Future of Workplace Health, Safety, and Wellness

As businesses continue to integrate technology into the workplace, the use of artificial intelligence (AI) and machine learning (ML) for health, safety, and wellness has become increasingly prevalent. By leveraging these powerful tools, companies can gain access to actionable data that can help them improve the overall health and well-being of their employees.

By analyzing patterns and trends in employee health data, AI and ML can provide valuable insights into potential health risks and help companies take preventative measures to address them. Additionally, these technologies can be used to monitor workplace safety and identify potential hazards before they result in accidents or injuries.

Furthermore, the use of AI and ML in the workplace can also support employee wellness initiatives by providing personalized recommendations for healthy habits and promoting healthy behaviors. By implementing these technologies, companies can create a safer and more supportive work environment for their employees, ultimately leading to improved productivity and satisfaction.

The Need for More Data

There is a growing need for more data in the workplace in order to gain a better understanding of employee health, safety, and wellness. With more data, companies can identify potential risks and take action to prevent them. Additionally, having a larger data set allows for more accurate analysis and more personalized recommendations for employees.

Furthermore, as the world becomes increasingly data-driven, having more data can help companies stay competitive and stay ahead of industry trends. By leveraging the latest data and technologies, companies can gain a competitive edge and improve their overall performance.

Overall, the need for more data in the workplace is essential for promoting employee health, safety, and wellness, and for supporting the success of businesses.

“What” data is needed?


The type of data needed in the workplace to support health, safety, and wellness initiatives will vary depending on the specific needs of the company and its employees.

Some common types of data that may be useful include:

1. Employee health data, such as medical records, fitness tracking data, and self-reported health information.

2. Workplace safety data, such as incident reports, safety inspections, and risk assessments, and

3. Employee wellness data, such as engagement and satisfaction surveys, mental health assessments, and productivity metrics Having access to this data allows companies to identify trends and patterns, and to make informed decisions about how to improve employee health, safety, and wellness.

Additionally, data can be used to measure the effectiveness of different initiatives and to make adjustments as needed.

The collection and analysis of this safety data digitally can lead to faster, more accurate, and more insightful conclusions.

“Why” is data needed?


Data is needed in the workplace to support health, safety, and wellness initiatives because it provides valuable insights into employee health, safety, and wellness. By analyzing data, companies can identify potential risks and take action to prevent them. Additionally, data can be used to monitor the effectiveness of health, safety, and wellness initiatives and make improvements as needed.

Furthermore, the use of data in the workplace can also support employee wellness initiatives by providing personalized recommendations for healthy habits and promoting healthy behaviors. By implementing data-driven approaches, companies can create a safer and more supportive work environment for their employees, ultimately leading to improved productivity and satisfaction.

Overall, the use of data in the workplace is essential for promoting employee health, safety, and wellness, and for supporting the success of businesses.

“Where” to collect and store data?


Data should be collected from employees, external inspectors, and IoT safety devices alike to provide the most all-encompassing sets of data. These will take advantage of human insights and creative thinking, as well as the unbiased, continuously-operating observations of digital safety systems.

Additionally, the storage and analysis of EHS&Q data should be organized and centralized in a single location. This location is often a database system local or external to the organization. Doing this avoids physical silos within the organization and allows information to be validated more easily. Just as important as the place where data is stored, database systems, when coupled with analysis, become the place where historic trends, key metrics, and actionable suggestions come from.

“When” is safety data needed?


Safety data is needed in the workplace at all times in order to identify potential hazards and take preventative measures to address them. By regularly collecting and analyzing safety data, companies can gain a better understanding of the risks in their workplace and take steps to reduce or eliminate them.

Furthermore, safety data is also needed in the event of an accident or injury. This data can be used to investigate the incident, determine the root cause, and implement preventative measures to prevent similar incidents in the future.

There is not a single right answer about when data collection is needed or even possible. Should it be collected at regular intervals, at a frequency that meets the requirements of legislation, or continuously? In an Industry 4.0 setting, data can be collected and analyzed in real-time, then used to generate immediate notifications and actionable suggestions. While this rapid turnaround may not be an essential aspect of a facility’s EHS&Q program, it certainly has its benefits. For example, if AI systems trained for computer vision applications spot an overlooked hazard or unsafe behavior, they can provide instant feedback to local EHS&Q teams and site supervisors, who can then take action before an incident occurs.

