Clinical documentation is very important for good patient care. It helps keep accurate medical records and makes billing and compliance easier. But many healthcare workers spend too much time on documentation. This means they have less time to see patients. AI technology can help by automating note-taking, entering data, and some other tasks.
Healthcare settings in the U.S. are very different. They vary in size, specialty, and IT setup. Clinics, practices with many providers, outpatient centers, and hospitals each have different needs and ways of working. AI tools that try to fit everyone may not work well everywhere. They may miss local rules, special terms, or laws like HIPAA and payer rules.
This is why customization using SDKs is needed. SDKs help developers and tech teams make AI models and workflows that fit their own clinical documentation needs. Using open-source tools or cloud AI platforms, practices can add special vocabulary, automate common tasks, and connect AI with their existing electronic medical record (EMR) systems.
For example, Canvas Medical’s Hyperscribe is an open-source AI scribing tool that works with customizable EMRs. It uses a technique called “chaining,” where AI agents perform tasks in order such as documentation, placing orders, scheduling patients, and managing referrals. Healthcare IT teams can use SDKs to adjust Hyperscribe to their workflows or types of patients while keeping safety through built-in limits. Because Hyperscribe is open-source, health systems can see how it works, control it, and change it as they need, which is important for U.S. healthcare’s varied rules and operations.
Healthcare organizations grow in many ways. They may expand their practices, join networks, or see more patients. This means their documentation tools must grow too. Many traditional AI tools work okay in small trials but may fail when used on a larger scale because they are not flexible.
SDKs let IT managers build AI systems that are modular and scalable. For instance, NVIDIA’s NeMo platform offers different parts to handle the full AI process from data preparation and model training to monitoring and learning. NeMo can be run on cloud servers, local servers, or both. This is useful for healthcare providers who want to keep data private but still need strong computing power.
NeMo breaks down AI tasks into microservices. These are small, independent parts that can be updated or scaled easily without changing the whole system. This modular setup works well for healthcare networks or groups with many specialties who need AI that stays consistent but can be customized for different workflows.
NeMo also has a data flywheel automation. This means it uses real-world data all the time to make the AI more accurate. This is important since healthcare rules and doctor preferences often change. The AI learns and improves without needing full retraining, making scaling cheaper and easier.
Several AI platforms help healthcare providers build custom AI documentation tools. They offer SDKs with development tools, APIs, and no-code options so medical IT staff can change the tools to fit their needs.
Google Cloud’s Vertex AI Platform is one example. It provides a system to build, launch, and manage AI models, including ones for healthcare. Its no-code tool, Agent Builder, lets both technical and non-technical healthcare workers create AI agents tailored to their documentation needs. This helps small and mid-sized practices that do not have many developers but want to use advanced AI.
Vertex AI supports over 200 base models and lets users fine-tune them. This helps healthcare systems adjust AI so it summarizes, classifies, and extracts data accurately for clinical use. This is useful because rules and payer demands differ by state and specialty. The platform works with any cloud and charges by use, fitting well with medical practice budgets.
PwC’s AI Agent Operating System (agent OS) is another platform. It connects many AI agents across cloud and local systems to create flexible workflows. A healthcare client used it to automate cancer care processes, lowering staff work by almost 30% and improving clinical insights by about 50%. This shows how managing AI agents with SDKs and unified systems can change care documentation and help with decisions.
PwC’s agent OS uses drag-and-drop workflow tools. These let healthcare users build AI workflows without deep coding skills. It also integrates with Oracle, Salesforce, and Microsoft Azure. This helps AI work with many healthcare IT systems in the U.S.
AI tools for clinical documentation do more than change speech to text or take notes. They can also automate complex workflows. This lets healthcare staff simplify both administrative and clinical tasks, needing less manual work. Automation helps make practices run better.
Canvas Medical’s Hyperscribe uses AI agents that work in sequence to handle many tasks at once during a patient visit. For example, the AI documents the visit, prepares lab or imaging orders, schedules follow-ups, and sends referrals while checking allergies or drug problems. This automation lowers doctor burnout from too much paperwork and cuts errors from manual order taking.
