Open Access

This paper examines how the Balanced Scorecard (BSC) and Digital Maturity Models (DMM) can be integrated as strategic management tools for Digital Transformation (DT) in manufacturing companies. The paper first presents the theoretical foundations of both models and then demonstrates their application in the context of digitalization. The BSC approach is enhanced with specific digital Key Performance Indicators (KPIs) and perspectives, while the acatech Industry 4.0 Maturity Index serves as an assessment framework for digital maturity. The integration of both models is presented in a matrix that links strategic objectives with operational development stages. This combined methodology provides companies with a structured framework to plan, implement, and monitor their Digital Transformation, considering both strategic alignment and operational maturity.

Introduction: The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study. Methods: A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI. Results: The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm. Conclusion: The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.