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Breaking news in the healthcare AI

Healthcare AI News from the HIMSS Conference

The Health Information and Management Systems Society (HIMSS) is one of the prominent voices in the field of healthcare information or data. It has become critical in recent years with more and more solutions coming from the artificial intelligence (AI) and healthcare field. The conference provides great platform for the vendors and consumers to showcase and interact about upcoming changes that going to drive the next cycle of growth and innovation in the healthcare. The recently concluded HIMSS annual conference had many exciting news from different vendors. Here are some of those highlights.

EPIC announces Agentic AI platform

These agents aim to streamline healthcare, aiding patients in appointment preparation and physicians in data-driven insights. Conversational AI will help with pre-visit tasks, like scheduling tests and summarizing patient goals. 2 Epic integrates AI into its EHR, with 125 features in development, using models like OpenAI. MyChart's response technology, already generating millions of drafts, highlights Epic's push to leverage AI for efficiency and addressing physician shortages.

Clinical Decision Making Support with Microsoft Dragon Copilot

One the biggest announcement came from Microsoft with Dragon Copilot integration which provides the healthcare industry’s first unified voice AI assistant that enables clinicians to streamline clinical documentation, surface information and automate tasks. UpToDate has integrated the Dragon Copilot.

• Streamline documentation: Clinicians can take advantage of multilanguage ambient note creation, automated tasks and multilanguage support, personalized style and formatting, natural language dictation capabilities, speech memos, editing, customized texts, templates, AI prompts, and more in one singular user interface.

• Surface information: The embedded AI assistant functionality allows clinicians to conduct general-purpose medical information searches from trusted content sources.

• Automate tasks: New capabilities allow clinicians to automate key tasks, such as conversational orders, note and clinical evidence summaries, referral letters, and after-visit summaries, in one centralized workspace.

Big Hospitals announced AI product integration

• Memorial Sloan Kettering Cancer Center taps Abridge for ambient scribing

• Rush announces expansion with Suki AI (ambient AI)

• NYU Langone partners with Amazon One for contactless check-in

Zoom launches betas for healthcare AI

Zoom launched AI features for healthcare, including Zoom Workplace for Clinicians with an ambient scribe (via Suki AI) for visit notes, and a Custom AI Companion for data-driven decisions. These tools aim to streamline workflows, reduce burnout, and integrate EHR data. Teladoc enhanced Prism with AI, improving referrals and providing data insights for clinicians. Prism now offers closed-loop referrals and real-time clinical transcription. Teladoc reported a $1 billion loss due to a BetterHelp impairment charge.

🧑🏼‍🔬 Bench to Bedside👨🏽‍🔬

Developments in healthcare AI research and innovations

Artificial Intelligence Driven Triage Decision Support on Emergency Department Care

Triage, a clinical method of assessing and categorizing patients in an Emergency Department (ED) care based on the severity of their conditions with profound implications for resources allocation with ultimate focus on better clinical outcomes of patients along with minimizing morbidity & mortality.

Risk stratifications as well as identifying critical illnesses has been a major issue of traditional ED triage. AI use has helped and driven the ED triage in strengthening the foundation along with increasing the outcomes exponentially.

A multisite quality improvement study of an AI informed triage clinical decision support (CDS) intervention was conducted and results reflect that AI can enhance CDS as it provides faster, accurate and data driven insights, with reduction of the risk of errors and eventually improving the patient care.

Findings:

Patient characteristics and triage performance were compared pre as well as post intervention (nurse vs. nurse + AI). Triage performance was assessed based on AI recommendations and nurse agreement with the AI’s acuity level.

Out of 174,648 ED visits (83,404 visits pre-intervention and 91,244 post-intervention) were included across three ED’s. The AI triage CDS intervention led to a shift in acuity levels, low acuity visits increased by 48.2% while mid and high acuity visits decreased by 18.7% & 8.8% respectively. These adjustments were deliberately driven by the AI recommendations.

The AI triage CDS intervention was associated with reductions in median times from arrival to the initial care area (33.0%; 12.0 to 8.0 minutes), ED disposition (4.2%; 190.0 to 182.0 minutes) and to ED departure (6.1%; 311.0 to 292.0 minutes) which reflects the benefit of AI driven ED care.

Benefits of AI in ED care:

·       AI can aid in better resource allocation & patient categorization in the ED.

·       AI triage CDS was associated with improved performance of triage nurses in the early identification of patients at risk for critical illness.

·       Synergistic potential increases with AI and human judgement which is beneficial for improving the patients’ clinical outcomes.

·       AI use in ED significantly decreases the wait times, ED length of stay as well as improved triage performance and ED patients flow.

·       It aids in managing the increased patient load which tends to decrease burnout in healthcare providers as well as financial pressure.

Limitations:

·       Study was conducted in a single healthcare system.

·       Increases the risk of intervention contamination for nurses who work across multiple ED sites over different pre and post intervention study periods.

·       Focusing only on short term ED outcomes & does not allow for an assessment of the long-term impact of the AI triage CDS on patient care.

Expert Insights: AI’s Role in Future Epidemic Response

• Dr. Moritz Kraemer, lead author of the Nature article, warns that AI’s success depends on responsible integration. AI can revolutionize epidemiology, but without ethical oversight, it risks reinforcing health inequalities.

