The role of context-specific proprietary data combined with advanced foundation models in improving AI decision-making accuracy in healthcare settings

In the United States, healthcare organizations are increasingly adopting AI technologies to support clinical decisions and administrative workflows.

Among these technologies, the combination of advanced foundation models with context-specific proprietary data plays an important role in improving the accuracy and relevance of AI decision-making within healthcare environments.

This article focuses on how this combination improves healthcare AI performance in the U.S., especially for medical practice administrators, owners, and IT managers who manage and integrate AI in healthcare facilities.
We also include a section on AI and workflow automations relevant to these roles.

Advanced Foundation Models: What They Are and Why They Matter

Foundation models are large AI systems trained on huge amounts of general data.

These models can understand language patterns, analyze images, and solve problems across many tasks.

Examples include transformer-based language models and big neural networks.

Such models have broad abilities but do not start with detailed knowledge about specific areas like healthcare.

Healthcare needs decisions based on detailed patient data, clinical rules, and medical history.

Generic AI models without customization can miss important details needed for accurate healthcare decisions.

This is why foundation models need to be combined with proprietary data, which is internal healthcare data unique to a medical practice, hospital, or health system.

The Importance of Context-Specific Proprietary Data

Proprietary data is information collected and owned by a specific healthcare organization.

Examples include electronic health records (EHR), lab results, imaging data, insurance claims, patient feedback, and operational data.

This data shows the unique patient population, treatment methods, and workflows of each organization.

When combined with foundation models, this data helps AI better match the specific clinical and operational setting.

AWS’s VP for Agentic AI, Swami Sivasubramanian, says using proprietary data with well-chosen foundation models improves AI’s reasoning and decision-making.

This leads to results that are more relevant, accurate, and efficient in healthcare.

Using detailed proprietary data lets AI understand patient histories, predict risks, and support clinical decisions tailored to certain patient groups.

For example, in a clinic serving one type of community, AI can prioritize care suggestions based on local health trends, insurance patterns, and social factors found in the data.

How AI Decision-Making Accuracy Improves with this Combination

• Greater Precision in Diagnostics and Treatment Planning

  AI models trained on generic data often can’t interpret complex medical histories well.

  Adding proprietary data helps AI give diagnoses and treatment plans that fit the patient’s own situation.

  Agentic AI includes systems that work on their own and adapt using different types of data—text, images, genetics—to improve their answers step by step.

  For example, including radiology reports and lab results helps AI spot small changes over time that might be missed otherwise.

  Next-generation agentic AI learns from its mistakes and gets better, which lowers errors and helps patients.

• Context-Aware Clinical Decision Support

  AI tools for clinical decisions work better when connected to real hospital data.

  Proprietary data helps AI understand hospital rules, care steps, and past treatment results.

  This is important for precise medicine where treatment needs to be personal.

  Agentic AI using foundation models and proprietary data can update suggestions based on new guidelines and patient details.

  This makes care safer and more effective.

• Enhanced Operational Efficiency

  Besides patient care, AI with proprietary operational data helps improve workflow and resource use.

  Hospitals and clinics can use AI to predict patient visits, manage staff, and plan procedures better.

  Data about appointment types, no-shows, and resource use gives AI the knowledge to suggest specific improvements.

  AWS’s AgentCore platform helps connect AI with hospital systems while managing complex tasks and memory for AI agents.

AI and Workflow Automation in Healthcare Settings

Automation is now important in healthcare offices, especially in clinics and hospitals trying to improve front-office work.

Tasks like scheduling, answering patient questions, billing, and paperwork take a lot of staff time.

Using AI automation can reduce this workload while keeping good patient service.

Simbo AI, a company that focuses on front-office phone automation and answering services, uses AI to handle routine patient calls.

Their AI can book appointments, answer common questions, and send urgent issues to staff.

This cuts down wait times and improves patient experience.

How AI Automations Align with the Role of Proprietary Data and Foundation Models:

• Accurate Patient Verification
  AI agents securely use proprietary patient data to confirm identity during calls. This cuts errors and speeds up calls.
• Customized Call Responses
  With data about patients and the practice, AI gives relevant answers about services, insurance, or doctor availability.
• Adaptive Learning from Data
  By combining foundation models with real-time feedback and proprietary data, AI improves its accuracy over time. It adjusts for seasonal changes, policies, or sudden healthcare needs.
• Integration with Existing Systems
  AI automation works smoothly with electronic health records and scheduling systems using secure connections. This supports full automation from calls to appointment confirmations.

This kind of automation lowers front-office work, cuts costs, and improves patient satisfaction by giving quick and accurate replies.

Overcoming Challenges in AI Use in Healthcare

Even with benefits, there are challenges in using AI in healthcare.

Ethical use of patient data, privacy, and regulations are very important.

Nalan Karunanayake, author of Informatics and Health, says deploying agentic AI needs strong governance to ensure transparency and data protection.

U.S. organizations must follow HIPAA and other rules to keep patient data private.

Using safe platforms like AWS’s AgentCore keeps AI sessions separate, offers strict permission controls, and monitors activity.

This protects sensitive healthcare data while allowing AI to work well.

Examples of AI Adoption in Healthcare Using Proprietary Data and Foundation Models

Some organizations have led the way in using AI with proprietary data to improve healthcare in the U.S. and worldwide.

• AstraZeneca has sped up healthcare research by using agentic AI that mixes clinical trial data with foundation models. This helps make drugs faster and find patient groups.
• Innovaccer, a U.S. healthcare data platform company, uses agentic AI with AWS Bedrock AgentCore. They turn administrative and clinical data into useful insights to help healthcare decisions.

These examples show how big healthcare companies gain by combining foundation models with proprietary data. This creates AI agents that work safely and handle complex systems.

Key Features Required for Effective AI Integration in Healthcare Practices

Medical administrators, owners, and IT managers should focus on these features for successful AI use:

• Agility and Adaptability
  Systems should be flexible to allow quick changes based on feedback. This helps AI stay useful in fast-changing healthcare.
• Secure Identity and Access Controls
  Detailed permissions and session separation protect patient privacy and reduce data risks.
• Memory Management
  Systems that handle short- and long-term patient data improve continuity in AI interactions, especially for multi-step clinical tasks.
• Real-Time Observability
  Dashboards and monitoring provide transparency for compliance, troubleshooting, and performance reviews.
• Cost-Efficient Data Storage
  Storing AI knowledge vectors in services like Amazon S3 Vectors cuts costs by up to 90%, making AI analysis cheaper.
• Model Customization
  Customizing foundation models with specific proprietary data improves decision accuracy for local health needs and patient groups.

Implications for Healthcare Practices Across the United States

Healthcare providers in the U.S. face pressure to give better care while controlling costs and managing complex administration.

AI systems that combine advanced foundation models with proprietary data offer ways to:

• Improve diagnosis accuracy and patient safety
• Personalize care based on local data
• Automate routine administrative tasks, helping staff efficiency
• Streamline scheduling and patient communication using phone automation
• Meet strict privacy and regulatory rules effectively

For administrators and IT leaders, investing in scalable, secure AI systems with strong customization will affect overall success and patient satisfaction.

Medical practices and hospitals that start using these AI solutions early can keep improving and better handle health challenges for their patients.

By focusing on their unique data and using advanced AI models, healthcare organizations in the U.S. can make better decisions, improve workflows, and provide care with more confidence.