Today, large amounts of clinical data, e.g. vital values and other measurements, are automatically collected. This is a challenge to the clinical staff as they need to quickly and effectively manage and assess these large quantities of data, e.g. validate, identify patterns and anomalies as well as make significant clinical decisions based on the clinical data. This demands for a focused and specialized decision support solution. The base for taking the next step has been made by the collaboration with Karolinska University Hospital, where Systematic has extended their medical device integration (MDI) solution with a vendor neutral archive (VNA) platform and a Viewer functionality that provides an intuitive and flexible overview of clinical data, e.g. vital values and other measurements. Furthermore, extensive experience with machine learning projects with Aalborg University Hospital and Regional Hospital Randers in Denmark create both clinical and technical insights in, how to work with and use machine learning and AI I to create prediction and decision making. The next step is to give clinicians an even better overview and more intelligent decision support by leveraging emerging technologies such as machine learning and AI. This technology can be used to automatically identify patterns and abnormalities that can help the clinician to gain insights from the vast amounts of data, and focus on critical or faulty measurement values, variations and deviations relative to the patient's normal values. In the long term, data can also be used to find abnormalities in relation to populations of patients with similar symptoms and diagnoses. This development enables the clinician to be able to effectively validate and act on critical patient data and make informed decisions. It will contribute to a clinically focused and patient-safe approach to the large amount of clinical data, e.g. vital values and other measurements. The Systematic MDI/VNA platform is designed to be the foundation in a well-functioning ecosystem - built from the ground up on HL7 FHIR - providing all data via standard HL7 FHIR interfaces. This ensures, through standards such as HL7 FHIR and SMART-on-FHIR, that third parties can develop dedicated and specialized decision support tools that leverage the intelligent machine learning algorithms provided by the platform. Key take-aways from the presentation: - In a world with more and more health data, how can technology help the clinician to effectively and safely get an overview of what is clinically significant. - How patient safety can be improved by bringing intelligent decision support tools to clinicians. - Key elements that are essential in a useful and supportive clinical decision-making tool