Contributor: Hephzibah Thomas MSN, RN-BC
Working in an outpatient setting is where you are simultaneously coordinating care in-person, triaging a symptom-based call on MyChart, refilling medications, screening labs so you don’t miss a critical value, managing continuous overflow of patients so providers are running on time, de-escalating patient concerns, attending to Rapid Response calls, being a patient advocate, requesting insurance approvals for same day procedures, and noticing that the patient who just walked in to the exam room does not look right, only because you have known them for years now.
That is outpatient nursing. Not the version that shows out in textbooks where you calmly educate patients, but the real version. I have been a nurse for twenty years and worked in different capacities. Currently I am a doctoral student, which means I also spend a meaningful portion of my life reading research and asking uncomfortable questions about things we assume are true. One such question has been sitting with me lately: What happens if artificial intelligence shows up in outpatient setting? What will happen if it starts doing some of what I do? Is this a threat to my clinical judgement and critical thinking skills? Or is it is a gift?
Artificial Intelligence in Outpatient Nursing
Here is something that does not get said enough: Outpatient nursing requires a different level of clinical intelligence than inpatient care. In the hospital, the patient is right in front of you, monitors are telling you a story, deterioration is visible, measurable, and help is immediate. In an outpatient setting, the deterioration happens between visits, at home, when no one is watching. Your job is not to respond to a crisis in front of you; it is to anticipate the crisis that has not yet arrived. That means holding a mental load of hundreds of patients simultaneously and noticing patterns across months of visits. Reading between the lines of a message and digging through the chart to recognize when something does not sound routine.
Is it a pattern recognition and clinical intuition firing all at once?
That is exactly where AI is beginning to show up. Predictive analytics tools that flag patients whose chronic disease markers are trending in the wrong direction, such as continuous glucose monitors. Machine learning algorithms can now predict hypoglycemic events before they happen, allowing patients to reach out to their providers in outpatient settings before a crisis develops rather than after they are in peril (Contreras & Vehi, 2018).
Heart failure monitoring platforms that track daily weight trends and symptom patterns have shown that exacerbation days before hospitalization become necessary, which means a nurse can intervene with a phone call or a medication adjustment instead of an ER visit (Angraal et al., 2020).
Remote monitoring systems that track a post-surgical patient’s recovery in real time and alert the care team before a complication becomes a crisis (Khan & Shah, 2025). Clinical decision support that indicates a medication interaction or an overdue screening right at the point of care, when there is still time to act. These are not hypothetical features. They are in use in outpatient clinics right now, and the evidence behind them is growing. When these systems work well, they function like a highly organized second set of eyes, catching what a busy nurse might otherwise miss (Moja et al., 2014). That is beneficence made practical.
Beneficence
I have been examining AI in outpatient nursing through the lens of beneficence, our moral obligation to actively promote patient welfare and prevent harm (Beauchamp & Childress, 2019). Beneficence is not an abstract framework for me. It is the reason I became a nurse and why I stayed through decades of patient care, education, and leadership. It is also the standard I use to evaluate every clinical tool, every protocol, and every algorithm that is used in my clinic. Now, does this help the patient in front of me?
I have heard the fear. I have felt it myself. What happens to nursing when machines can analyze data faster than we can? What happens to the therapeutic relationship when care is mediated through sensors and algorithms? What happens to clinical judgment when a computer is doing the flagging?
Clinical Power Theory (Polifroni, 2010) grounds this in a way that feels visceral. Dr. Polifroni argues that power is knowledge and that every nurse already possesses it. Not borrowed, not granted, but inherent. When an AI algorithm enters my clinic, I already hold the clinical knowledge, the relational awareness, and the intentionality that Polifroni describes as the foundation of clinical power. The question is not whether nurses are qualified; the question is whether we will show up and do it.
Conclusion
When AI handles pattern recognition, we as nurses are freed to do the things that only humans can do. Sit with the patient, analyze their context, and prioritize the complexity of their problem. You can notice that the patient who just came in for a pain pump med refill is not actually doing well although the chart says so. That is not a diminished role, that is an elevated role. The best AI tools do not replace the art of nursing. They protect the space for it.
