Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.

The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research into antimicrobial resistance (AMR). ARMD encompasses data from adult patients, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset's acquisition, structure, and utility while detailing its de-identification process.

We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for efficient training and deployment in resource constrained environments, while also having deterministic and explainable behavior. Using data from 25 patients, we demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead, highlighting its potential for real-time, low-resource monitoring applications.

Background: The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. Methods: A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, re-usable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure the compliance with FAIR principles. Results: The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion and frame levels. Conclusion: Our study shows that the proposed framework facilitates the storage of the visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

Artificial intelligence holds great promise for healthcare, and it is already being put to use by many forward-looking hospitals and health systems. One challenge for healthcare CIOs and clinical users of AI-powered health technologies is the biases that may pop up in algorithms. These biases, such as algorithms that improperly skew results because of race, can compromise the ultimate work of AI – and clinicians. We spoke recently with Dr. Sanjiv M. Narayan, co-director of the Stanford Arrhythmia Center, director of its Atrial Fibrillation Program and professor of medicine at Stanford University School of Medicine. He offered his perspective on how biases arise in AI – and what healthcare organizations can do to prevent them.

The United States has the highest maternal mortality rate of all high-income countries. Compared to women in Canada and France, women in the United States are twice as likely to die from childbirth complications. This crisis is especially pronounced in ethnic and racial minority populations: Black and Native American women in the United States are much more likely to perish from pregnancy-related complications than their white counterparts and are more likely to suffer severe maternal morbidity due to postpartum hemorrhage, hypertensive disorders, and sepsis. The impact of the Covid-19 pandemic on these groups is not yet known, but given the way it has exacerbated racial inequities nationally and globally, it is expected to have made the situation worse. However, data from the U.S. Centers for Disease Control and Prevention (CDC) suggests that approximately 60% of maternal deaths are preventable. Not only would this strategy improve outcomes, it would also significantly reduce medical costs.

Offered by Stanford University. Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses. In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.

Registration for this conference is now closed. This conference is anchored and building on the preview of the Special National Academy of Medicine (NAM) publication titled: "Artificial Intelligence in Healthcare: The Hope, The Hype, The Promise, The Peril." Co-led by Michael Matheny and Sonoo Thadaney Israni. Registration includes course materials, certificate of participation, breakfast and lunch. CME Certificate Fee: $25.00 Note: If you would like to receive CE Credit for your attendance, there will be a $25.00 fee option after the conference evaluation is completed and your conference attendance is verified. Your email address is used for critical information, including registration confirmation, evaluation, and certificate.

Worldwide, about 44 million people are living with Alzheimer's disease or a related form of dementia. Although 82 percent of seniors in the United States say it's important to have their thinking or memory checked, only 16 percent say they receive regular cognitive assessments. Many traditional memory assessment tools are widely available to health professionals, though deficiencies in screening and detection accuracy and reliability remain prevalent. But even with increasingly available tools like MemTrax, an online memory test based on image recognition, the clinical efficacy of this approach as a memory function screening tool has not been sufficiently demonstrated or validated. In practice, there are numerous integrated and complex factors to consider in interpreting memory evaluation test results, which presents a real challenge for clinicians.

I'm about to start a session with Woebot, billed as the first chatbot clinically shown to improve symptoms of depression and anxiety. Chatbots do everything these days, from helping people manage bank accounts and practice language skills to keeping them company when they can't sleep. But can an AI really get into my head the way a therapist would? Turns out Woebot, created by a Stanford University psychologist, is more about getting me into my own head -- and teaching me to better manage the chatter in there. Using brief daily conversations, mood tracking, curated videos and word games, the new bot relies on principles of cognitive behavioral therapy (CBT), a short-term, goal-oriented treatment that aims to rewire the thoughts that negatively affect how we feel.