A 49-year-old female notices new-onset vaginal bleeding over the past several days. She becomes concerned and seeks advice from her long-time family physician. When she calls, she is surprised to hear responses from an artificial intelligence (AI) platform. The longtime secretary, who knew her well and would quickly arrange appointments or connect her with the doctor, has been replaced by this expensive new AI-based system. The call begins with an extensive library of prompts. When she presses 0 to speak with a human, she is told the next available appointment is in nine weeks. She hangs up and redials to discuss her problem with a pleasant computer voice, which almost sounds like a real person and asks her to describe her problem—eventually responding with a long-winded response with possible explanations for her bleeding. It then utilizes a proprietary algorithm to make recommendations which include lifestyle changes and watchful waiting, with instructions to dial back if the problem persists.

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Eventually, she loses patience and decides to visit the office in person. After briefly seeing her in the office, her doctor is concerned and orders a CT scan with the smart scheduler that uses a complex triage algorithm to schedule her imaging in 1-2 days. She then receives the results of the CT scan in an email and again goes through the scheduler system to book her surgery, which is again triaged based on perceived medical urgency. The night before the operation, the pre-operative anesthesia system automatically calls and asks dozens of questions through various menus, ending with lengthy instructions regarding eating, drinking, and pre-operative care. The program does not offer time to address her fears of going under anesthesia.

The day of surgery, everything is increasingly efficient due to new AI-based systems. The operating room team already has her medical history in the electronic record, and she immediately goes to the operating room without needing to meet with the anesthesiologist or surgeon. All goes well, and four days later she gets an email with instructions to call a number and use a six-digit code to get information on the results of an ovarian biopsy. She won’t need to waste any time traveling to the doctor’s office or sitting in the waiting room for her appointment. Instead, a computer-generated AI voice informs her that she has high-grade serous ovarian cancer with metastasis.

The platform then automatically redirects her to a line where a compassionate, AI voice explains the prognosis and her various treatment options based on the latest research. She breaks down and drops the phone in tears. There is nobody to comfort her, let alone answer her endless questions. Is this the nightmare scenario for future patients in our rapidly evolving healthcare system, or a reality in the setting of ongoing physician shortages, skyrocketing medical costs, and “manmade medical errors”? Will this Boost patient health and reduce obstacles to accessing care, or will it create discomfort and dissatisfaction in the healthcare setting?

A exact piece by New York Times health columnist, Gina Kolata, hints that such a future, encompassing such a nightmare patient encounter with our tech-enabled and evolving AI-paradigm of care, may not be far off.

In order to preserve our sense of  compassion and humanity, healthcare providers must prioritize human-to-human communication when we deliver delicate news in order to foster caring relationships with our patients. This forms the basis of the humanity of medicine and the sacred doctor-patient relationship.

That said, no health care provider can disagree that tedious tasks currently take us away from face-to-face, direct patient care. A study in JAMA Internal Medicine found that AI assistants may hold value in composing routine notes or drafting responses to a skyrocketing number of electronic messages from patients as physician demands and burnout rise. Simply put, this work is a call to action for the medical establishment to look inward and determine how we can prioritize the human connection among doctors and patients while taking advantage of AI.

Overall, it appears that doctors are optimistic—but also expressing caution—regarding the potential for AI and large language models becoming part of a toolkit for promoting more effective communication between patients and healthcare providers. Certainly, this technology holds promise, as science communication for years has been marred by complexity and inaccessibility to the lay public. Using AI to better communicate health advice and medical literature with the public will be valuable.

There is certainly promise of large language models to help busy health care professionals with composing emails, reviewing medical records, and answering prior authorizations. Moreover, AI may help triage the patients and the questions that reach physicians, with more routine or unnecessary items being answered by technology. The potential to reduce time spent on tasks that lead to anger, frustration, and ultimately burnout is invaluable. Additionally, administrative costs are estimated to drop by over 35% given this evolution.

As total medical knowledge grows exponentially, it is impossible for doctors to stay abreast of all medical advances and retain such detailed knowledge in our brains. On the contrary, both AI and robotics will inevitably be more effective in cataloging constantly changing medical knowledge. This can support evidence-based management for patients. However, physicians must hold onto their unique and special gifts of humanism and empathetic care for patients.

