J P Systems, Inc.

Digital transformation has become a major priority across the healthcare industry with the rise of advanced technologies like AI. Building and integrating these tools presents unique hurdles -- including data-related challenges, AI transparency and health equity concerns -- for health systems and digital health companies. To spur innovation in this area, Mayo Clinic Platform recently launched Solutions Studio, a program designed to accelerate digital health solutions' development, validation and integration into healthcare workflows. "Mayo Clinic Platform operates in a de-identified federated model, which we refer to as data behind glass," he said. "That means the data doesn't leave the [Solutions Studio] environment," says Steve Bethke, vice president of the solution developer market at Mayo Clinic Platform. #MayoClinic https://lnkd.in/gedYD2qP

Ready Computing serves government clients at the federal, state, and local levels, such as the United States Department of Veterans Affairs, the UK National Health Service, the New York eHealth Collaborative, and the Georgia Health Information Network. They partner with the largest data management corporations in the world, including InterSystems, Google, Amazon, Microsoft, Red Hat, and MongoDB. https://lnkd.in/gi_txgkz

Safeguarding against cybersecurity threats is always an ongoing task. Places like hospitals and other healthcare facilities are frequently targets of ransomware that can prevent clinicians from accessing medical records or tools. Proper asset management can help to prevent cyberattacks. Asset management involves all of the policies, processes and procedures shepherding IT assets from acquisition to retirement. If an IT asset is on the network, it needs to be in a configuration management database (CMDB). This gets at the heart of proper asset management and how it supports security operations. #Cybersecurity #ITAssets https://lnkd.in/gdKaffiN

The Veterans Health Administration is taking steps to identify how one's home can be an integral part of their health and safety. By “monitoring and tracking” Veterans within their homes, “analytics can feed back to you what you need to do better, or what risks you have within your own house" said Joe Ronzio, Deputy Chief Health Technology Officer for the VHA. The smart home technologies in production are aimed at reducing long-term care needs and some skilled nursing needs for aging Veterans. The VHA is also experimenting with various forms of artificial intelligence, including a deep learning model that digests radiological imaging. The co-computing system analyzes images to offer early detection of skin cancers and other types of malformations or abnormalities, Ronzio said. #AI #MachineLearning https://lnkd.in/ejW5WcXP

Frustrated by a lack of clinical data for research or other purposes? Synthetic data, artificially generated information not taken from real-world sources, has been proposed as a potential solution to many of healthcare's data woes, but the approach comes with a host of pros and cons. Relevant applications for synthetic data generation and use within healthcare are identified as application development, clinical research, real data substitutes and patient privacy preservation. #SyntheticData https://lnkd.in/eaeSDuCr

AI holds tremendous potential to aid healthcare providers in diagnosis, treatment selection, clinical documentation, and other tasks to improve quality, access, and efficiency. By taking a proactive and standardized governance approach, The Federation of State Medical Boards may be able to promote safe and effective integration of AI in its various forms, while prioritizing patient wellbeing. However, these technologies introduce risks if deployed without proper “guardrails” and understanding which may impact considerations in clinical practice as well as regulatory processes of state medical boards. https://lnkd.in/gDeS4twg

Model collapse happens when AI models are trained on synthetic, AI-generated content and degrade. These new models will rely too heavily on patterns, overestimating probable events and underestimating improbable events. This means these synthetically trained models will compound errors, misinterpret data and give increasingly wrong and homogeneous outputs. #LLM #AI https://lnkd.in/gZ6BFxJ4

The Technical Language Processing Community of Interest (TLP COI) in collaboration with the National Institute of Standards and Technology (NIST) will hold a TLP COI Meeting and Workshop. This virtual 2 day event on 9/23/24 aims to foster connections and exchange insights across all facets of the TLP Community: from researchers and developers to practitioners and consumers of TLP. #TLP https://lnkd.in/gFVMAzs5

One of the major hurdles to AI adoption in healthcare is the phenomenon of AI hallucination, which occurs when a model generates false or misleading information. To mitigate these risks, the research team set out to develop a hallucination detection framework that could be applied to LLMs tasked with generating medical summaries. Researchers from the University of Massachusetts Amherst and healthcare AI company Mendel have published a framework for hallucination detection in AI-generated medical summaries. #AI https://lnkd.in/g-U7wCwb

Leadership from John Snow Labs, writing in Forbes last year, asserted that “while synthetic data may be useful for demonstrating healthcare software user interfaces, it is currently not suitable for analytics, data science or training medical machine learning models” due to limitations stemming from data leakage, patient cohort generation, and bias. Synthetic data typically lacks the “noise” present in RWD, and models trained on synthetic data may achieve much higher performance than other tools, potentially due to data leakage. In these models, the generated synthetic patient data is too similar to the test set data, which can lead to overly optimistic assessments of performance. #JohnSnowLabs https://lnkd.in/gZHQJG-c