Advanced Analytics Chart the Future of the Healthcare Supply Chain

As healthcare increasingly embraces digital transformation and employs more modern data strategies, predictive and prescriptive analytics will play an essential role in building the supply chain of the future.

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In the wake of the COVID-19 pandemic, healthcare providers and suppliers are rethinking their supply chains with renewed vigor. The near-term focus has been driving greater levels of automation and digitization to address long-standing issues that have simmered below the surface for decades. The goal is to build more agile, resilient supply chains like those found in other industry sectors. As healthcare increasingly embraces digital transformation and employs more modern data strategies, predictive and prescriptive analytics will play an essential role in building the supply chain of the future.

Imagine an environment where the complexity of supply chain logistics could be simplified in the same way that Waze and Google Maps GPS-enabled navigation advise on different route choices, supply a predicted estimated time of arrival for each one, and then recommend the optimal route based on personal preferences. While the industry isn’t quite there yet, the possibilities predictive and prescriptive analytics afford shouldn’t be underestimated.

Analytics are transforming the healthcare supply chain

Together, predictive and prescriptive analytics represent significant change agents in healthcare. Not only are these models helping the industry provide more personalized patient experiences, but they are also essential to the reinvention of the healthcare supply chain.

Predictive analytics is widely used across the healthcare supply chain today. These models analyze historical and current data to help predict future outcomes. Essentially, they help answer questions about what is likely to happen in the future. Today, predictive modeling solutions are commonly used in supply and demand forecasting, logistics and cost optimization, and invoice and payment automation. Since the start of the pandemic, the use of predictive analytics has surged, particularly as it relates to anticipating and matching PPE supply and demand.

Prescriptive analytics is a more advanced form of analytics that extends the insight gained through predictive analytics, providing organizations with insight about the steps they can take to change or improve future outcomes. Prescriptive analytics is the key to making data-driven decisions a reality in any industry, not just healthcare. Venture capital firms use it to guide investment decisions, banks and insurance companies use it for fraud detection and marketing teams use it for product pairing, pricing and targeted campaigns. In healthcare, it’s expected that prescriptive analytics will support financial and clinical operations, providing actionable insight on everything from inventory management and budgeting to patient risk and staffing levels.

As we look ahead, a more advanced technique called digital twins is generating excitement and quickly gaining popularity. A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. It can represent an entire healthcare supply chain, or a collection of twins can model the different components or processes of the supply chain. It allows for a risk-free environment to conduct what-if analysis and simulations to optimize these processes and disruptions for optimal outcomes.

Accenture’s 2021 Digital Health Technology Vision report found that a quarter of healthcare executives report their organizations were experimenting with digital twins. Moreover, 66% of the respondents expect their organization’s investment in intelligent digital twins to increase between 2021 and 2023.

While digital twins are used in the healthcare supply chain today, the approach is largely still emerging. However, digital twins do offer a good way to bring together predictive and prescriptive models. We are already seeing some exciting examples of organizations using digital twins in their predictive models. Medtronic is using digital twins to improve supply chain optimization, while Johnson & Johnson Consumer Health is using digital twins to get to market faster as well as for product innovation.

The same is true for prescriptive analytics. Virtonomy is using digital twins to shorten the time-to-market for medical devices, accelerating medical innovation and reducing costs. And the Babylon Health Healthcheck application builds a digital twin to increase patient care.

The importance of the right data at the right time

Predictive and prescriptive analytics models only work as well as the data feeding them. Success requires a deep understanding of how healthcare data is managed and the ability to ensure the data is known and trusted. In healthcare, this is easier said than done. Historically, healthcare data has resided in silos, in different formats with systems unable to communicate. To mitigate these challenges, organizations must establish a modern data strategy that ensures clean, accurate data can flow easily across systems. Forward-thinking organizations have begun to modernize their data management practices using automation to centralize and streamline data, adopting standardization and integrating data and systems. Taking steps to break down silos and align data will enable supply chain teams to use predictive and prescriptive analytics to make data-driven decisions and track the financial and clinical outcomes of those decisions.

The digitization of healthcare presents an incredible opportunity to use the industry’s massive volume of data to transform the supply chain and the way we deliver care. Predictive and prescriptive analytics, and even digital twins, can be applied to almost all supply chain challenges. The new insights gained will help the industry to increase efficiency across the supply chain, while reducing waste and costs. And, we can do this while still making care more affordable and personalized exactly where and when the patient needs it.