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Clinical Decision Support System Enhancement & Modernization

rolemodelsoftware.com

Health & Fitness

Information technology

Medical

Challenges in Oncology Treatment Planning

Oncologists face the challenge of efficiently analyzing vast amounts of treatment-related data from EHRs to create data-validated, personalized treatment plans within limited timeframes. The lack of readily available, actionable insights can lead to overlooked effective and lower-cost treatment options, and difficulty adapting to evolving regulatory standards like FHIR. Manual data entry and analysis are time-consuming and prone to error.

About the Client

A medical technology company specializing in clinical decision support software for oncologists, focused on improving patient care through data-driven treatment planning.

Project Goals

Enhance the existing clinical decision support software to streamline treatment plan development.
Improve data extraction and integration from diverse EHR systems.
Ensure compliance with evolving FHIR standards for interoperability.
Expand the software's capabilities to support value-based care regulations.
Maintain a user-centered design approach to ensure ease of use and adoption by oncologists.
Facilitate rapid iteration and release of new features and updates.

System Functionality

Decision Graph Engine: Matching patient observations with NCCN treatment recommendations.
EHR Data Extraction: Automated extraction of relevant patient data from EHR systems.
FHIR Compliance: Adherence to Fast Healthcare Interoperability Resources standards.
Customizable Treatment Plans: Ability to tailor treatment plans based on individual patient characteristics.
Regulatory Reporting: Generation of reports to support value-based care initiatives.
User-Friendly Interface: Intuitive and user-centered design for ease of use.

Technology Stack

Agile Development Methodologies (Scrum/Kanban)

User-Centered Design (UCD) Practices

Test-Driven Development (TDD)

Cloud-based infrastructure (e.g., AWS, Azure, GCP)

FHIR API integration

Database technology suitable for large healthcare datasets

External System Integrations

Electronic Health Record (EHR) systems (e.g., Epic, Cerner)
Medicare/Medicaid APIs for value-based care reporting

Non-Functional Requirements

Scalability: Ability to handle a large and growing number of users and data volumes.
Performance: Fast response times and efficient data processing.
Security: HIPAA compliance and robust data security measures.
Reliability: High system uptime and availability.
Maintainability: Code should be well-documented and easy to maintain.

Expected Business Impact

This project is expected to significantly improve the efficiency and effectiveness of oncology treatment planning, leading to better patient outcomes, reduced healthcare costs, and enhanced compliance with regulatory requirements. The nationwide adoption of the software demonstrates a significant market need and the potential for substantial return on investment. It will also solidify Proventys' position as a leader in clinical decision support for oncology.