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Morgan Health Invests in Merative to Help Employers Turn Health Data into Action

Healthcare IT Today

Data is also necessary for training AI models to drive improvements in care navigation and more effective consumer choice. For years, employers have increased their spending on health care without commensurate improvements to the quality of care.

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The Digital Transformation of Patients – Update from Rock Health and Stanford

Health Populi

First and foremost, it’s for fitness training, losing weight, sleeping better, and managing a chronic condition. Peoples’ willingness to share their health data with physicians is also the top-trusted share-point, followed by sharing personal health information with family which substantially grew in trust between 2018 and 2020.

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Obstacles to Healthcare Training Data Accuracy and How to Overcome Them

Healthcare IT Today

One of the biggest problems with AI solutions in healthcare right now is getting quality data that you can use to train your AI models. When you train an AI model using generic data, it is like a child trying to teach another child. You couldn’t just throw more data at the problem.

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Bringing Contextualized Health Data into the Diagnostic and Treatment Process

Healthcare IT Today

What’s changed is the explosion of data in healthcare and the availability of this data to clinicians as well as a whole host of healthcare professionals. Bringing context and meaning to this vast amount of data including unstructured health data is going to be key for every healthcare organization.

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Health Data and Interoperability Infrastructure Needs

Healthcare IT Today

However, amidst the IT infrastructure responses we received a number of health IT experts talking about the importance of health data and interoperability infrastructure. If the future of healthcare is built on the back of data, then it makes sense why health data infrastructure would be such an important topic.

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Physicians’ Confidence In and Use of AI is Rising, AMA Finds – Coupling Demand With Many Enabling Factors

Health Populi

Top among these factors include feedback loops for channeling experience and input, data privacy assurances by the hospital/provider and EHR vendors, integration with EHRs and workflows, training and seeing a physician-leader overseeing the Ai implementation.

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Harnessing Large Language Models for Mental Health

Digital Health Global

Challenges and Ethical Considerations Despite their potential, LLMs face key challenges: Bias and Fairness: AI can reflect biases in training data. Data Privacy and Security: Since mental health data is highly sensitive, strong privacy protections are required. Therefore, continuous validation is essential.