The outpatient setting where care is proactive rather than reactive and where relationship spans for years rather than days of hospital admission, let us embrace these tools. It clears the clutter, catches the trending data, flags the overdue screening, and hands the patient back into the precious hands of the nurse who actually knows them. Every patient deserves someone who is paying attention to them. Not the data, not the dashboard, not the metrics, not the alert. The Nurse!!! You are that nurse and AI is just here to assist.
References
Angraal, S., Mortazavi, B. J., Gupta, A., Khera, R., Ahmad, T., Desai, N. R., Jacoby, D. L., Masoudi, F. A., Spertus, J. A., & Krumholz, H. M. (2020). Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction. JACC:
Heart Failure, 8(1), 12–21. https://doi.org/10.1016/j.jchf.2019.06.013
Beauchamp, T. L., & Childress, J. F. (2019). Principles of Biomedical Ethics (8th ed.). Oxford University Press.
Contreras, I., & Vehi, J. (2018). Artificial Intelligence for Diabetes Management and decision support: Literature review. Journal of Medical Internet Research, 20(5). https://doi.org/10.2196/10775
Moja, L., Kwag, K. H., Lytras, T., Bertizzolo, L., Brandt, L., Pecoraro, V., Rigon, G., Vaona, A., Ruggiero, F., Mangia, M., Iorio, A., Kunnamo, I., & Bonovas, S. (2014). Effectiveness of computerized decision support systems linked to electronic health records: A systematic review and meta-analysis. American Journal of Public Health, 104(12). https://doi.org/10.2105/ajph.2014.302164
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Polifroni, E. C. (2010). Power, right, and truth: Foucault’s triangle as a model for clinical power. Nursing Science Quarterly, 23(1), 8–12. https://doi.org/10.1177/0894318409353811
Polifroni, E. C. (2018, October). Clinical Power.
About Hephzibah Thomas
Hephzibah Thomas, MSN, RN-BC is a doctoral candidate in Nursing at Texas Woman’s University, where her research examines how clinical judgment develops in undergraduate nursing students, a question born directly from what she has observed at the intersection of practice, education, and a rapidly changing healthcare landscape. She has more than 20 years of nursing experience and currently serves as a Nurse Manager and an Adjunct Clinical Faculty. When she moved into Ambulatory care setting, she had to relearn how to be able to safely discharge a patient after 30 minute of a visit which made her sensitive to the structural gap in outpatient care where nurse’s reach have always been limited by time. She believes that nursing profession is at its most powerful when it engages the tools of its era with confidence and discernment. Nurses are not just capable of leading the integration of AI into outpatient care, they are the right people to do it.
I feel AI can be useful as a tool. Not as the only answer. It is changing and we still need to monitor that as critical thinkers. Reliance on a 1 tool can be detrimental to nursing.
Use caution.
Years ago, when diving into some tangential rabbit hole of semantics, I read Alfred Korzybski, whose most famous statement is “the map is not the territory” (1931). In the same way, the chart is not the patient, the lab values are not the patient, the AI algorithm and probabilities are not the patient. Of course AI can play chess better than I, can crunch a larger universe of data than I, but AI is neither you nor I. Humans are relational beings noted for their abilities and needs — unpredictable, creative, imaginative, individualistic, odd, defiant, abilities and needs. This is to say that, as you note,
“Polifroni argues that power is knowledge and that every nurse already possesses it. Not borrowed, not granted, but inherent. When an AI algorithm enters my clinic, I already hold the clinical knowledge, the relational awareness, and the intentionality that Polifroni describes as the foundation of clinical power… When AI handles pattern recognition, we as nurses are freed to do the things that only humans can do.”
Humans are relational beings, and nursing ethics is relational. While you use “the lens of beneficence, our moral obligation to actively promote patient welfare and prevent harm,” the ANA Code of Ethics for Nurses provides an even richer source of moral guidance, especially provisions 1-3. For example (2.1) “Every clinical encounter and plan of care must reflect the fundamental commitment of nursing to the inherent dignity, worth, unique attributes, and human rights of the patient. Nurses provide patients with opportunities to participate in assessing their capacity for, planning and implementing their plan of care, and deciding what supportive services are acceptable to them.” So, crunch away AI…while I sit and hold the hand of the patient who says “please don’t leave me; I am scared.” The clinical power of nursing.