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Strategies & Market Trends : Value Investing -- Ignore unavailable to you. Want to Upgrade?

To: Sean Collett who wrote (78004)	8/31/2025 3:19:42 PM
From: E_K_S	Read Replies (1) \| Respond to of 78497

It's already happening but in a slightly different way. ORCL maintains a cloud based customer medical records systems and now uses AI to help identify drug discovery & secondary or "serendipitous" findings. Oracle Cloud and Health Record Management Oracle's presence in the health record management space is primarily through its acquisition of Cerner in 2022, a major provider of electronic health record (EHR) systems. This business is now known as Oracle Health. Oracle is leveraging its cloud infrastructure, Oracle Cloud Infrastructure (OCI), to transform these healthcare solutions. 1. EHR System(s): Oracle Health EHR: The core of Oracle's health record management is the Cerner EHR system, which has been a prominent player for decades. Next-Generation EHR: Oracle is actively developing and rolling out a new, modernized EHR built on OCI. This "next-generation" system is designed to be cloud-native and "AI-first," with a strong focus on features like voice-activated commands, conversational AI, and personalized, streamlined workflows for clinicians. The goal is to reduce administrative burden and "reimagine" the clinician experience. 2. Customers and Revenue: Customers: Oracle Health (formerly Cerner) has a significant customer base, with over 2,000 hospitals using their EHR systems. Notable clients include Atrium Health, Baystate Health, Dignity Health, and government institutions like the Department of Veterans Affairs (VA). Revenue: Oracle's health business, primarily from Cerner, has been a major revenue stream. In 2023, Cerner contributed approximately $5.9 billion to Oracle's total revenue. However, recent reports from industry analysts have noted some challenges, including customer dissatisfaction and a decline in market share for Oracle Health in the acute care hospital segment. 3. Forecasted Growth: The U.S. electronic health records market is expected to have moderate growth, with a forecasted CAGR of around 2.55% from 2025 to 2030. The overall market is dominated by a few key players, with Oracle Health holding the second-largest market share behind its competitor, Epic. Despite recent challenges, Oracle's strategy is to grow its market share by modernizing its EHR and integrating it with its powerful cloud and AI services. The company's focus on an "AI-first" and "cloud-based" solution is a key part of its growth strategy. The Importance of Medical Records for AI and Drug Discovery Medical records, especially when in a structured, digital format, are crucial for advancing healthcare through AI and drug discovery. Here's why: 1. For AI: Training Data: EHRs provide the massive, rich datasets needed to train powerful AI models. These records contain a wealth of information, including patient demographics, diagnoses, lab results, medications, and clinical notes. AI systems can use this data to identify hidden patterns, predict patient risk factors, and aid in diagnosing complex cases. Clinical Decision Support: AI, powered by EHR data, can create intelligent "assistants" for clinicians. These systems can summarize a patient's entire chart, highlight critical insights, and provide contextual decision support at the point of care, helping physicians make more informed and efficient decisions. Population Health Management: By analyzing aggregated and de-identified data from many patients, AI can help healthcare systems identify at-risk populations and design more effective, coordinated care programs. Operational Efficiency: AI can automate administrative tasks like note-taking, scheduling, and billing, which reduces the cognitive burden on healthcare staff and allows them to focus more on patient care. 2. For Drug Discovery: Accelerating Research: EHRs contain invaluable real-world data on treatments and patient outcomes. AI models can mine this data to identify side effects of drugs, evaluate their efficacy in different demographic groups, and even screen molecular compounds for new therapies. Personalized Medicine: By combining EHR data with genetic information and other sources, AI algorithms can provide insights into how individual patients might respond to a particular drug. This helps practitioners determine the optimal timing and dosage for personalized treatment plans. Clinical Trials: EHR data can be used to more effectively assemble and manage clinical trial panels by identifying patients with specific genetic profiles or conditions that are relevant to the research. This can accelerate the development and approval of new drugs. ------------------------------------------------------------------------------------------ It's about the 'collective' data and the LLM that can be developed from this data.

To: Sean Collett who wrote (78004)	8/31/2025 3:28:00 PM
From: E_K_S	Respond to of 78497

Here is a real world example BEFORE LLM's Metformin and Alzheimer's Disease The link between metformin and Alzheimer's disease is a fascinating and ongoing area of research that exemplifies how a drug's "secondary findings" or "off-label" effects can lead to new therapeutic avenues. Primary Use: Metformin has been a first-line treatment for Type 2 diabetes for decades. Its primary mechanism of action is to lower blood glucose by increasing insulin sensitivity and decreasing glucose production in the liver. Secondary Findings and Observations: Over time, observational studies began to show a correlation: people with diabetes who were taking metformin seemed to have a lower risk of developing dementia and Alzheimer's disease compared to those who weren't. This was a "secondary finding" from analyzing large-scale health data, as the drug was not originally prescribed for cognitive issues. The Research Connection: This observation sparked significant research into how metformin might affect the brain. Scientists have since found several potential mechanisms, including: Neuroprotective effects: Metformin has been shown to reduce neuroinflammation, oxidative stress, and neuronal cell death in animal models. Metabolic link: There's a strong connection between insulin resistance (a hallmark of Type 2 diabetes) and Alzheimer's disease. Metformin's ability to improve insulin sensitivity might also benefit brain health. AMPK activation: Metformin activates an enzyme called AMPK, which plays a critical role in cellular energy metabolism. This activation may help clear abnormal proteins (like amyloid-beta and tau, which are associated with Alzheimer's) from the brain ---------------------------------------------------------------------------------------------- The initial discovery of the link between metformin and Alzheimer's disease was not due to AI or LLMs. It was a classic example of observational research using large-scale medical records, sometimes referred to as "real-world data." The Role of Large Medical Records For years, researchers have been analyzing large datasets from health systems, insurance companies, and national health registries. These datasets contain anonymous information on millions of patients, including their diagnoses, prescriptions, and health outcomes over many years. When studying these records, scientists began to notice a compelling pattern: People with Type 2 diabetes who were prescribed metformin appeared to have a lower risk of developing dementia and Alzheimer's disease compared to those with diabetes who were not taking the drug or were on other medications. This observation, repeated across various studies and different populations, was the key "secondary finding." It was the direct result of a human researcher or team asking a question of a large database, identifying a correlation, and then forming a hypothesis to test in a more controlled, mechanistic way. The Role of AI and LLMs While AI and LLMs were not involved in the initial discovery of this link, they are crucial tools in the subsequent research. Their role is to accelerate the process of understanding the "why" and "how" behind the observation. AI for Deeper Analysis: AI and machine learning algorithms can be used to analyze these same vast datasets with greater speed and precision. They can identify subtle correlations that might be missed by human researchers, and they can help to control for confounding variables that could skew the results. For example, AI can analyze a patient's entire health history to determine if other factors (like cholesterol or blood pressure) are more responsible for the observed link. LLMs for Synthesis and Hypothesis Generation: While a correlation in the data is a starting point, it doesn't explain the biological mechanism. LLMs are not used to make the initial "discovery," but they can be invaluable for synthesizing the latest scientific literature to help researchers formulate new hypotheses. They can quickly process thousands of scientific papers to find connections between metformin's known effects on the body and the biological processes involved in Alzheimer's. In summary, the initial observation was a product of traditional data analysis on large, ORCL/EPIC-like databases. The deeper understanding of that link is where modern AI and LLMs are playing an increasingly significant role. ----------------------------------------------------------------------------- You want to own/manage the data and the LLM's. ORCL is now one of my top 5 holding for this reason.