But let’s be clear—practicing medicine is an art, and no technology can take away that fact. When facing patients themselves, human interaction with a doctor is vital. Patient satisfaction and shared decision-making will continue to rely heavily on this humanism. Medicine is a profession that still requires compassion, reassurance, and most importantly, empathy. Even with the advent and ongoing evolution of AI and other large language models, empathy is best learned and communicated in the form of bedside teaching by humans—not AI or chatbots.

However, considering that AI and chatbots were supported by some experts in Kolata’s piece as an approach for teaching healthcare professionals how to express empathy and compassion to patients or families, it’s likely time for us to hit the “reset button” on how we approach conversations and communications with patients.

We feel that the most effective way to restore empathy and compassion as the cornerstone of physician communication to patients is not by modeling or a framework suggested by AI or chatbots; instead, this requires a focus on human-to-human teaching and dialogue. Education surrounding humanities, social sciences, and the science of communication are just as vital as teaching physicians about anatomy and physiology. This applies to not only medical students, but to those in residency, and all healthcare professionals. Certainly, education about emerging technologies and medical devices will also become important so providers can best incorporate these innovations to Boost care without compromising patient experiences.

In the past, bedside teaching in medical school was an art practiced by careful observation and listening, with particular attention to eyes and body language as our professors handed down the invaluable unwritten and unspoken ways to express care, concern, and empathy for our patients. This human interaction has proven implications for patient satisfaction, motivation, and adherence to treatment recommendations.

Such unique and unspoken methods of communication of human emotions and interactions cannot be taught by AI or chatbots. Granted, medicine is often criticized for being decades behind in innovation, and as AI technology grows, health professionals should certainly embrace its benefits. However, they must also remain true to their values and the oath they have taken to serve their patients. Upholding these professional standards requires a strong adherence to humanistic care and continued development of communication skills. It is in the patient’s best interest to stay attuned to these trends and understand the benefits and risks of modern innovations in care.

Contact us at letters@time.com.

Medical students who reported a disability to their school increased by more than 25% during the COVID-19 pandemic, a study shows.

The proportion of students reporting attention deficit hyperactivity disorder or chronic health and/or psychological disabilities has increased between 2015 and 2021.

Despite the increase in medical students reporting these conditions, the requests for more inclusive preclinical testing accommodations, like extra time for test completion or a less distracting environment, decreased during the pandemic between 2019 and 2021.

According to authors of the new research letter in JAMA Network Open, the remote curriculum delivery during the pandemic may have allowed students to create an optimal learning and testing environment, decreasing the need for accommodation.

"Medical education was at its most flexible during COVID," said Lisa Meeks, Ph.D., clinical associate professor of learning health sciences and family medicine at the University of Michigan Medical School.

She adds that this could have reduced the need for testing accommodations, but it is unclear whether the need for accommodations will rise again after the exact return to in-person lectures and testing.

Documenting the rise

The study results are part of a long-term research project led by Meeks that follows the prevalence of medical students in the United States who disclose disabilities to their respective schools.

This study on disability disclosure in medicine was the first large scale study of its kind, encompassing all types of disability, including psychological, learning, sensory, physical and chronic health conditions.

Since 2015, researchers have seen an increase of medical students reporting a disability to their institution from 2.8% in 2015 to 4.7% in 2019, and to 5.9% in 2021.

When asked to describe why we see such large increases in the population of medical students with disabilities, Meeks posited that "growth in this population could mean that we are reducing bias and stigma, and therefore people who were already in medicine are more willing to disclose."

"It could also mean that our research sparked a conversation to change policies, which then led to individuals with disabilities who didn't think they could make it in medical school choosing to apply to these schools."

Doctors with disabilities Boost patient care

According to Meeks, there is still significant work to be done to increase the representation of doctors with disabilities in medicine.

Only 5.9% of medical school students report a disability, but 27% of adults in the U.S. currently live with some type of disability.

As the population ages, this number is expected to increase.

"Physicians in the U.S. and many other countries report that they do not feel confident in their ability to provide equal quality of care to patients with disabilities as they provide to patients without disabilities," said Karina Pereira-Lima, Ph.D., a research fellow in the Michigan Medicine neurology department.

"The inclusion of professionals with disabilities in medicine can greatly Boost the care for patients with disabilities and the health of the population overall."

Retaining medical trainees with disabilities

Increasing the number of physicians with disabilities requires both the recruitment and retention of medical trainees.

