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In Part 1 of this series, we laid out the AI landscape for nursing in clear terms: which roles are changing, which are growing, and why the nursing profession as a whole is not going away. However, a question kept surfacing in that research, and it deserves its own honest treatment. If a recession hits in 2027, will nursing respond the way it always has? Or has something fundamentally changed? The short answer is: we genuinely do not know. And that uncertainty itself is new. For every previous recession in modern memory, the answer was obvious. This time, for the first time, it is not. What Made Nursing Recession-Proof and Why It May Not Be EnoughNursing's recession resilience was never magic. It was structural. People get sick regardless of GDP. Government programs fund half of all healthcare spending regardless of market conditions. And the healthcare workforce shortage was always so severe that even a surge of returning nurses couldn't push employment negative. Those structural forces still exist. But the 2027 version of "recession" carries a variable that 2008 did not: an actively deployed, commercially available, and rapidly improving set of AI tools that hospitals can substitute for human nursing labor at a fraction of the cost. Frier Levitt 2008 Recession: The Rules We KnewAI deployment: Artificial intelligence was not yet deployable in clinical settings at scale. Budget cuts: Hospitals cut budgets by reducing elective procedures rather than reducing nursing headcount. Staffing dynamics: Returning experienced nurses filled existing shortages instead of replacing current staff. Government funding: Federal stimulus packages successfully protected healthcare spending. New graduates: Increased competition for open positions was the worst outcome experienced by most nurses. 2027 Recession Risk: What's ChangedAI tools as substitutes: Artificial intelligence tools are being explicitly marketed as nurse substitutes at a fraction of the cost, such as $9/hour compared to $90/hour for a licensed RN. Pre-recession staff cuts: Healthcare organizations are already cutting staff and citing AI or automation before an official economic downturn even begins. Job elimination: AI-driven utilization review, triage, and case management roles are being permanently eliminated rather than just shifted to other areas. Reduced government buffer: Federal healthcare cuts, such as the Department of Health and Human Services reducing 10,000 positions, are actively weakening the government funding buffer that historically protected nursing during recessions. New financial escape valve: Hospitals facing financial pressure now have an AI-driven cost-cutting alternative that simply did not exist in previous economic cycles. -
Walk into almost any hospital today and you will find AI already embedded in the workflow — flagging deteriorating patients, auto-populating nursing notes, and optimizing shift schedules. The question is no longer whether AI will change nursing. It already has. The real question is: how much, in what ways, and what does it mean for the nurses asking that question right now on this forum? This article breaks down the honest, data-driven answer — the roles being changed, the roles being created, the legitimate fears worth taking seriously, and the strategic moves every nurse can make today. The Big Picture: Nursing Is Not Going Away Nursing consistently ranks among the lowest-risk professions for full automation, according to the Bureau of Labor Statistics, McKinsey, and Oxford researchers who study AI's labor market impact. The reason is structural. Nursing is built on physical presence, emotional attunement, real-time adaptive judgment, and therapeutic relationships. Those are not things current AI can replicate, and most experts do not expect that to change in the near term. Terry McDonnell, senior vice president and chief nurse executive at Duke University Health System, told HealthLeaders Media that AI will be "a plus one," meaning additive to nursing rather than a replacement. The American Nurse Journal affirms that AI should remain a supportive tool, not a substitute for nursing expertise. The Washington State Nurses Association put it simply: technology can assist nurses, but it cannot replace their heart, judgment, or expertise. That sentiment echoed loudly at the ViVE 2026 healthcare conference in February, where nursing leaders pushed back hard against tech vendors marketing products as AI nurses. Bonnie Clipper, founder of the Virtual Nursing Academy, told the audience at Chief Healthcare Executive that the term "AI nurse" is flat-out wrong and that companies building healthcare AI need to actually talk to nurses rather than asking ChatGPT to diagnose the profession's problems. Susan Grant, chief clinical officer of symplr, was just as direct: "The thing that technology and AI cannot do is provide the critical thinking, the observations, those subtle changes that only nurses can see when they're with a patient." APRN Employment Growth 35% Projected 2024–2034, much faster than average for all occupations Source: BLS Occupational Outlook Handbook RN Employment Growth +5% 2024–2034 — 189,100 new openings projected annually Source: BLS Registered Nurses OOH NP Growth (2022–2032) 45% Fastest-growing master's-degree occupation in the U.S. economy Source: BLS Economics Daily -
