How Is Data Engineering Used In The Pharmaceutical Industry?

Big data has emerged as a formidable tool for addressing some of the most critical scientific research and drug discovery concerns in recent years. Laboratory information systems and data management techniques are one area where data engineering solutions are having an influence. To make sense of the massive amounts of information regarding drugs, their interactions with human bodies, clinical studies, and other topics, the pharmaceutical business primarily relies on big data and analytics. Consider how much time a researcher could save if they could quickly locate clinical trial records or other previous work involving similar patients. Currently, researchers can spend days combing through innumerable files to find an answer to this issue. What happens, though, when we use predictive algorithms like machine learning models? The challenge is transformed into an optimization problem, which yields new insights into pharmaceutical research problems. This article covers some of the data handling issues that pharma is now dealing with before highlighting the opportunity provided by advanced analytics techniques such as machine learning in helping to overcome these issues.

What is Big Data, exactly?

The term "big data" refers to the enormous and diverse databases created by recording digital touchpoints all over the world. Website analytics, social media activity, customer feedback, factory records, and anything else where computers are monitoring over our daily lives can all contribute to big data. It's frequently gathered automatically by algorithms that look for specific trends in online usage (such as what people search for). This data is used to improve website content, sell more products through targeted advertising, and even anticipate disease outbreaks ahead of time. The data sources for the life sciences business are slightly varied, and they include the following:

• research into drug development
• clinical trials and clinical research
• patient records and other health-related data
• manufacturing facilities/processes
• data on distribution
• records of raw materials,
• wholesalers, retailers, and distributors' marketing and sales records

Through data engineering services, these data entries are aggregated and used to improve medication discovery and development procedures, clinical trials, drug production, and distribution. To make sense of the data, business intelligence tools and techniques such as predictive analytics, sentiment analysis, text mining, and anomaly detection must be used.

Another important data-heavy concept in the drug development and pharmacovigilance processes is lab management systems (LMS), which can provide a single point of entry for laboratory requests to be processed by various laboratories within the organization or by different organizations that share resources.

The pharmaceutical industry's demand for better data management

The pharmaceutical sector is extremely complicated, and this complexity has resulted in the demand for better data management. Tracking drug delivery, for example, can be difficult due to the numerous categories that must be recorded (ex: Point-of-Sale Data from wholesalers, retailers, distributors). In clinical trials and the medication development process, there's a lot of data that needs to be filtered through for insights, such as spotting patterns between groups or examining efficacy by distribution mechanism. Because of this complexity, pharma need improved data management in order to extract useful insights from a large volume of data.

Because they are independent from the main business database and have no correlation, database pools in laboratories can be challenging to administer. This leads to a variety of challenges, including inaccuracies, reporting delays, missed opportunities for real-time observation actioning, and waste due to workflow duplication.

Data Management in Clinical Practice

Clinical Data Management (CDM) is responsible for all clinical data and information, including raw clinical trial datasets and coded patient medical records. HIPAA, ICH GCP principles, FDA rules, and other pharmaceutical industry standards are used by CDMs to manage this massive amount of data. In order to implement tighter medication safety rules in the future, there is a greater need for clinical research transparency and better collaboration among all stakeholders. Furthermore, pharmacovigilance processes necessitate the development of a sound clinical data management strategy.

Clinical Data Management is a misnomer since it indicates that only clinical trial records are involved but, in fact, any document connected to pharmaceutical research projects should be included in the system.

There's a lot of clinical trial data out there

The ever-increasing volume of data created by life sciences firms has made data administration and analysis a difficult undertaking. Clinical trials, for example, frequently collect large amounts of trial site reports on paper or by email over time; these may be laboriously recorded into databases days, weeks, or months later - by people who may not understand how these pieces fit together in the larger picture. As a result, it's tough to keep track of all of the clinical study data. The volume of clinical trial data generated by corporations is the main difficulty CDMs face in today's drug discovery and development climate.

The pharmaceutical CDM must manage a variety of data input types, both structured and unstructured, in addition to the high volume. Nonstandard parameters across numerous documents, such as lab reports and electronic health records (EHRs), as well as a lack of compatibility between diverse laboratory instruments, result in redundant processes and erroneous results. Because the majority of clinical trial documents are PDFs or Excel files that are not normalized and have little structure (i.e., only title information), manually retrieving needed content from them is a time-consuming process that requires scanning through countless pages/documents for relevant content while ignoring irrelevant ones.

Is volume a challenge that just major pharma businesses face?

No, it's not the case. With the amount and variety of clinical trial records in its pipeline, the pharmaceutical industry's data management presents a significant problem. Big pharma companies require more sophisticated systems to manage this massive quantity of data and create an interface with end-users so they can search for and retrieve specific information without having to sift through all available sources. Although data engineering solutions difficulties in smaller firms are more limited in scope, the same algorithmic methodologies can be used to remove manual, time-consuming labor.

What obstacles can pharmaceutical businesses face as a result of utilizing Big Data?

Implementation costs could be high

When it comes to integrating machine learning systems, the largest barrier that pharmaceutical businesses will confront is the possible cost of deployment. It can be difficult to determine which data sources are important and how much money should be invested in this development project. Furthermore, a data governance framework must be in place so that all parties involved have access to the same data at all times in order for it to be of actual value.

Poor data results in poor outcomes

When applying artificial intelligence technologies, low quality data from a pharmaceutical company's database might lead to bad results. It's possible that the data will need to be cleansed before it can be used to provide useful insights. This clean-up process comes at a high expense, which might be difficult to justify when the data isn't immediately useful.

The price of storing vast amounts of information

Another potential stumbling block would be the cost of preserving all of the datasets collected over time by biopharmaceutical companies, especially if they wish to keep them permanently. These expenditures can soon mount up if there is no clear return on investment on how big data can help them improve their business operations even further than it already does.

Within the company, there is a lack of competence

Data Scientists aren't found in every biotech company. Implementing big data techniques that produce value might be tough when there is a dearth of skills and understanding. For deep learning algorithms, you'll require professionals with statistical programming abilities and business intelligence skills for data exploration and visualization, as well as a focus on cooperation to create.

Making sense of pharma datasets is a difficult task

In addition to a lack of experience, it is challenging to comprehend all of the various forms of pharmaceutical data that must be acquired, evaluated, and handled. Many unstructured forms, such as PDFs and scanned documents, are difficult to interpret using typical techniques. For the best results, engineers working on the problem should collaborate closely with pharma business professionals.

How to Implement AI-powered Data Processing in Your Business

Consider tackling the implementation in an iterative manner that embraces the culture of experimentation to reduce the risk of failure. Select a strategic partner or solution provider with the expertise to assist you in achieving your objectives.

Start small with a data engineering solutions to get everyone on the same page about the fundamental problem, business objectives, and potential solutions. Then proceed to the Proof of Concept step to confirm the solution and delve further into the data and processes you already have. The next phase is to figure out the costs, risks, and timescale for your production-ready project before moving on to a scalable solution. Create automated processes, scale your artificial intelligence program, and put it into production.

Summary

The artificial intelligence revolution has already begun in the pharmaceutical industry, and it will only accelerate as more businesses realize its potential. The possibilities for what can be accomplished are nearly endless. Defining goals, selecting solution partners, evaluating current processes, refining use cases, and knowing technology capabilities are all steps that must be taken before implementing an AI-powered workflow.