9.  AI-Augmented Healthcare

AI in healthcare, or augmented intelligence, focuses on augmenting the capabilities of physicians and has shown promising results in reducing mortality rates, decreasing ICU transfers, and potentially saving 500 lives per year.

• Dr. Vincent Liu, a senior research scientist and regional medical director of Augmented Clinical Intelligence at Kaiser Permanente, discusses the implementation of AI in healthcare and its early results.

• AI in healthcare is referred to as augmented intelligence because it focuses on augmenting the capabilities of physicians and prioritizes people, patients, communities, and clinicians over algorithms.

• Kaiser Permanente has been using the Advanced Alert Monitor program, which utilizes AI technology to identify high-risk patients in the hospital and prevent adverse events.

• Using machine learning algorithms paired with robust workflows and collaboration with clinicians, the implementation of augmented intelligence in healthcare reduced mortality rates, decreased ICU transfers, and potentially saved 500 lives per year.

AI is being used in different areas and is important in healthcare, with one example being a technology that analyzes and sorts patient-physician messages to prioritize urgent cases and ensure timely attention to concerns.

• AI is being utilized in various domains and is playing a key role in the overall strategy.

• Using natural language processing, a team has developed a technology that analyzes and sorts messages between patients and physicians, helping to prioritize urgent cases and ensure timely attention to concerns in healthcare.

Computer vision algorithms can identify high-risk features in breast mammograms, improving breast cancer detection and allowing for personalized screening recommendations, while the integration of AI in healthcare can reduce physician burden and improve patient care.

• Computer vision algorithms can identify high-risk features in breast mammograms that were called normal by radiologists, potentially increasing the identification of patients at risk for breast cancer and allowing for personalized screening recommendations and targeted outreach.

• The integration of AI technology in healthcare can help reduce the burden on physicians and improve patient care.

• The implementation of AI and augmented intelligence in healthcare has shown promising results, reducing death rates among hospitalized patients and decreasing high-risk readmissions, although the process is complex and clinicians are overburdened.

Integrating AI and machine learning in healthcare aims to improve efficiency and reduce burden on clinicians, with a focus on cautious implementation, testing, and collaboration to ensure patient outcomes are improved and resources are allocated effectively.

• Integrating AI and machine learning in healthcare should either replace tasks to make them more efficient or remove unnecessary elements to avoid alert fatigue and distraction.

• The goal is to cautiously approach and test AI tools in healthcare, ensuring they improve patient outcomes and reduce burden on clinicians, in order to build support for their use and create an unburdened future.

• Health systems are currently focusing on the implementation of AI-powered programs and updating policies to address the risks and benefits, as well as collaborating with partners and allocating resources to this area of growth.

Health systems must establish forums and frameworks for stakeholder engagement and oversight to make informed decisions about implementing AI in healthcare, while training physicians and clinicians to understand AI; caution is needed when leveraging large language models, and human oversight remains essential.

• Health systems need to establish forums and frameworks for stakeholder engagement and oversight in order to make informed decisions about implementing AI in healthcare, and there is a need to train physicians and clinicians to understand AI.

• Data science involves understanding and evaluating various technologies such as reinforcement learning, large language models, bias, and fairness evaluations, which requires specialized training and development of a workforce.

• The speaker discusses the importance of understanding both the clinical deployment and technology of AI in healthcare, and the need for governance, oversight, and a capable workforce to guide its implementation, with computer vision being a tangible next step.

• Leveraging large language models can improve communication with patients, help with information retrieval, and assist in risk prediction, but caution is necessary and human oversight is still essential.

Advancements in AI technology in healthcare, funded by the AIM-HI Program, aim to solve real problems for patients and clinicians by improving patient outcomes and developing best practices.

• Advancements in technology, such as treatment recommender systems and robotics, will provide more capabilities in healthcare, solving real problems for patients and clinicians in a safe and sustainable manner.

• The AIM-HI Program, funded by the Gordon and Betty Moore Foundation, aims to provide grants to health systems to conduct rigorous tests on the impact of AI on patient outcomes, in order to develop best practices and prove the important role of AI in improving healthcare.

Striking a balance between regulations and innovation is crucial in AI and machine learning in healthcare to prevent technology companies from gaining control over patient data and reducing autonomy in healthcare.

