Using AI to Improve Healthcare Data Analytics

Today’s Trends: Drug Development

From pre-clinical studies to clinical trials, Artificial Intelligence (AI) revolutionizes data analytics processes by enhancing the accuracy and efficiency of the various phases of testing, drug development, and optimizing the outcomes. Advanced AI algorithms can rapidly analyze colossal databases of chemical compounds and identify the potential compounds that can be bonded to the target. AI can also predict the effects of a compound and its potential side effects, reducing scientists’ time and effort by allowing them to focus on promising candidates rather than irrelevant ones.

AI is transforming the drug development process, and clinical trials are no exception. AI can help to:

• reduce costs,
• accelerate time to market,
• improve data analysis,
• enable personalized medicine,
• and improve patient outcomes.

Use cases of AI and Benefits

APV has deep expertise in providing thought leadership for application and implementation of AI at Federal and state agencies to enhance their services to citizens. APV worked with Centers for Medicare and Medicaid Services (CMS) and Health Resources and Services Administration (HRSA) to provide an Intelligent Document Processing plan (IDP), case deduplication, and data analytics solutions in AI space. AI algorithms improve drug development from clinical research by automating and streamlining a variety of tasks, including data entry, analysis, and diagnosis. Specific use cases include the following:

Predictive modelling

Using predictive analytics, pharmaceutical companies can create models of how different groups of patients will respond to drugs. These insights provide baseline expectations and help researchers improve their products before clinical trials, making trials more efficient and effective. Additionally, predictive analytics can help reduce the risk of trial failure and patient injury, while increasing the chances of success.

Personalized medicine

AI can be used to identify distinct patient populations that are most likely to benefit from a given medication, based on factors such as genetic composition and lifestyle. This has the potential to make personalized medicine a reality, where patients receive the treatments that are most likely to work for them.

Improved patient outcomes

By improving the efficiency and effectiveness of clinical trials, AI can help to improve patient outcomes. AI can also be used to develop new treatments and therapies that are more targeted and effective.

Predictive Intelligence – Incident Assignment

Predictive Intelligence is used for incident assignment within the Service Center. The solution analyzes the short description provided by the end user using Natural Language Processing (NLP) with previously submitted incidents and assigns the ticket to the appropriate assignment group

Enabling real-time expertise

AI can be used to create a comprehensive knowledge network that connects researchers and clinicians around the world. These connections improve communication and collaboration and facilitate the sharing of information and expertise.

Conclusion

AI, machine learning, data analytics, predictive analytics, and generative AI are rapidly transforming healthcare and life sciences. These technologies can be used to digitize clinical trial processes and provide valuable insights into every aspect of drug development, internal processes, and patient experience. Overall, AI is a powerful tool that has the potential to revolutionize clinical trials. By automating tasks, improving data analysis, and enabling real-time expertise, AI can help to reduce costs, accelerate time to market, and improve patient outcomes.

Reach out to us on emergingtech@apvit.com, for more details on how we can help you!