Predictive Analytics Solutions for the Pharma Industry
First, we need to understand the impact of predictive analytics solutions in the pharmaceutical industry. The healthcare industry has seen a complete overhaul in recent times. Players in the industry have undergone transformations and moves over from a volume-based business to a value-added business. The industry has always given priority to providing optimum outcomes to patients and that’s set to remain, but the importance of value-based care has significantly increased.
Since the industry focuses on a significant number of patients along with their data, it has become complex for the players to derive any significant value from the data. Recent technological advancements call on the organizations to use advanced analytics and uncover valuable actionable insights from the data and deliver a personalized, value-added experience to their customers.
Considering how the role of value-based reimbursement has increased in the industry, the healthcare players have started to understand the importance of predictive analytics in pharma industry in managing population health and deliver care in a cost-effective manner. Predictive analytics also helps pharma players reduce readmission rates while also predicting different types of healthcare trends.
Predictive analytics not only helps organizations improve the lives of their patients rather it also helps them improve their operational management, including improving overall business operations, enhancing accuracy of diagnosis, customization of medicinal therapies, assessing potential risk factors, and more!
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Due to the nature of the industry, every task is complex and fraught with its own set of challenges, such as developing drugs, it is a long research-intensive process involving a plethora of stakeholders. There are a lot of stages involved when dealing with drug production, such as investing a huge chunk of resources in analyzing the data, followed by research and development, clinical trials and finally drug development. Superior predictive analytics in pharma can help organizations predict drug behavior, enhance clinical trials’ success rates, and expedite drug development itself.
Unlike traditional medicines, the modern methods focus on anticipating and reducing risk based on historical data of the medicine and patients both. Considering the industry, healthcare officials often take difficult decision without complete certainty, but predictive analytics becomes a very powerful tool for the officials. Predictive analytics brings data-backed answers for healthcare officials when taking difficult decisions. Predictive analytics helps healthcare officials in the pharma industry focus on understating the impact of the clinical trials of the medicines even before the medicine goes for the trials.
Predictive Analytics in Pharma Challenges Faced by the Client
Considering the impact of technological advancements across the globe, the pharmaceutical giant aimed at analyzing their sales operations and optimizing their revenue cycles to drive profitability and cash flow. By leveraging predictive maintenance technologies that use IIoT, the client wanted to run diagnostic on equipment to reduce unscheduled downtime and ultimately optimize its after-sales support services offered to its customers. Superior predictive analytics in pharma could also help find anomalies and possible failures in real-time and proactively.
Just like every other industry in the industry, the client had a huge explosion of data that turned out to be extremely complex for their in-house data teams and experts. These datasets could have helped the client drastically improve their business efficiency. The client was struggling with another complex challenge: accumulating and analyzing data regarding HACs (Hospital Acquired Conditions) and HAIs (Healthcare Associated Infections) to understand their causes and gain insights on how to counter them at a granular level.
Since the industry is fraught with human interactions, there are a lot of reasons things might go awry, due to the human factor in the mix, any uncertainty in a clinical decision can result in healthcare officials undertreating or overtreating their patients. A superior predictive analytics tools would help the client’s healthcare workers and officials administer medicine and treatments consistently and accurately.
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Considering the high cost of medical treatment in the times today, most patients opt for medical insurance to help them out in their time of need. However, some patients try to benefit from this widespread service and might turn out to be fraud resulting in major loss in business. Such abuse of medical insurance has turned out to be a significant challenge for the client and resulted in huge monetary losses.
From its inception to it becoming a huge buzzword, predictive analytics has become a necessity in the field of healthcare. Planning to leverage the most out of any medical advances in the healthcare industry, players are constantly looking out for ways to enhance their service efficiencies and data analytics has become a major part of this endeavor. After attending one of our seminars, the pharma giant decided to connect with our experts to understand how they can improve their revenue.
Revolutionary Predictive Analytics Solutions for the Client
Once the challenges were defined to their extent by our experts, our research teams started working on methods to solve them. Our sales analytics experts started by adopting a comprehensive approach to help the healthcare giant address its supplier challenges. Our experts first started by understanding the existing sales operations and sales models to identify issues and factors impacting sales. By undertaking this exercise, our experts were able to gain a holistic understating of the client’s exact requirements and develop strategies to address those needs by enhancing their current development efforts.
Once the challenges were identified, our experts started focusing on the use of predictive analytics to drive results of various datasets including unstructured data across the organization. Our framework also included the use of Natural Language Processing (NLP) and predictive analytics in pharma to understand the data and resulting in deeper insights and precise predictions of reasons that have a direct impact on their sales and growth. The last step was to integrate both internal and external data into one single warehouse to effectively identify regional market penetration and compare the resulting data with others across the country.
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Once that was done, we started by implementing data analytics algorithm and AI-driven system solution that identify new genome. The framework linked the new genomes with large-scale data to help increase the success rates of disease biology and reduce failure rates. Also, the algorithm created by our experts helped the client map disease passageways, protein, and more based on experimental survey data. This exercise helped the client discover new pathways and chemical spaces to expedite the process of drug development.
Our predictive maintenance solutions handled the client’s maintenance schedules by creating a monitoring system that tracked several data points and created early warning alerts. These early warning alerts enabled the client to track equipment parts, and proactively enable the client to repair the parts of the machinery or order spare parts even before any breakage happened. Instead of working independently, the framework was integrated with the client’s current system to reduce resource requirement and help the client improve the uptime of their equipment and increase their customer base with positive feedback.
Once all of this was handled, then our experts diverted their attention to the fraud detection challenges faced by the client. To reduce the number of fraud medical insurance patients, our experts implemented a three-pronged approach. The first prompt called for the creation of a predictive analytics-based healthcare dashboard to help the officials expedite their decision-making process.
For the second phase, we enabled the client to leverage AI and IoT to identify gaps and unusual patterns to prevent any insurance frauds in the future. This became the primary benefit for the client since the client was hemorrhaging their profits due to a huge number of insurance frauds. In the last phase, we implemented predictive modeling solutions based on ML, IoT, and advanced algorithms to help the client predict and prevent HACs and HAIs.
Quantzig’s predictive sales analytics solutions also enabled the client to:
- Gain better visibility into sales operations
- Improve profit margins by 14%
- Increase Success Rates of Clinical Trials by 40%
- Increased product uptime by 10-30%
- Reduced unexpected downtime of the medical device by 20%
- Reduced spare part supply chain costs by up to 15%
- Optimized complete aftermarket supply chain to improve the availability of spare parts at the right time
- Achieve a 3x increase in sales post the implementation of predictive sales analytics models
- Prevention of over 80% of HACs and HAIs
- Improve profit margins by 14%, increase success rates of clinical trials by 40%