This study develops a big data-driven predictive platform for hospital staff attrition, integrating machine learning (ML) with psychological constructs. Negotiable Fate (NF), a culturally rooted belief system, is examined as a predictor of turnover via psychological capital (PC) and organizational citizenship. Structured HR data from 400+ employees at a tertiary public hospital, covering 20+ features, were analyzed. Due to attrition imbalance (~5%), SMOTE was applied to balance the dataset. Four ML classifiers-logistic regression, decision tree, random forest, and XGBoost-were evaluated using accuracy, precision, recall, and F1-score. Statistical analyses assessed mediation, moderation, and construct validity using survey variables: NF, PC, perceived organizational support, job performance (JP), and organizational citizenship behavior. Random Forest and XGBoost achieved superior recall for attrition cases. Feature importance consistently highlighted working hours, income, job type, and satisfaction as key predictors. NF significantly predicted JP (β = 0.30, p < 0.001) and organizational citizenship (β = 0.36, p < 0.001) through PC (β = 0.33, p < 0.001). Perceived organizational support moderated the NF → PC pathway (β = 0.16, p < 0.001), confirming mediated moderation. Integrating ML with psychological theory enhances both the prediction and understanding of hospital staff attrition. The platform enables culturally sensitive, data-driven HR interventions, helping administrators identify high-risk employees and implement targeted strategies to reduce attrition, stabilize the workforce, and improve patient care.

The Executive Session, Trust by Design: Enabling Responsible Precision Health through Blockchain-Powered Digital Twins and Trusted AI, explores how the convergence of blockchain, artificial intelligence, genomics, 6G wireless technology, and other advanced technologies can be leveraged to power precision health digital twins. The dialogue focused on governance, interoperability, cybersecurity, and the impact of blockchain and trusted AI-powered digital twins on advancing precision healthcare and personalized medicine. Use cases—for genomics, radiology, theranostics, and end-of-life care—illustrated both opportunities and barriers. Throughout the discussion, speakers emphasized the centrality of trust, patient sovereignty, and resilient infrastructures for the next generation of healthcare.

The adoption of Artificial Intelligence (AI) is accelerating in the life sciences sector, offering opportunities to enhance biopharmaceutical research, development, and commercialization. However, the sector lacks structured tools to prioritize AI initiatives across varied business domains. This study presents a framework to categorize and evaluate potential AI applications in the life sciences industry, organizing use cases along two critical dimensions: Phase of Product Lifecycle and Operational Domain. A structured mixed-methods approach was employed, including a modified Delphi consensus process with industry experts, a qualitative case study review, and iterative framework refinement between August 2023 and August 2024. The resulting matrix framework enables life sciences professionals to assess AI opportunities across research, clinical development, commercialization, and post-marketing activities. Key findings highlight the pervasive nature of AI impact, the emphasis on data-driven strategies, and the regulatory and ethical challenges facing biopharma firms. This framework provides a practical model for strategic AI adoption decisions within the life sciences sector and lays the groundwork for future research, policy development, and enterprise transformation efforts.

In simple terms, existential means "related to human existence" or relates to the experience of being alive. It often refers to deep questions about the responsibilities of being human. In the era of AI, if you have your ear to the ground, you know fear abounds across social strata and cultural divides, the one thing we all have in common is birth, death, and all along that continuum, we find health, and this industry has a responsibility to all its participants.

We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.