In honor of Martin Luther King, Jr. Day, it seems appropriate to reflect on the conversations about race in the last year. One that stands out in my mind and that may have the biggest impact for years to come is halting the use of facial recognition software. IBM went the farthest with their decision to stop developing or offering facial recognition software altogether. Microsoft and Amazon paused use by law enforcement to allow lawmakers time to come up with rules. Why? Because several studies - including one by the federal government published in 2019 - concluded this software shows bias against minorities and women. It misidentifies people of color and women more often than white men, and in some cases was 100 times more likely to produce a false positive. The implications go beyond facial recognition software itself. As Artificial Intelligence (AI) becomes more common in healthcare, nurse leaders should be asking questions: Is the Artificial Intelligence we use biased? How are we introducing bias to AI? Could AI make existing health disparities even worse? If you are new to the topic of Artificial Intelligence in healthcare, take a look at my Guide to AI for Nurses. How Can AI Be Biased? Most of us think technology is more objective than humans. We know there can be defects that cause strange glitches. But it is hard to imagine technology having racial or gender bias. Machine learning is a form of AI where computer systems can learn and adapt without instructions. In order to learn the systems must be trained on large sets of data where information is tagged appropriately. For example, if you want to train the system to recognize orange cats, you would give it 1,000 pictures of different types of cats and label which ones are orange. The application of machine learning to healthcare raises the questions of what these systems are learning and from whom. There are three problem areas where bias can be introduced: (1) lack of inclusiveness in the data used to train AI; (2) bias encoded in the data used to train AI; (3) algorithm errors paired with a lack of human critical thinking. Lack of Inclusiveness in Data Used to Train AI One powerful use for AI is in prediction. For example, Netflix uses AI to make better movie recommendations and Amazon uses AI to help you find products you want to purchase faster. Both of these applications of AI use information about you to predict what you may want in the future. In healthcare, predictive applications could help identify someone likely to develop a health condition, like diabetes, or who will experience side effects of a drug or vaccine before they receive it. But, in order to make predictions, AI has to be trained on those large data sets, which is where bias can occur. Joy Buolamwini, a researcher at MIT, focused her thesis around a strange interaction she had with the AI in her lab. She was using facial recognition software for a project. But for some reason the software could not tell that Ms. Buolamwini had a face. When she saw it identify faces on the other people from her team, she realized it may be because she has dark skin. With further research she found the problem was a lack of diversity in data used for training the algorithm. After testing facial recognition technology from three major software companies, she and her team found error rates of less than 1% for light-skinned men, but over 30% for dark-skinned women. They later discovered the software was trained on a data set that was 77% male and 83% white. When we extend this issue into healthcare, it is easy to imagine how lack of inclusive data for training AI could make health disparities worse. An algorithm used to identify melanoma trained only only light skin would miss cases on dark skin. It is similar to how women were excluded from cardiac research, and researchers later discovered women present heart attack symptoms differently. Bias Encoded In Data Used to Train AI Bias can also enter AI through the way training data is tagged and categorized. For example, clinical notes are widely used in areas like psychology and social work. Natural Language Processing - a form of AI that understands and interprets human language - can extract information from those notes for machine learning. However, that also means bias baked into those notes can also carry over. If a social worker routinely describes female patients as 'dramatic,' their notes could lead to algorithms that cannot detect anxiety disorders in women. This is not a far-fetched example. A recent study used Google's cloud image recognition service to evaluate pictures of male and female politicians. It gave men labels like 'official,’ 'white collar worker,’ and 'business person' while it gave women labels like 'smile,’ 'beauty,’ and 'hairstyle.’ This happened because the datasets of labeled photos to train these algorithms already had gender bias, such as showing women cooking and men going to work. The large technology companies are trying to address this problem by being transparent about the bias in their AI instead of correcting the bias. It is basically like a nutrition label on fast-food. Algorithm Errors and Lack of Human Critical Thinking One of the big examples of AI bias is the criminal risk assessment instrument used to determine how likely someone is to commit another crime. This algorithm made headlines when a tool used in Wisconsin, New York, California, and Florida labeled African American defendants twice as likely to commit another crime as white defendants. The criminal risk algorithms take information such as where the defendant lives and employment status to create a score. Judges in states with this tool have used the score to impose harsher sentences. This resulted in an African American teenage female with no prior offenses receiving a harsher sentence for stealing a bicycle than a middle-aged white male who shoplifted and had several prior arrests. Follow-up done several years later found the man went on to commit armed robbery, while the young woman had no further crimes. This example highlights how humans cannot afford to turn off their critical thinking while working with technology. Algorithms can produce errors and are not necessarily more objective. Moving back to the healthcare context, algorithms are not free from error here either. In a study looking at prediction error in psychiatric readmissions at a New England Hospital, the model was found to have a higher error rate for predicting readmission for African-American patients than any other group, and the rate of error for women was much higher than for men. The bottom line is that nurse leaders need to stay alert, questioning, and cautious. As machine learning becomes increasingly involved in health care decisions, it will be crucial to look at the impact on different demographic groups. What Can Nurse Leaders Do to Make Sure AI Supports Health Equity? There are ways in which AI could help to decrease health disparities if channeled in the right direction. To get there, nurse leaders should advocate for guidelines and a shared common goal of eliminating these disparities. At a minimum we will need: Counter-bias algorithms to test and correct for systemic discrimination This should be made a basic part of the process prior to an application's approval for use in healthcare. Greater diversity in data science training and workforce We are lucky that Joy Buolamwini was in that MIT lab, working with that technology at that time. But what if she was not there? What if she had a different project that did not use facial recognition software? We should not have to rely on luck. Healthcare leaders should push for diversity both at a national level as a requirement for research funding, but also as part of the criteria for AI selection by health systems. Education of the healthcare workforce that includes how to evaluate algorithm results We need to do better than the legal system and question the output, especially when it goes against our better judgement. In Closing I believe in the promise of what AI can do for humanity. But I also see how important it is for us to understand what tools we are using, who built them, how they were trained, and their impact. To avoid the pitfalls of the legal field, those of us in healthcare must question the technology. We cannot simply delegate all critical thinking to the algorithm and hope it is right. We are moving into the next great age technology - we must try to leave our biases behind us. Resources/References Can AI Help Reduce Disparities in General Medical and Mental Health Care? Exploring the Potential of Artificial Intelligence to Improve Minority Health and Reduce Health Disparities Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century AI Could Worsen Health Disparities
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What is Artificial Intelligence? We all know the concept of Artificial Intelligence, and understand the use it has in our lives. Whether you appreciate it or not, it is here to stay and will only continue to weave it's way into human life. There are two broad types of A.I., narrow and general1. Heath tells us in his article, "What is AI? Here's Everything You Need to Know About Artificial Intelligence", that narrow is defined by its use with defined tasks (Siri), and general is a more malleable form that can learn and do specific tasks1. A.I. is a system that can learn and modify, and then affix that knowledge to new situations. This ability to learn and adapt to new scenarios can now be used to detect adenomas in the colon during a colonoscopy. Adenoma Detection Rate Gastroenterology physicians perform colonoscopies among other procedures. Colonoscopies are meant to screen patients beginning at age 50 for precancer or cancer. The new recommended age is 45 by the American Cancer Society, so hopefully insurance will follow suit soon. If you have ever seen a colonoscopy, you know that there are many folds and corners that polyps can hide. There are many factors to consider in how well an exam results in