Maximizing Workplace Injury Prevention and Safety with the Synergy of Artificial Intelligence and Human Expertise
With its rapid evolution, artificial intelligence (AI) has triggered a revolution in the modern workplace, transforming work processes and creating fresh opportunities for innovation and productivity. In a changing work environment, artificial intelligence and human analysts are joining forces to design the most effective injury prevention programs, providing a glimpse into the future of workplace safety. Of course, we're talking about a match made in heaven, like peanut butter and jelly, Batman and Robin, or Tom and Jerry (well, maybe not Tom and Jerry, that cat is always trying to harm the mouse). Anyway, you get the point. Whatever your analogy, one thing is clear: AI and human analysts have the potential to make a great team.
The Importance of Injury Prevention in the Workplace and How AI Can Help
First, let's talk about why injury prevention is so crucial in the workplace. In case you haven't heard, workplace injuries can be a big problem. They can cost companies a ton of money in healthcare expenses and lost productivity, not to mention the pain and suffering of the injured employee. And as if that wasn't enough, workplace injuries can also lead to OSHA fines, lawsuits, and bad PR.
The Bureau of Labor Statistics (BLS) report provides data on workplace injuries and illnesses in the United States. The report covers nonfatal injuries and illnesses that occurred in the workplace in 2020 and includes information on the types of injuries and illnesses, the industries and occupations in which they occurred, and the demographic characteristics of the affected workers.
According to the 2020 report, there were 1,062,700 nonfatal injuries and illnesses among US workers in 2020, which is similar to the number reported in 2019. The incidence rate of injuries and illnesses decreased from 2.8 cases per 100 workers in 2020 to 2.7 cases in 2021. The total number of days away from work, job transfers, and work restrictions (known as DART) also decreased from 1.1 million in 2019 to 895,500 in 2020. So, even in a "good" year, the impact of workplace injuries is astronomical.
Now, here's where AI comes in. Artificial intelligence is a powerful tool that can analyze vast amounts of data quickly and accurately. In the case of workplace injury prevention, AI can sift through thousands of injury reports and identify patterns and trends that human analysts might miss. For example, AI might notice that a particular type of machinery is responsible for a higher-than-average number of serious injuries, or that injuries tend to happen more frequently at a specific time of day. These metrics can then be used to implement targeted interventions and initiatives to reduce the risk of future injuries.
Click to skip ahead:
- Types of Artificial Intelligence for the Workplace
- The Power of Combining AI and Human Analysts for a Comprehensive Injury Prevention Program
- Tips for Seamless Integration of AI and Human Analysts in Injury Prevention Programs
Types of Artificial Intelligence for the Workplace
There are various forms and applications of Artificial Intelligence (AI); however, in the realm of injury prevention at the workplace, three main types stand out.
We are getting deeper into the cutting-edge world of advanced, or "smart," sensors that far surpass traditional sensors in terms of their remarkable capabilities. Smart sensors can be implanted inside the body, worn on the body, or attached to workplace objects to monitor different parameters, providing a wealth of data that can be utilized in various ways. By connecting these "Internet of Things," also known as IoT-enabled devices, to the internet and each other, they create a vast network of data that can be analyzed to improve workplace safety measures.
These AI-enabled sensors have the potential to revolutionize the field of occupational safety and health. By utilizing continuous data from workplace sensors, practitioners can intervene early to prevent toxic exposures, moving away from traditional sampling methods. With the aid of ML-enabled algorithms, large datasets produced by a 24/7 sensor network can be analyzed, improving safety and health surveillance, reducing uncertainty in risk assessment and management practices, and driving new avenues of research.
Despite the many benefits, there are also challenges to be faced with the use of AI-enabled sensor technology. The privacy dilemma associated with monitoring and tracking all aspects of worker performance is one such challenge. While more businesses are adopting sensor technology and cloud-based human resource systems to manage their workforces, it is important to establish best practices for employer-sponsored worker monitoring programs, including using only validated sensor technologies, ensuring voluntary worker participation, ceasing data collection outside the workplace, disclosing all data uses, and ensuring secure data storage.
Overall, the potential benefits of advanced sensor technology are vast. By balancing these benefits with the need for privacy and data security, we can pave the way for a safer, more efficient, and more effective workplace.
What role does a human play in the use of smart sensors?
It is important to understand that while AI can collect and analyze vast amounts of data, humans are still needed to make sense of this data and develop effective solutions.
