Due to the valuable and sensitive nature of its data, the Australian healthcare sector is increasingly targeted by cyberattacks. Existing cybersecurity evaluation methods often lack the specificity required to address the unique vulnerabilities within this sector, especially in terms of engaging stakeholders and fostering a proactive security culture. These evaluations often overlook psychological empowerment, which enhances individuals’ confidence in managing cybersecurity. This study aims to develop a tailored cybersecurity self-assessment index for the Australian healthcare system. It will focus on enhancing psychological empowerment alongside technical assessments to improve overall sector resilience against cyber threats. Using a design science research approach, the index was developed using expert reviews, online surveys, and in-depth interviews with key stakeholders, including healthcare providers, consumers, and government entities. This iterative process involved identifying gaps in existing cybersecurity measures and designing an index to address technical and human factors. The index’s evaluation through a pilot study revealed that it effectively raised awareness and empowered individuals within the healthcare sector to take ownership of cybersecurity practices. Participants reported increased confidence in managing cybersecurity risks and found the index’s actionable recommendations helpful in improving their security posture. However, challenges related to its applicability across diverse healthcare environments and regulatory constraints were identified. The Australian Healthcare Cybersecurity Self-Assessment Index shows promise as a tool for strengthening cybersecurity in the healthcare sector by integrating psychological empowerment with technical assessments. Further research is needed to refine the tool, incorporate quantitative data, and explore its scalability across different healthcare settings and global applications. • The healthcare sector is vulnerable to cyber-attacks; lacking tailored evaluation • We propose a cybersecurity self-assessment index for the Australian healthcare sector • The index supplements technology defences and enhances psychological empowerment • Expert reviews, surveys, and interviews shaped the index’s creation • Pilot results show the index aids healthcare cybersecurity and empowers individuals

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.