Revolutionizing Pharma Commercial Operations with Data-Centric AI/ML

Revolutionizing Pharma Commercial Operations with Data-Centric AI/ML

Table of Contents:

1. Introduction
2. The Problem of Providing Personalized Education to Doctors
3. Rapid Innovation in Cancer Therapies and Rare Diseases
4. Challenges in Keeping Up with Innovations and Guidelines
5. The Role of Multi-Disciplinary Teams in Cancer Treatment
6. The Need for Coordinated and Personalized Education
7. Leveraging External and Internal Data Sources
8. Understanding Patient's Needs and Deviations from Standard Care
9. Predicting the Next Best Intervention
10. Delivering Actionable Insights to Different Teams
11. Challenges of Using Real-World Data in Pharma Commercial
12. Enriching Data and Addressing Missing Values
13. Labeling Data for AI Model Training
14. Evaluation Criteria for Data Enrichment
15. Opportunities for Scale in Data Enrichment
16. The Relationship between Machine Learning and Domain Experts
17. The Role of Human Input in Data Enrichment
18. The Future of AI in Data Enrichment

Enabling Personalized Cancer Treatment through Data-Centric AI and ML

Introduction

In the field of cancer treatment, personalized education plays a crucial role in ensuring that doctors make the right treatment decisions at the right time. However, with rapid innovation in cancer therapies and the complexities of rare diseases, providing personalized education becomes a significant challenge. This article discusses the opportunities and challenges of using data-centric AI and ML in the pharma commercial sector to enable personalized education for doctors and improve patient outcomes.

The Problem of Providing Personalized Education to Doctors

In the field of cancer therapies and rare diseases, there is a constant influx of new targeted therapies that are highly effective but limited to specific indications. However, many patients are not receiving the right treatment at the right time, primarily due to the challenges faced by doctors in keeping up with innovations and changing guidelines. Additionally, the multidisciplinary nature of cancer treatment, involving various specialties, further complicates the need for coordinated education.

Rapid Innovation in Cancer Therapies and Rare Diseases

Cancer treatment is witnessing rapid innovation, with the introduction of hundreds of new targeted therapies. These therapies have shown remarkable efficacy in specific indications. However, due to the constant evolution of treatment options, doctors struggle to keep up with the pace of innovation and changing guidelines. This creates a gap in providing personalized education and delays the administration of appropriate treatments to patients.

Challenges in Keeping Up with Innovations and Guidelines

The fast-paced nature of cancer treatment innovation presents challenges for doctors in staying updated with the latest advancements. With the constant introduction of new therapies and guidelines, it becomes increasingly difficult for doctors to provide optimal care to their patients. This challenge is magnified in the case of rare diseases, where expertise from multiple specialties is required to make informed treatment decisions.

The Role of Multi-Disciplinary Teams in Cancer Treatment

Cancer treatment, particularly in rare diseases, involves multi-disciplinary teams comprising surgeons, radiation oncologists, medical oncologists, and other specialists. These teams collaborate to determine the most suitable treatment plan for each patient. Coordinating education across these different specialties becomes crucial to ensuring that patients receive the right investigations and treatments.

The Need for Coordinated and Personalized Education

Personalized education is essential to address the challenges of coordinating treatment decisions across multiple specialties. A one-size-fits-all approach fails to account for the individual needs of patients. Therefore, there is a significant opportunity for AI software and systems to provide coordinated and personalized education, tailored to the specific requirements of each patient.

Leveraging External and Internal Data Sources

The key to enabling personalized education lies in leveraging a wide range of data sources. External real-world data sources, although noisy, provide valuable insights into the patient Journey and unmet needs. Internal data sources within pharmaceutical companies also contain critical information about drugs, indications, and preferred treatment approaches. Bringing together these data sources allows for a comprehensive understanding of patient needs and deviations from standard care.

Understanding Patient's Needs and Deviations from Standard Care

To deliver personalized education, it is crucial to gain insights into what is happening at an individual patient level. This includes identifying unmet needs, understanding deviations from standard care, and identifying drivers of prescribing behaviors. By answering the questions of what and why, AI systems can predict the next best intervention and provide actionable insights to the Relevant teams.

Predicting the Next Best Intervention

Once the key questions of what and why are answered, AI systems can be developed to predict the next best intervention for each patient. By analyzing the available data and identifying Patterns, AI models can provide recommendations that are tailored to the individual patient's needs and treatment history. These predictions empower healthcare professionals to make informed treatment decisions and provide optimal care.

Delivering Actionable Insights to Different Teams

In the pharma commercial sector, various teams, including commercial, medical, and field-Based representatives, play a crucial role in engaging with doctors. To deliver personalized education effectively, actionable insights generated by AI systems need to be seamlessly communicated to these teams. By providing easily consumable information, the communication can be tailored to the needs of doctors, ensuring the right level of engagement.

