FINLAND: Doctoral research highlights the need for a lightweight tool for predicting absences – Finnish QWL pulse offers a solution

Pulse survey helps the team address potential problems in a timely manner

M.Sc., H.D., Docent Marko Kesti

Novogain AI, developed in Finland, predicts absences: a six-question pulse produces a monthly absence forecast for each team without health information

"Human resource risks can be hidden in the dispersion of responses. If even one employee becomes exhausted, the consequences will burden the entire work community. That's why listening to every employee is important."

— Marko Kesti

OULU, FINLAND, November 2, 2026 / EINPresswire.com / -- Employees' experiences of working conditions contain important information about future sickness absence risk, a recent Finnish doctoral thesis shows. The

study highlights the potential of short, repeatable surveys. Developed in Finland, Novogain AI uses the six-question QWL pulse to predict sickness absence on a team-by-team basis without employee health data.

The timing is significant. On October 6, 2026, the European Parliament approved a legislative initiative to strengthen the prevention of psychosocial risks. Parliament asks the Commission to assess the need for and possibilities for binding EU regulation.
The aim is to shift the focus of work ability management from the post-remediation of problems to their prevention.

EU emphasizes proactive psychosocial risk management
The European Parliament adopted the resolution by 413–210 votes. It emphasises the regular assessment of psychosocial risks, employee participation and monitoring the effectiveness of preventive measures.

The parliamentary committee preparation also presented the use of scientifically validated assessment methods and early identification mechanisms.
This is not yet a valid directive. However, the EU regulatory direction is clear: work-related stress should be prevented primarily by developing work organisation, working conditions and management.

Employee experience predicts sickness absence
Anniina Anttila's doctoral dissertation published at the University of Tampere in 2026 examined survey responses from almost 12,000 employees, occupational health care data and sickness absence. Regarding
psychosocial working conditions, in particular the employee's experience of their opportunities to influence the content, pace and working hours predicted repeated short sickness absences. This question was more informative in predicting the type of absence in question than the individual questions from the depressive symptom survey.
The study also combined survey and registry data with machine learning models. The best combined model achieved an AUROC value of 0.79 in predicting long-term sick leave, which describes the model’s ability to distinguish between people at higher and lower risk.
The 11-page survey used in the study was difficult to complete. Anttila highlights the possibility of shorter surveys: they could be used more often, which would allow for earlier detection of increasing work capacity risks.
The doctoral thesis did not address Novogain, but its findings support the development of predictive measurement based on employee experience.

Six questions, a prediction for every team
Novogain’s AI QWL pulse measures physical and emotional safety, cohesion and identity, as well as goals and creativity.
The internal consistency of the six-question measure was very good in the validation data of 699 respondents (Cronbach's alpha 0.898).
The validation data for the QWL-based sickness absence forecast included 10,322 respondents and 85 organizational units. The correlation between the forecast and the actual sickness absence rate was 0.427 and the degree of explanation was 0.18.
The model shows statistically significant but limited predictive power at the group level. The forecast can be calibrated with the organization's own sickness absence data if necessary.

A monthly or bimonthly pulse provides the supervisor with a proactive assessment of the team's absence risk. Automatic analysis and AI-assisted development recommendations reduce manual analysis work.
The value of the forecast comes from the opportunity to influence future developments before sickness absences occur.

A good average can mask work-related illness
A team's average work-related well-being can be good even if some employees experience significant strain.
That's why Novogain looks at the dispersion of responses in addition to the QWL average. A signal indicating possible work-related illness can activate the Zero Work-Related Sickness traffic light to red.
The red light guides the supervisor to have confidential one-on-one work-related illness discussions with all team members. The purpose is not to identify individual survey respondents, but to ensure that everyone has the opportunity to be heard.

"Employment risks can be hidden in the dispersion of responses. The team's average work-related well-being can be good even if one of the employees is seriously stressed. If one employee gets exhausted and goes on long sick leave, the consequences put a strain on the entire work community. That's why listening to every employee is important," says Novogain developer Marko Kesti.

Treili has significantly reduced sick leave
The social and health service organization Treili implemented Novogain AI in January 2026 and team-specific sick leave prediction in March.
According to preliminary monitoring results, sick leave days per employee decreased by 9.2 percent in January–June compared to the corresponding period in 2025. The result corresponds to approximately one less day of absence per employee. There were fewer absences during the six-month period, even though there was an exceptionally strong flu wave in Finland in February. In March–June, the total number of sick leave days decreased by 21 percent compared to the corresponding period in the previous year.
Reduced sick leave means cost savings potential and better availability of human resources.
The results are promising, but observational monitoring does not yet show how much of the change is due to the use of Novogain.

Zero sickness at work – cost-effective proactive management
Improving productivity is at the heart of many organizations’ strategies. Productivity means the ratio of outputs to inputs, so the decisive factor is not only how much sick leave can be reduced, but also what costs are involved in achieving the reduction.
In traditional reactive work capacity management, measures are often taken only when sick leave accumulates. In this case, more expensive work capacity support measures, substitute arrangements and rehabilitation may have to be resorted to. With proactive management, some of these costs can potentially be avoided.

Novogain AI’s QWL pulse, which predicts sick leave, is available for one euro per employee per measurement. The cost of the measurement is therefore small in relation to the costs caused by even one day of sick leave. The overall profitability is also affected by the working time used by superiors and the development measures implemented.

The Zero Sickness Initiative aims to make proactive work ability management a continuous, effective and financially sustainable activity. When employees are listened to regularly, risks are identified in time and supervisors receive AI-assisted development recommendations, supporting work ability becomes an investment that improves productivity.

The most important task of a sickness absence forecast is not to tell how many people will get sick, but to help prevent illness – as effectively and cost-effectively as possible. Financial benefits arise when supervisors know how to utilize predictive information and take the right measures at the right time.