Advanced Predictive Analytics: Driving Operational Efficiency across Pharma ProcessesAdvanced Predictive Analytics: Driving Operational Efficiency across Pharma Processes https://datazymes.com/wp-content/uploads/2022/07/predictive_analytics-1024x576.jpg 1024 576 deepak https://secure.gravatar.com/avatar/acfa5c400ac94d9a541d4f108658dcbb?s=96&d=mm&r=g
Post-pandemic Pharma Landscape: Toward Agile Data-driven Efficiency
The pharmaceutical industry is facing growing competition with increasing pricing pressures and strict regulations. Evolving drug regulatory landscape and supply disruptions caused by the pandemic cast a shadow on operations. Such challenges have been instrumental in keeping businesses on their toes. To reduce their costs and increase profitability, adopting operational excellence and transforming in an agile way is the way forward.
mbracing data-driven decision-making that is proficient in data ingestion and generating actionable insights through analytics platforms is driving change in pharma. Most companies are in the hunt for agile data platforms that integrate with third party sources. Cutting-edge digitization and automation practices like integrating AI/ML analytics as a part of the business strategy is the proactive approach is driving change. The global healthcare predictive analytics market size was valued at USD 9.3 billion in 2021 and is expected to grow at a compounded annual growth rate (CAGR) of 24.5% from 2022 to 2030.
Pharma companies use advanced machine learning algorithms to process huge amounts of raw data thereby generating predictive models. These algorithms and mathematical equations crunch data across a varied set of variables and factors to forecast future outcomes to decide what the probability of certain drugs failing in the research and development phase to which patient characteristics are likely to lead to adverse reactions to a particular drug.
Predictive analytics enable manufacturers to proactively identify production line optimization issues, recognize the drug batches that are more likely to fail in development or in safety issues. Robust process excellence via use of predictive analytics, directly impacts profitability and digitizing the entire drug research, manufacturing, sales and marketing, supply chain and availability network means focusing on maximizing operational competency.
Dissecting Operational Efficiency: A Case in Point
Operational efficiency broadly refers to the ability of an organization to deliver quality services with relatively less resources. The more output an organization can produce from a given amount of input, the more efficient those operations are. It is the function of two variables such as the quality of an entity’s operations and its operating expenses. This goes beyond cost management and draws a strategy that looks both externally and internally at all processes in a pharma organization.
Ongoing strides in data mining, data modelling, simulation and analytics open new and definitive opportunities to apply data-driven insights to improve upstream drug discovery and clinical trials. Data analytics is influencing downstream go-to-market strategies and post-patent lifecycle management. Computing, cloud capabilities alongside modelling and simulation methodologies with data analytics techniques shape essential activities from upstream drug development and regulatory approval to downstream commercialization and lifecycle management efforts that go beyond patent expiry.
A good case in point has been Elekta’s push to achieve operational excellence in pharma data management. In 2017, Elekta, a manufacturer of radiotherapy treatment for cancer and brain disorders found its paper-based records were very costly and inefficient. The manual execution of processes like compliance checks led to costly errors. Physical documents for a medical product required 800 manual signatures and 1200 folders per year. To address this, the company decided to implement a centralized data management system enabling staff to view its processes, products and orders via a digital platform. The payback was significant as there was cost savings, increased efficiency and easier compliance with the company doubling its revenue in 5 years and orders at one facility going up by 35 per cent. The business also achieved a cost savings of $85,000 from printing and storage as well because of the digitization.
How Predictive Analytics is Refining Pharma Processes?
The goal of these data analytical models is to improve the operational efficiency of all phases of the clinical trial. Their focus is to increase the probability of high-validity clinical findings that can support product development and help define therapeutic indications with the right patient populations to target and then optimize the protocols to improve the odds of success of these trials. Advanced analytics also help ensure that clinical trials produce higher-validity, regulatory quality data by differentiating and sorting data-entry errors, inconsistencies and outliers with misreported adverse events that can undermine clinical development findings.
These technologies are helping biotech companies and their research partners identify and select promising therapeutic molecules with a potential to treat certain illnesses. To do this, predictive analytical models in drug discovery process consider varied data points about individual targets. This involves collating historical information on how a particular target behaves while interacting with other proteins and data about the behavior of the target in previous experiments and what type of drug did not work before.
Drug Safety and Pharmacovigilance:
In drug safety processes and pharmacovigilance, analytics are also playing a pivotal role. Drug safety remains a touchy topic in a pharma company and involves a lot of cost, especially if it involves recalling batches of medication. Companies also must consider adverse drug reaction or ADR that could lead to severe legal implications. Predictive analytics can reduce this risk and identify patient populations who might not tolerate these drugs and those who are at a greater risk of experiencing an ADR.
One of the biggest challenges in pharma are the time and cost of patient recruitment in clinical trials. Predictive analytics makes this process more operationally efficient by identifying which patient populations have specific characteristics and would fit in certain clinical trials. Predictive models can thereby reduce the number of test subjects needed for research while ensuring that only those who fit stay engaged till the end of trial. This results in more effective results with less wasted resources and shorter market time to develop a drug.
Drug Supply Chain:
Next key area of impact is in the drug supply chain. Predictive analytical models help pharma companies understand how demand is impacted by variables such as regional demographics and economic conditions – thereby allowing drug manufacturers to make use of efficient resources and improving patient access to critical medicines thereby streamlining the supply chain management.
Using advanced machine learning algorithms and predictive analytics models, pharma companies can also forecast drug sales correctly. This leads to a more efficient drug distribution process through improved inventory management that reduces risks of over stocking and having too few drugs to meet demand. These technologies also are used alongside historical data points about past sales and trends in regions by customer segments (pharmacies and hospitals) with other variables that could contribute to future sales forecasts.
These analytical models also help pharma companies identify and prioritize new sales opportunities. Pharma companies can identify profitable customer segments for a drug or medication and can focus on those with better potential for a return on investment.
Drug Marketing and Advertising:
Another area of operational efficiency is drug marketing and advertising. Companies rely heavily on predictive analytical models during early stages of product development – from identifying those patient populations most likely to benefit from a new drug to its long-life cycle. Customer Relationship Management or CRM is also an essential aspect of pharma process. These companies can better understand how customers react and interact with aspects of business operations through each phase or touchpoint within an overall sales process. Predictive modelling and advanced analytics can track this information throughout the entire customer experience life cycle via numerous channels such as email campaigns, call center interactions, online ads, and more, giving companies valuable insights needed to market their products well.
Although predictive analytics can help pharma companies take note of the abundant data generated in the sales marketing processes and the entire drug lifecycle, there are growing challenges in terms of organizational preparedness and strategy to implement data analytics solutions.
Using Advanced Analytics: The Challenges
Considerable number of pharma companies seem interested in applying prototyped analytics solutions and have sought to operationalize them. Besides data scientists, other user groups such as business analysts can also explore the use of predictive analytics. But there are conditions for the successful use of advanced analytics like using the right tool for the company’s needs and training business users in how to analyze data sets while having a holistic data strategy in place. Across the company, data access and governance issues must also be addressed. Improving data management and employee skills with training are top concerns too.
Data literacy is also critical and training business analysts and algorithmic transparency go a long way to address trust in automated decision-making. To derive optimum benefits, a company-wide strategy to mobilize analytics is needed. Advanced analytics poses a real and significant advantage to gather unstructured and structured data and build models for turning insights into impact at scale. But identifying and prioritizing how to invest money, time and efforts remains crucial here and pharma companies have been slow to adopt to automation changes.
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