"Anonymous research with medical trainees with disability shows that about one in every five medical students and more than half of resident physicians do not request accommodations when they need them," said Pereira-Lima.

The two main reasons for not requesting needed accommodation were fear of stigma or bias and lack of a clear institutional process.

"Program access, or simply having the ability to access accommodations should they need them, improves medical trainees with disabilities performance in relation to testing and patient care. It also reduces the likelihood of reporting depressive symptoms or burnout," added Pereira-Lima.

Meeks advocated for "standardization in support for students with disabilities in medical education."

"Medical education strives for parity and continuity between medical schools, but when it comes to disability services and reasonable accommodations, there's no standardization whatsoever," said Meeks.

"One school could have an incredible specialized disability support services with a qualified disability resource professional running the office, while another school does not have a specialized disability support service at all."

'A wave of change'

The team notes that addressing the second common barrier to attaining needed disability accommodations and fear of stigma or bias requires a continued culture shift in medicine.

"Disability is still incredibly stigmatized, and ableism is rampant in medicine and medical education. At the same time, I think the work from our lab, the Association of American Medical Colleges, the Accreditation Council for Graduate Medical Education and others in medicine started a wave of change that is extraordinarily strong," said Meeks.

This work is bolstered by the matriculation of individuals that Meeks calls the post Americans with Disabilities Act generation into medical school.

"This generation has a lot of disability pride. They've had accommodations their entire lives, they know the law, they know their rights and they're not ashamed of being disabled," said Meeks.

Next steps

As this long term study continues, the research team plans to assess how other identities interact with the disability identity.

"People with disabilities have different racial and ethnic backgrounds, sexual orientations and socio-economic statuses. We want to learn more about how the interaction between these different identities impacts the performance and mental health of medical students with disabilities," said Pereira-Lima.

"We're also developing methods to measure the efficacy of accommodations. We need to do more research on the quality of received accommodations and how easy the process was for them to receive the accommodations they needed" added Pereira-Lima.

"Investing in a culture that acknowledges disability as a valuable form of diversity will Boost patient care."

There is a lot to learn if you are making a career transition into or entering the medical packaging field.

Thankfully, the Medical Device Packaging Technical Committee (MDPTC) of the Institute of Packaging Professionals is here to help. This committee invested in developing Fundamentals of Medical Device Packaging to enable rapid learning about medical device packaging.

The course was first offered at Pack Expo International 2022 and featured classroom and expo floor instruction.

If you are considering the course, listen in as five medical device packaging leaders debriefed the merits of this course in a SPOT Radio podcast, by Charlie Webb, Founder and President of Van der Stahl Scientific and CPPL (“lifetime” Certified Packaging Professional).SPOT stands for Sterile Packaging on Track.

The five leaders interviewed are myself, Dannette Casper, Package Engineer, Edward LifeSciences; Sarah Rosenblum, Senior Director of Sales & Marketing, and Cassandra Ladd, Senior Marketing Manager, both at Packaging Compliance Labs; Art Castronovo, Director Package Engineering and Product Labeling at Johnson and Johnson; and Jennifer Benolken, MDM & Regulatory Specialist, Packaging Engineering, DuPont Tyvek, Healthcare Packaging.

Highlights of their conversation include:

1. Synopsis of the new Medical Device Packaging Fundamental’s class, which covers all the steps/functions of packaging a medical device, from design to launch.

2. Review of the premier offering at Pack Expo. It was sold out! And attendees were highly engaged. Their peer-to-peer participation added to the richness of the content presented by the experts.

3. Expectations for the second Medical Device Packaging Fundamental’s class at MD&M West in Anaheim, Calif., The show runs from February 7-9, but the Fundamental’s class starts on the afternoon of February 6 and runs until February 8.

Registration is now open for this second class and you can sign up here. MD&M West is co-located with WestPack.

FLINT, MI. (WJRT) - A pilot mindfulness program through the Crim Fitness Foundation in Flint is helping medical residents at Hurley Hospital and McLaren Hospital.

The Search Inside Yourself training program is proven to reduce stress, Boost focus and peak performance, and Boost interpersonal relationships.

Crim Fitness Foundation Associate Program Director Marie Jones-Watts said the program was created by an engineer at Google.

“The goal was to increase emotional intelligence, develop empathy, compassion so that the employees could thrive.”

