Resetting Data Management- Creating a foundation for your Data lake and Large Learning Management Systems (LLM, ChatGPT).

For years, I’ve worked with business leaders at hospitals and health systems who’ve told me their biggest issues is data management, both from a quality and maturity perspective.

Today I want to ask a question: Why hasn’t this changed? If the biggest challenge to driving meaningful change in health systems is still data quality & maturity, why have we not collectively found ways to address this issue?

With data at the center of every decision a health system leader must make, surely there’s a way to advance the quality and usefulness of data. So I’d like to share what SupplyCopia has been working on with business partners (health systems, hospitals and suppliers), in recognition of the shared issues related to data:

1.???? We must resolve data inconsistencies across multiple systems.

Generally speaking, health system data is stored everywhere but the linen closet and it’s been nearly impossible to move it and use it to support meaningful analysis. Siloes remain, and although I’ve seen supply chain leaders work hard to address gaps in data sets in the systems they manage, that doesn’t always drive change across the organization.

2.???? We must build a single location for aggregation & data intelligence.

Until recently, there’s never been a place for a health system’s data to be combined and understood in ways that let it be used meaningfully. With a smart and secure location, we can combine and aggregate data, combine it with an AI- and ML-propelled tool, and layer on a methodology that creates a data set that can actually get used for analysis and predictions. ?

3.???? We have to implement processes that increase data maturity & quality.

Even within a single organization, data maturity varies from system to system. Recognizing the need for technology that can span data and systems, we architected a digital “data lake,” which creates a single location to combine all data. Through a sophisticated data pipe, a health system’s entire data set can be brought together in the data lake, where the addition of smart technology enables greater analysis and understanding.

4.???? We have to get to actionable insights.

It’s by understanding our complex environments (our patients, our service lines, products, etc.), and our goals (how can we improve costs, how do our surgeons measure up, what are we missing in our reimbursements?) that data can be used to improve decisions and results.

5.???? We must add practical recommendations from the real world.

Provider organizations need meaningful insights and practical recommendations based on the real world. So we work with health systems – from most prominent to smallest – to consider their data, aggregate it in meaningful ways, and apply smart technology to run machine learning algorithms, ultimately revealing answers and recommendations needed to improve end-to-end results, from patient to profitability.

6.???? We must actually improve outcomes.

Our team has created ML-driven algorithms that constantly examine data to deliver useful insights. The algorithms look at standard data: price parity, demand, functionally equivalent products, and contract compliance, and then go beyond to factor in patient outcomes and reimbursements. With flexibility built into the data, evolving and ever-smarter recommendations are revealed, showing organizations a path to savings while ensuring quality outcomes.

7.???? We must understand whether our supply chain approach serves our needs.

Is your organization’s supply chain driven by a long-term vision or a series of short-term tactics? If the supply chain is more of a tactic, creating broad change can be challenging. If supply chain is seen as a strategy, there are more ways to create and support organizational resilience, drive transformation and reframe the future.

8.???? We must look at best practices, beginning with a Bill of Materials.

By bringing together cost, quality, and outcome data from the entire enterprise and combining available data points from health systems around the globe, a standardized Bill of Materials for every procedure can be created. (Read more about the value of using a Bill of Materials to support your supply and procedure decisions.)

9.???? We must collaborate across teams to improve results.

It’s a game changer when your clinical team fully understands its role in cost, quality and outcomes. One way to start is with a scoring matrix. This approach has helped organizations look at their surgical team’s performance and create a standard method of reducing costs while improving outcomes.

10.? We must use data to tell a compelling story.

We’ve not yet become great at using analytics to tell compelling stories. Stories that actually change the way we select products, standardize processes, improve results. Often important insights get mixed with noise. So let’s learn how to create a meaningful foundation from our data, and in turn, help teams focus on what’s most important, and then discard what’s not.?

It’s time that our industry leverages the power of data to change the way we actually deliver care. It’s been my mission for years and I’d love to get more of us working together to make this change.

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