Smart sensors can provide a wealth of data that can be used to improve workplace safety and health. Occupational safety and health practitioners can use this data to intervene early and prevent toxic exposures. However, just because the sensors collect data, it does not mean that the data will be useful on its own. Humans are still needed to interpret the data and determine how it can be used to improve workplace safety and health.
For example, while smart sensors can collect data on worker exposure to hazardous substances, it is still up to human experts to determine what levels of exposure are safe and to develop strategies to reduce exposure. This may involve changes to work processes, the use of personal protective equipment, or the implementation of ventilation systems to reduce exposure.
In addition to interpreting the data collected by smart sensors, humans are also needed to ensure that the data is collected and used ethically and responsibly. This requires clear guidelines for the collection, storage, and use of data from wearable sensors, as well as worker training on how to work safely with these sensors.
Lately, there has been a move from workplace robots that perform repetitive tasks (automated robots) to more sophisticated robots that can interact with humans and their surroundings (autonomous robots). These cutting-edge, AI-powered robots are known as collaborative robots or "cobots". However, the presence of a cobot and a human worker in the same workspace raises safety concerns, primarily related to collision control. In 2016, the International Organization for Standardization (ISO) established safety standards to ensure safe human-cobot collaboration. For industrial cobots equipped with AI-enabled sensors, the ISO suggested: (1) monitored stopping controls for safety purposes, (2) human-guided control of the cobot, (3) monitoring controls for speed and separation, and (4) limitations on power and force.
Furthermore, AI technology enables robotic devices to learn from each other. The sensors in robotic devices can be connected to the cloud, allowing the learning experience of one AI-powered robot to be shared with all other connected robots through "cloud robotics".
What role does a human play in the use of robotic devices?
Implementing collaborative robots: Collaborative robots, or "cobots," have the potential to improve workplace efficiency and productivity. However, their presence in the same workspace as human workers raises safety concerns. While the ISO has established safety standards for industrial cobots equipped with AI-enabled sensors, these standards are not enough on their own to ensure safe collaboration between humans and robots.
Humans are still needed to ensure that the robots are used safely and efficiently. This may involve worker training on how to work safely with cobots, as well as ongoing monitoring of the robots to ensure that they are functioning as intended. In addition, humans are needed to make decisions about the deployment of cobots in the workplace. For example, it may be necessary to consider the potential impacts of cobots on worker health and safety, as well as the potential impacts on worker jobs and job security.
Decision-Support Systems (DSS)
DSS powered by AI are being adopted by companies with large data collections and strong computational and computer engineering capabilities. These systems aid in financial, operational, and organizational risk decision-making by using AI to mine knowledge from real-time data. However, there are concerns about the transparency and bias of AI algorithms used in DSSs, and the "black box" nature of machine learning methods can diminish trust in the outputs produced.
The impact of AI on work also raises questions about automation and human-machine interactions. Some estimates suggest that around half of all job tasks in the US economy could be automated with current AI technologies. Still, not all studies agree on the extent of job loss, and some factors may prevent companies from adopting job-displacing AI technologies. It is crucial to consider the implications of AI in the workplace and to conduct a thorough safety and health review before implementing AI devices or systems. The introduction of AI raises questions about how to manage uncertainties in human-machine interactions. A systems safety approach should be taken to manage the risk and ensure the reliability and safety of AI technologies in autonomous systems.
What role does a human play in the use of DSS?
DSS powered by AI can aid in financial, operational, and organizational risk decision-making by using AI to mine knowledge from data. However, the human aspect is still crucial in the implementation of these systems. A thorough safety and health review should be conducted before implementing AI devices or systems, and a systems safety approach should be taken to manage the risk and ensure the reliability and safety of AI technologies in autonomous systems.
While the DSS can provide valuable data and insights, humans are still needed to make decisions based on this information. This requires a deep understanding of the data and the ability to critically evaluate the outputs produced by the DSS. Humans are also needed to address any biases or limitations in the AI algorithms used in the DSS, as well as to ensure that the algorithms are functioning as intended.
The Power of Combining AI and Human Analysts for a Comprehensive Injury Prevention Program
Here's the thing: AI is great at identifying patterns and trends but not so great at understanding the nuances of human behavior. That's why human analysts are irreplaceable. Human analysts are trained to understand the social and psychological factors that influence worker safety. They can interview employees, observe work practices, and identify potential hazards the data may not capture.
Together, AI and human analysts can create a powerful injury prevention program that considers both quantitative and qualitative aspects of safety programs.
