KXCON23 | AI Superheroes in Healthcare | kdb at Syneos Healthcare

Nataraj will share his experience of building cloud-based, time-series enterprise applications using optimal hardware-software architectural decisions, at 1/10th of the usual cost with kdb+

KXCON23 | AI Superheroes in Healthcare | kdb at Syneos Healthcare

Nataraj Dasgupta is the VP of Advanced Analytics at Syneos Health, a global Clinical Research Organization (CRO) that has helped develop 92% of novel new drugs approved by the FDA in the past 5 years.

Nataraj was a founding team member of RxDataScience, a Healthcare AI/ML startup, which was acquired by Syneos in 2021. Using kdb+, RxDataScience deployed enterprise-scale solutions at some of the world’s largest pharmaceutical companies.

Syneos Health is a global clinical research organization (CRO) that uses kdb+ to power its data analytics and AI capabilities. kdb+ is a high-performance database and analytics platform that is used to process and analyze large amounts of data in real time.

KXCON23 | AI Superheroes in Healthcare | kdb at Syneos Healthcare

Syneos Health uses kdb+ for a variety of purposes, including:

  • Clinical trial data management: kdb+ is used to manage clinical trial data, including patient demographics, medical records, and laboratory results.
  • Data analytics: kdb+ is used to analyze clinical trial data to identify trends and patterns. This information can be used to improve the design and conduct of clinical trials.
  • AI: kdb+ is used to develop and deploy AI models to improve the efficiency and accuracy of clinical trial data analysis.

Syneos Health’s use of kdb+ has helped the organization to improve its clinical trial data management, data analytics, and AI capabilities. This has led to better outcomes for patients, such as shorter clinical trial timelines and improved patient safety.

Here are some specific examples of how Syneos Health uses kdb+:

  • Syneos Health uses kdb+ to manage data from clinical trials that involve thousands of patients. This data includes patient demographics, medical records, and laboratory results. kdb+’s speed and scalability allow Syneos Health to process and analyze this data in real time, which helps them to identify trends and patterns that can be used to improve the design and conduct of clinical trials.
  • Syneos Health uses kdb+ to develop and deploy AI models to predict the risk of adverse events in clinical trials. This information can be used to identify patients who are at risk of developing adverse events, and to take steps to prevent them.
  • Syneos Health uses kdb+ to monitor the performance of clinical trials in real time. This information can be used to identify any potential problems with the trials, and to take corrective action as needed.

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Syneos Health’s use of kdb+ is a good example of how AI and data analytics can be used to improve the efficiency and effectiveness of clinical trials. As AI and data analytics technology continues to develop, we can expect to see even more innovative applications of these technologies in the healthcare industry.

AI superheroes in healthcare refer to the powerful and transformative applications of artificial intelligence (AI) in the medical field. These applications have the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance the efficiency of medical processes. Here are some examples of AI superheroes in healthcare:

  1. Diagnostic Assistants:

    • AI-powered diagnostic tools can analyze medical images (such as X-rays, MRIs, and CT scans) and identify abnormalities or potential diseases. These tools can assist healthcare providers in making more accurate and timely diagnoses.
  2. Predictive Analytics:

    • AI algorithms can analyze patient data to predict disease progression, identify patients at risk of certain conditions, and provide personalized treatment plans.
  3. Natural Language Processing (NLP):

    • NLP enables AI systems to understand and extract valuable information from unstructured text, such as clinical notes, medical records, and research papers. This can facilitate data-driven decision-making for healthcare professionals.
  4. Drug Discovery and Development:

    • AI can significantly expedite the process of drug discovery by analyzing large datasets to identify potential drug candidates, predict their efficacy, and optimize dosage regimens.
  5. Virtual Health Assistants:

    • AI-powered virtual assistants and chatbots can provide patients with immediate access to medical information, answer common questions, and even assist in scheduling appointments.
  6. Robotic Surgery:

    • AI-driven robotic systems can enhance surgical precision and accuracy, leading to better outcomes for patients. Surgeons can use robotic assistance for procedures that require a high level of precision.
  7. Remote Monitoring and Telemedicine:

    • AI can enable continuous monitoring of patients’ vital signs and health metrics, allowing for early detection of any anomalies or changes in health status. This is particularly valuable for managing chronic conditions and providing care in remote or underserved areas.
  8. Personalized Treatment Plans:

    • AI can analyze genetic, lifestyle, and clinical data to create personalized treatment plans for patients. This ensures that interventions are tailored to an individual’s unique needs and characteristics.
  9. Clinical Trial Optimization:

    • AI algorithms can identify suitable candidates for clinical trials based on specific criteria, which can accelerate the research and development of new therapies.
  10. Natural Disaster Response:

    • AI can be used to analyze data in real-time during natural disasters or health emergencies to help allocate resources efficiently and provide timely medical assistance.
  11. Mental Health Support:

    • AI-powered chatbots and virtual assistants can offer mental health support, provide coping strategies, and even detect signs of distress in text or speech.
  12. Medical Research and Literature Review:

    • AI can sift through vast amounts of medical literature to extract relevant information, helping researchers stay up-to-date with the latest advancements in their field.

These AI superheroes are not meant to replace healthcare professionals but to empower them with advanced tools and insights, ultimately leading to improved patient care and outcomes. However, it’s important to ensure that these technologies are ethically and responsibly developed and deployed in order to maintain patient privacy and safety.

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