The Role of NLG-based Reporting Automation in the Pharma Industry

Romil Shah
10 min read

 

The healthcare and pharma industries have always been among the leading adopters of cutting-edge technologies like AI. AI applications in pharma, such as the use of deep learning for developing new drugs and machine learning for clinical trial design, are well documented. However, AI-driven automation in pharma and healthcare is not only limited to these long-term R&D activities. AI is also adding value to the day-to-day operations of pharma and healthcare organizations.

 

One of the leading ways in which AI is helping healthcare organizations is by interpreting the results of CT, X-ray, and MRI scans. These scans can be interpreted with the help of Natural Language Generation (NLG), an advanced AI technology. First, computer vision and deep learning are used to analyze the images and other patient-related data to detect disorders.

 

These findings, which are in the form of structured tables and images, are automatically converted to a natural text report that outlines the patient’s health using natural language technology. These reports highlight the exact points that need attention from doctors, leading to diagnosis within minutes.

 

Similar reporting automation applications are providing healthcare and pharma companies with innumerable benefits, such as:

 

  • increasing efficiency,
  • minimizing errors in reports,
  • eliminating the misinterpretation of data,
  • allowing non-analyst employees to focus on and improve their core functions,
  • improving data analysts’ productivity, and
  • enabling pharma companies to understand and keep up with market trends.

 

To achieve these benefits, the following are the biggest ways in which healthcare and pharma companies are leveraging NLG-driven reporting automation:

 

Monitoring medical representative performance to increase sales

 

The volume of sales achieved by pharmaceutical companies is closely tied to the performance of their medical representatives. These medical representatives travel from place to place, meeting doctors, drug stockists, and distributors to expand the company’s distribution channels. Their performance is based on metrics like:

 

  • the number calls they make on average to doctors,
  • the number of meetings attended with doctors and stockists,
  • the number of meetings missed, and
  • the number of incentive-driven doctors visited.

 

Helping medical reps to monitor these metrics can bring out insights into improving their performance. Businesses are using ERPs and advanced analytics tools to record and measure these representatives’ performance. However, these insights aren’t often delivered to the representatives themselves in a language they can understand.

 

Medical Representative Performance Report
Medical Representative Performance Report

 

Instead, this data is often provided to them in the form of complicated spreadsheets. These spreadsheets not only take time to interpret but can also lead to misinterpretation due to a lack of data literacy skills. And spending hours trying to decipher their performance-related data is not the most productive use of the reps’ time.

 

That’s why leading pharma companies are using AI to automate medical representative (MR) performance reporting. Advanced reporting automation tools use natural language generation technology to convert the MR performance data from spreadsheets into concise reports written in simple, natural language.

 

These reports can be read by medical reps to quickly understand how they are performing. And when combined with predictive analytics, reporting automation can even suggest measures to improve their performance through the reports. By using these reports, medical reps can keep a close tab on making enough sales calls and visits to meet their targets.

 

Exploring IMS data to aid strategy and decision-making

 

In addition to analyzing individual medical reps’ performance, pharma companies also use market data gathered from the industry to make informed decisions driving overall performance. While there is no shortage of accurate market data due to sources like IMS Health, extracting the necessary insights from the massive IMS database and providing it to different people in the pharma supply chain can be challenging. Especially when you have to customize data for hundreds of different people based on their designation and region.

 

IMS Health Data Analysis

 

The use of reporting automation in pharma companies is helping them make the most of their market data. With the help of NLG-based pharma reporting software, the IMS health data can be translated from tables and graphs into easy-to-understand trends written in simple understandable language.

 

IMS health data analysis can highlight the trends and patterns that would allow these enterprises to make performance-enhancing decisions. For instance, marketing leaders can use AI-driven reporting automation to:

 

  • identify their best and worst-performing brands, 
  • measure their market share in different drug categories and
  • analyze their region-by-region sales statistics.

 

Using this information, they can make quick decisions in response to market trends that can lead to improved business results.

 

Conclusion

 

Many leading pharma companies are already utilizing NLG-enabled reporting automation to improve their ability to make sense of data. Using NLG, these companies are augmenting their data analysis capabilities and extending the same to all of their employees, regardless of their function and data literacy level. As a result, all members of the pharma supply chain -- from C-suite executives to on-field medical representatives -- are reaping the benefits of data-driven decision making, thanks to reporting automation.

 

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