According to a recent US Pharmacopeia, or USP, survey, seven of 10 U.S. physicians say that the COVID-19 pandemic has heightened drug supply chain problems, limiting their ability to provide quality patient care. It’s no secret: The pandemic has driven supply chains to their breaking points, causing out-of-stocks and sky high prices. As the pandemic slows in the United States (though cases are currently rising again in Europe and Asia), its lasting impact on supply chains is likely here to stay for months, maybe years, to come.
Even before COVID-19 emerged in the United States, managing drug store supply chains has always been challenging. Illness seasons are historically unpredictable, and the pandemic only exacerbated this fact. What if drug stores could know when and where illness is rising, and how quickly it’s spreading? What would it mean to remove some of the unpredictability before, and during, an illness season?
Knowing when and where customers will want products begins with high-quality demand-sensing data that is timely, consistent and regional. Drug stores can leverage such data to help prevent out-of-stocks and effectively plan their workforce.
Drug stores typically leverage medical claims-based data, like data from the Centers for Disease Control and Prevention, which publishes aggregated data with up to a two-week delay. Making supply chain decisions using this lagged data can have serious consequences for retailers because illness patterns and trends typically change quickly. Illness data aggregated at illness onset, before someone enters the healthcare system, can show when and where symptoms are rising and falling in real time, helping drug stores make more nimble decisions about stock, staffing and more.
If drug stores have context from past illness seasons, they can compare them to present activity to help identify and predict exceptional illness levels. “Consistent” year-over-year data enables retailers to put upcoming illness levels, including COVID-19 waves, in context, and this more complete picture of any given illness season is key for informed decision-making. This is particularly helpful as illness activity (flu, RSV, etc.) decreased when people employed preventive behaviors in response to COVID-19 (i.e., masking and social distancing).
Illness levels vary greatly in different parts of the country. It’s common for one region to experience increasing illness levels while others near or pass seasonal peaks. Local illness trends require different resource strategies, and with high-definition “regional” data, retailers can pivot appropriately to best serve the communities where their stores are located.
In the case of Kinsa, an illness insights solutions business working with two of the three largest pharmacy chains in the United States, local illness insights are rooted in high-quality demand-sensing data.
These insights begin with aggregated, anonymized real-time illness data (temperature, symptom and demographic) from millions of households across the country using Kinsa’s app-enabled smart thermometers (“timely” data). Kinsa’s aggregated data can also show the rate at which illness is transmitted within a household, and the origin of illness within the household (child versus adult). This information helps understand how illness might spread throughout the larger community and the region.
Kinsa’s real-time data is aggregated with other inputs of publicly available health data, weather data and more (“consistent” data), and the output is an illness data hub with predictive power to forecast when and where illness will rise up to 20 weeks in advance while simultaneously monitoring in real time for symptom spikes at a hyperlocal level (“regional” data).
High-quality demand-sensing data has the power to help drug stores optimize their supply chains in anticipation of illness season and throughout. Timely, consistent and regional data can help keep the right products on drug stores’ shelves to help customers and communities feel better faster.