What are the AI Tools Currently Used by EHRs?
Artificial Intelligence (AI) is revolutionizing healthcare by significantly enhancing the functionality of Electronic Health Records (EHRs). AI tools, including ambient AI, are being seamlessly integrated into EHR systems, promising to revolutionize clinical documentation, streamline workflows, and ultimately enhance patient care. This blog explores the current AI applications in EHRs, highlighting specific tools and their potential to positively impact healthcare practices. The information spotlights what is available for consideration for your physician practice. The assessment you perform to determine the capacity of AI tools could benefit and enhance the practice workflows. If/when the physician practice decides to pursue implementing AI tools, try or pilot it before investing in the tool.
1. Ambient AI Assistants
Ambient AI, a term referring to systems that operate in the background, capturing and processing data without disrupting users’ natural workflows, is a game-changer in healthcare. In the context of EHRs, ambient AI assistants listen to patient-clinician interactions and automatically generate clinical documentation. This not only ensures accurate and timely documentation and significantly reduces the administrative burden on healthcare providers, allowing them to focus more on patient care.
- Microsoft’s Dragon Copilot: Introduced in early 2025, Dragon Copilot is an AI assistant designed for healthcare applications. It leverages voice-dictation and ambient listening technologies developed by Nuance, a company Microsoft acquired in 2021. Dragon Copilot automates tasks such as note-taking, summarizing clinical evidence, and drafting referral letters. It also supports multilingual documentation and natural language dictation, allowing clinicians to focus more on patient care and reducing burnout. Early surveys indicate high satisfaction rates among users.
- Abridge: Founded in 2018, Abridge is a startup that applies AI to clinical documentation by generating structured clinical notes from patient-clinician conversations. The technology captures key details from medical dialogues, creating organized summaries that integrate seamlessly into EHR systems. In 2024, Abridge partnered with Yale New Haven Health and the University of Vermont Health Network to implement its AI-driven documentation platform. The company has raised significant funding from investors like CVS, Kaiser Permanente, and Mayo Clinic, underscoring its impact in the healthcare AI landscape.
2. Natural Language Processing (NLP) Systems
NLP is a branch of AI that enables machines to understand and interpret human language. In the context of EHRs, NLP systems process unstructured clinical notes to extract meaningful information, facilitating better data analysis and decision-making.
- Apache cTAKES: The clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source NLP system developed by the Apache Software Foundation. It extracts clinical information from unstructured text in EHRs, identifying entities such as drugs, diseases, symptoms, anatomical sites, and procedures. cTAKES enhances the utility of EHRs by structuring free-text data, making it more accessible for clinical decision support systems and research.
3. Predictive Analytics Tools
AI-powered predictive analytics tools analyze EHR data to forecast patient outcomes, identify potential risks, and support preventive care strategies. By identifying high-risk patients and potential health issues in advance, these tools can help healthcare providers intervene early, potentially preventing serious health complications and improving patient outcomes.
- Risk Prediction Models: AI algorithms can evaluate individual patient records to predict the likelihood of developing specific conditions based on medical history and genetic information. For instance, rule-based systems analyze large datasets to create associations between observations and potential diagnoses, aiding early detection and intervention.
4. AI in Medical Billing and Coding
AI tools assist in automating the coding process and identifying billing discrepancies, leading to more accurate claims and reduced administrative workload.
- Grok: Patients are increasingly using AI-driven tools like Grok to analyze medical bills and detect overcharges. For example, a patient used Grok to review a $14,000 emergency room bill and discovered that all charges exceeded state and national averages. This empowered her to challenge the charges and secure financial aid. Such tools benefit patients and providers by promoting transparency and accuracy in medical billing.
5. Integration with Connected Medical Devices
Integrating AI with networked medical devices enhances data collection and patient monitoring, contributing to more comprehensive EHRs.
- Medical Internet of Things (MIoT): Connected devices like smart implants and monitoring systems collect real-time patient data, which AI algorithms analyze to detect anomalies and predict medical events. For instance, smart knee-replacement implants by Zimmer Biomet and Canary Medical enable post-operative monitoring, allowing for timely interventions and improved recovery outcomes. The MIoT market is projected to grow significantly, reflecting the increasing adoption of such technologies.
6. AI-Powered Clinical Decision Support Systems (CDSS)
AI-enhanced CDSS analyzes EHR data to provide evidence-based recommendations, supporting clinicians in making informed decisions.
- Diagnostic Assistance: AI algorithms process patient data to suggest potential diagnoses, recommend treatment options, and identify possible drug interactions. By integrating these insights into EHRs, clinicians receive real-time support, improving diagnostic accuracy and patient safety.
Challenges and Considerations
While AI integration into EHRs offers numerous benefits, several challenges must be addressed:
- Data Privacy and Security: Ensuring that AI systems comply with regulations like HIPAA is crucial to protect patient information.
- Interoperability: AI tools must be compatible with various EHR systems to facilitate seamless integration and data exchange.
- Bias and Accuracy: AI algorithms should be trained on diverse datasets to minimize biases and ensure accurate, equitable healthcare outcomes.
- Effective Use of AI Tools in Healthcare: AI tools in healthcare require continuous learning and skill development. Healthcare providers need adequate training to understand AI tools’ capabilities, interpret their outputs, and integrate their insights into patient care. This not only ensures the effective use of AI but also promotes a culture of learning and innovation in healthcare.
Conclusion
Integrating AI tools, including ambient AI, NLP systems, predictive analytics, and connected devices, transforms EHRs and significantly improves healthcare delivery. These technologies enhance clinical documentation, support decision-making, and improve patient outcomes. However, responsible implementation requires addressing privacy, interoperability, bias, and user education challenges. As AI continues to evolve, its thoughtful integration into EHRs will play a pivotal role in advancing healthcare efficiency and quality, giving healthcare professionals more confidence in their tools.
