2023 Global Summit

AI and NLP are universally applicable and malleable technologies that can be applied to many domains in the pharma industry, improving both patient outcomes and business performance. These tools offer invaluable insights from unstructured data, particularly in reporting and analytics for late stage and launched products. However, transitioning from a proof of concept (POC) to a productive environment presents challenges, especially in demonstrating the value of these AI/NLP solutions and establishing the role of the product owner an organization function with no prior experience.

We will address transitioning from a POC to a productive environment including strategies for communication and cross-functional collaboration as well as strategies for quantifying and communicating the value of AI projects in the medical domain in the pharma industry.

Finally, we will discuss the creation of a product owner role in an organization that has little experience of this role. We will outline the specific competencies, roles, and responsibilities, which may need to be acquired or developed within the organization. Additionally, we will discuss strategies for fostering a culture of innovation and collaboration is crucial to successfully implementing AI/NLP solutions.

This presentation will focus on a case study showing how EMR-equivalent data provides high quality, longitudinal patient data to support product launch in an uncommon disease in four European countries.

Uncommon disease background:

• Rare disease <1,500 patients in each major European country. Treatment is loaded towards a subset of the institutions and life science companies have a list of target institutions.
• Diagnosis via complex combination of genetics, diagnostic tests, plus S&S.
• Progressive condition with acute relapses. Acute episodes are treated differently from progression – a key data target.
• Until recently no labelled treatments.
• Client need and data objectives
• In each of four major European countries (Germany, France, Italy and Spain)

Detailed patient journey:

• Diagnostics and interaction with comorbidities
• Progression and treatment
• Relapse dynamics

Data objectives:

• Market structure
• Dynamic market evolution
• Validation of existing data sources
• Registries

The client need defined as follows:

• Longitudinal patient clinical records tracking from pre-diagnosis (EMR data)
• Data from target institutions
• Forward-looking tracking to monitor shifts in treatment behaviors and new treatments

Data capture and analysis. Proprietary data capture tools used to achieve:

• Diagnostic journey and adherence with guidelines
• High response from target tiers
• Representative data coverage
• Full EMR records on each patient, allowing for complex multi-dimensional analytics

Conclusions: This presentation will show how this method can be applied and shows how the method is being expanded by specialty and geography.

The session will be focused on answering the following questions:

• Why is it important for LS companies to strategically plan the promotion budgets in Italy?
• How Market Mix Modelling is a valuable solution to make data-backed informed decisions?
• How to effectively build an MMM solution despite limitations like lack of HCP level sales and other associated constraints given the market landscape?
• What are the expected outcomes of this MMM solution?
• How to scale this up for other EU countries?

Precision medicine powered by novel targeted therapies is revolutionizing oncology care for patients based on their protein expression levels and genomic phenotype. As the pharmaceutical landscape continues to evolve with these treatment advances, there has been a shift in strategy to focus more on improving patient health outcomes and equity. This shift in focus ensures that more patients receive best-in-class and optimal treatment. This requires health care providers to be more aware and empowered to administer comprehensive care based on the latest approved guidelines. Improving the ability to identify these unmet needs, both for patients as well as physicians, enables pharmaceutical organizations to become strategic partners in ensuring delivery of optimal care to patients.

To adapt to these changes in practice, its important to understand the needs of both patients and physicians. Understanding deviations from optimal treatment and quantifying gaps in care can define priority focus areas for the pharma industry. These insights can impact clinical trial planning and enhance the specificity of commercial and medical initiatives including targeted educational programs. These insights will help define medical strategy and guide proactive and integrated evidence generation planning. Finally, the pharma industry can use these analyses to better plan their resource allocation and drive enhanced physician and patient engagement experiences.

AI and NLP are universally applicable and malleable technologies that can be applied to many domains in the pharma industry, improving both patient outcomes and business performance. These tools offer invaluable insights from unstructured data, particularly in reporting and analytics for late stage and launched products. However, transitioning from a proof of concept (POC) to a productive environment presents challenges, especially in demonstrating the value of these AI/NLP solutions and establishing the role of the product owner an organization function with no prior experience.

We will address transitioning from a POC to a productive environment including strategies for communication and cross-functional collaboration as well as strategies for quantifying and communicating the value of AI projects in the medical domain in the pharma industry.

Finally, we will discuss the creation of a product owner role in an organization that has little experience of this role. We will outline the specific competencies, roles, and responsibilities, which may need to be acquired or developed within the organization. Additionally, we will discuss strategies for fostering a culture of innovation and collaboration is crucial to successfully implementing AI/NLP solutions.

This presentation will focus on a case study showing how EMR-equivalent data provides high quality, longitudinal patient data to support product launch in an uncommon disease in four European countries.

Uncommon disease background:

• Rare disease <1,500 patients in each major European country. Treatment is loaded towards a subset of the institutions and life science companies have a list of target institutions.
• Diagnosis via complex combination of genetics, diagnostic tests, plus S&S.
• Progressive condition with acute relapses. Acute episodes are treated differently from progression – a key data target.
• Until recently no labelled treatments.
• Client need and data objectives
• In each of four major European countries (Germany, France, Italy and Spain)

Detailed patient journey:

• Diagnostics and interaction with comorbidities
• Progression and treatment
• Relapse dynamics

Data objectives:

• Market structure
• Dynamic market evolution
• Validation of existing data sources
• Registries

The client need defined as follows:

• Longitudinal patient clinical records tracking from pre-diagnosis (EMR data)
• Data from target institutions
• Forward-looking tracking to monitor shifts in treatment behaviors and new treatments

Data capture and analysis. Proprietary data capture tools used to achieve:

• Diagnostic journey and adherence with guidelines
• High response from target tiers
• Representative data coverage
• Full EMR records on each patient, allowing for complex multi-dimensional analytics

Conclusions: This presentation will show how this method can be applied and shows how the method is being expanded by specialty and geography.

The session will be focused on answering the following questions:

• Why is it important for LS companies to strategically plan the promotion budgets in Italy?
• How Market Mix Modelling is a valuable solution to make data-backed informed decisions?
• How to effectively build an MMM solution despite limitations like lack of HCP level sales and other associated constraints given the market landscape?
• What are the expected outcomes of this MMM solution?
• How to scale this up for other EU countries?

Precision medicine powered by novel targeted therapies is revolutionizing oncology care for patients based on their protein expression levels and genomic phenotype. As the pharmaceutical landscape continues to evolve with these treatment advances, there has been a shift in strategy to focus more on improving patient health outcomes and equity. This shift in focus ensures that more patients receive best-in-class and optimal treatment. This requires health care providers to be more aware and empowered to administer comprehensive care based on the latest approved guidelines. Improving the ability to identify these unmet needs, both for patients as well as physicians, enables pharmaceutical organizations to become strategic partners in ensuring delivery of optimal care to patients.

To adapt to these changes in practice, its important to understand the needs of both patients and physicians. Understanding deviations from optimal treatment and quantifying gaps in care can define priority focus areas for the pharma industry. These insights can impact clinical trial planning and enhance the specificity of commercial and medical initiatives including targeted educational programs. These insights will help define medical strategy and guide proactive and integrated evidence generation planning. Finally, the pharma industry can use these analyses to better plan their resource allocation and drive enhanced physician and patient engagement experiences.