Healthcare
Transforming Healthcare with Human-Guided AI
Healthcare organizations generate a vast array of unstructured data, from clinical notes and medical images to research papers and patient feedback.
This data holds immense potential for improving patient care and advancing medical knowledge, but it also presents significant challenges.
Traditional AI solutions often struggle to fully grasp the nuance and context of healthcare data, leading to inaccurate or incomplete insights. Moreover, without human oversight and validation, these insights may not align with the real-world needs and priorities of healthcare professionals.
To truly leverage the power of unstructured data, healthcare organizations need a solution that combines the capabilities of AI with the expertise and judgment of human experts.
Human-Guided AI for Healthcare
AgileDD offers a unique approach to healthcare data management and analysis, centered around the concept of human-guided AI. Our platform seamlessly integrates with your existing data systems and workflows, enabling you to:
Collaborate with AI:
Our intuitive interface allows healthcare professionals to work hand-in-hand with AI models, teaching them to understand the specific language, context, and requirements of your organization.
Validate and Refine Insights:
Human experts can review and validate the insights generated by AI, ensuring their accuracy, relevance, and alignment with clinical goals. This feedback is then used to continuously improve the performance of our AI models.
Customize and Adapt:
Our platform can be easily customized to meet the unique needs of your organization, with workflows and tools tailored to the specific requirements of different healthcare domains, from clinical care to research and administration.
Transforming Healthcare Delivery for Stakeholders at Every Level
AgileDD’s human-guided AI platform delivers significant value to all stakeholders in the healthcare ecosystem, enabling them to collaborate, innovate, and drive better outcomes.
Frequently Asked Questions
Can AgileDD capture information from diverse healthcare documents (i.e., office visit summaries, test results, claims), including handwritten ones, and enable users to chat with them?
Then, you can define LLM prompts or train highly specialized AI models to capture a variety of data from the documents to populate a dashboard or a database.
We also provide a chat interface that allows you to have a conversation with your entire set of documents using our innovative retrieval augmented generation (RAG) approach that can be used in conjunction with search and filtering of key metadata values.
How is AgileDD's RAG approach different, and why is it effective?
Therefore, we have designed an approach to RAG that combines search and filtering with RAG methods. From our search interface you can narrow down the documents that you need to chat with either with keywords or metadata values, and then chat with the resulting documents.
AgileDD makes possible the use of commercial and very large LLM such as ChatGPT or Gemini only available on external APIs or opensource LLM as Mistral, Llama, Falcon or Bloom which can be installed locally and eventually locally fine-tuned.
How can information captured by AgileDD be integrated into healthcare databases?
The APIs can be used to extract the information from documents using existing models or to access pre-captured information and to output into structured formats such as CSV, JSON formats or SQL input records.
How can the quality of AI outputs from AgileDD be controlled before using them in decision-making or analytics?
The AgileDD platform was originally built just for this purpose.
Our intuitive UI allows you do interact with your documents and the outputs of AI.
You can apply AI to highlight various attributes (for example, symptoms or medications), and then review and correct any mistakes in the UI.
Your input is saved by AgileDD and becomes a record in the AgileDD for future reference or analytics.
Enhance clinical decision-making by providing physicians with timely, accurate, and contextually relevant insights from unstructured patient data.
Streamline care coordination and communication among providers, enabling seamless sharing of patient information and treatment plans.
Optimize resource allocation and operational efficiency by identifying opportunities for process improvement and cost reduction.
Improve risk assessment and stratification by leveraging AI-powered analytics to identify high-risk patients and predict potential health issues.
Streamline claims processing and prior authorization by automating data extraction and analysis tasks, reducing manual errors and delays.
Enable more effective population health management by identifying trends and patterns in patient data that can inform targeted interventions and wellness programs.
Reduce administrative burden by automating data entry and documentation tasks, allowing physicians to focus on patient care.
Provide clinical decision support by delivering evidence-based recommendations and insights at the point of care.
Facilitate research and discovery by enabling physicians to easily search and analyze large volumes of clinical data, identifying new treatment options and best practices.
Enhance patient experience by providing personalized, data-driven recommendations and resources tailored to each patient’s unique needs and preferences.
Optimize resource utilization and capacity planning by leveraging AI-powered forecasting and simulation tools.
Enhance supply chain management by predicting demand for medical supplies and medications, reducing waste, and ensuring availability.
Improve revenue cycle management by automating coding and billing processes, reducing errors and denials, and accelerating reimbursement.