Utilizing digital data to estimate working conditions in health care

We use data-driven methods to create objective digital indicators that monitor the working time characteristics and workload of healthcare employees in real time.
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Workload is an important factor in an organization's operations and employees' health. Factors affecting well-being at workplaces are usually investigated through personnel surveys. 

The aim of the project is to find out whether the factors that affect the well-being of health care employees can be investigated with data accumulated in daily activities from employees and organizations into different hospital systems. 

Data and methods

The research data is based on the Hospital District of Southwest Finland and its approximately 8,000 employees who provide specialist health care to an area of approximately 500,000 inhabitants. We utilize two large, existing digital collections of data: 

  • Working time data extracted from the Titania® shift planning software from 2008 to this day 
  • Medical records from the Auria data lake. 

The comparative data consist of employee survey data collected since 1998. The surveys have been conducted every two years. 

We will combine the collections of data and use statistical modeling and machine learning methods to create prediction models. We will assess how the workload metrics obtained from digital data relate to sickness absences, occupational accidents and the quality of care. 

Results and impact

The project will provide information to assess the workload of health care by utilizing the automatically accumulated data on employees and patients in the hospital district's systems. Workload management in nursing is especially important due to the aging, turnover and safety of employees.