Monitor Infection Control in Hospitals with Surface Hygiene Monitoring

Flow Diagram

Technology Stack

Problem Statement

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Increasing cases of hospital-acquired infections (HAIs): Hospital-acquired infections are a significant problem that can lead to serious complications, longer hospital stays, and increased costs. Inadequate cleaning and disinfection of surfaces can contribute to the spread of HAIs.

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Limited staff resources: Hospitals have limited staff resources that can be allocated to cleaning and disinfecting surfaces. Manual cleaning processes can be time-consuming and labour-intensive, which can make it difficult to keep up with the demand for cleaning in a busy hospital environment.

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Inconsistent cleaning practices: Hospitals may have inconsistent cleaning practices that can lead to uneven cleaning of surfaces. Different cleaning staff may have different approaches to cleaning, which can lead to inconsistencies in the cleaning process.

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Inadequate monitoring of cleaning activities: Hospitals may not have an adequate system to monitor cleaning activities. This limitation can make it difficult to track and evaluate the effectiveness of cleaning activities, which can lead to missed spots and an increased risk of HAIs.

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High cost of infection control: Infection control is essential for preventing the spread of infections in hospitals, but it can be costly. Manual cleaning processes can be time-consuming and require significant labour, and the cost of disinfectants, cleaning equipment, and other supplies can add up, making infection control an expensive process.

Solution Overview

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OptiSol has worked with the client in building an AI based surface hygiene monitoring solution to help hospitals control hospital acquired infections.

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The solution utilises computer vision technology, which involves training a machine learning model to recognise different types of surfaces in a hospital setting, such as floors, walls, and medical equipment.

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Cameras are placed strategically throughout the hospital to capture images of these surfaces in real-time.

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The images are then analysed using the trained machine learning model to detect areas that require cleaning and disinfection.

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The system generates alerts and notifications to cleaning staff to indicate which areas need attention, and the severity of the issue.

Business Impact

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Surface hygiene monitoring using computer vision has reduced the risk of hospital-acquired infections, leading to improved patient outcomes, increased patient satisfaction, and reduced healthcare costs.

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By adopting surface hygiene monitoring using computer vision, hospitals have improved their operational efficiency, allowing them to allocate staff resources to other critical areas of healthcare delivery and reducing the time and labour required for manual cleaning processes.

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Hospitals that have adopted surface hygiene monitoring using computer vision have enhanced their reputation and brand image by demonstrating a commitment to patient safety and infection control, leading to increased patient confidence, better community relations, and improved recruitment and retention of healthcare professionals.

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The adoption of surface hygiene monitoring using computer vision has resulted in cost savings for Relhospitals by making the cleaning process more efficient and effective, leading to cost savings in labour, supplies, and other operational expenses.

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Smart hospitals that have adopted surface hygiene monitoring using computer vision have gained a competitive advantage by offering a higher level of infection control than other hospitals, leading to increased patient referrals, improved financial performance, and a stronger market position.

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