While this presents an obvious benefit, there is also the potential for EHS&Q leaders and managers to receive an unrelenting influx of notifications. As such, it is important that the safety software being used is well-designed to benefit your team.

“Who” needs this data?


There are several stakeholders who need and use data on employee health, safety, and wellness in the workplace. These include employees, employers, and regulatory agencies.

Employees are the primary beneficiaries of data on employee health, safety, and wellness. By having access to this data, employees can gain a better understanding of their own health and take steps to improve it. Additionally, employees can use this data to identify potential safety hazards and take action to prevent them.

Employers also need and use data on employee health, safety, and wellness in order to create a safer and more supportive work environment. By analyzing this data, employers can identify potential risks and take preventative measures to address them. Additionally, employers can use this data to monitor the effectiveness of their health, safety, and wellness initiatives and make improvements as needed.

Regulatory agencies also need and use data on employee health, safety, and wellness to ensure that companies are compliant with health and safety regulations. By having access to this data, regulatory agencies can monitor the safety of the workplace and take action if necessary.

Overall, data on employee health, safety, and wellness is needed and used by a variety of stakeholders in the workplace.

Data Collection Methods

There are several methods for collecting safety data using forms, QR codes, and computer-vision powered by cameras.

Mobile-Friendly Digital Forms are a common method for collecting safety data. Forms can be used to gather information on safety incidents, such as accidents and injuries. Forms can be completed by employees, supervisors, or safety personnel, and can be submitted electronically or in paper form.

QR codes are another method for collecting safety data. QR codes can be placed on safety equipment, such as fire extinguishers and first aid kits, and can be scanned using a smartphone to access safety information and instructions.

Computer-vision powered by cameras is a more advanced method for collecting safety data. Cameras equipped with computer-vision technology can be used to monitor the workplace and identify potential safety hazards. This data can be used to take preventative measures to address the hazards and prevent accidents and injuries.

The transition to digital data collection can be fast and effective. As reported in EHSToday, safety managers and directors found actionable insight from safety programs powered by digital data in as little as four weeks.

Importantly, data collection does not only come from EHS&Q professionals. Employee engagement and empowerment are key to the success of new policies. For this, mobile and website applications are great tools, as employees can access them via mobile phones for an easy way to input data which can then be immediately stored and analyzed.

The techniques and methods developed for data collection should be inclusive and easy to use by everyone. Moving from traditional methods will likely face resistance from employees if the tools developed are hard to use. In fact, an independent study showed that 55% of EHS professionals thought that their team required more data science expertise.

This can be addressed in two ways. Firstly, it will likely be useful to provide more training to EHS personnel on the use of advanced tools and how they can take advantage of big data. Secondly, we can demand more of our safety tools, taking advantage of the most modern AI solutions to provide more important safety insights with less human involvement. For example, video analytics systems can analyze live or recorded files to inform EHS&Q professionals of potential safety threats, such as employees not wearing personal protective equipment or entering restricted areas.

It is worth noting that the quality and characteristics of the data collected can drastically change the quality of the results from its analysis. For instance, something as simple as the record of incidents can become misleading if the total number of personnel or the production levels are not considered. For example, finding out that most of the incidents in your plant happen in September is both unhelpful and non-actionable unless you also consider that there are more personnel on-site and production is at an all-time high. As such, we know that a clear understanding of safety performance can only happen if a complete and accurate set of data is collected and analyzed.

The Past, Present, & Future of EHS&Q Data Analysis

Today’s Industry 4.0 revolution has come with new opportunities and challenges as companies venture into a new era of big data featuring levels of automation and connectivity that would have never been possible before. These changes have sent ripple effects through EHS&Q, particularly when it comes to how data is collected, managed, analyzed, and more importantly, the actionable conclusions that can be drawn from the data. To have a better understanding of how EHS&Q got to this point, we can explore the evolution of data collection and analysis over time.

The past


This is essentially data collection with pen and paper, paired with basic statistics and the generation of short-sighted or incomplete graphs and plots. In a rather reactive fashion, incidents and quality issues would only be recorded and acted on when something went wrong.