PwC’s AI agent OS also enables AI workflows to handle things like insurance checks, prescription steps, and follow-up calls. This can reduce wait times in clinical call centers by about 25% and cut call transfers by 60%. This makes patients happier and helps clinics run smoother.
NVIDIA NeMo supports reinforcement learning, which means it keeps learning from healthcare work to improve AI actions and change as clinical rules update. Safety controls and performance checks help make sure AI automation follows healthcare rules and keeps patients safe.
AI automation helps staff with routine and repeated tasks. This lets doctors spend more time with patients and less time on paperwork. For medical administrators and IT teams, SDKs let automation be customized. It works for one specialty, a large multi-site group, or a big health network in the U.S.
Medical practice leaders in the U.S. should pick AI documentation tools that support SDK customization and scalability. This helps make sure AI fits with current workflows instead of forcing the practice to change how they work.
Open-source tools like Canvas Medical’s Hyperscribe make AI clear and safe. They avoid worries about “black box” AI, which some U.S. healthcare rules watch carefully. Open-source also helps with audits, edits, and following HIPAA and federal laws.
Cloud platforms such as Google Cloud with Vertex AI provide ways to host and manage AI models that can grow. They also let data be processed locally to follow state data laws. No-code tools lower tech barriers, helping smaller practices use AI easily.
Enterprise platforms like PwC’s agent OS and NVIDIA NeMo offer strong modular systems for big health systems or networks that span many states. They help organize many AI agents and improve clinical workflows efficiently.
Software development kits help healthcare providers:
As AI keeps improving in healthcare, using SDKs and scalable systems will be important. These tools will help AI fit the needs of many types of healthcare practices across the U.S. Medical administrators who invest in them will be better prepared for long-term success and better patient care.
In short, AI-assisted clinical documentation platforms with SDKs give healthcare organizations the tools to build custom workflows and grow operations effectively. Using these technologies helps reduce paperwork, improve accuracy, and automate complex administrative tasks. This supports delivering good clinical care.
Hyperscribe is an open-source AI-enabled clinical copilot developed by Canvas Medical, designed to assist clinicians by using ambient audio to document clinical notes and execute tasks within an EMR.
Hyperscribe captures ambient audio during patient encounters to continuously update patient records and clinical documentation in real time, providing clinicians with a live view of notes and orders.
Unlike traditional scribes, Hyperscribe not only drafts documentation but also executes clinical tasks by collaborating with multiple AI agents, automating processes such as scheduling, referrals, and prescription drafting with clinical safety guardrails.
Canvas uses built-in organizational and system-driven guardrails, human-in-the-loop oversight, safety logic, and transparent open-source code with evaluation benchmarks to maintain high AI governance standards and minimize risks.
‘Chaining’ enables multiple AI agents to collaborate by passing outputs from one task to trigger subsequent actions, streamlining workflows like scheduling, insurance verification, and medication safety within clinical encounters.
Hyperscribe is highly customizable via the Canvas SDK, allowing integration with any large language model and enabling organizations to tailor the AI copilot to specific workflows or clinical use cases through its open-source codebase.
By continuously updating patient records in real time and automating documentation and orders, Hyperscribe significantly reduces clerical burden, improves documentation accuracy, and allows clinicians to focus more on patient care.
It accesses the full medical record and integrates ongoing data updates to ensure context-aware documentation while observing contraindications, allergies, and safety protocols to prevent harm when drafting orders.
Hyperscribe acts as an advanced AI agent within the customizable Canvas EMR platform, augmenting provider workflows by enabling AI agents to collaborate and automate clinical and administrative tasks effectively.
Transparency and benchmarking allow healthcare organizations to understand AI behavior, ensure accountability, enable customizations, and build trust in AI tools, addressing risks and liabilities associated with clinical AI deployments.
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