Doctors often rely on digital tools called clinical decision support systems (CDSS) to help diagnose and treat patients. Current systems, however, face big challenges. Some are “black box” AI models that make recommendations without explaining why—making doctors reluctant to trust them. Others require too much patient data upfront, which isn’t always available, or follow rigid step-by-step paths that don’t reflect how real medical care happens (where diagnosis and treatment often overlap). These systems also struggle with conflicting medical studies and don’t fit smoothly into busy clinical workflows.
The Invention
This patent introduces a modular, knowledge-based system built on decision trees designed to mirror real clinical practice. Key features include:
- Flexible structure: Instead of rigid “one-way” paths, the system alternates between data input, decision points, recommendations, and alerts—better reflecting how doctors work.
- Modular design: Specialized modules (like emergency care, intensive care, or rehabilitation) link together, so a patient’s case can seamlessly move across disciplines.
- Minimal data entry: Doctors can start with just one symptom (like chest pain), and the system requests more details only if needed.
- Built-in calculators: Tools that quickly process parameters (e.g., vital signs, lab results) to shorten decision paths.
- Evidence hierarchy: Decision trees are built strictly on trusted clinical guidelines and protocols, with clear ranking to resolve conflicting research.
The Inventive Step
Unlike existing systems, this design uniquely combines modular connectivity, symptom-first entry, and evidence-based decision trees, making it both clinically practical and trustworthy.
The Benefits
- Saves doctors’ time by reducing paperwork and unnecessary data entry.
- Increases reliability by showing transparent reasoning.
- Covers the full patient journey, from emergency through recovery.
The Broader Impact
This invention could improve healthcare worldwide by making medical decision support faster, safer, and easier to trust. It has the potential to reduce errors, improve patient outcomes, and standardize best practices across hospitals—ultimately advancing global healthcare delivery.