Health Forward Grant

Introduction

We are thrilled to announce that the HSG Healthspan Lab has been awarded a new grant to develop an advanced AI-driven system for nutritional monitoring in healthcare settings in collaboration with the implementation partner Alpinasana AG. The team includes distinguished members such as Prof. Dr. Dietmar Grichnik, Prof. Dr. Tobias Kowatsch, Dr. Raban Iten, and Dr. Robert Schreiber. We are excited to work with our first pilot partners, who will play a crucial role in this important initiative:

• Stadtspital Waid, represented by Karin Blum, Prof. Dr. Heike Bischoff-Ferrari, and Enrico Dahl
• Kantonsspital St.Gallen, represented by Dr. Stefan Bilz, Dr. Sarah Sigrist, Rahel Giger, and Salome Lex
• Kantonsspital Frauenfeld, represented by Prof. Dr. Pascal Probst, Dr. Kathrin Hauser, and Dominique Scherrer

This project aims to tackle the critical issue of malnutrition among hospital patients by leveraging technology to enhance the accuracy and efficiency of nutritional assessments. Food is one of the last and greatest joys in life, and improving nutritional care can significantly boost patients' well-being, life quality, and health outcomes. Malnutrition affects 20%-37.7% of hospitalized patients and up to 54% of the elderly in care homes, leading to longer hospital stays, higher readmission rates, and increased healthcare costs. By ensuring that patients receive the nutrition they need, we strive to make their hospital experience more comfortable and enjoyable, ultimately improving their recovery and quality of life.

Objectives

1. Enhance Nutritional Monitoring: Develop an AI-based system to monitor and assess the nutritional intake of hospital patients accurately.
2. Improve Patient Outcomes: Ensure patients receive appropriate nutritional support, leading to better life quality, health outcomes and reduced hospital stays.
3. Reduce Healthcare Costs: Decrease the financial burden on hospitals by minimizing malnutrition-related complications.

Approach

Our innovative approach integrates advanced 3D imaging technology with state-of-the-art AI techniques to provide precise and comprehensive nutritional monitoring. The system will identify and quantify food items on patients' trays, even in complex and mixed food scenarios, and calculate their nutritional content using data from hospital recipes and established nutrition databases.

Scientific and Social Contributions

• Scientific Advancements: This project will push the boundaries of AI and 3D imaging technology, contributing to the fields of computer vision, machine learning, and healthcare informatics. The development of an accurate food recognition and volume estimation system will set a new standard in nutritional monitoring technology.
• Social Impact: By improving nutritional monitoring, our system aims to enhance the quality of life for hospitalized patients, particularly the elderly, by ensuring they receive adequate nutrition. This will lead to better health outcomes, life quality, shorter hospital stays, and reduced healthcare costs. Additionally, the project addresses a critical public health issue, contributing to the overall improvement of healthcare services and patient care.

We are excited about the potential of this project to revolutionize nutritional monitoring in healthcare, ultimately enhancing patient care and reducing healthcare costs. Stay tuned for updates on our progress and findings.

Photo source: iStock, Svitlana Hulko