The Crim Fitness Foundation was able to adopt and implement the program and pass the training on to other community leaders.

Crim Fellows Dr. Barbara Wolf and Dr. Anju Sawni said mindfulness is more important than ever for healthcare workers struggling to cope.

“It’s always related to work. You know, you can say I'm burnout at home, but for our definition, it's always related to work. It's a personalization, a level of cynicism, emotional exhaustion, as well as a disconnection from joy or meaningfulness in your work,” said Wolf.

She said the healthcare industry is seeing stress scales jump since the COVID-19 pandemic.

“It has to do with the systems that we live and work in that cause distress. So extra hours, a primary care doctor spends another 30 hours a week besides the four weeks in a month on paperwork, which is not computer work, which they do when they're not at work."

And it hits home for Sawni. She said several years ago, she was seriously considering leaving medicine.

“After 38 years and seeing the change in the health system, I was feeling down and burnt out I really was not having any joy coming to work and I love what I do. I love being a pediatrician. I love taking care of kids my whole life. I love teaching residents. I love teaching medicine. I mean that's really... been my joy in medicine.”

In 2021, Sawni and Wolf brought their mindfulness training to pediatric residents, and then medicine and pediatric residents. Their program involves simple kinds of skill sets and feedback.

“So they're not sitting down for 20 minutes and being quiet. They're maybe putting their hand on the doorknob, where they go into the next patient and breathing in and breathing out kind of separating from the last patient and being ready to face the next one" said Wolf.

Dr. Rashee Gupta was part of the pilot program. She told us she still uses the training as she begins her third year of pediatric residency.

“The body scan is really helpful. So like when I feel tension in my body... you just scan through your body from top to down. It can be done sitting, standing, laying down, and just sort of relax as you move through. So by the end, you are completely relaxed.”

Looking ahead, Swani said the goal is to expand the training to include more resident physicians, and also frontline nurses.

The key to understanding proteins -; such as those that govern cancer, COVID-19, and other diseases -; is quite simple. Identify their chemical structure and find which other proteins can bind to them. But there's a catch.

"The search space for proteins is enormous," said Brian Coventry, a research scientist with the Institute for Protein Design, University of Washington and The Howard Hughes Medical Institute.

A protein studied by his lab typically is made of 65 amino acids, and with 20 different amino acid choices at each position, there are 65 to the 20th power binding combinations, a number bigger than the estimated number of atoms there are in the universe.

Coventry is the co-author of a study published May 2023 in the journal Nature Communications.

In it his team used deep learning methods to augment existing energy-based physical models in 'do novo' or from-scratch computational protein design, resulting in a 10-fold increase in success rates Tested in the lab for binding a designed protein with its target protein.

We showed that you can have a significantly improved pipeline by incorporating deep learning methods to evaluate the quality of the interfaces where hydrogen bonds form or from hydrophobic interactions."

Nathaniel Bennett, study co-author, post-doctoral scholar at the Institute for Protein Design, University of Washington

"This is as opposed to trying to exactly enumerate all of these energies by themselves," he added.

Readers might be familiar with popular examples of deep learning applications such as the language model ChatGPT or the image generator DALL-E.

Deep learning uses computer algorithms to analyze and draw inferences from patterns in data, layering the algorithms to progressively extract higher-level features from the raw input. In the study, deep learning methods were used to learn iterative transformations of representation of the protein sequence and possible structure that very rapidly converge on models that turn out to be very accurate.

The deep learning-augmented de novo protein binder design protocol developed by the authors included the machine learning software tools AlphaFold 2 and also RoseTTA fold, which was developed by the Institute for Protein Design.

Study co-author David Baker, the director of the Institute for Protein Design and an investigator with the Howard Hughes Medical Institute, was awarded a Pathways allocation on the Texas Advanced Computing Center's (TACC) Frontera supercomputer, which is funded by the National Science Foundation.

The study problem was well-suited for parallelization on Frontera because the protein design trajectories are all independent of one another, meaning that information didn't need to pass between design trajectories as the compute jobs were running.

"We just split up this problem, which has 2 to 6 million designs in it, and run all of those in parallel on the massive computing resources of Frontera. It has a large amount of CPU nodes on it. And we assigned each of these CPUS to do one of these design trajectories, which let us complete an extremely large number of design trajectories in a feasible time," said Bennett.

