Advanced Analytics Charts the Future of the Healthcare Supply Chain


In the wake of the COVID-19 pandemic, healthcare providers and suppliers are rethinking their supply chains with renewed vigor. The short-term goal has been to increase levels of automation and digitization to solve long-standing problems that have been simmering below the surface for decades. The goal is to build more agile and resilient supply chains, like those found in other industrial sectors. As healthcare increasingly embraces digital transformation and employs more modern data strategies, predictive and prescriptive analytics will play a critical 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 GPS navigation and Google Maps advise different route choices, provide an estimated time of arrival for each, then recommend the route. optimal route based on personal preferences. Although the industry is not quite there yet, the possibilities offered by predictive and prescriptive analytics should not be underestimated.

Analytics Transforms the Healthcare Supply Chain

Together, predictive and prescriptive analytics represent important agents of change in healthcare. Not only are these models helping the industry deliver more personalized experiences for patients, they are also key to reinventing the healthcare supply chain.

Predictive analytics is widely used in 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 onset of the pandemic, the use of predictive analytics has increased, particularly with respect to anticipating and matching PPE supply and demand.

Prescriptive analytics is a more advanced form of analytics that extends the insights gained through predictive analytics, providing organizations with insight into actions 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 capitalists use it to guide their investment decisions, banks and insurance companies use it for fraud detection, and marketing teams use it for product matching, pricing and targeted campaigns. In the healthcare industry, prescriptive analytics is expected to support financial and clinical operations, providing actionable insights into everything from inventory management and budgeting to patient risks and risk levels. endowment.

Going forward, a more advanced technique called digital twins is generating excitement and rapidly gaining popularity. A digital twin is a virtual representation that serves as a real-time digital counterpart of a physical object or process. It can represent an entire healthcare supply chain, or a set of twins can model the different components or processes of the supply chain. It enables a risk-free environment to perform what-if analyzes and simulations to optimize these processes and disturbances for optimal results.

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

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

The same goes for prescriptive analytics. Virtonomy uses digital twins to shorten the time to market of medical devices, accelerate medical innovation and reduce costs. And the Babylon Health Healthcheck app creates a digital twin to augment patient care.

The importance of having the right data at the right time

Predictive and prescriptive analytics models only perform as well as the data that powers them. Success requires a deep understanding of how health data is managed and the ability to ensure the data is known and trusted. In healthcare, this is easier said than done. Historically, health 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 and accurate data can flow easily between systems. Forward-thinking organizations have begun modernizing their data management practices by using automation to centralize and streamline data, embracing 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 any supply chain challenge. The new insights gained will help the industry increase supply chain efficiency, while reducing waste and costs. And we can do it while making care more affordable and personalized exactly where and when the patient needs it.


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