Here are a few ways they can work together:
1. Identifying High-Risk Areas
AI can analyze data from injury reports to identify workplace areas at high risk for accidents. Human analysts can then visit these areas and conduct interviews with employees to understand the underlying causes of the risk. For example, as we discussed earlier, AI might identify a particular type of machinery that is responsible for a high number of injuries, but it's the human analysts who can determine whether the employees are using the machinery incorrectly or if there's a design flaw that needs to be addressed.
2. Predicting Future Injuries
AI can use predictive modeling to predict future injuries' likelihood based on past trends. This information can be used to prioritize prevention efforts and allocate resources where they are most needed. Human analysts can then work with employees to understand the specific circumstances that lead to these injuries and develop targeted interventions to prevent them.
3. Designing Effective Training Programs
AI can analyze training program data to identify which are most effective at preventing injuries. Human analysts can then work with trainers to refine these programs and make them even more effective. For instance, AI might determine that employees who receive training on a particular piece of machinery are less likely to be injured, but it's the human analysts who can work with the trainers to ensure that the training is delivered in a way that resonates with employees and actually changes their behavior.
4. Conducting Safety Audits
AI can use computer vision to analyze video footage of the workplace and identify potential safety risks. Human analysts can then verify these hazards and work with employees to develop solutions. An illustration would be AI identifying a worker not wearing the correct protective gear. Still, human analysts can determine whether the worker needs to be appropriately trained or if there is a problem with the availability or suitability of the equipment.
5. Validation of Human Analyst Best Practices
The use of AI in injury prevention programs has the significant benefit of objectively verifying the best practices identified by human analysts. Human analysts provide insights into the social and psychological factors that affect workplace safety. However, their findings can be subjective and biased by personal experiences. In contrast, AI utilizes data analysis and pattern recognition to identify trends and irregularities, providing an impartial perspective on ergonomics and injury prevention.
AI's analysis of large data sets can validate the effectiveness of the best practices identified by human analysts in reducing the risk of workplace injuries. For example, Briotix Health conducted a case study in which they used wearable AI sensors to monitor job tasks, confirming all the findings that Industrial Sports Medicine Professionals (ISMP) had identified. The AI's objective perspective improved management's buy-in, which resulted in the implementation of human analysts' recommendations.
Collaboration between AI and human analysts results in a holistic injury prevention program that addresses both quantitative and qualitative aspects of workplace safety hazards, ultimately leading to improved safety outcomes for employees, additional tools for safety managers, reduced workers’ compensation costs, and increased business success.
Tips for Seamless Integration of AI and Human Analysts in Injury Prevention Programs
So, there you have it: AI and human analysts can make an excellent team for workplace accident and injury prevention. But, like any good team, they must communicate effectively to ensure their efforts are aligned.
Here are a few tips for making sure that AI and human analysts are working together seamlessly:
1. Set Clear Goals
Before starting any injury prevention program, ensure everyone involved is on the same page. Define clear goals and objectives for the program and ensure everyone understands how their role contributes to those goals. This will help ensure that AI and human analysts work together toward a common purpose.
2. Use Data to Inform Decisions
Data is the foundation of any good injury prevention program. It's important that everyone involved in the program is using the same data to inform their decisions. This will help AI and human analysts work from the same information and can make informed decisions together.
3. Embrace Innovation
The world of AI is constantly evolving, and new technologies and approaches are always emerging. Your injury prevention program should adapt to these changes, and you should always look for new and innovative ways to prevent injuries. This might involve investing in new technologies, exploring new data sources, or experimenting with new approaches to training and education.
The Collaborative Force of AI and Human Experts in Injury Prevention Programs
Integrating artificial intelligence and human analysts is ideal for creating the most effective workplace injury prevention programs. AI excels at data analysis and pattern recognition, while human analysts offer contextual understanding and insight into the human aspect of workplace safety culture. By working together, they can develop a comprehensive injury prevention program that considers quantitative and qualitative elements.
Incorporating AI in business operations provides a competitive advantage by enabling the analysis of vast amounts of data and facilitating informed decision-making. Despite concerns about the displacement of human workers, AI can create new opportunities and enhance existing jobs by enabling humans to handle more complex and strategic tasks. By integrating AI and human expertise, companies can drive innovation and growth, positioning themselves ahead of competitors.
Rather than fearing the impact of AI on their operations, businesses should welcome its integration and leverage its potential to boost efficiency, productivity, and profitability. By doing so, they can unlock the full potential of their operations, outcompete rivals, and thrive in a constantly evolving technological landscape. So let's embrace the possibilities of AI while working alongside human colleagues to achieve success.