: I think MA growth should be slowed or stopped, at least until we end the extraordinarily high subsidies for MA plans, which are unfair to traditional Medicare and burdensome to the public treasury and many beneficiaries. Many beneficiaries can get better care for themselves and greater choice through traditional Medicare, and that option should remain robust and available. …

Clark: Let’s talk about … how MA plans are overpaid. They’ll receive $88 billion more this year than what is spent for the same patients in traditional Medicare. That is a huge concern. But I don’t think it’s clear to readers how the plans are paid, that it’s not necessarily for better care.

Berwick: It’s a really complicated process and, I must say, possibly intentionally so, because I think it serves the interests of the private plans to have payment rules that are so hard to understand.

What the benchmark begins with is a comparison to the traditional Medicare population: people like me, who are in fee-for-service, traditional Medicare. There’s a calculation of the cost of my expected care… based on demographics and county, because care is more expensive in some counties than others. ..

Most importantly, the amount that an MA plan gets is adjusted for the number of codes for diagnoses that a beneficiary has, like atrial fibrillation or diabetes, and for each code the plan gets more money, supposedly reflecting the additional care the patient needs. And that’s where the gaming nonsense occurs. Many codes have no real implications at all for evidence-based clinical care, but they carry with them extra payment nonetheless. The coding system has created opportunities for these plans to upcode. They comb through patients’ histories and try to stuff in as many diagnoses as they possibly can, even if they have nothing to do with the care of the patient.

Clark: During a January 12 MedPAC meeting, one of the commissioners mentioned that the plans were paying doctors to go through each record and look for anything that could add to capitated payment.

Berwick: That’s right. That’s worth a bit of a dive. There are three ways health plans manipulate the coding processes. What they’re after is what you said, which is, comb through the patient’s record, send a nurse into the patient’s home to find additional diagnoses. Sometimes, in some of the MA systems, they actually pay the doctor to code using a software package the insurance plan gives to the doctor. …

And a third and now much more popular, certainly to insurers, is to simply employ the doctors. Once the doctor is an employee, then you can set up all sorts of ways to accelerate upcoding. You can train the doctors or give them incentives to upcode. And now, the largest employer of doctors is a health plan, an MA plan. In this third tactic, the MA plan gets the benefit of all the upcoding, and that’s part of the game they are playing.

Now rather than lower costs, MA has much higher costs — something to the tune of $80 billion a year. Other estimates are as high as $120 billion. For the most part, that money doesn’t represent the needs of the patient. In fact, we know that beneficiaries in MA are, on the whole, healthier than those in traditional Medicare, and ought to cost less, not more.

It’s just a transfer of money to the private sector. Most of that goes to profit for the plan, or for stock buybacks, high compensation for plan executives, …

And further, because of all the gaming of diagnoses, it gets really, really hard to compare quality of care and outcomes between Medicare Advantage and traditional Medicare patients.

Clark: You’ve said you think MA plans should be slowed or stopped. How do you think that should happen?

Berwick: … We need to stop these abuses as quickly as possible. And we need simultaneously to improve the attractiveness of traditional Medicare, which is what tens of millions of Americans still have. …

Clark: [The MedPAC director] said that for chronic conditions, there is evidence that MA plans deliver better care.

Berwick: … There is some information that suggests that for some conditions, some MA plans do offer better chronic care but that’s a really hard statement to prove, and it certainly doesn’t apply across the board. And remember: MA plans are upcoding patients, so they make the patients look sicker, so when you try to assess outcomes, adjusted for severity, you’ve already fallen into the hole created by this game in which you’re no longer comparing apples to apples….

Clark: You spoke of the quality bonus program, which is a factor in MA capitated payments. How does it work?

Berwick: … the quality bonus system needs a big overhaul. It now has been pretty thoroughly gamed by MA plans. They focus on the scored variables, not overall better care – “teaching to the test,” as it were. Something like 80% of plans now are four-star or five-star on a five-star scale. I call that the “Lake Wobegon” effect, where everybody is above average. It’s a tricky system with unintended consequences. For example, when you’re treating a very distressed population with limited resources and there are barriers to treatment, you don’t want to take money away from the very organizations that need more money because their populations are harder to treat. But it has just become too easy to get a high score. …

The rating system is flawed and amounts to a check off the box analysis.

Other restriction for care require prior authoization for certain expensive tests, or imaging (MRI), and durable medical equipment (DME) for which they have strict limited vendors

On the other hand Advantage plans provide free transportion to senior citizens, many of which have no transportation

Caveat emptor---Let the buyer beware

 Comment by: Jim Kahn

Don Berwick is a hero for many of us, for his decades of foundational work in quality of medical care and his forthright criticism of Medicare Advantage, the private insurance arm of Medicare.

In this interview, he reviews how insurers thoroughly game the MA systems for payment and quality rating, yielding tens of billions in excess payments with no evidence of clinical benefit. Indeed, there is evidence of impaired access to care for the sickest and poorest, and financial harm to providers who treat the most needy.

You should read the entire interview, it’s wide-ranging and informative, and only a little technical. We’ll be writing more on Medicare Advantage issues in coming days.

As Dr. Berwick points out, traditional Medicare – in which the government pays providers directly – works more efficiently to assure access to care. But it needs some coverage gaps closed. That is, we need an improved traditional Medicare. Then we could extend that to everyone. That’s an “improved Medicare for All”. Aka: single payer.

About the Commentator, Jim Kahn

Jim (James G.) Kahn, MD, MPH (editor) is an Emeritus Professor of Health Policy, Epidemiology, and Global Health at the University of California, San Francisco. His work focuses on the cost and effectiveness of prevention and treatment interventions in low and middle income countries, and on single payer economics in the U.S. He has studied, advocated, and educated on single payer since the 1994 campaign for Prop 186 in California, including two years as chair of Physicians for a National Health Program California.

Health Justice Monitor – Medicare Advantage: How Private Insurers Exploit Medicare