Enhancing patient autonomy in data ownership: privacy models and consent frameworks for healthcare

2024-12-30T18:00:25+08:00

Patient autonomy in healthcare has become increasingly significant in the digital age as individuals seek greater control over their health data. This review examines the ethical, legal and technological aspects of patient data ownership, emphasizing the need for privacy models and consent frameworks to empower patients, safeguard privacy and enhance transparency. Traditional doctor-patient confidentiality faces challenges due to advancements such as electronic health records, artificial intelligence and wearable technologies, necessitating updated frameworks to protect patient rights. Privacy models such as private, public and hybrid models present varying implications for data control, security and societal benefits. Emerging technologies such as blockchain and AI are revolutionizing data privacy by decentralizing data storage and enabling patient control while ensuring secure and ethical data utilization. Advanced consent frameworks, including dynamic and granular consent, provide patients with flexibility and transparency and promote trust and active participation in data-sharing decisions. Real-world implementations, such as Australia’s My Health Record and Estonia’s e-Health system, demonstrate the potential of patient-centric privacy frameworks to enhance healthcare quality and innovation. However, significant challenges persist, including regulatory ambiguities, cybersecurity risks and gaps in digital literacy. Addressing these issues requires collaboration among stakeholders to develop adaptable, secure and interoperable systems that prioritize patient autonomy. By integrating patient education, fostering interoperability and leveraging adaptive technologies, healthcare systems can balance privacy and innovation, build trust and ensure ethical data practices that empower individuals while advancing public health objectives.

MedBrain® for making diagnosis of childhood illnesses: a case report

2025-02-17T13:49:23+08:00

Diagnosing childhood diseases can be particularly challenging, especially in areas with limited access to specialists. This case report explored the application of MedBrain^®, an artificial intelligence–powered Clinical Decision Support System (CDSS), in supporting diagnosis in paediatric practice. We presented two clinical cases from Nigeria: a 4-year-old girl diagnosed with urinary tract infection and a 3-year-old girl diagnosed with acute gastroenteritis. These cases were selected to demonstrate common but diagnostically challenging paediatric conditions with overlapping symptoms. For each case, MedBrain^®was used prospectively to generate a ranked list of differential diagnoses with corresponding diagnostic confidence scores based on patient symptoms and clinical findings. The system utilized a hybrid algorithm combining rules-based logic and machine learning to assess input data and compute likelihood estimates. In both cases, MedBrain^®'s top-ranked diagnoses— urinary tract infection (96%) and gastroenteritis (95%)—were confirmed by attending paediatricians, validating its clinical utility. Comparatively, the standard clinical diagnosis was initially uncertain in both scenarios due to nonspecific presentations. These findings emphasized the potential of MedBrain^® to augment paediatric diagnostic accuracy, particularly in low-resource or non-specialized settings. Future studies should evaluate MedBrain^® in larger prospective cohorts or randomized control trials and compare its diagnostic performance with established clinical guidelines.

Journal of Digital Health

Enhancing patient autonomy in data ownership: privacy models and consent frameworks for healthcare

MedBrain® for making diagnosis of childhood illnesses: a case report