Electronic health records (EHRs) store patient information in healthcare. But many EHR systems were made mainly for billing and paperwork, not to help doctors make decisions. This can cause extra work for doctors and take time away from patients.
AI clinical assistants help by working inside the EHR to quickly look at large amounts of data. They use tools like machine learning, natural language processing, and rule-based systems to find key medical information from both organized data and medical notes. This helps make diagnoses more accurate, offers treatment ideas, and predicts health risks.
One example is Tempus, a company linked to about 65% of academic medical centers and over half of US cancer doctors. Their AI assistant, Tempus One, works right inside the EHR. It lets doctors search patient data and create special tools that speed up work, improve decisions, and find the best treatments and trials for cancer patients.
AI assistants do not replace doctors but help them by gathering and showing important information. They provide real-time data from medical notes, genetic tests, and patient history to help make better and faster diagnoses.
AI programs can predict how diseases might change and how well treatments may work. This leads to medicine that fits each patient instead of using one plan for everyone. For example, in cancer care, Tempus xT uses a mix of tests to better find targeted treatments and help patients join clinical trials across the US.
AI tools also help find diseases early. They can analyze X-rays and MRIs faster and sometimes more accurately than people. For example, an AI stethoscope from Imperial College London can detect heart problems like valve disease and irregular beats in just 15 seconds by combining heart sounds with ECG data.
Doctors often spend too much time on paperwork and tasks that lower the time they have for patients. AI clinical assistants can take care of routine jobs like data entry, transcription, and processing claims.
For instance, Microsoft’s Dragon Copilot writes referral letters, after-visit reports, and clinical notes, which helps reduce paper burden for doctors. This makes work faster and lets doctors focus more on patient care.
Still, adding AI to existing EHRs is not easy. It requires complex technical work so AI and EHRs communicate smoothly. Some doctors may not trust or understand AI yet. Privacy rules and concerns about bias also need careful attention. Overcoming these issues often needs teamwork with AI companies or major system updates.
AI also helps in office and clinical tasks beyond decision support. Automating phone calls, appointment schedules, and patient communication lowers mistakes and waiting times, improving patient service.
For example, Simbo AI uses AI to manage phone calls for healthcare providers. The system sorts calls, handles urgent needs first, and schedules appointments automatically. This reduces staff work, missed calls, and speeds up patient access to care.
More healthcare offices in the US use smart call routing powered by AI to answer patient questions faster. These systems judge how urgent calls are and send patients to the right place, helping prioritize urgent care and use resources better.
On the clinical side, AI tools in EHRs cut down repetitive tasks like data entry, coding, and note-writing. Natural language processing turns free text into data that can be searched easily. Predictive analytics looks at past patient info to flag possible problems like medication errors or allergies, keeping patients safer.
AI clinical tools are being used more in the US. A 2025 AMA survey showed that 66% of doctors now use AI tools, up from 38% in 2023. Also, 68% say AI helps improve patient care. This shows more doctors trust AI as a helpful tool.
Cancer care leads in AI use. Tempus links to over half of US cancer doctors and works with 95% of top cancer drug companies. It uses millions of research records and huge amounts of data to help improve patient outcomes.
As AI grows, healthcare faces both chances and challenges. Adding AI to EHRs takes technical work but can make workflows smoother, reduce doctor exhaustion, and provide better decision support. At the same time, patient privacy, data protection, and avoiding bias need careful work. Agencies like the FDA are paying more attention to AI oversight.
Healthcare leaders need to understand how AI clinical assistants and automation tools affect daily work. Bringing these technologies in should include checking current EHR systems, getting staff ready, and planning how to involve patients.
For smooth implementation, IT managers should focus on:
In the future, AI is expected to get more advanced. This includes automated diagnostic helpers and AI that assists with medical documentation and decision-making. This should lower doctors’ workload more and help provide care based on live data.
For healthcare organizations in the US, using AI assistants with EHRs will become more important to stay competitive and improve care quality. Thoughtful use of these tools helps doctors spend more time with patients and less on paperwork while keeping care accurate and safe.
Adding AI clinical assistants to electronic health records is changing how doctors work and make decisions in healthcare throughout the US. These systems quickly analyze medical data, automate clerical tasks, and give useful information to support personalized and timely care. Companies like Tempus and Simbo AI show how AI can help in cancer treatment and office work. More doctors and healthcare groups are using AI, showing its growing role. Leaders in healthcare need to carefully plan AI use by checking compatibility, training staff, protecting data, and following rules to make sure patients stay safe and trust the system.
AI accelerates the discovery of novel targets, predicts treatment effectiveness, identifies life-saving clinical trials, and diagnoses multiple diseases earlier, enhancing personalized patient care through advanced data analysis and algorithmic insights.
Tempus provides an AI-enabled assistant that helps physicians make more informed treatment decisions by analyzing multimodal real-world data and identifying personalized therapy options.
Tempus supports pharmaceutical and biotech companies with AI-driven drug development, leveraging extensive molecular profiling, clinical data integration, and algorithmic models to optimize therapeutic strategies.
The xT Platform combines molecular profiling with clinical data to identify targeted therapies and clinical trials, outperforming tumor-only DNA panel tests by using paired tumor/normal plus transcriptome sequencing.
It uses neural-network-based, high-throughput drug assays with light-microscopy to predict patient-specific drug response heterogeneity across various solid cancers, improving treatment personalization.
Liquid biopsy assays complement tissue genotyping by detecting actionable variants that might be missed otherwise, providing a more comprehensive molecular and clinical profiling for patients.
~65% of US Academic Medical Centers and over 50% of US oncologists are connected to Tempus, enabling wide adoption of AI-powered sequencing, clinical trial matching, and research partnerships.
Tempus One is an AI-enabled clinical assistant integrated into the Electronic Health Record (EHR) system, allowing custom query agents to maximize workflow efficiency and streamline access to patient data.
xM is a liquid biopsy assay designed to monitor molecular response to immune-checkpoint inhibitor therapy in advanced solid tumors, offering real-time treatment response assessment.
Fuses combines Tempus’ proprietary datasets and machine learning to build the largest diagnostic platform, generating AI-driven insights and providing physicians a comprehensive suite of algorithmic tests for precision medicine.
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