As an EHS&Q strategy, this is no longer standard practice for the majority of modern companies. Unfortunately, this practice can still occur unintentionally, even in companies taking advantage of modern EHS&Q tools, if some areas of operations are left out of EHS&Q development scopes.

While the “ratio of accidents”, sometimes called “Heinrich’s Law” or the “Heinrich ratio”, has been modeled in the past and found to be relatively constant, over time and across companies, it has also been well documented that the severity, cause, and frequency of these accidents can vary widely between industries, companies, and even departments. This clearly indicates a need for an increased understanding of safety trends, which points to the need for improved EHS&Q data collection and analysis across all industries.

The present


With the growth of ML and AI, new and powerful tools have become increasingly available to EHS&Q professionals. The use of more complex statistical methods and larger data sets means AI systems are increasingly capable of identifying key insights for improving safety performance. Autonomous data collection can be used to generate live graphs that lead to actionable recommendations by EHS&Q leaders and management. The additional data and analysis allow management to better plan safety events and initiatives, like training updates and internal quality inspections.

Unfortunately, current EHS&Q data collection can still be inexact, pushing safety programs in the wrong direction or missing important insights. This can potentially lead to initiatives with minimal impact benefits for their cost, or the potential for overlooked weaknesses in safety, quality, and productivity programs that persist and decrease efficiency.

The future


The accelerated development of AI and ML, coupled with the interconnectivity of Industry 4.0, will bring data analytics for EHS&Q into a new area of optimization and performance. This is possible when IoT networks integrate all enterprise assets into a live ecosystem. The vast amount of data from collection tools like smart sensors and video cameras could be fed into advanced data mining, AI, and ML methods to generate live actionable recommendations and automated EHS&Q responses. In the future, businesses with advanced EHS&Q programs will be preferred by clients because of their advanced capabilities, enhanced efficiency, and improved EHS&Q track record.

It should be noted that EHS&Q professionals will not be out of their jobs. In fact, they will still be needed to guide AI and ML solutions, applying data science expertise to help optimize EHS&Q programs. The future will also bring the application of EHS&Q standards to AI and ML systems, which then would feed right into updating international standards like the ISO 9000s. EHS&Q professionals will be essential in the conversations surrounding how to develop advanced software solutions for workplace safety, quality, and productivity.

The Best Way to Collect & Manage Your EHS&Q Data

With the future of EHS&Q and productivity on the horizon, companies will need to adapt soon. This adaptation will come to businesses as great opportunities for improvement and growth. Some of the first steps to move forward into the future are:

Data collection


The times when one-fits-all assumptions and rules of thumb were used to define EHS&Q and productivity policies are long gone. In the future, as much as in the present, your organization should find the most suitable and effective data collection methods for your industry. Before collecting data, you should make sure that the data is centralized, clean, normalized, and part of your organization’s context. The data collection itself should then be standardized, monitored, part of a culture of inclusion, and done with a clear purpose.

Integration of new technologies


The possibilities for integrating technologies like AI, ML, and IoT seem endless. This applies to EHS&Q as much as it does to other parts of your organization and can result in distinct improvements in EHS&Q performance. That said, the integration of any new solutions should be planned carefully to make sure it is worth the investment and to foster improved adoption of the tech by workers and management.

Actionable recommendations


Get the most out of new technology. Regularly monitor and assess EHS&Q and productivity performance indicators to ensure that the results from the data collection and analysis lead to meaningful, actionable recommendations for improvement.

EHS at the core of your business


New data collection and analysis technologies are showing their worth across a wide range of industries, improving business financials and operational efficiency. The EHS&Q sector is no different, and with the increasingly widespread adoption of a safety culture and initiatives like the “Zero Accident Vision”, EHS&Q continues to prove itself as an essential component of a sustainable and successful business.

About Us


Knowella AI Inc. offers industry-leading digital solutions to help companies make data-informed operational decisions.

On Knowella platform, you’re be able to automate your health, safety, and quality-focused programs, processes, and workflows! Our people-centric and data-driven solutions boost frontline engagement and well-being, while helping companies save time and money.

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