• AI researcher Eric Topol emphasizes its decision-support role: AI’s predictive power is extraordinary, but it must work alongside human experts, not replace them.

The Future: Smarter, Faster, and More Ethical Epidemic Response

AI has the potential to redefine global health security, making epidemic response more data-driven, proactive, and precise. However, its impact will depend on responsible implementation, transparency, and human oversight. The future lies in AI-human collaboration, where technology enhances, but does not replace, expert decision-making.

🧑🏽‍⚕️ AI in Clinic 🏥

Developments in healthcare AI research and innovations

The Role of Virtual Voice Assistants in Healthcare: Enhancing Clinician Efficiency and Overcoming Challenges.

• Virtual Voice Assistants( VVAs), are AI-powered tools designed to assist healthcare professionals by automating tasks through voice commands. They manage clinical workflows, help with documentation and improve patient-provider interactions.

• VVAs use natural language processing and speech recognition technologies to reduce administrative burdens by automated note taking, voice-guided test ordering, and enabling realtime clinical decision support.

• With the recent launch of Microsoft Dragon Co-pilot , a new voice activated AI assistant which uses voice recognition to streamline clinical documentation and automate healthcare administrative tasks, we want to highlight a few companies working on AI- driven voice recognition technologies in healthcare.

• Abridge uses AI-powered speech recognition to automatically transcribe and summarise patient-clinician conversations integrating directly into EHR workflows.

• Commure combines advanced speech recognition technology which can handle over three hours of conversations between multiple speakers with different accents and dialects with tailored documentation templates to act as an automated scribe.

• Suki is a healthcare AI startup specialising in ambient documentation, dictation, ICD-10 and HCC coding all while integrating with existing EHRs.

• Teladoc health is a virtual healthcare platform connecting patients with licensed doctors via phones, video calls, and app-based interactions. It utilises AI-driven chatbots and virtual assistants to guide patients through symptom assessment and triaging. Its integration with Amazon Alexa and AI driven speech recognition automates virtual healthcare through voice commands.

• K health is another virtual healthcare company using an AI driven chatbot to interact with patients to identify potential diagnoses and connect them with the correct specialists for consultations.

  What is the Future of VVAs in healthcare?

• As AI-driven VVAs continue to evolve, their role in healthcare is set to expand significantly. These technologies are already in use enhancing clinical documentation, real time risk assessments and improving multi-lingual communications enhancing clinical care efficiency.

• The integration of AI translators and advanced voice recognition for documentation reduces time spent on administrative tasks. Voice guided test orders and information retrieval can also help in reducing clinician burnout ensuring quality patient interactions.

• The main limitations for VVAs at present include HIPAA compliance and potential for errors which can be minimised with adequate training and ensuring safety checks.

🤖 Patient First, AI Second🤖

Ethical and Regulatory Landscape of Healthcare AI

Beyond Productivity: How Generative AI Can Transform Women's Careers

Generative AI presents challenges and transformative opportunities for gender equity in the workplace. While women face disproportionate risks from AI-driven job displacement and adoption gaps, strategic implementation can turn these tools into powerful enablers of empowerment. Below are key strategies supported by recent research and initiatives:

1. Targeted Upskilling and Career Navigation

• AI literacy programs: Women using generative AI report 25% lower adoption rates than men despite equal access, often due to ethical concerns or fear of stigma. Companies like Indeed and PwC advocate for employer-funded AI training during work hours, focusing on high-displacement sectors like administration and customer service.

• Negotiation and leadership tools: Generative AI can simulate salary negotiation scenarios, analyze gendered feedback patterns, and provide real-time coaching—addressing the 20% confidence gap in career advancement.

2. Bias Mitigation in Talent Systems

AI systems designed with gender equity in mind can:

• Reduce hiring discrimination: Tools like PwC’s AI-driven recruitment platforms expand talent pools and flag biased language in job descriptions.

• Improve performance reviews: Algorithms trained on diverse datasets help identify and correct gender disparities in feedback and promotion processes.

Case study: Salesforce’s internal AI teams prioritize “testing and learning” cultures, achieving near-equal adoption rates between genders by creating psychological safety for experimentation.

3. Building Equitable AI Ecosystems

The Gender Equitable AI Toolkit (NetHope) provides nonprofits with frameworks to ensure female participation in AI problem-solving and deployment.

4. Structural and Cultural Shifts

• Policy integration: UNESCO and Deloitte advocate for “gender lens” AI governance, including pay equity audits and childcare subsidies to support women in tech roles.

• Normalization campaigns: Companies like Atlassian attribute 50-50 gender adoption rates to leadership transparency about AI’s role in workflows.

The productivity paradox: While 75% of women feel technologically prepared for AI transitions, only 41% report tangible productivity gains compared to 61% of men. Closing this gap requires pairing technical training with cultural interventions that validate AI use as professional growth—not cheating.

By addressing technical and systemic barriers, generative AI can help dismantle workplace inequities rather than amplify them. The key lies in human-centered implementation that prioritizes women’s leadership in shaping these tools.