Challenges of Using Real-World Data in Pharma Commercial

While real-world data is a valuable resource in enhancing personalized education, there are challenges associated with its use. One of the significant challenges is the breadth of data required to understand the patient journey adequately. Real-world data sets, such as electronic medical records (EMR), are often fragmented, making it challenging to gain a holistic view. Additionally, EMR data has missing values, limited capture rates for essential information, and privacy limitations.

Enriching Data and Addressing Missing Values

To overcome the limitations of real-world data, data enrichment techniques can be employed. Enrichments involve leveraging internal and external data sources to fill in missing values and obtain a comprehensive view of the patient journey. For example, insurance claims data can provide breadth, although it may be noisy and have missing data due to privacy and business reasons. Machine learning and AI techniques can be used to make enrichments, enabling the development of more accurate predictive models.

Labeling Data for AI Model Training

Labeling data is a critical aspect of model training. The process involves expert medical and business professionals manually labeling the data to provide insights and enrichment. This process is time-consuming and resource-intensive but plays a crucial role in training AI models to accurately predict patient eligibility for specific drugs and interventions. The challenge lies in ensuring consistency and accuracy across the labels generated by different experts.

Evaluation Criteria for Data Enrichment

Evaluating the effectiveness of data enrichment approaches is essential to ensure the quality and accuracy of the insights generated. While the specific evaluation criteria may vary depending on the use case, a combination of curated data sets and expert validation can provide reliable results. The evaluation criteria should focus on minimizing false negatives and validating the results with established sources, such as academic experts.

Opportunities for Scale in Data Enrichment

Scaling data enrichment efforts is critical to meet the increasing demand for personalized education and insights. Current approaches heavily rely on expert labeling, which limits scalability. To address this, efficient approaches, such as slice-based data evaluation and programmatic labeling, can be explored. Leveraging multi-modal data sets and consulting with domain experts within and outside the pharmaceutical industry can further enhance knowledge representation and labeling consistency.

The Relationship between Machine Learning and Domain Experts

Machine learning models and domain experts complement each other in the process of data enrichment. While machine learning enables triage, filtering, and enrichment of the data, the expertise of domain experts is crucial in ensuring high-quality labeling and capturing nuanced insights. Human input will Continue to play a significant role in the accuracy and reliability of AI systems, as they possess the knowledge and experience to make informed decisions.

The Role of Human Input in Data Enrichment

Human input, particularly from domain experts, is indispensable in the data enrichment process. The dynamic nature of medical science and guidelines requires human intervention to keep AI systems up-to-date and accurate. AI systems can aid experts by providing decision support tools and leveraging their insights to develop more comprehensive predictive models. However, the final decision-making authority will always rest with human experts.

The Future of AI in Data Enrichment

The future of data enrichment lies in effectively combining the capabilities of machine learning and domain experts. Streamlining processes, such as expert labeling, through the development of efficient tools and workflows, will be instrumental in scaling AI applications. The collaboration between the data-centric AI community, pharmaceutical companies, and experts from diverse fields will play a pivotal role in driving innovation and advancements in data enrichment. With continuous improvement and integration of AI systems into healthcare workflows, personalized cancer treatment can be significantly enhanced.

Highlights:

• The use of data-centric AI and ML in pharma commercial holds great potential for enabling personalized education to doctors in cancer treatment.
• Rapid innovation in cancer therapies creates challenges in keeping up with advancements and changing guidelines.
• Coordinated and personalized education is needed to address the complexities of cancer treatment involving multidisciplinary teams.
• Leveraging external and internal data sources is crucial for understanding patients' needs and deviations from standard care.
• Data enrichment techniques, such as labeling and business rules, can overcome the limitations of real-world data.
• Expert input and collaboration between machine learning models and domain experts are essential for accurate and reliable data enrichment.
• The future of data enrichment lies in developing efficient tools and workflows and collaborating with the data-centric AI community and healthcare experts.

FAQ:

Q: What is the main challenge in providing personalized education to doctors in cancer treatment? A: The main challenge is keeping up with rapid innovation in cancer therapies and changing guidelines.

Q: How can AI help in delivering personalized education to doctors in cancer treatment? A: AI can leverage data from various sources to understand patients' needs, predict the next best intervention, and provide actionable insights to relevant teams.

Q: What are the challenges in using real-world data for data-centric AI in pharma commercial? A: Real-world data is often fragmented, has missing values, and limited capture rates. Privacy limitations and noise further complicate its use.

Q: What is the role of human input in data enrichment? A: Human input, especially from domain experts, is crucial in labeling and validating data, ensuring accuracy and reliability in AI models.

Q: How can data enrichment efforts be scaled for personalized education in cancer treatment? A: Efficient approaches, such as programmatic labeling and leveraging multi-modal data sets, can enhance scalability. Collaboration with domain experts and the data-centric AI community is vital for knowledge representation and labeling consistency.

Q: What does the future hold for data-centric AI in pharma commercial? A: The future involves streamlining processes, developing efficient tools and workflows, and continued collaboration to drive innovation and advancements in data enrichment, ultimately enhancing personalized cancer treatment.