An entirely new AI model that can identify people at high risk for accidents or illnesses, and predict what efforts can prevent emergencies from occurring. The information from the model becomes a decision support and helps us to put in place preventive measures at an earlier stage, which works better and affects the individual less. The model can also identify which individuals and which efforts are most likely to provide lasting improvement.

In this way, we get help to achieve the goal of individualized care with the human being at the center, and save on unnecessary suffering for the customer. For development assistance officers and care staff, this means support at work and help to tailor efforts, and direct the resources where they are most useful.


Care that is adapted to the individual has great benefits. The experience of the person being cared for is better if you feel that the effort is adapted for you and the effect is better. At the same time, we save resources when we do not have to make expensive efforts that may not be in demand or needed. Those resources can instead be devoted to providing the care that is really needed and appreciated.

Costs and suffering caused by, for example, fall accidents, cognitive impairment, cardiovascular disease, diabetes are partly possible to prevent and alleviate more effectively - if only information can arrive in time.

Given the demographic development with more elderly people in need of care and care, and with resources that are not increasing at the same rate, initiatives such as these are not only good but necessary.

It is an overall need that will generate benefits in all our business areas and ultimately for all our customers.


The AI model is based on the assessments made by nurses. Based on that data, patterns can be identified and decision material presented. The development of services for making forecasts using AI is fast and it is reasonable to assume that there are similar projects around the world. What makes this extra interesting is that it is done together with Ensolutions, with which we already collaborate in our quality work and in harmony with existing working methods.

During the autumn of 2021, the solution will be developed together with the administration's employees in home care to be tested sharply during the winter. In 2022, the solution will be realized and implemented throughout the administration if the tests are successful.

Impact target

Reduction of accidents and illnesses among our customers

Impact target

Reduced time in administrative work for administrators and care staff

Impact target

Reduced stress among staff through reduced risk of incorrect and too late decisions about interventions.


Name: Eric Semb
E-mail: Eric.Semb@helsingborg.se