SOAP notes were created by Dr. Lawrence Weed in 1968 to make clinical documentation clearer and organized. Today, SOAP notes are widely used in the U.S. healthcare system as a basic way to record patient visits. They divide information into four parts:
Good SOAP notes help health teams communicate, keep care consistent, and meet legal rules. They are also needed for billing by showing what services were provided.
Despite their value, writing SOAP notes by hand has many problems. Nurses spend 25 to 50 percent of their work time on documentation. Doctors may spend up to 15.5 hours a week completing notes and paperwork. This reduces the time they can spend with patients and causes tiredness. Mistakes in notes cause 10 to 20 percent of medical malpractice lawsuits in the U.S. These errors are often from incomplete or wrong information.
Handwriting SOAP notes takes a lot of time, can cause mistakes, and adds to burnout. With fewer staff and more complex patients, these issues hurt patient care and how well organizations run.
Artificial intelligence (AI), using natural language processing and machine learning, can change how SOAP notes are made. AI systems pull data from voice recordings, interview texts, and electronic health records. They then fill in SOAP notes with little human help.
Studies and real cases show benefits of AI-created SOAP notes:
John Snow Labs uses Medical Large Language Models with Amazon Web Services (AWS) tools like HealthLake, SageMaker, and Bedrock to make SOAP notes automatically. Their AI pulls information from recorded visits and electronic records, creating SOAP notes with real-time accuracy checks. Accuracy is over 95%, and data stays HIPAA-compliant.
This AI is used in clinics like oncology, primary care, and precision medicine. In oncology, it helps doctors understand complex test results faster and make quicker treatment decisions.
MetroHealth System in Cleveland uses Pieces Technologies’ AI platform to improve documentation. The platform makes reports such as progress notes and discharge summaries inside existing EHRs. Their AI summaries follow SOAP note rules and help doctors find key patient information fast.
Short summaries and patient history views help manage patient data in clinics and hospitals. Pieces reports saving doctors 40 to 50 minutes and case managers about 60 minutes daily. Certified doctors check the AI’s work to make sure notes are accurate and safe.
MetroHealth and Pieces also work on research supported by NIH and the National Cancer Institute on using conversational AI in cancer care.
Leveled Platforms, Inc’s Medical Scribe app runs on Apple devices and uses speech recognition to write down doctor-patient talks in real-time. It creates SOAP notes automatically and offers templates for different medical fields. The app connects with EHRs to reduce paperwork.
Its cloud storage lets doctors access and change notes anywhere. Security measures follow HIPAA rules to protect patient information.
Sunoh uses silent listening technology to record patient-provider talks. It turns conversations into structured SOAP and progress notes that fit easily into many EHR systems. The app works on phones and tablets and is used by over 90,000 U.S. healthcare workers.
This tool decreases paperwork and speeds up tasks like order entry. It also lets users review and adjust notes before saving them, which builds trust in the AI’s work.
Using AI for SOAP note creation shows how automation can change clinical work. Automation helps in more areas than just writing notes.
For healthcare leaders in the U.S., adding AI SOAP notes to current EHRs offers many benefits:
Adding AI-created SOAP notes into current EHR systems offers a clear way for U.S. healthcare providers to improve work and documentation. The technology cuts time spent on paperwork, makes records more accurate, and follows privacy rules. Examples from groups like John Snow Labs with AWS, MetroHealth with Pieces Technologies, and apps like Medical Scribe and Sunoh show real benefits for doctors and administrators.
Healthcare leaders focused on efficiency and good patient care should think about using AI tools for documentation. As healthcare needs grow, using AI SOAP notes can help reduce workloads, keep data correct, and improve care processes across the United States.
SOAP notes, developed by Dr. Lawrence Weed in 1968, are a standardized method for documenting patient encounters. They organize clinical information into Subjective, Objective, Assessment, and Plan sections, aiding clear communication among healthcare providers and ensuring structured, consistent clinical documentation essential for patient care and legal compliance.
Manual SOAP note creation is time-consuming, with nurses spending up to 50% of shifts on documentation and physicians dedicating 15.5 hours weekly on paperwork. It is also prone to errors, contributing to 10–20% of malpractice lawsuits, increases clinician burnout, and presents legal and compliance risks due to inaccurate records.
AI agents use NLP and machine learning to extract data from voice recordings, transcripts, and EHRs. The system converts raw inputs into structured SOAP notes by identifying and populating the Subjective, Objective, Assessment, and Plan sections, with real-time validation to minimize errors and seamless integration with existing EHR systems.
AWS HealthLake organizes unstructured clinical data into structured formats while maintaining compliance and security. Amazon SageMaker deploys scalable machine learning models for real-time or batch processing. Amazon Bedrock enables AI workflow management for autonomous agents that integrate with John Snow Labs’ Medical LLMs, ensuring accurate and efficient AI-generated documentation.
Automation reduces documentation time, freeing clinicians to focus more on patient care, which enhances interaction quality. It decreases administrative burden, reducing clinician burnout and improving work-life balance. The system also ensures timely, accurate documentation, reducing clinical errors and improving patient safety and outcomes.
AI models use context-driven NLP and real-time validation to continuously cross-check data accuracy and completeness, achieving over 95% accuracy in clinical settings. Models are built on peer-reviewed research and real-world cases, providing reliable and trustworthy documentation for healthcare professionals.
The system integrates with AWS HealthLake to ensure HIPAA compliance, securing personal health information. Data is anonymized automatically during processing to protect patient identities while allowing the AI to learn and generate insights without compromising privacy.
They seamlessly connect to major EHR platforms like Epic and Cerner without requiring retraining or workflow overhaul. For other systems, flexible APIs enable easy integration, ensuring minimal disruption and rapid adoption by healthcare professionals.
In oncology, automation reduces time spent reconciling complex imaging and reports, enabling quicker treatment decisions. In primary care, it increases clinic productivity by allowing clinicians to see more patients. It also aids precision medicine by facilitating rapid data analysis and tracking longitudinal patient information for personalized care.
It addresses inefficiencies and errors of manual documentation, reduces clinician burnout, ensures accurate and timely notes to avoid legal risks, maintains privacy compliance, and supports scalable data handling necessary for growing patient volumes and complex clinical workflows.
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