Overview
A new partnership between C2-Ai and Netcall is set to enhance collaboration among the NHS, local councils, and housing organizations. This initiative aims to improve understanding of the risks and needs of individuals in care, ultimately reducing hospital demand.
Key Objectives
- Facilitate coordinated decisions to ease demand on NHS services.
- Prevent avoidable harm and emergency department visits.
- Target community interventions effectively.
Partnership Details
The collaboration combines the strengths of:
- C2-Ai: A clinical analytics specialist with a focus on AI-driven insights.
- Netcall: A software company enhancing productivity in healthcare.
Data Integration for Better Outcomes
This partnership will allow for:
- Combining multi-sector data to create detailed risk profiles for individuals.
- Utilizing AI models to identify high-risk patients waiting for treatment.
Practical Applications
For instance, a patient on a waiting list for COPD treatment who reports worsening symptoms through various channels could be flagged as high-risk. This early identification allows:
- NHS teams to prioritize medical interventions.
- Housing and social care teams to address underlying issues, such as poor living conditions.
- Community pharmacists to take proactive measures to maintain patient health.
Statements from Leaders
John Clarke from Netcall emphasized the importance of viewing patients holistically, stating:
“By combining up-to-date information from various services, we can create a comprehensive risk profile that allows for better decision-making.”
Dr. Mark Ratnarajah from C2-Ai highlighted the potential for a shift towards a more person-centric approach:
“We aim to enable providers to keep individuals safe and well, preventing avoidable costs and hospital admissions.”
Future Implications
This partnership aligns with the government’s goals for:
- Effective use of digital technologies.
- Preventing health deterioration and rising demand.
- Supporting patients within their communities.
It is anticipated that this model could be relevant to healthcare systems globally.