This talk introduces Trustworthy Computing as a shift toward systems built on verifiable, transparent, and privacy-respecting foundations. Using blockchain and AI, it explores real-world impacts—from food safety and identity protection to AI transparency and machine unlearning—emphasizing the transition from reputational trust to algorithmic trust in everyday digital life. Health care and Life Sciences professionals will find particular relevance in applications that enhance drug authenticity, patient data privacy, and AI reliability in decision making. This 45 minute keynote presents new information and research in development in the theme areas below: Enhanced Security Frameworks Advanced approaches to drug authenticity verification Next generation patient data privacy protection Cybersecurity strategies for healthcare systems AI Reliability &amp; Transparency Methods for ensuring AI decision making transparency Implementing "machine unlearning" for data protection Building auditable AI systems for clinical use Blockchain Applications Real world blockchain implementations in healthcare Supply chain integrity from lab to patient Identity protection in digital health ecosystems Practical Implementation Actionable strategies for healthcare organizations Risk mitigation in digital transformation Regulatory compliance in trustworthy systems AI Disclosure: The PDF transcript was transcribed entirely from the keynote video using automated AI transcription tools. About Jerry Cuomo With decades of software innovation and 100+ U.S. patents, Jerry Cuomo stands as one of the most influential voices in trustworthy computing. As author of the acclaimed Think series (including Think Blockchain and Think Artificial Intelligence) and co-author of Blockchain for Business, his insights have guided countless organizations through digital transformation. Currently lecturing at NC State University and leading Wild Ducks, LLC, Jerry continues to pioneer solutions in trustworthy AI, blockchain, and cybersecurity – making this presentation invaluable for healthcare leaders navigating our digital future.

Disruptive digital health technologies are reshaping how patients interact with health professionals, how data are shared among providers, and how treatment plans and health outcomes are determined. While the COVID-19 pandemic has accelerated the adoption of digital technologies, challenges remain in realizing the potential of digital transformation programs in healthcare. Specifically, health data need to remain secure, usable, and shareable across multiple stakeholder groups in a world where silos between organizations and information systems persist. The implementation of innovative and disruptive digital technologies such as blockchain can offer a solution to these challenges. This article explores how blockchain technology can be used to accelerate digital health transformation programs. It provides an overview of the technology applications (i.e. data management, Internet of Medical Things [IoMT], supply chain management, and health insurance) and key players based on a literature review and secondary data. It also identifies challenges and success factors in implementing blockchain in healthcare. At the organizational level, we discuss the careful planning and specialized expertise required to overcome the technical, regulatory, and adoption-related hurdles associated with implementing blockchain technology. At the system level, the authors discuss the regulatory constraints, standardization and interoperability issues, and stakeholder engagement challenges linked to implementing blockchain technology.

