A panel of licensed healthcare professionals preferred ChatGPT responses 79% of the time and rated ChatGPT responses as higher quality and more empathetic. Gulp. Understandably, some doctors are not happy with this study. And many were not pleased with me for not diving deeper into the complexities inherent in the research in my initial LinkedIn post. Message heard. Understood!

The news headlines and the initial study callouts overplay the immediate importance of ChatGPT in the physician-patient relationship. Physicians do not fair poorly.  However, the authors provide an inflection point that should not be ignored and must be acknowledged – Communication is Part of the Care and Cure! Physicians must be trained and have time to deal with patient curiosity and urgencies. #Patientexperience is different. They do not want to sit idle or silent. They are curious and concerned.

As generations have become more familiar with technology in their day-to-day lives, perhaps they place more trust in machines’ “unbiased nature” over humans. That assumption has led to the rise of “#misinformation.” We believe our Twitter feeds if we don’t explore the facts further. But, our screens reduce the press of needing to engage with people at the moment – they give us time to think and check in with this “on-call” information aggregator. To let the information sink in without being confronted about the next step. Doctors are too often pressured into an eight-minute per-patient provider reimbursement model. It’s not their fault – it’s the system that they must co-exist within. But that tilted system leads to the consumer seeking – needing – alternatives. If so, even imperfect ChatGPT4 and beyond will be a go-to.

There are changes afoot that we need to make happen sooner rather than later by moving minds, systems, and behaviors so that life-sustaining and life-saving approaches to patient care may eventually tip the scale of human survival toward health and wellness. However, we see data from a human perspective – sometimes self-interests or emotional needs for control. ChatGPT is the aggregate of data and human input. It is not divorced from us but a faint mirror of the human experience.

Yes, this study is worth reading. Yes, many have criticized its design and the intent of the authors. Yes, many are fearful that machines may replace physicians. But, the latter assumption is doubtful. Reading between the lines reinforces that, as industry colleague Ritesh Patel often says, “If it moves, digitize it!” People get their information in ways that are quick and convenient. That is a reality everyone in the health community must face!

The medical community and health communicators must rise to the moment if they want to harness this technology. Learn about ChatGPT and how it operates – its prompts. Also, read words from experts on the digital health news platform Medika Life including the insightful words by innovation theorist John Nosta. John will rock your boat; however, often, he points to where this is going.  Read the words of Tom Lawry, former head of Microsoft’s AI team, author of the best-seller Hacking Healthcare, and a global counselor on the practical application of AI.

Almost one year ago, I penned a piece titled: Health Possibilities We Cannot Afford to Block. There were 10 ideas/technologies included in that piece – #1 was #AI. That’s the heart of ChatGPT. Fixing one part of the healthcare puzzle is encouraging – but is it transformational? What can we do to make things work better for patients? Medicine can harness the power of ChatGPT to make it work even better for patients seeking healing solutions. Perhaps we can give physicians more time to help patients feel their doctors have and always are among their greatest advocates. We can also bring technology companies and leading medical associations together to talk about ChatGPT influence on trusted people-to-people connections, particularly with physician-patients.

Why do consumers turn to machines instead of people for medical counsel?  Well, we haven’t been able to clone or at least develop teaching models drawing upon the many outstanding physicians who demonstrate incredible patience and empathy for patient woes and questions – doctors like WebMD’s John Whyte and NHS’s and Microsoft’s Junaid Bajwa—many answers to consider. Among the most important are skill, collaboration and empathy.

Consumers may feel that devices are better listeners and work with them in partnership. We should expect this outcome due to the fragmented health ecosystem that consumers must navigate with difficulty. We must recognize that ChatGPT’s interest and popularity among health information seekers didn’t just happen. It is possible to realize that these same information seekers feel they are not getting what they seek.

Keep learning! This is not the end of humanity and the beginning of the Matrix – where people, software and machine battle for survival. The world will be changing in amazing ways in the short years ahead. Collaboration and communications go hand-in-hand as essential tools for healing.

The post Does Artificial Intelligence (#AI) Chatbot Outperform Physicians in Patient Experience? appeared first on Medika Life.

This is not totally surprising. While it would be ideal for a vaccine to prevent infection – i.e., catching the virus at all – at the very least, a good vaccine will prevent you from getting very sick from the disease. On this latter front, the vaccines continue to hold up well.