finding polyps. The prep must be good so that the doctor can see well. Also, the equipment must be adequate and working, and the physician must spend the recommended six minutes withdrawing in order to be able to find polyps. The adenoma detection rate is the percentage of patients in which polyps are found in screening colonoscopies. Since colon cancer is preventable through these screenings, the more polyps that are detected decreases a patient's chances of colon cancer. If the polyps are removed and then studied by a pathologist, a patient can be sure of a diagnosis. This diagnosis helps the doctor to define the path of treatment that best fits the patient. Artificial Intelligence in GI Colon cancer has a high incidence in America, the third highest in both men and women according to the American Cancer Society. They estimate that there will be 104,270 new cases of colon cancer this year, and 45,230 of rectal cancer2. Education combined with screenings is super important in decreasing cases. Using this new artificial intelligence program, decreased screening age, and increased patient awareness, the number of colon cancer cases will decrease in our country. Too many cases are found in advanced stages and that increases the patient's mortality. The A.I. program uses a computer program that has a very accurate data set that can recognize polyps of all shapes and sizes3. When an abnormal area of tissue is detected, it puts a circle around it on the screen so that the doctor can go right to it. Medtronic has developed a program, one that has a 99.7% sensitivity3. The program studied 2684 lesions that were proven with pathology in order to learn what to look for3. In the GI lab, using this program decreases procedure time by pointing out polyps to the physician. The physician can then remove the polyp and continue on with the exam. Human eyes are good and combined with this program, no polyp will be missed. With a glance away from the screen, a polyp can slip past, not to be seen until years later when the patient returns for another screening or diagnostic colonoscopy. And literally, this could mean the difference between having surgery or not. I have seen a sample of this program, and I hope to one day get it for the lab that I work in. Eventually, this program should be built into all of the processors so that every patient can have the best outcome possible. References 1What is AI? Here's everything you need to know about artificial intelligence 2About Colorectal Cancer 3Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped
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Mobile Health According to Statista, during the second quarter of 2019, there were nearly 50,000 health apps available in the iOS app store. By 2020, the mobile health market is expected to be worth 21 million dollars globally. Many consumers turn to mobile health (mHealth) for overall health and wellness. You can do things like track your meals, log chronic symptoms, keep detailed records of the amount of water intake, or keep track of your workouts. More healthcare companies and practitioners are turning to mobile health to reach patients, and some are using chatbots to increase how quickly they can connect. Health insurer Anthem is taking a shot at a new digital service where patients can pay for a text chat with a physician to review symptoms and receive treatment. However, their first interaction is with artificial intelligence (A.I.) chatbot that asks about symptoms and suggests diagnoses. The patient is then connected to a physician for follow-up that happens at the patient's convenience for an agreed-upon fee. As more people turn to mHealth for disease management, we need to get a clear picture of the pros and cons. Kevin Campbell, MD, took an in-depth look into the good and bad of mobile health and why he thinks patients will like it and physicians won't. Here is a look at the good and bad around using mHealth and A.I. for medical care. Understanding the Benefits Most medical care and treatments come with pros. Here is a look at the positives of using AI-based apps for healthcare treatment. Price Transparency Most care happens with little or no conversations about what it might cost the patient. However, in our current healthcare market, more patients want to know what their out-of-pocket contribution will be before they sign on the line consenting for treatment. Anthem understands this desire of patients and is meeting them halfway by giving them the cost of their chatbot visit and MD appointment upfront. Not only do patients know the cost of the visit, but they also get an appointment that fits into their schedule from the comfort of their home, office, or breakroom. Of course, price transparency doesn't only come from apps. The Affordable Care Act requires hospitals to publish a master list of costs so that consumers can shop around for the best price. This rule was enforced on January 1 of this year but has become nothing more than a long list of expenses that mean little to most consumers. With the Anthem app, prices are clearly communicated to the patient before care so that an informed decision