Legionella risk assessments can become costly, with some industries having to test the water systems and drinking water quality more often than the budget allows. Still, assessing whether water systems contain Legionella bacteria remains essential to ensure public health.
Government statistics show that 604 confirmed cases of Legionellosis, a serious lung infection, were reported in Wales and England in 2023, with 388 cases involving individuals over 60. The public health risk remains high, but using artificial intelligence (AI) may reduce the costs.
What Impacts the Cost of Legionella Risk Assessments
Evaluating the presence and risk of Legionella bacteria in freshwater environments, storage tanks, and cooling towers costs more for commercial properties than it does for privately owned premises. However, some factors driving the legionella risk assessment cost include:
- How far must engineers travel to assess the risk for Legionnaires disease.
- The size of the buildings or water systems on the property.
- The complexity of the systems being assessed for Legionella growth.
- The presence of unused systems, including where the water remains stagnant.
- The need for PAT testing using manual reading and monitoring processes.
- The number of premises and water systems that need to be checked.
- A need for environmental water samples from nearby locations.
How Can AI Effectively Manage Assessments and Reduce Costs
Artificial intelligence (AI) has made waves in the last few years, with the technology being able to transform UK public services, including the opportunity to reduce and control public health risks related to Legionella growth in water systems across the UK. A manual approach isn't working anymore. Artificial intelligence will reduce Legionella assessment costs by streamlining various processes.
AI Detects Legionella Bacteria Faster Than Manual Reading Processes
The authors declare that a new AI technology can detect Legionella bacteria with 99% accuracy in only 48 hours in a recent Health and Safety International article. The breakthrough from the Dubai Central Laboratory Department found that the technology could even quantify Legionella Pneumophila bacteria.
The Legionella Pneumophila bacteria is one of the pathogens connected to the dangerous spread of a more critical form of Legionnaires disease. The teams used a computational model to conduct various tests on environmental water samples using microbiological and chemical analysis processes.
The lab's equipment and testing methodologies are compliant with ISO/IEC 17025:2017 standards to ensure consumer safety and reduce the health risks of consumer products. Ultimately, the Dubai-based lab found an AI-powered model to improve operational efficiency and detect pathogens faster.
AI Technology Can Automate How You Monitor Water System Temperatures
Improper water temperatures are a primary cause of Legionella growth and are responsible for many incidents in the UK. However, many organisations are moving away from paper-based systems that help them monitor and assess water conditions with the help of artificial intelligence (AI) and sensors.
For example, one study confirmed how the use of artificial intelligence in public health prevention of Legionellosis improved the quality of drinking water with AI-driven technology that monitors water temperature inside water tanks. The AI model could successfully monitor and control the temperature.
Automating the manual approach of constantly monitoring and adjusting water temperatures in the tanks can reduce costs in the long run. Many organisations can safely use artificial intelligence models to monitor and control water temperatures remotely without the need for manual readings.
AI-Driven Technology Can Monitor More Than Water System Temperatures
Organisations and engineers must monitor and control various parameters in water systems to reduce the growth of Legionella bacteria, including the temperature, chemicals, and presence of pathogens. Real-time monitoring of large amounts of water is impossible and costly if you don't use AI-driven tools.
Fortunately, another study focused on the development of a machine learning model found that the real-time monitoring capabilities for public health were beyond impressive, while the tools and sensors reduced the cost of Legionella bacteria control. The ML tools were able to monitor various parameters.
The study focused on large plumbing systems that often cost a pretty penny to monitor and control, and the lack of technology makes real-time monitoring impossible. Instead, the AI-powered tools improved human health parameters and reduced on-site energy consumption, further cutting costs.
Automation and IoT Technology Contribute to Lower Long-Term Costs
Developing automation tools to monitor water systems in large buildings or multiple premises can certainly reduce the costs long-term by ensuring all monitoring processes are efficient and accurate. Automation is the answer, but sensors and other equipment with the technology ensure efficiency.
Many of the studies mentioned have used AI-driven software with sensors and other equipment as an Internet-of-Things (IoT) solution to ensure that data collection about the water parameters was accurately done in real-time in water tanks and other building plumbing and water systems.
For example, large-scale environmental monitoring of Legionella in hospitals took five years to complete because of manual processes. However, automating the processes can save time, money, and lives. The cost of developing IoT monitoring systems is worth it to reduce the risk of Legionnaires disease.
AI Helps Building Managers and Employers Achieve Compliance
The other end of Legionella bacteria assessment costs is the failure to conduct risk assessments as recommended by the Health and Safety Executive (HSE), leading to the spread of Legionnaires disease. The human cost of developing Legionnaires disease is sure to bite your budget hard.
However, one company recently paid a £900,000 fine for failing to protect health and safety in their workplace. Every building manager is responsible for Legionella bacteria control, and every employer must contribute to the exceptional health and safety of all staff on the site.
The failure to test water systems and other plumbing in buildings or the direct environment can lead to excessive financial losses. Instead, streamline operations, gain valuable insights with machine learning, and prevent Legionnaires disease to reduce costs in the long run.
How AI Reduces the Cost of Legionella Bacteria Control Summary
Artificial intelligence technology combined with some physical equipment can automate monitoring and control activities to ensure you manage Legionella growth in your water systems. It can also ensure that you remain responsible for your compliance requirements, ultimately saving you time, money, and frustration.