Yet, if it were to happen – if someone were to get a breakthrough infection – research shows that this person ends up with very robust immunity.

Researchers from the Oregon Health & Science University studied 26 people who were fully vaccinated against SARS CoV-2 and subsequently got COVID and compared them with 26 people who were vaccinated but did not get COVID. They found the immunity after a breakthrough infection was substantial:

In fact, the immunity after a breakthrough infection helped protect against multiple variants of SARS CoV-2, including Delta (the study was conducted before Omicron was born):

This is also not surprising, as an infection after vaccination serves as a “booster shot,” of sorts, and it therefore greatly enhances one’s immunity.

Now, of course, this is not the best way to boost your immunity against COVID if you are vaccinated. Sometimes, even mild COVID infections can have lasting long-term effects.

The best way, in fact, to boost your immunity against COVID is to get a booster shot. In the United States, everyone over the age of 16 is eligible to get a booster shot 2 months after the J&J vaccine and 6 months after the mRNA vaccines. They are safe. They are effective. And they boost your immunity to a great degree.

Again, I do not wish a breakthrough infection on anyone. This disease is horrible, and I have seen its horror up close. If one does get a breakthrough, though, the silver lining is that the immunity after such infection is very, very robust.

Reference: Bates TA, McBride SK, Winders B, et al. Antibody Response and Variant Cross-Neutralization After SARS-CoV-2 Breakthrough Infection. JAMA. Published online December 16, 2021. doi:10.1001/jama.2021.22898

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Some excellent, large data sets were either being put to purposes which they weren’t well suited or authors were drawing conclusions that really weren’t the results of analyzing the data.

Why can’t they just report their results accurately and transparently and not be afraid of doing so?

Journals are a big part of the problem. They seduce researchers with temptations that they cannot resist.

A while ago the JAMA family of papers spent millions of dollars revamping their publishing platform. The publisher announced that editors were going to make decisions about papers based on altmetrics — how much traffic similar papers have attracted to the journal website, and how much time readers spent there, and therefore how much the journal could charge for advertising.

That means that some papers that are solid science and have important messages for mental health care will be given lower priority. The papers are not in hot, trending topic areas and would not attract broader audiences.

Despite being obviously weak in their methods or data, other papers will be accepted because they are trendy in their conclusions. Some papers will be rejected because they persuasively present inconvenient findings.

In the case of this issue of JAMA Psychiatry, an obvious decision was made by the editors to emphasize racial and ethnic health disparities in a special section Psychiatry and COVID.

A call went out for papers. I doubt that some of the authors even had that intention of examining health disparities or racism in mind when they gathered their data and made plans to analyze it.

I am picking one article to dissect because I have a great deal of respect for one of the authors. I’ve followed his work closely for decades because he does very good work. I think he knew what he and his co-authors were doing in putting a message on their results that weren’t there. He was having harmless fun and not trying to corrupt science.

Naively or on purpose, Greg Simon allowed his co-authors to say silly things in the section discussing implications, where such speculations seem natural.

Maybe I am giving too much credit to Greg but what I liked was that this paper was so transparent in how he manipulated the reviewers and the readers. Right away, he revealed what he was up to in an incredibly transparent abstract. Like a magician spoiling the trick by explaining what he was doing, he was communicating to the smart, attentive members of the audience and allowing them to feel smart. Too bad that most people do not read carefully.

The article could be paywalled by now, but you can access the abstract here and see the context of the statements that I am going to pick out.

The authors state as their research question:

*Could implementation of suicide prediction models reinforce and worsen racial/ethnic disparities in care?”

Translation: How white people’s science hurts black and indigenous people and perpetuates systemic racism.

The huge retrospective study consists of administrative data of 13,980,570 visits by 1,433,543 patients. The data came from seven health systems providing integrated physical and mental health services.

The authors used sophisticated statistics to construct a model predicting which patients would die by suicide within 90 days of when observation of a particular patient started.

The authors conclude

These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients…Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.

Unraveling this conclusion

In epidemiological/mental health services studies, it is not how big your data set is that matters, but how many events you are available for particular groups.

From the clearly written and transparent abstract, you can tell right off that the data set is not very good to be used to predict black and Native American suicides because there were so few Blacks and Native Americans in the sample.