• Regulations are important for putting appropriate guardrails in place for AI and machine learning in healthcare, but there is a balance to be struck to avoid stifling innovation.

• The speaker emphasizes the importance of allowing health systems and practices to innovate and use patient data to improve outcomes without being burdened by excessive regulations, as it could lead to technology companies gaining control over patient data and reducing autonomy in healthcare.

AI in healthcare is making significant progress, countering the hysteria surrounding it, and the speaker will provide updates on the results in the future.

🧠 "This is technology that augments the capability of our physicians rather than just focused on the development of algorithms."

💡 The use of AI in healthcare aims to leverage technology to prevent and respond to patients at risk, improving patient outcomes.

💡 The implementation of AI and machine learning in healthcare, when closely tied with clinicians and patients, has the potential to reduce mortality, reduce ICU transfers, and save lives.

📊 AI technology is already being used to analyze and sort an average of a million messages per month in the healthcare system, improving workflow and patient care.

🏥 This technology unlocks opportunities for personalized screening recommendations, targeted outreach, and avoiding unnecessary second visits for patients, improving efficiency and patient care in healthcare.

💪 The implementation of AI and augmented intelligence in healthcare has shown promising results, reducing death among hospitalized patients by as many as 500 per year and decreasing high-risk readmissions by up to 10%.

💪 "If they produce better patient outcomes, it really strengthens the level of support for these types of tools, even when, occasionally, some of them may produce some excess burden or work for our clinicians, but really to prevent adverse outcomes."

🚀 The future of healthcare will involve treatment recommender systems and precision medicine, utilizing omics data to provide personalized and targeted treatments.

Despite the high cost of healthcare in the US and the availability of numerous treatments, doctors still struggle to determine the most effective treatment for each patient, leading to a need for AI assistance.

We can use AI and machine-learning methods with electronic medical records to predict and address medical challenges, augmenting both clinicians and patients.

Machine learning can help guide the selection of antibiotics for urinary tract infections by analyzing antibiotic susceptibility profiles and providing real-time recommendations.

Using a machine learning algorithm, doctors can predict which antibiotics will be most successful in resolving infections, resulting in 20% fewer inappropriate prescriptions and a 50% reduction in second line antibiotic usage, which can help prevent the growth of antibiotic-resistant superbugs.

Algorithms can use patient data to predict personalized responses to cancer treatments, including survival time and quality of life.

Our deep Markov models accurately forecast a patient's future biomarkers, adverse events, and progression-free survival under different treatment options, outperforming earlier linear modeling approaches.

Patients can now access the notes written by clinicians about their visit, but these notes are often difficult for patients to understand due to the complexity of medical language.

AI can help patients understand their health records and improve patient care by translating clinician notes, defining clinical concepts, generating Q&A, and making detailed cancer patient data available through apps.

• AI can be used to help patients understand their health records by translating clinician notes into patient-friendly language, defining clinical concepts, and generating questions and answers to address patient concerns.

• Algorithms require detailed information about cancer patients and their outcomes, and to improve patient care, it is necessary to make this data available to patients through apps and collect higher quality data on a larger scale.

🏥 Patients often find it challenging to navigate the healthcare system, understand their diagnosis, and make informed decisions about their treatment options.

📊 AI and machine-learning methods can utilize electronic medical records to predict and address medical challenges, potentially improving healthcare outcomes.

📊 AI has the potential to significantly improve the decision-making process for urinary tract infections by providing real-time analysis of antibiotic susceptibility profiles, reducing the reliance on delayed laboratory results.

🦠 AI-powered tools can help reduce the use of last-resort antibiotics by 50%, potentially preventing the growth of antibiotic-resistant superbugs.

📊 The future of cancer treatment involves going beyond traditional randomized controlled trials and incorporating data from genomics and medical history to make more informed decisions.

📊 The new algorithms outperform earlier linear modeling approaches in matching the ground truth predictions of a patient's biomarkers.

📝 The availability of clinicians' notes to patients through patient portals presents an opportunity for patients to be more informed about their healthcare, but the complexity of medical language remains a challenge.

💡 AI can help patients better understand their health records by translating clinician speak to patient speak, defining clinical concepts, and generating questions and answers.