Improved patient safety through a clinical decision support system in laboratory medicine

Abstract

Laboratory diagnostics are essential for diagnosis, initiation of therapy, and monitoring of patients. Laboratory results that are overlooked or incorrectly interpreted lead to adverse events and endanger patient safety. Clinical decision support systems (CDSSs) may facilitate appropriate interpretation of results and subsequent medical response. The research project on digital laboratory medicine (AMPEL) aims at developing aCDSS based on laboratory diagnostics, which supports practitioners in ensuring the necessary medical consequences. Aliterature review of CDSSs describes the current state of research. The research project AMPEL is presented with its objectives, challenges, and first results. Furthermore, the development of aframework and reporting system is illustrated through the clinical example of severe hypokalemia. Through interdisciplinary development and constant optimization, aspecific CDSS with high acceptance among clinicians was developed. Initial results in the case of severe hypokalemia show apositive effect on patient care. Thereby, more complex frameworks such as sepsis diagnostics or acute coronary syndrome are implemented. The limited availability of standardized and digital clinical data is challenging. In addition to the application of classic decision trees in CDSS, the use of machine learning offers apromising perspective for future developments.