Hiding in plain sight in the abstract, the authors openly admitted that they only had n=65 black who died by suicide within 90 days of when tracking starred. They only had n= 21 Native Americans who died by suicide within 90 days of when tracking starred.

These are horribly small numbers, even useless, to be used in making generalizable scientific statements. But the situation is even worse. The sample was split between building and validating the model. In the validating sample, there were only 30 black patients and 15 Native Americans.

The authors claim that their predictive model worked well for whites. That would be expected without even seeing the predictive model, because it was built to a predominately white sample.

But who should care about this predictive model?

Recall this was an administrative data set constructed from information collected for other purposes. The diagnosis and service utilization data in the final predictive equation are so crude that the model would not be clinically useful in real-world settings. Maybe it delights epidemiologists for looking so good in terms of statistical significance, but clinicians should ignore it. I am sure that they will.

Every suicide is a tragedy, but because it is such an infrequent event, a base rate “won’t die by suicide” is likely to be more accurate than a predictive model based on group data. Aficionados will recognize that is a classic Paul Meehl observation.

Here comes the pablum.

Pablum is boring tasting baby food. Authors put pablum in papers because they need a filler.

Readers expect certain things to be said about a trending topic, even if things were not among the findings of the paper. Alert reviewers play gatekeeper and keep pablum out of the discussion section if they are not actual findings. In this paper, the authors make the unsubstantiated claim:

Relative benefits and harms of suicide prevention interventions vary by race/ethnicity. Additional attention from a mental health care professional may increase access to beneficial services and likely presents limited harm but could cause stigmatization or discrimination and damage patient-practitioner therapeutic alliances, particularly for patients from marginalized communities already less likely to trust or engage with traditional mental health care.

This may be true or not in other findings that were not cited. Why are the authors bringing this up near the end of a paper that was doomed from the start not to advance our understanding of suicides by ethnic and minority persons?

Of course, a journalist from MedPage Today fell for the trick and gave the authors some extra publicity. One of the authors of the study is quoted in the MedPage Today article:

“We must not ignore unintended consequences of suicide prediction models,” she said via email. “Identifying patients at high risk of suicide could initiate a cascade of more intrusive interventions, including involuntary psychiatric hospitalization and ‘wellness checks’ that put a patient in contact with law enforcement. We have to recognize, due to structural racism, that the risk of these harms is greater for BIPOC [Black, Indigenous, and people of color] populations.”

A touch of moral panic attracts more readers to MedPage Today and the JAMA Psychiatry article, keeping MedPage Today, JAMA Psychiatry, and the authors’ institution happy.

But science suffers from the confirmation of a hypothesis that was not tested, especially when the paper is cited for this conclusion that did not arise in the data.

What important message about racism and health disparities is missing in this paper?

Unwarranted talk about systemic racism distracts from looking for real, modifiable racial disparities.

Mental health services researchers like these authors require large data sets, which come from settings that are organized enough to provide them. This particular study relied on integrated care settings in which patients could easily access both primary medical and mental health treatment. These settings depend on an alignment of incentives, insurance benefits that make integrated care possible.

There were not many black or Native American suicides for which data were available in the settings. That is a serious health disparity. Such patients don’t get to such settings.

Black or Native Americans don’t have the right insurance or live in close proximity to such quality care. It is a long way from most Native American reservations to the doorsteps of a well-resourced integrative care setting. Those who reside on a reservation mostly won’t have the proper insurance nor the transportation to get there.

That was strongly suggested by taking a peek at what data the authors collected or even where they collected it. Nothing fancy had to be done, but then again, they probably would not get their paper in the prestigious JAMA Psychiatry,

You might think from this article that there is harm and even danger for Native Americans to be treated in one of the settings that were studied.

I have a close native American relative by marriage whose 90+ years old (It has to be a guess because birth certificates of many Native Americans are not preserved from back then.) grandmother was a reservation orphan from another tribe who was sent to a Navajo reservation because that nation was able to keep polio victims in the 50s. I wish she could get all the treatment of post-polio syndrome that she deserves in the reservation. She wants to keep working for the US National Parks Service, as she has for decades, and accessible integrated care would help.

Algorithms that poorly predict individual suicides of any race or ethnic background be damned. If we care about social disparities in health, we must concentrate on getting more blacks and Native Americans into treatment in integrated care settings.

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