Is the Healthcare Supply Chain Ready for the Netflix Effect?

Have you ever marveled at how Netflix seems to know what you want to watch, when you want to watch it, and sets expectations with a high percentage of likelihood that you’ll enjoy their recommendation? What if we could apply that same degree of accuracy to the healthcare supply chain? Armed with the right information, we could know who needs which supplies and when, down to the patient level. The result would be a more resilient supply chain and better patient outcomes.

If you recall, Netflix’s original recommendations didn’t always have a high level of accuracy. Through a combination of artificial intelligence, data, and predictive analytics, Netflix’s picks are now so accurate that 80% of viewer activity is driven by personalized recommendations. Along with boosting customer loyalty, this saves Netflix more than $1 billion per year, according to an Insider interview with Carlos Gomez-Uribe, the company’s vice president of product innovation.

The healthcare supply chain is a lot like Netflix before it went all in on predictive analytics.

Due to a variety of factors, predictive analytics is poised to play a larger role in the healthcare supply chain. First, healthcare is at an inflection point. COVID-19 has accelerated the necessity for change and hospitals recognize that they can’t operate as they did in the past or they won’t succeed. But forging ahead will require greater flexibility, resiliency and cost efficiencies.

Second, there is greater alignment among providers, payers, suppliers and patients. This alignment drives collaboration, innovation and the marriage of data sets.

Finally, the abundance of data available to improve decision-making continues to grow. Making sense of it requires predictive analytics.

From a supplier’s perspective, predictive analytics will help get ahead of shortages, identify and address leakage, and maintain the integrity of the supply network. For healthcare providers, it will enable them to align the right products with the right patients at the right time. Yet, as we’ve all learned over the past year, there’s room for improvement.

Building a more resilient healthcare supply chain through the use of predictive analytics comes down to using data points from the past, converting them into actionable information, and using them to ask the right questions to accurately forecast what could happen next. The more data and insight you have, the better the recommendations and results.

Healthcare doesn’t suffer from a lack of data. Where the industry can—and needs to—improve is aligning the right data sets and identifying variables within it to make recommendations in real time.

For example, if you’re a hospital administrator trying to predict ICU capacity during the next week, one variable could be the number of current COVID-19 cases.

Another example is predicting the likelihood of a patient needing knee surgery in six months. Variables could be a patient’s age and ZIP code, coupled with their medical record. However, a few general data points aren’t enough to make accurate predictions. Conversely, an abundance of data makes it difficult to know which variables are most relevant to a specific situation at that moment in time.

The most accurate predictions come from identifying the variables and studying them. To do this, you need models that can turn data into predictions. These models have historically been statistics, regression, and neural networks. In the past decade, we’ve seen advances in all three areas. It’s these advances that will further modernize and fortify the healthcare supply chain.

For example, using predictive analytics, medical device suppliers can gain the insight they need to better understand their customers and markets, deliver products more efficiently and effectively, and match the right product to the right patient and the right case. They can also have deeper insight into the clinical impact of products and know which will drive adoption and profit and where to allocate resources to get the best returns. Finally, predictive analytics can vastly improve research and development, and drive innovation to continuously produce better products that yield even stronger patient outcomes.

That’s not to say there isn’t already good work happening with suppliers using predictive analytics. More recently, suppliers have deployed predictive analytics to better identify where to increase or decrease shipments of PPE supplies. Now that the technology is advancing and healthcare is at an inflection point, the opportunities to optimize the value of predictive analytics for suppliers will increase. By extension, providers will also benefit as they seek to better understand factors such as inventory levels, forecasted demand, and upstream manufacturing capacity from suppliers.

Suppliers in the healthcare supply chain can learn great lessons from the Netflix experience. By consistently optimizing data and applying predictive analytics, the more accurate the recommendations. In turn, this increases loyalty while delivering greater cost savings.

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