integrated nursing and social care through the digitization of the care counselling visit in line with § 37.3 SGB XI
The aim of the INGE project is to support cross-sector care for those in need of care and who are being cared for at home by relatives. Ultimately the project aims at improving the overall home care situation. In North Rhine-Westphalia, approximately 417,300 individuals are being exclusively cared for at home by their relatives and entitled to receiving an attendance allowance. These individuals are entitled to a so-called “care counselling visit” which is being carried out on a compulsory basis according to § 37.3 SGB XI of the Social Care Act (SGB=Sozialgesetzbuch). Depending on the level of care required, the counselling visit takes place quarterly or semi-annually. Currently no instrument is continuously planning those counselling visits. Therefore, almost no knowledge transfer regarding the individuals’ care situation is occuring from one sector of care to another, since information is only partially recorded, often handwritten on a varying number of print forms at different moments in time. Communication between formal and informal caregivers as well as other nursing and care providers is also limited.
Against this background, INGE is developing a digital service platform that will facilitate the communication and knowledge transfer between the core players involved in the provision of care and provide information about the care situation at home. For this purpose, a service is being developed to provide IT support for the above mentioned counselling visit in line with § 37.3 SGB XI. A set of counselling instruments will be developed which will be used to assess the care situation from a care professional perspective. Reliable assessment instruments will be used and – based on a series of interviews – enhanced with contents adapted to individual situations. As a result, a continuous and sustainable care planning shall be developed, which can be made available to other care providers through e.g. e-nursing reports that can be integrated into other health information systems. Knowledge transfer between care providers will be enabled by healthcare-standard Application Programming Interfaces (APIs) as well as interoperable data formats and elements, such as FHIR.
the project will allow for early prevention as the machine learning (ML)
element integrated into the IT platform will help analyse specific use cases throughout the collected data. The intended early
alert system will detect typical developments in the patient nursing record and
notify the user to help slow down or even prevent more severe regressions.
Prototypes of the digital service platform will be evaluated and improved in order to develop an IT-solution most suitable to the care counseling visit. Additionally, business models will be analysed and adjusted to guarantee for a smooth realization and continued development of the platform.
Further results of the project will be innovative services developed in dialogue with all users. The implementation of these services will look at creating new jobs in the context of demographic change and increased numbers of people receiving care at home.
INGE thus offers a concrete cross-sector, integrated, approach to the digital transformation of care processes for informal caregivers, who will continue to be the main source of care provision for individuals in need of care in the foreseeable future.
The gewi-Institut für Gesundheitswirtschaft e.V. (gewi-institute for healthcare studies) is leading the consortium further composed of the following partners: Fraunhofer-Institut für Angewandte Informationstechnik FIT (Fraunhofer Institute for Applied Information Technology FIT), Hauspflegeverein Solingen e.V. (home care Solingen), smart-Q Softwaresysteme GmbH (smart-Q software systems), and Universität zu Köln (University of Cologne).
Funding program: Leitmarkt Gesundheit.NRW (European Regional Development Fund and State of North Rhine-Westphalia)
Project duration: 01.01.2020 – 31.12.2022
Keywords: integrated care, knowledge transfer, care consultation, early prevention, use cases, machine learning, business models, IT platform, informal caregiver
Contact: gewi-Institut für Gesundheitswirtschaft e.V.
Dr. Alexia Zurkuhlen