Leading Change in Malnutrition: Improving RDN Value by Leveraging Data Analytics in Long Term Care

Malnutrition is frequently undiagnosed and untreated which translates to a substantial loss of reimbursement. Specifically, malnutrition is diagnosed in less than 12% of malnourished residents. This represents an important gap in care that should be addressed to improve resident health outcomes as RDNs move to a value-based care model. Real time dashboards can gather data across medical records to efficiently capture and manage risk.

In this session, we present how dashboard technology can aggregate data and support clinicians to identify and intervene against malnutrition. This creates a more efficient workflow for better standards of practice. Attendees will experience best practices and use cases that include dynamic tracking of intake, weights, and wounds. Dietitian leaders and care providers can efficiently assess status of related factors, easily filter the data by level of risk, and additional critical criteria.

With real time data visualization, RDNs are able to provide timely, optimal interventions and modernize their impact in long term care. Providing a global view across an organization, analytics tools that address malnutrition allow RDNs and interdisciplinary leaders to collaborate effectively. The integration of analytics with current practice empowers clinicians to consistently be proactive and drive policy change.

Planned with the Nutrition Informatics Dietetic Practice Group

CPE Level: Level 2 (intermediate knowledge/experience)
CPEU: 1.5

Performance Indicators

• 4.2.6 Integrates relevant information with previous learning, experience, professional knowledge, and current practice models.
• 5.2.1 Integrates technology platforms with other internal and external services (e.g., risk management, disease management, data tracking and reporting).
• 7.4.6 Analyzes data to identify trends and improvements and to determine cost-effectiveness of nutrition-related clinical care, or to support the delivery of health, food and nutrition services.

Learning Objectives

• Explain how analytics in relation to reimbursement in long term care can improve knowledge on malnutrition identification.
• Demonstrate the importance of capturing risks related to malnutrition in real time using dashboards that correlate critical data.
• Outline the benefits of technology using analytics in managing nutrition.

Moderator

• Sharon Solomon

Speakers

• Amy Wootton
• D’Jeanne Florence
• Joy Herring