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December 13, 2023 | Premangsu Bhattacharya
Blog / 5 Transformative Ways You Can Revolutionize Patient Experience with Generative AI
Data Rich, Insight Poor. This is the fundamental truth plaguing the pharmaceutical industry today. It’s a paradox where an abundance of data fails to translate into actionable insights.
Let’s look at an example:
A pharmaceutical company, with a global supply chain, is on a mission to revolutionize patient experience. Despite having vast amounts of data at their fingertips, they face a formidable opponent – data fragmentation.
Here’s a deeper dive into their struggle:
In this scenario, the company’s ability to make swift, informed decisions impacting patient care is severely hindered. Each day lost in data compilation and analysis amplifies the risk of stock shortages or surpluses, directly affecting patient accessibility to essential medications.
The Core Issue: In the pharma world, the struggle isn’t just about collecting data; it’s about seamlessly connecting the dots. Most data teams today have tools like BigQuery at their disposal for data crunching, and Generative AI holds immense promise. Yet, the missing link is a unified platform that brings these technologies together, tailored to the industry’s unique challenges.
With Planning in a Box, a Generative AI-powered Decision Intelligence platform, this challenge is addressed head-on. This platform brings together all your data from various sources, enabling you to converse with your enterprise data effortlessly, make data-driven decisions rapidly, and unlock actionable insights in real time.
In this blog, we will explore 5 transformative ways you can revolutionize patient experience with Generative AI leveraging a decision intelligence platform like Planning in a Box, demonstrating the power of integrated data and advanced AI for impactful healthcare outcomes.
Generative AI excels in quickly integrating and analyzing data from diverse sources. This includes clinical trial data, patient health records, and supply chain information, which are traditionally siloed.
By asking questions like, “What patterns emerge across different clinical trials for Drug X?”, you get instant, consolidated insights. This rapid integration and analysis facilitate more informed decision-making, crucial in fast-paced healthcare environments.
|Traditional BI Tools Approach
|Planning in a Box Approach
|What patterns emerge across different clinical trials for Drug X?
Requires manual integration of data from various trials, extensive data cleaning, and complex analysis.
Involves querying separate databases, potentially leading to fragmented insights.
Analysis time can span several hours to days.
Automatically integrates data from multiple clinical trials, both internal and external sources.
Utilizes Generative AI to understand context, recognize patterns, and identify anomalies, providing comprehensive insights.
Delivers results in minutes, offering actionable recommendations.
Imagine being able to simply ask your data a question and receive an answer almost instantly. That’s the power of Generative AI. It enables you to interact with your enterprise data through natural language.
You can ask complex questions like, “What are the current trends in patient feedback for our cardiovascular drugs?” or “How will a delay in the supply chain affect our drug distribution in Europe?” Instead of navigating through multiple systems or writing complex SQL queries, Generative AI processes these questions and delivers concise, insightful answers.
|Traditional Analysis Time
|Time with Generative AI
Analyzing patient feedback trends
Forecasting medication demand in specific regions
Assessing the impact of supply chain delays
Evaluating clinical trial data for new drug approvals
Identifying patient preferences for treatment methods
Understanding patient experiences involves laboriously sifting through feedback from various channels, including digital platforms and direct surveys. The process is usually manual, slow, and often leads to outdated insights, making it challenging to adapt and enhance patient care strategies effectively.
Generative AI changes this dynamic by transforming complex datasets into user-friendly, conversational formats. This AI-driven approach allows for seamless integration of varied data types.
Consider a scenario where you need to analyze patient responses to a new treatment protocol. Traditional methods would require manually compiling and analyzing feedback from different sources, a process taking several days or even weeks.
With Generative AI-enabled Decision Intelligence Platforms like Planning in a Box, you can instantly query, “What do patients think about the new treatment protocol?” The AI would quickly analyze feedback across platforms, presenting a comprehensive view of patient experiences. This could reveal specific areas of patient concern, like side effects or treatment efficacy, enabling healthcare providers to quickly address these issues and enhance overall patient care.
Forecasting healthcare demand traditionally relies on historical data and slow, manual analyses. This often results in either overestimations or underestimations, leading to either excessive inventory or critical shortages, especially during unexpected health crises.
Generative AI revolutionizes this process by enabling predictive analytics that incorporates real-time data and external market trends. This approach offers a far more accurate prediction of healthcare demands.
Let’s consider a pharma company that wants to forecast the demand for flu vaccines for the upcoming season. The traditional method would involve looking at past years’ data and a slow, cumbersome analysis process.
With a platform like Planning in a Box, the hospital can ask, “What is the projected demand for flu vaccines in the next six months, considering current health trends?”
The AI would analyze not just historical data but also current health trends, news, and Google Trends data to predict a more accurate demand figure. This enables the hospital to plan vaccine orders more precisely, ensuring adequate supply without overstocking.
Clinical trial data processing in its conventional form is marred by inefficiencies. A typical scenario involves manually collating data from various stages, which, for a complex drug trial, can involve data points from thousands of patients.
This process, often stretching over weeks, is riddled with potential inaccuracies and inconsistencies. For example, a diabetes drug trial might take up to two months just to aggregate and analyze patient responses, blood sugar levels, and side effects from different trial phases.
Consider the analysis of a new cancer treatment’s clinical trial. Traditionally, synthesizing data from multiple patient groups, control data, and varied responses would extend over six to eight weeks.
With Planning in a Box, the same analysis is completed in under 48 hours. This swift turnaround time allows for immediate identification of patterns and anomalies.
For instance, the AI could quickly highlight that while effective in reducing tumor sizes, the treatment shows higher side-effect frequencies in patients above 60.
In the pharmaceutical industry, lack of inventory visibility is more than a logistical challenge; it directly impacts patient care. Globally, pharma companies are grappling with the complexities of managing their inventory across diverse regions. This challenge becomes acute during health crises or seasonal disease spikes, where the demand for specific medications surges unpredictably.
Here is an example. A global pharmaceutical company struggles with disparate inventory levels: excess stock in North America and shortages in Asia and Africa due to seasonal diseases. This imbalance, stemming from fragmented data across regional ERP systems, leads to inefficiencies and impacts patient care. The challenge is not only managing the inventory but also adapting quickly to varying regional health demands.
Planning in a Box revolutionizes their approach. By integrating data from regional ERP systems and external health trend reports, it provides a real-time, comprehensive view of the global supply chain. This enables the company to:
In this blog, we’ve delved into several transformative use cases of Generative AI that have the potential to significantly improve patient experiences in the pharmaceutical industry. The sco[e of Generative AI is immense, but we’ve focused on scenarios that offer the quickest and most impactful value. Each of these use cases, ranging from advanced patient analytics to streamlined clinical trials, can be rapidly deployed through Planning in a Box within a 4-week timeframe.
If you find yourself intrigued by the possibilities of Generative AI but uncertain about where to begin, or how to overcome the challenges of data fragmentation, we invite you to join our Gen AI Bootcamp. Here, we collaborate closely with you to identify the most suitable use case for your needs. We then guide you through a swift implementation process on your own cloud infrastructure, all within a 4-week timeframe.
Discover the path to rapid, impactful digital transformation in healthcare with us.