Aims

Factors affecting well-being and working conditions at workplaces are typically measured using personnel surveys. In this DigiWork-project we will examine whether these factors can be assessed using routine, daily accumulating digital data on personnel and organizational performance at hospitals. With data-driven approaches, we will construct objective digital indicators for online monitoring of working hour characteristics and workload and evaluate their relevance for employee health and well-being at work measured e.g., as absences from work due to sickness, or quality of care.

Data

The target organization is the Hospital District of Southwest Finland which includes around 8 000 employees and provides specialized health care for a population of 500 000 Finnish citizens. We will use two large existing digital information pools:

  • Personnel Data Lake which comprises daily-level registry data of working hours from a shift scheduling software Titania® with existing and prospective data for each employee from year 2000 onwards
  • Auria Data Lake that is the administrative Clinical Data Repository containing electronic health records of treated patients.

A third source of data, used as a reference, is responses to biannual questionnaire surveys by the entire personnel from 1998 onwards.

We will merge these databases and use modern statistical methods of data analysis, including advanced machine learning (such as hierarchical clustering and association rule analysis or regression, decision trees, and Bayesian classifiers) to construct predictive models. Our outcome measures include employee absence from work due to sickness, occupational injuries, and measures of quality of care operationalized e.g. by hospital readmissions. We will identify indicators of working conditions to estimate workload from routine digital data that are linked to our outcome measures and assess whether they outperform traditional survey-based predictors.

The project will expand the use of ‘big data’ into working life research in the context of working life. It represents a proof-of-concept to determine the feasibility of digitalised on-time assessment of routine data as a tool to improve monitoring of working conditions that affect employee well-being and quality of care in health care organisations.

Consortium partners

DigiWork Consortium is led by Finnish Institute of Occupational Health (Adjunct prof. Annina Ropponen), and partners are University of Eastern Finland (Prof. Marianna Virtanen), and University of Helsinki (Prof. Mika Kivimäki).

Schedule

2020-2022

Funding

Academy of Finland DIGIHUM-program