The authors used the RifDock docking program to generate six million protein 'docks,' or interactions between potentially bound protein structures, split them into chunks of about 100,000, and assign each chunk to one of Frontera's 8000+ compute nodes using Linux utilities.

Each of those 100,000 docks would be split into 100 jobs of a thousand proteins each. A thousand proteins go into the computational design software Rosetta, where the 1,000 are first screened at the tenth of the second scale, and the ones that survive are screened at the few-minutes scale.

What's more, the authors used the software tool ProteinMPNN developed by the Institute for Protein Design to further increase the computational efficiency of generating protein sequences neural networks to over 200 times faster than the previous best software.

The data used in their modeling is yeast surface display binding data, all publicly available and collected by the Institute for Protein Design. In it, tens of thousands of different strands of DNA were ordered to encode a different protein, which the scientists designed.

The DNA was then combined with yeast such that each yeast cell expresses one of the designed proteins on its surface. The yeast cells were then sorted into cells that bind and cells that don't. In turn, they used tools from the human genome sequencing project to figure out which DNA worked and which DNA didn't work.

Despite the study results that showed a 10-fold increase in the success rate for designed structures to bind on their target protein, there is still a long way to go, according to Coventry.

"We went up an order of magnitude, but we still have three more to go. The future of the research is to increase that success rate even more, and move on to a new class of even harder targets," he said. Viruses and cancer T-cell receptors are prime examples.

The ways to Boost the computationally designed proteins are to make the software tools even more optimized, or to trial more.

Said Coventry: "The bigger the computer we can find, the better the proteins we can make. We are building the tools to make the cancer-fighting drugs of tomorrow. Many of the individual binders that we make could go on to become the drugs that save people's lives. We are making the process to make those drugs better."

© 2023 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

To control our behavior, the brain must be able to form associations. This involves, for example, associating a neutral external stimulus with a consequence following the stimulus (e.g., the hotplate glows red - you can burn your hand). In this way, the brain learns what the implication of our handling of the first stimulus are. Associative learning is the basis for forming neural connections and gives stimuli their motivational force. It is essentially controlled by a brain region called the dopaminergic midbrain. This region has many receptors for the body's signaling molecules, such as insulin, and can thus adapt our behavior to the physiological needs of our body.

But what happens when the body's insulin sensitivity is reduced due to obesity? Does this change our brain activity, our ability to learn associations and thus our behavior? Researchers at the Max Planck Institute for Metabolism Research have now measured how well the learning of associations works in participants with normal body weight (high insulin sensitivity, 30 volunteers) and in participants with obesity (reduced insulin sensitivity, 24 volunteers), and if this learning process is influenced by the anti-obesity drug liraglutide.

In the evening, they injected the participants with either the drug liraglutide or a placebo in the evening. Liraglutide is a so-called GLP-1 agonist, which activates the GLP-1 receptor in the body, stimulating insulin production and producing a feeling of satiety. It is often used to treat obesity and type 2 diabetes and is given once a day. The next morning, the subjects were given a learning task that allowed the researchers to measure how well associative learning works. They found that the ability to associate sensory stimuli was less pronounced in participants with obesity than in those of normal weight, and that brain activity was reduced in the areas encoding this learning behavior.

After just one dose of liraglutide, participants with obesity no longer showed these impairments, and no difference in brain activity was seen between participants with normal weight and obesity. In other words, the drug returned the brain activity to the state of normal-weight subjects.

"These findings are of fundamental importance. We show here that basic behaviors such as associative learning depend not only on external environmental conditions but also on the body's metabolic state. So, whether someone has overweight or not also determines how the brain learns to associate sensory signals and what motivation is generated. The normalization we achieved with the drug in subjects with obesity, therefore, fits with studies showing that these drugs restore a normal feeling of satiety, causing people to eat less and therefore lose weight," says study leader Marc Tittgemeyer from the Max Planck Institute for Metabolism Research.

"While it is encouraging that available drugs have a positive effect on brain activity in obesity, it is alarming that changes in brain performance occur even in young people with obesity without other medical conditions. Obesity prevention should play a much greater role in our healthcare system in the future. Lifelong medication is the less preferred option in comparison primary prevention of obesity and associated complications," says Ruth Hanßen, first author of the study and a physician at the University Hospital of Cologne.