Part 1 Decentralized healthcare is no longer a distant vision—it’s an emerging reality that demands secure, scalable, and intelligent infrastructure. This podcast explores how enterprise grade blockchain platforms and artificial intelligence are converging to create next generation health ecosystems – there is a significant value proposition that professionals are waking up to. Topics include: Drivers pushing the healthcare industry towards decentralized technology How blockchain and AI address data silos and trust issues in healthcare Critical components for decentralized healthcare—a secure data layer, intelligent automation, etc. How the Oracle blockchain platform meets scale, performance, and compliance needs in healthcare How AI and blockchain technically integrate How decentralized technologies with AI ensure compliance with HIPAA for data sharing Example of blockchain + AI solution delivering real value in healthcare How blockchain and AI are impacting supplier risk management in the pharmaceutical industry The next frontier for AI and blockchain in healthcare Whether you're an enterprise CTO, innovation lead, or digital health strategist, the discussion delivers leading edge insight into the platforms and protocols shaping decentralized care—and how AI is supercharging it. Part 2 – Join Ravi Sarkar, Chief Technology Officer, Microsoft and others! This podcast is the precursor to the In Person discussion delving deeper into decentralized health at BHTY's Annual ConV2X Event: Driving Platform and Decentralized Technology in Healthcare and Life Sciences Date: Sept 25 and 26, 2025 Place:The Foundry in Cambridge, MA Registration Link: https://conv2xsymposium.com/shop/?add-to-cart=6644 ________________________ Speaker Biographies Mark Rakhmilevich, Group VP, Product Management and Development Fintech, Blockchain, Transaction Management, Mission-Critical Systems, Oracle Corporation E-Mail: mark.rakhmilevich@oracle.com Mark Rakhmilevich is Vice President of Product Management and Development at Oracle, leading the Fintech, Blockchain, and Transaction Management portfolio within Oracle’s Mission-Critical Systems group. He drives technology and product strategy for key solutions including Oracle Blockchain Platform, Blockchain App Builder, Oracle Database Blockchain Tables, Tuxedo, Transaction Manager for Microservices, and the Fintech Data Platform initiative—delivering innovation both in the cloud and on-premises. With a focus on the complex needs of financial institutions, public sector, and enterprises across manufacturing, logistics, healthcare, and other industries, Mark guides global organizations in leveraging blockchain, digital assets, and data platforms to drive measurable outcomes—accelerating growth and innovation, deepening customer engagement, streamlining operations, reducing fraud and risk, and bringing to market innovative solutions that solve real-world economic and societal challenges. He plays a pivotal role in Oracle’s blockchain and digital assets strategy, working with customers and partners across sectors to move from experimentation to production—sharing industry use cases, advising on deployment architectures, and aligning technology with regulatory and business imperatives. Mark has been actively involved in numerous enterprise blockchain projects across industries and digital asset initiatives with financial services innovators worldwide. He’s actively exploring intersection of Blockchain and AI, where decentralized ledgers and smart contracts enable privacy-preserving model training and Agentic AI collaboration. A recognized thought leader, Mark is shaping Oracle’s innovation in Digital Assets, Decentralized Identity, and AI-powered compliance through the Fintech Data Platform initiative. He has spoken at global forums including the OMFIF Digital Money Summit and regularly contributes insights on Oracle Blockchain Blog and on LinkedIn. Mohan Venkataraman, Principal Consultant, Block10.ai With over 35 years in tech, Mohan has had the privilege of serving more than 100 clients. From 2013, he served as CTO of Chainyard, where he established the “Consulting and Advisory” Practice. His journey into blockchain began in 2015, and his team went on to deliver over 40 projects across industries from healthcare to fintech. Subsequently, he expanded the services into AI and machine learning solutions. Mohan's work in healthcare is particularly notable, leveraging blockchain and AI for solutions such as The Health Wallet, secure document sharing, and transparent supply chains. He is a recognized speaker at forums like the Hyperledger Global Forum, and a contributor to several patent-pending blockchain solutions.. Today, under block10.ai, he offers independent advisory consulting. His focus is on blockchain, AI, and machine learning, helping clients refine their ideas, develop solid business models, and create implementation strategies.

This study underscores blockchain technology's potential to tackle critical healthcare challenges, including data security, interoperability, and collaboration, delivering a scalable and efficient framework that enhances patient trust, operational efficiency, and compliance with global data protection standards. The advent of blockchain technology has radically altered centralized data management to adopt decentralized distributed systems with their inherent features-such as transparency, immutability, and security-that offer a promising answer for the challenges faced by modern healthcare systems. The authors introduce a smart healthcare solution for a secured digital identity of a patient by maintaining its Confidentiality, Integrity, and Availability (CIA-triad). It customizes an open-source Hyperledger Fabric-based framework for developing and utilizing the healthcare ecosystem as per the requirements of maintaining the digital identity of a patient. In addition, it uses fabrics' key components, such as privacy-preserving channels, endorsing peers, anchor peers, orderer nodes, and a secure consensus for an efficient collaboration among stakeholders that ensures data integrity and confidentiality. The decentralized storage feature allows secure Secure Hash Algorithm 256-bit (256-bit) during digital signature generation and verification algorithms across the network, and its one-way cryptographic function feature adds an advantage to maintain digital identity encryption both on-chain and off-chain during sharing and storing. This acts as a resistance to different cyberattacks as a record of the Common Vulnerability Scoring System scorecard. The efficiency of the proposed operational model tested in a closed experimental network gets a more balanced output than that of a test network, which may be chosen for an adoption.

Background: AI tools permeate research efforts, offering speed yet posing risks of bias, privacy breach, and eroded trust. Objective: To announce BHTY’s new AI Policy and put it into context for readers. Content: The editorial summarizes requirements around the use of AI in conjunction with authorship, review, and editorial activities. Conclusion: By codifying that AI can augment research efforts, but does not replace human expertise and accountability, BHTY reinforces its commitment to rigorous, transparent scholarship and science.