AI, Machine Learning, Genomics, Precision Health and Robotics in Nursing Practice: Innovative Informatics Tools (Discussion and Sample Blog)
7 min read Discussion: Innovative informatics tools and applications to clinical practice
For this Discussion, you will explore various topics associated with innovative technology and your healthcare organization or nursing practice. You will consider how you might utilize these advancements, as well as how these advancements might influence nursing informatics.
- Review the Learning Resources associated with the topics: AI, Machine Learning, Genomics, Precision Health, and Robotics.
- Consider the role of these technologies in your healthcare organization or nursing practice.
- Analyze the differences of these technologies as they may impact healthcare delivery and nursing practice.
- Reflect on the potential use of each of these topics and your personal experiences with their implementation into practice.
Post a response to your blog for the following:
- Choose one of the five topics: AI, Machine Learning, Genomics, Precision Health, or Robotics. How has this technology been applied in your practice or in the lives of your patients?
- How do you think this innovation will impact the future of your practice?
Five innovations reshaping healthcare
Nursing informatics is moving beyond the electronic health record (EHR) toward technologies that can predict, personalize and automate care. Before choosing one topic for the blog, it helps to understand what each technology is and how differently they affect healthcare delivery and nursing practice.
| Technology | What it is | Clinical examples | Impact on nursing practice |
|---|---|---|---|
| Artificial intelligence (AI) | Computer systems that perform tasks normally requiring human intelligence, such as language, reasoning and image recognition. Generative AI produces text, images or summaries. | Ambient AI documentation, chatbots for patient questions, AI triage of messages, image interpretation | Can reduce documentation burden and support decisions; requires oversight for accuracy, bias and privacy |
| Machine learning (ML) | A subset of AI in which algorithms learn patterns from large datasets to make predictions | Early warning scores for deterioration and sepsis, readmission risk models, fall-risk prediction | Changes how nurses detect risk; nurses must interpret alerts and avoid alert fatigue and automation bias |
| Genomics | Study of a person’s genes and how they interact with health, disease and medications | Pharmacogenomic testing (for example, CYP2C19 and clopidogrel), hereditary cancer screening (BRCA), newborn screening | Nurses take family histories, provide genetic education and support informed consent |
| Precision health | Tailoring prevention and treatment to each person’s genes, environment, behaviors and social context | Targeted cancer therapies, personalized risk prediction, wearable-guided lifestyle plans, research programs such as NIH All of Us | Shifts care from “one size fits all” to individualized plans, with nurses as educators and coordinators |
| Robotics | Machines that carry out physical tasks, sometimes guided by AI | Robot-assisted surgery, pharmacy and medication-dispensing robots, delivery and supply robots, telepresence robots, UV disinfection robots | Can relieve nurses of non-clinical tasks; needs training, workflow redesign and safety checks |
Key differences: AI and ML mainly change how information and decisions are produced. Genomics and precision health change what care is chosen for each individual. Robotics changes how physical work is done. All of them depend on high-quality data and informatics infrastructure, and all need nurses to keep care safe, ethical and human-centered (American Nurses Association [ANA], 2022).
Sample blog post: artificial intelligence in my practice
The following first-person blog post is an illustrative example. Your own post should describe your real practice and experiences.
How AI has been applied in my practice and in the lives of my patients
I work as a registered nurse on a busy medical-surgical unit, and artificial intelligence has entered my practice in ways I did not expect only a few years ago. The most visible example is our EHR’s machine-learning early warning score. The system continuously analyzes vital signs, lab results and nursing assessments, and alerts the team when a patient’s risk of deterioration or sepsis rises. On one night shift, the score flagged a postoperative patient whose vital signs individually looked “borderline.” When I assessed him, he was newly confused and his urine output had dropped. Because the alert prompted an early rapid response call, he received fluids and antibiotics before he developed septic shock.
I have also seen the limits of these tools. Some alerts fire for patients who are clearly stable, and the volume of alerts can contribute to alert fatigue. A widely used proprietary sepsis model was found, when tested externally, to miss many sepsis cases while generating many false alarms (Wong et al., 2021). This reminds me that an algorithm supports, but never replaces, my clinical judgment.
Our organization recently piloted ambient AI documentation, in which a secure AI tool listens to a patient conversation, with consent, and drafts a note for the clinician to review. Early reports from large health systems suggest that these “AI scribes” can reduce documentation time and help clinicians focus on patients (Tierney et al., 2024). Colleagues who used it said they could make more eye contact with patients instead of typing. Every draft must still be checked carefully for errors.
For my patients, AI appears in everyday life too: patient portal chatbots that answer questions, smartwatches that detect irregular heart rhythms, and apps that remind them to take medications. I now ask patients how they use these tools and help them interpret the information safely.
How AI will affect the future of my practice
- Less documentation, more time for patients: ambient documentation and AI-generated handoff summaries could give nurses back time for assessment, education and comfort, which could help reduce burnout.
- Earlier detection of deterioration: better-validated predictive models, combined with continuous wireless monitoring, could help nurses intervene hours earlier.
- Personalized patient education: AI could tailor discharge teaching to a patient’s language, literacy level and condition.
- Smarter staffing and workflow: AI could forecast admissions and acuity to support safer staffing decisions.
- New roles and responsibilities for nurses: nurses will need AI literacy: understanding how algorithms work, recognizing bias, and knowing when to override a recommendation. Nurse informaticists will help select, test and monitor AI tools, and bedside nurses must be included in governance.
- Ethical vigilance: algorithms trained on biased data can widen health disparities (Obermeyer et al., 2019). Privacy, transparency, informed consent and accountability will remain essential nursing concerns.
Overall, I believe AI will make my practice more data-informed and, if implemented well, more human by removing tasks that pull me away from the bedside. The nurse’s role will shift from data entry toward interpreting, questioning and acting on intelligent information, with the patient’s safety and dignity always at the center.
Tips for writing your own blog post
- Choose one technology and give a specific, real example from your workplace or a patient’s experience. If you have no direct experience, describe how the technology is being used in organizations like yours.
- Explain the impact on patients, nurses and the organization, including both benefits and risks.
- Look ahead: describe how the technology will change your role, the skills you will need, and the ethical issues to watch.
- Support your points with current scholarly sources and your course Learning Resources, in APA style.
- Keep the tone reflective and professional, and protect patient privacy: no names or identifying details.
Related reading: Reflection on informatics and knowledge work and Contemporary issues in advanced nursing practice, which includes AI-supported clinical decision-making.
Conclusion
AI, machine learning, genomics, precision health and robotics are each changing healthcare in a different way: by improving information and decisions, by personalizing treatment, or by transforming physical work. For nurses, these innovations offer earlier detection, less administrative burden and more individualized care, but they also demand new competencies, careful evaluation and strong ethical oversight. Nurses who understand these technologies, and help shape how they are implemented, will ensure that innovation improves care for every patient.
References
American Nurses Association. (2022). The ethical use of artificial intelligence in nursing practice [Position statement]. American Nurses Association.
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
Tierney, A. A., Gayre, G., Hoberman, B., Mattern, B., Ballesca, M., Kipnis, P., Liu, V., & Lee, K. (2024). Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catalyst Innovations in Care Delivery, 5(3). https://doi.org/10.1056/CAT.23.0404
Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., Ghous, M., & Singh, K. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070. https://doi.org/10.1001/jamainternmed.2021.2151
