I have selected an article from HIMSS Healthcare IT News titled, “How AI is Reshaping Clinical Decision-Making in 2025” (HIMSS, 2025). This article, published on May 22, 2025, details the evolution of artificial intelligence from a supportive tool to a central component of strategic decision-making in healthcare. It highlights how AI is being embedded into clinical infrastructure to influence care in real-time.

2. Article Summary & Technology Definition

The article discusses how AI has moved beyond a theoretical concept in healthcare and is now an integral part of clinical decision-making. The key takeaway is that healthcare organizations are leveraging AI to shift from reactive to predictive care models. The article details how AI tools are being used for real-time patient insights, risk prediction, and workflow optimization. It emphasizes that these AI tools are intended to augment, not replace, human clinicians, offering a second layer of insight for faster and safer decisions. A central theme is the importance of trust and transparency in these technologies.

The emerging technology described in the article is AI-powered predictive analytics for risk stratification. Predictive analytics in this context uses machine learning algorithms to analyze large datasets from sources like electronic health records (EHRs), patient wearables, and genomic information to identify patterns and forecast future events, such as a patient’s risk of developing a complication, being readmitted to the hospital, or experiencing an adverse event. Risk stratification is the process of categorizing patients into different risk groups to guide care, and AI enhances this by identifying complex risk factors that may be missed by traditional methods. This technology enables a shift from a reactive to a proactive care model (Khajuria et al., 2022).

3. Example of Technology

My intended area of practice is in an acute care setting on a medical-surgical floor, where I would manage a diverse patient population with various chronic and acute conditions. A significant part of my role would be to prevent patient deterioration and ensure safe discharges.

An example of how this emerging technology could be used in my practice is through a predictive risk stratification dashboard integrated into the EHR. When I log in at the start of my shift, this dashboard would display a color-coded risk score for each of my assigned patients. For a patient who has just undergone surgery, the AI might analyze their vital signs, lab results, medication history, and recent charting notes to flag them as “high risk” for developing a post-operative complication like sepsis or a fall. This score would be based on complex data patterns that a human might not synthesize as quickly, such as a subtle trend in a patient’s temperature or a specific combination of medications. A high-risk score would prompt me to perform a focused assessment, implement specific fall prevention protocols, or escalate my concerns to the interdisciplinary team for further evaluation. This allows me to prioritize my care based on real-time, data-driven insights, moving beyond standard protocols to a more personalized, risk-based approach. Studies have shown that predictive analytics can significantly reduce hospital readmission rates and improve patient outcomes by identifying at-risk patients early (Rani et al., 2021).

4. Ethical, Legal, and Safety Issues

• Legal Issue: A significant legal issue related to AI-powered predictive analytics is liability and accountability. If a predictive algorithm provides an incorrect risk assessment that leads to a patient harm, who is legally responsible? Is it the clinician who followed the recommendation, the hospital for implementing the technology, or the vendor that created the algorithm? This ambiguity of liability creates a “black box” problem where the decision-making process of the AI is not transparent, making it difficult to assign blame in a malpractice case (Weng et al., 2020).
• Ethical Concern: A primary ethical concern is algorithmic bias, which can perpetuate and exacerbate existing health disparities. If an AI model is trained on a dataset that primarily represents a specific demographic (e.g., a white, male population), it may not accurately predict risks for underrepresented groups, such as women or ethnic minorities. This could lead to a situation where the AI consistently underestimates the risk of adverse events in certain populations, resulting in inequitable care and poorer health outcomes for those groups (Larson et al., 2022).