One major trend is the rise of hyper-automated AI. This technology utilizes advanced machine learning algorithms and advanced data analytics to quickly analyze vast streams of data, identify promising drug candidates, streamline experiments and trials, and accelerate the overall development process. With the steep increase in the rate at which data is getting generated (the healthcare industry alone contributes approximately 30% of the world’s data volume), hyper-automated AI has the potential to bring new therapies to patients faster and improve healthcare outcomes significantly.
Another important trend is the development of AI and ML-powered next-best action engines. These engines analyze extensive patient and physician data to provide real-time suggestions on engagement strategies, enhancing decision-making, improving brand performance, and optimizing physician and patient experiences at scale.
AI-powered patient-centricity solutions too are a significant and evolving trend. It involves developing and innovating drugs and treatments specifically tailored to the needs, demographics, and behaviors of individual patients. By leveraging the power of AI, life sciences companies can gather and analyze vast amounts of patient data to gain valuable insights and make informed decisions.
The Internet of Things (IoT) is also a prominent trend. These devices possess swarm intelligence, enabling them to serve as autonomic decision-makers via AI systems and social networks. This helps with real-time monitoring, patient alert generation, and continuous tracking, paving the way for convenient and less time-consuming communication with a focus on better HCP and patient connect.
We closely collaborate with our clients to understand their specific needs, regulatory requirements, and existing infrastructure. This enables us to develop tailored strategies that align with their business objectives and ensure compliance with industry regulations. Our expertise lies in implementing customized digital technologies which enhance research and development processes, optimize clinical trials, streamline commercial models, and improve operational efficiency.
We also offer robust data management systems that adhere to data privacy and security regulations, establish reliable data governance frameworks, and provide data integration and interoperability solutions. Indegene’s analytical solutions leverage real-world evidence and big data to gain valuable insights into patient outcomes, treatment effectiveness, physician preferences, and market trends, empowering lifecycle teams worldwide to make informed decisions, personalize patient care, engage physicians effectively, and identify new opportunities for innovation.
We tackled the challenge of managing patient data across multiple products by designing an integrated data platform. We established a single source of truth by implementing a data source-agnostic common data model and analytics-ready datasets. This enabled real-time analysis of patient data from diverse sources. We standardized data visualization across brands and markets for easier and shared consumption of insights.
As a result, our customers experienced a 60-80% reduction in time to obtain patient insights and a 40-50% cost reduction compared to previous methods relying on full-time employees.
In another case, we helped a global consumer health company implement an effective omnichannel re-launch strategy. By creating a multidimensional HCP segmentation model, integrating data from various sources into a data warehouse, and leveraging AI-based digital scoring and affinity models, we provided a comprehensive view of the HCP journey and real-time persona enrichment. This allowed for optimized channel-campaign performance and customized assets creation.
Within three months of the campaign launch, the company achieved significant outcomes, including unified HCP views, standardized data ingestion, accelerated generation of insights, reduced time-to-market for campaigns, increased HCP engagement, and a 10% increase in new and repeat prescriptions.
Indegene’s USP lies in its ability to deliver the unique blend of domain and analytical expertise that life sciences teams need. We understand the specific challenges and intricacies of the industry, allowing us to provide tailored solutions that address unique needs.
Our technology stack includes a wide range of components. We utilize natural language processing techniques to extract insights from unstructured text data. Deep learning models, built upon neural networks, allow us to tackle complex tasks like image recognition and voice recognition. Predictive analytics techniques enable us to forecast future outcomes based on historical data, empowering our clients to make informed predictions about disease outbreaks, patient behavior, treatment responses, and more. We employ data visualization tools and techniques to present complex healthcare data, in a visually compelling and intuitive manner.
We also employ robust data extraction, transformation, and integration techniques. Middleware platforms facilitate data flow between systems, while data warehousing and ETL processes enable efficient storage, management, and processing of integrated data. We also implement data governance, master data management, and API integration practices to ensure data quality, security, and interoperability. In addition, we incorporate MLOps practices to manage and deploy machine learning models at scale.
Our technology stack is continuously evolving to keep pace with emerging trends and advancements in the life sciences industries.
AI and ML are central to our services and solutions, shaping the future of health in collaboration with our clients. We leverage AI and ML across all our offerings. Our focus is on delivering high-quality outcomes, quick turnaround times, and scalability. To drive our data and analytics innovation, we have several initiatives in progress, these include (but are not limited to):
Integrated Patient Insights: This platform aims to provide a comprehensive view of patient data, utilizing AI and ML techniques for deeper insights
Hyper-Automated AI: This initiative focuses on developing cutting-edge automation capabilities to optimize processes and increase operational efficiency at scale
Generative AI: We use this technology to create personalized and streamlined commercial solutions tailored to individual needs
Recommendations Engine: Powered by AI and ML algorithms to provide personalized next-best actions in real-time, improving patient and physician engagement
AI Query Resolver: These are typically chatbots, powered by large language models, that serve as the first line of defense in resolving sales rep queries
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