Location: Trøndelag, NORWAY
Website: https://synplan.ai/
Technology area: Artificial Intelligence
BOWI, the EU funded project that stands for Boosting Widening Digital Innovation Hubs, has announced the names of the 24 selected experiments presented by SMEs and Midcaps that will enter the Support Programme for accessing innovative digital technologies.
SynPlan (from VNNOR AS) is one of the winners and, like every selected experiment, their experiment will enter a 10-month long BOWI Support Program which includes both technical and business-related support and get access to training sessions offered by the BOWI network partners .
Digital innovation hubs that will be supporting the company during the experiment are NTNU and RWTH Aachen University.
Through the experiment, SynPlan aims to test out and develop further their new Artificial Intelligence System for Healthcare Workforce Planning that would help municipalities, hospitals, and healthcare institutions. The company’s research has shown that nurse managers spend more than 70% of their time on workforce planning which, most of the time, is done by rough estimates, repeating past practices, which turns out to be inefficient. Uncertainty of sick-leaves in planning often leads to over budget spending problems.
The company is using advanced Artificial Intelligence methods to analyze large amounts of data (historical data of workloads, competence, payroll, activities, patient’s data, and weather data) to predict in advance the demand of healthcare as well as the absence of core employees. That would help planners to be more productive and make more efficient workforce plans, thus avoiding overspending the budget and increasing the services at the same time.
With some sample data from Trondheim municipality, SynPlan has built the first version of the system and run demonstrations. The system has 3 main features:
Further in the project, the company plans to run an experiment to test the system in Trondheim municipality. The municipality will adopt the new way of healthcare workforce planning by taking into account the predictions of sick-leaves provided by VINNOR’s system. The real effect and possible places for improvements will be measured during the experiment.
The project has 3 main steps, in which the previous step will be completed before the next step starts.
The project has 3 main steps.
Also, SynPlan plans to work with Norway Health Tech, which is a technology cluster facilitating the growth of new and innovative healthcare solutions. This collaboration will bring them more connections to potential customers and better understanding of their problems in healthcare workforce planning.
SynPlan’s solution will empower managers and planners at the healthcare organization to plan their workforce more efficiently and more proactively with the sick-leave prediction taken into account, thus reducing the cost, saving the budget, and at the same time increasing the quality of services.
Main customers are municipalities, hospitals, and healthcare institutions. Currently, Trondheim municipality has agreed to become the company’s test user and a potential customer. Considering the fact that at Trondheim municipality there are more than 100 healthcare units with thousands of employees, SynPlan expects to have their first deal to be closed soon after the conclusion of the experiment.
SynPlan’s purpose is to bring Artificial Intelligence and Machine Learning (AI & ML) technology advantages to their customers and community through its solutions and services. The company’s mission is to focus on AI & ML for better solutions in healthcare and environmental issues, such as in combating micro-plastic issues and climate changes.
The company’s international team consists of longtime and experienced, highly skilled experts in AI & ML, professors at universities, and professional advisors. Currently, their customers and partners are municipalities, businesses, and Innovation Norway.
#artificial-intelligence #medical-devices #productivity
This article was originally published in Fundingbox Spaces.