David Ting, Tausight's CTO, and Larry Ponemon from the Ponemon Institute highlight the escalating risks and inefficiencies in healthcare data security during a webinar. Their research identifies a pervasive lack of visibility into where and how Protected Health Information (PHI) is stored and accessed, weaknesses exacerbated by frequent cyberattacks and inadequate cyber-hygiene practices. They emphasize the need for heightened responsibility and advanced solutions to locate, protect, and manage PHI effectively, advocating for better awareness, data management policies, and rapid incident responses to mitigate risks. Ting’s firm provides technology that seeks to improve PHI visibility across systems, which is crucial in preventing data breaches and ensuring patient trust in their healthcare providers.

"You Can't Rebuild Data": The Key Role of Visibility in Protecting Information | healthsystemcio.com healthsystemCIO.com

One Medical, led by CEO Trent Green, is planning further expansion but distances itself from the term "disruption" typically associated with such moves. The article outlines potential changes and expansions for One Medical's clinics amidst the backdrop of Amazon's workforce adjustments earlier in the year, aiming to differentiate the Amazon-One Medical partnership from other healthcare market entries by large retailers. The specifics of market targets, possible clinic closures, and staffing plans are highlighted as part of the strategic growth effort.

Why Amazon's One Medical is plotting more expansion publication

Artificial Intelligence (AI) and robotics are increasingly being integrated into healthcare settings, with a focus on enhancing patient care and operational efficiency. A recent Morgan Stanley report highlights that 94% of healthcare companies use AI, predicting a significant budget increase for AI and machine learning. Various hospitals in Los Angeles are already deploying AI applications—from robots managing medication delivery to AI systems predicting ER wait times and infection risks in patients. However, experts caution that the implementation and validation of AI technologies in clinical settings are complex and must be approached slowly to ensure safety and effectiveness. Examples include UCLA Health's robotic surgeries controlled by surgeons, AI-enhanced decision tools at City of Hope for detecting sepsis in vulnerable patients, and Cedars-Sinai's patient-facing AI app improving pre-visit diagnostics and care efficiency.

LA Hospitals AI Use Grows publication

In an article published on March 28, 2024, by Julian Horsey, IBM highlights five essential principles for creating trustworthy AI models: explainability, fairness, transparency, robustness, and privacy. These principles aim to enhance trust in AI systems by ensuring they are understandable, ethical, and secure, catering to a broad audience without requiring specialized knowledge. The article underscores the importance of these criteria in facilitating trust between users and AI, especially in sectors like healthcare and finance where decisions have significant implications. Additionally, IBM emphasizes the ongoing need for AI developers to engage with diverse data sets, uphold data privacy, and implement robust security measures to safeguard against threats and biases, thereby aligning technological advances with ethical standards and user security requirements.

5 Essential principals for creating trustworthy AI models Geeky Gadgets