The DADAP project aims to transform the diagnostic assessment process in the clinical care pathway of Child and Adolescent Psychiatry (CAP) by digitizing and automating it using an innovative AI-based solution which is explainable and that optimizes the delivery of health and care services across a multitude of different settings. In several recent national projects, Region Västmanland (RV), a hospital in Sweden, has developed two innovative instruments for digitizing the psychiatric assessment process. The first one is the Electronic Psychiatric Intake Questionnaire (EPIQ) and second one is the Electronic Psychiatric Semi-Structured Interview for Children and Adolescents (EPSI-C), to screen, triage, prioritize, and diagnose patients. As a result of using these instruments, the throughput of patients at the hospital has increased by 130%. Despite this initial success, the overall information gathering is still time-consuming, and the lead time for care needs to be further reduced, particularly due to the escalating number of patients seeking care. Consequently, we have gathered a strong international consortium from Sweden, Spain, Norway, and Romania to firstly, expand the usage of the existing instruments to new countries by translating and adapting them. This first step will contribute to validation of the concept pilot studies which in turn increases the project data. Secondly, by automating the process (potentially down
to 60% less manual work) using AI, we wish to increase the precision of diagnosis and treatment choices and improve patient satisfaction and trust in the healthcare system. We will verify whether we have achieved these goals in each pilot. Moreover, we estimate the social and economic impact of our proposed method in pilot countries. The DADAP project will also develop an AI-based screening and diagnosis system trained on standardized questions and
answers from care seekers and their relatives, clinically validated assessments, and psychiatric diagnoses. The proposed system aims to find and ask the questions that most effectively lead to the best possible basis for the diagnosis and treatment selection, thereby improving the efficiency of several steps in the assessment process. The project expects to achieve several impact goals related to the United Nations’ Agenda 2030, such as improved
availability and reduced waiting times for healthcare, reduced work-related stress for healthcare workers, less subjective and biased diagnosis, equal access to efficient diagnosis regardless of social status or location, and
reduced need for travel. This will contribute to a sustainable healthcare system that is future-proof. The success of this project could significantly benefit the societies it is deployed to by enabling healthcare systems to scale up and efficiently organize their operations, leading to better health outcomes, gender equality, decent work, economic growth, reduced inequality, and sustainable cities and communities.
The DADPAP project’s expected benefits include: – enhanced patient satisfaction, treatment, and trust in the healthcare system; – reduced stress of healthcare workers contributing to a healthier work environment; – increased efficiency and scalability of healthcare systems.

The consortium has key expertise and experience for:

• Supporting implementation of existing solutions on a large-scale, or in different settings,
• Development of innovative explainable AI based tools, including adaptation, testing and integration,
• Making health and care systems economically, socially, and environmentally sustainable, while keeping people at the center of the care process.

Overall, the DADAP project proposes an innovative solution to a long-standing problem in healthcare and we expect a significant positive impact on society as a result of deployment of our solution.

Website of the Project