can be made. Increased Patient Engagement As nurses, we know that a highly engaged patient typically sees better outcomes. When dealing with complex medical issues like cardiovascular disease or diabetes, being well-versed in their symptoms, medications, and any possible side effects can keep patients healthy. App visits can also provide a level of anonymity that may allow some individuals to ask questions that they may not feel comfortable asking during a face-to-face visit. Understanding the Possible Drawbacks Just like all medical treatments, there are potential cons to using A.I. and mHealth. Here are a few of the potential dangers of chatbot visits. Legal Implications for ChatBots As Dr. Campbell points out, artificial intelligence is an excellent tool for healthcare professionals. However, seeing your physician or nurse practitioner and their office staff will always be the gold-standard for medical diagnoses and treatment. If a doctor does not have the ability to see the patient and do a physical exam, the risk of misdiagnosing the condition is significant. One question that is concerning for some experts is who would be responsible if an incorrect diagnosis is given to a patient during the chatbot conversation. Chatbots can't be sued, but physicians, nurse practitioners, and other care providers can be held responsible for misdiagnosing a patient's condition. Physician Burnout Could healthcare systems start expecting physicians to see patients all day and then go home and be connected to their phones? More doctors are talking about symptoms of burnout they feel from their day jobs. The American Academy of Family Physicians called burnout an epidemic in 2015, with about 46 percent of physicians reporting symptoms of the condition. Burnout can lead to low job satisfaction, anxiety, depression, and lower quality of patient care. Quick Fixes Aren't Always a Good Thing Our society likes a good quick fix. You can find a hack for almost anything these days. However, when it comes to your health, choosing the quick fix may not be the best answer. Dr. Campbell worries that patients may chat with the bot, get a few possible diagnoses and then end the visit before ever-texting an actual human. This could lead to poor outcomes and misdiagnosis because the patient didn't take the time to speak with the physician. The Future of MHealth and A.I. Healthcare was slow on the uptake of technology. Today, the industry has caught up and is even leading the charge in many areas of technology. So, what do you think about mHealth and chatbots? Would you use this service for yourself, and would you recommend it to your patients? Share your thoughts in the comments below.
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Artificial intelligence (AI) is reshaping healthcare and promising transformative changes, bringing about opportunities to enhance patient care and reduce clinical workload. Meanwhile, the implementation of AI technology comes with potential risks, which Dr. Carrie Nelson, the chief medical officer at Amwell, recently commented on in an interview with Health Care IT News. "There's talk that ChatGPT could be used to help physicians respond to messages from patients that are received via patient portals – but is that the right use of AI in healthcare? As challenging as that inbox is, we must pause and assess the risks before charging ahead," commented Nelson. "We also know longstanding healthcare system inequities and bias will insert themselves into and be potentially magnified by AI algorithms. This bias, including the type of data that has and hasn't been collected and documented in our medical records, limits the potential for AI to improve quality of care today, especially for vulnerable populations," she continued. AI in Healthcare Settings According to Nelson, some of the potential use cases for AI in healthcare settings are: Documentation Chat-based check-ins Digital behavioral health tools Referral management Authorization requests General administration Other use cases include "certain aspects of care," as "automation could be used to gather information regarding a patient's complex family medical history or other risk factors for disease," said Nelson. Related... Pennsylvania Mandated Ratio Law One Step Closer Nelson continued, explaining that the use of AI in healthcare could be pivotal as the advancements in medical science have exceeded the capacity of healthcare providers to fully utilize the body of knowledge and that AI can be used as a filter to refine and aid in diagnosing complex cases or formulating treatment strategies. When used correctly, Nelson is hopeful about the future of AI in healthcare, allowing clinicians to optimize their skills and time and focus on providing the greatest care they can to their patients. To address the risks associated with AI in healthcare, Nelson commented that more time is needed with AI-supported care models, allowing for medical professionals to identify best usage and to establish guardrails, as "any margin of error in healthcare is unacceptable. While I'm optimistic, recent data shows that we still have a long way to go."
