Data-Driven Decision Making in Supply Chain and Value Analysis Management

Over the past two years, the COVID-19 pandemic’s disruption of the healthcare supply chain has only heightened the need for data-based decision making in the healthcare ecosystem. For example, while healthcare leaders use data to understand the clinical nature of the novel virus, data is also the backbone of the supply chain, informing decisions like never before. 

With lives at stake, the need for accurate, comprehensive data is highlighted not as a fleeting trend, but as a vital part of the future of healthcare, where achieving a clinically integrated supply chain is paramount. 

What is data-driven decision making?

Rather than relying solely on intuition or observation, data-driven decision making (DDDM) leverages multifaceted analytics to achieve high-functioning results in the healthcare supply chain. For DDDM to be successful, data must not only be easily accessible to stakeholders, but also must be strategically integrated throughout the system. To realize the largest impact from DDDM, data cannot, for example, be managed exclusively at the facility, as is the case now in many healthcare systems.

Throughout the pandemic, integrated data proved vital for strategic decision making ranging from EHR integrations to discovering alternative products under a strained supply chain. While there are a wealth of resources available to support product decision making—from clinical evidence to regulatory and safety information—utilizing comprehensive data systems provides amplified information power for decision makers. However, what’s possible is not always what’s practical. 

It may be possible to source ad-hoc insights, but the time invested to do so and the ability to aggregate information for advanced comparative ability may not be practical.

How DDDM enables system-wide improvements

Rather than relying on individual, manual information gathering, leveraging integrated data through APIs and technology partners allows for systemwide improvements. In addition, it makes congruent data available and accessible across the entire health system.

It is evident that virtually all industries are increasingly relying on data-driven decision models. Just a few examples of the benefits are that it enables forward-thinking leaders to avoid bias, assumptions, and wasted time when it comes to making decisions.

While DDDM is not necessarily unique to the healthcare industry, it is arguably the most important setting where it's being applied, due to the high levels of risk and numerous overlapping data channels.

For example, during the early months of the pandemic, the supply chain was severely crippled by spiked demand for products such as respirators and personal protective equipment. It was vital then for hospitals to make informed decisions with integrated data surrounding sourcing equivalent products when the desired product was simply unavailable. And it remains just as vital today.

Why DDDM is the new standard

While the value of data-based decision making was accentuated during the pandemic, it will remain an important part of the healthcare ecosystem long afterward. Without data-driven decision making, incomplete information wastes valuable time, resources, and expertise that could be put to better use if high-quality, integrated data was used to inform vital product decisions.

Ultimately, enhancing data-driven decisions in healthcare supply chain and value analysis management has the ability to impact frontline care by making effective, clinically-backed product decisions that improve patient care.

Learn how GreenLight Medical, now a part of symplr, supports data-driven decision making though our Clinical Evidence database, which includes aggregated clinical evidence and summaries on thousands of products, alongside powerful analytics on safety and financial impact.  

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[1] A Guide To Data Driven Decision Making: What It Is, Its Importance, & How To Implement It. Tableau. (n.d.). 

[2] Healthcare Data Integration: 2 Success Strategies. Health Catalyst. (2021, April 6). 

[3] Miller, K. (2021, April 23). Data-Driven Decision Making: A Primer for Beginners. Northeastern University Graduate Programs. 

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