Employees are often described as one of the corporate world’s most important resources and key success factors. Too many companies, however, are not as aware as they might be about the true well-being of their people. ODUM is dedicated to helping them audit where they are and find the best solutions for promoting better well-being.
A reduced sense of well-being among employees can be a major drain on a company’s resources, and a number of methods exist for assessing the current situation, some more effective than others.
Recent research has begun to focus on the possibility of predicting the number of sick leave days a given workforce is likely to take over the next year, and why. One way to do this is by modelling the relationship between the results of routine health screening questionnaires and the number of subsequent sick leave days taken. Collecting the information to do this is very timeconsuming and complicated, however, and using employees’ self-reported absence represents a simpler alternative, particularly as this data correlates well with actual sick leave.
Promoting a proactive approach
A project being led by ODUM – Finland’s market leader in company health auditing and responsible for auditing over 2,000 companies and more than 200,000 employees to date – is now addressing this challenge together with VTT Technical Research Centre of Finland. Building on ODUM’s expertise in Internet-based health screening software and VTT’s neural network know-how, the project is designed to develop and evaluate models for making long- and medium-term predictions.
|ODUM and VTT are working together to help companies understand the true well-being of their employees and how to predict future trends and plan HR and occupational health care initiatives to maximise employees’ contribution – for everyone’s benefit.
The project expects to be able to generate large sets of self-reported absence data in combination with health screening results that can be used to capture the underlying relationships using a mix of mathematical- and statistics-based models, as well as a data-driven approach. The project will start with health-screening data and self-reported absence information at the companies involved over a six-month period. Annual follow-up data from health screening over the next three years will be used to analyse how employees’ answers to questionnaires develop and correlate with sick day reporting.
The project started with 36,000 questionnaires and 5,000 self-reported absence observations, and this set is being steadily expanded as the project progresses. This will allow the model’s predictive capability to be extended as more data becomes available and enable greater use to be made of the potential for alerting management and HR departments to people most at risk.
Being able to predict personnel likely to benefit from preventive rehabilitation, support such as mentoring, and greater motivation, for example – rather than simply respond to problems as they materialise, as happens now – could take occupational health promotion a valuable step forward.