Digital Solution for Pharmaceutical Quality systems

Digital solutions for pharma quality systems


In the present market of the pharmaceutical industry, there is no clear idea about the influence of digitalization in the industry. This is due to the lack of understanding of potential opportunities created by digitalization. Let us discuss some cases to understand how digital solutions for pharma quality systems can transform Quality in the pharmaceutical industry.

Now let us understand the term Quality actually means in the present market

Quality: In today’s pharmaceutical industry Quality of the product is defined as the extent of its compliance with the regulatory requirements. But, the definition of quality should be much more than compliance and focused mainly on the patients perspective. It must be defined as how well the product can improve the quality of life for the patient. Quality is driven by the availability of different medicines, reliable manufacturing devices and processes and patients. There is a larger impact of development processes, technical operations, manufacturing etc.,     

Overview of Digital Quality

The quality of product starts in the development phase through design, formulating and testing during the manufacturing of robust medicines and reliable devices associated with it. After commercialization, It became compulsory for the organisation to ensure the products are improving the Quality of life for patients. Quality is a cross-functional effort between different departments of the manufacturing process.

Let us look into the use cases of different phases of the quality lifecycle   

Product / Process Development:

Optimal product designs can be produced such as manufacturing processes, formulations, specifications, test methods, etc., using AI technology. It uses data from all previous manufacturing and development processes. There are few machine learning models built on previous data which can predict the outcome of health interventions on human bodies of individuals. these are built after extensive research conducted for many years, these can tell us how the body works and reacts when subjected to therapies and treatments. This will help us to conduct targeted and better clinical trials decreasing the time to market and improve the health outcomes.

Clinical Data Quality Management: 

Data quality management is a very important aspect of AI. It is used to monitor the completeness, accuracy and timeliness of the data. It is also useful in identifying trends across different data points for potential integrity issues. Finally, it will correlate the clinical results with the manufacturing and QMS data. You can look example such as examining clinical results after the usage of insulin and correlate back to an individual lot of insulin used. This is how we can shift compliance centric Quality management to patient-centric quality management.

Optimizing the Process:  

Massive amounts of process data obtained from analytics is used to correlate with the output data to get useful insights about the key process parameters. This will help to optimize those important parameters which have high correlation with output quality. These optimized parameters will result in improving process capability which yields great results.

Predicting Batch/Process Failures: 

Identify the potential failures in the key process parameters with real-time monitoring through IOT-based sensors. This will reduce the number of errors and non-conformance issues in the manufacturing process significantly.

Identifying Defects:    

With the help of visualization algorithms developed using machine learning models, it is easy to identify potential defects in manufacturing lines such as liquid filling, tabletting and device assembly lines.

Laboratory Automation:

Data generated through the Lab automation is an integral part of digital strategy for Quality which helps in understanding the development and manufacturing processes. Lab automation plays a crucial role in the improvement of product quality.

Batch Release: 

Real-time batch release is very crucial objective in the manufacturing process, it can reduce cycle time and manufacturing cost significantly. Blockchain and smart contracts could be used for real-time monitoring and batch release of products manufactured.


Enhance the training experience of the operators through Augmented reality coupled with SOPs and work instructions can reduce human error significantly during the manufacturing and testing of products. 

Monitoring Regulatory Environment: 

As pharma companies are highly regulated, programs can be developed to monitor regulatory trends globally and highlight the changes which can impact your companies production.

Inspection and Risk Management: 

Automation of internal audits can be done through analytics which can highlight the potential areas of risk. For example, if proper root cause analysis is not performed to the required depth, it can be reported immediately. Heat maps can be generated indicating potential risk areas through reporting metrics for management review.

Vendor Management: 

We can make use of existing tools which provides valuable insights about global suppliers and also risk assessments. These services, when coupled with analytics related to supplier quality, can help us to correlate with the process, product and patient outcomes.

Complaint Investigations: 

With the data acquired from batch records, lab results, previous complaints etc., we can partially automate the complaint handling process. It assists us in rapidly closing the complaints with the help of algorithms made using relevant data by conducting the in-depth analysis.

Recall Management: 

Streamline the recall process by understanding the impact of recalls using the analytics. This will help us to speed up the process and manage recalls efficiently.

Annual Product Review (APR): 

When Post market Analysis is linked with the continuous ongoing process of validation, thus resulting data can help us to automate the APQR. It can even perform real-time analysis and generate graphs, final reports automatically. A lot of programs and algorithms already exists in the market which will enable us to automate this process.

Management Reviews: 

Real-time data, insights and trends on key products and processes are useful for management review to reduce quality issues.

Post Market Surveillance:   

Identify adverse events and medical device reports using NLP. It is used in complaint management to generate various reports automatically with minimum human interference.

Social Media:

By analysing social media trends, real-time sentiment analysis can be done to highlight potential quality issues.

Call Center Optimization: 

Customer complaints can be captured automatically and operators can be assisted for the right question and the tone of voice should be used to offer pleasant customer experience.  


There are a lot of implications of digital quality in the organisation. Data scientists need to work with other teams to develop efficient algorithms to improve quality across the life cycle.

Big data has a major role in Artificial intelligence. As there is more available data algorithms become more efficient exponentially for accurate prediction of outcomes.

Regulatory industries should also be comfortable with AI. Generally, AI is considered as the unvalidated. Introduction of new Guidelines will help agencies and companies to coordinate with each other.


Leveraging the continuously available data for faster development of medicines and to improve the speed of the market. Digital solutions for pharma quality systems can predict and prevent failures in the manufacturing process to improve quality.

So far we have understood, how digital solutions for pharma quality systems can improve the product and process quality. Companies will uncover new opportunities in the field of quality management by digitalization. It will not replace humans but augment the human capability to enhance the quality of products.

Many companies are already using digital solutions for pharma quality systems to get more pleasing results in the field of manufacturing and quality management. However every industry has its own unique requirement, there is no one fit solution for all companies. AI needs a robust dataset to determine the path of your journey towards digitalization.

About the author: amplelogic123

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