Data Represents the Key to Jumpstarting Elective Surgeries BusinessData Represents the Key to Jumpstarting Elective Surgeries Business
Predictive and prescriptive analytics can help medtech identify trends and avoid missteps.
January 19, 2021
When the U.S. Centers for Disease Control and Prevention (CDC) recommended in April that hospitals postpone or eliminate elective surgeries to deal with the potential surge of COVID-19 cases, it was like a gut punch to the medical device industry.
The CovidSurg Collaborative estimates that more than 4 million surgeries were canceled over the peak 12 weeks of the pandemic (roughly mid-March to mid-June) in the United States, part of the 28.4 million cancellations worldwide. Recovery from this scenario is expected to be slow.
For example, a May 2020 Bain and Company survey of 160 U.S. surgeons showed the respondents expected surgery rates to be at 60% of pre-COVID-19 rates by June, and only 75% by September. Additionally, an article published in The Journal of Bone & Joint Surgery projected that even under the most optimistic circumstances, there would be a backlog of 1 million orthopedic surgeries two years after the end of elective surgery deferment.
In other words, there is currently far more demand for elective surgeries than there is capacity. This creates a dilemma for medical device manufacturers: on which product lines should they begin ramping up production and marketing/sales efforts to ensure they are ready to reap the rewards once the pent-up demand is unleashed?
A logical starting point is situations where patient need is greatest or most urgent—those at risk of a precipitous deterioration or an adverse event if their surgery continues to be delayed. Patients who are in great pain from their conditions (especially if they are unable to work) would most likely also fall into the high-priority group. The need increases even more if value-based payments make up a significant portion of surgeon revenue since they are more invested in the long-term outcomes.
Still, that may not be the right decision for every hospital or health system. Some may instead choose to ease back into elective surgeries by focusing on simpler, higher-volume procedures that carry lower risk to help them make up lost revenue quickly.
They may also look upon it as an opportunity to revamp workflows to improve efficiency and throughput, starting small and building out from there. This approach might include breaking traditional geographic barriers by directing some patients to their hospitals or clinics in neighboring counties to reduce costs, spread the workload, or take advantage of a concentration of surgical expertise as they bring their organizations back up to speed.
The reality is there is no single, simple answer. With so many options available, medical device manufacturers will need more than past sales figures to forecast production and marketing needs. They will need insights into where the greatest opportunities exist by county so they can ensure they have adequate supplies to meet the demand—wherever it leads. This is where sophisticated predictive and prescriptive analytics can help them identify trends and avoid missteps that can further damage already significantly reduce revenue.
Understanding the Patient Landscape
The key to informed decision-making is understanding what types of elective surgery are typically most prevalent in each county and whether there has been a noticeable decline in those surgeries from 2019 to 2020. This information should then be overlaid against publicly available data showing the impact of COVID-19 by county. Counties that have the highest instances of the virus by population, as well as the highest fatality rates, are the ones that will be most likely to have the highest backlogs of elective surgeries overall. Putting the two together shows which surgeries have the highest probability of being in high demand once restrictions are lifted.
For those medical device manufacturers that want to get more granular, analytics offer the ability to dig even deeper. By adding data on demographics (such as age and gender), psychographics, pre-existing conditions, recently administered procedures, and social determinants of health (SDOH), life sciences organizations can create a more comprehensive risk score that shows the level of severity for each condition within that county, which could also have an effect on which surgeries will be prioritized.
Armed with this information, life sciences companies can begin ensuring that they have sufficient supplies/equipment for each region, educational materials for surgeons and patients, copay support, and other necessities to meet the demand as soon as providers once again begin scheduling elective surgeries at high volume levels. At the same time, they can avoid wasting money and resources on procedures that are unlikely to show high demand, helping them avoid taking on more long-term debt.
Data in Practice
Here is an example of how this type of data analytics might work. A medical device manufacturer sees that sales of heart stents are down 50% in County A in 2020 versus 2019. Public data shows that County A was hit hard by COVID-19, and all resources over a two-month period were focused on managing the resulting surge.
The public data also shows that the trendline on COVID-19 patients is dropping rapidly, which means healthcare resources are freeing up. Because it is a smaller county, however, there is uncertainty whether the return of elective surgeries will focus on heart procedures or knee and hip replacements. A review of demographics and SDOH factors indicates that the overall population has been more prone to heart conditions than joint pain since they primarily have sedentary jobs and lifestyles, made worse by the fact they live in a food desert.
Taken together, these factors indicate that County A will likely prioritize heart procedures over joint replacements, which means the organization should begin working now to meet the upcoming demand.
While much has been made about the revenue impact on hospitals and health systems of delaying elective surgeries, the effects have been felt further upstream as well. Many medical device manufacturers have watched helplessly as once-reliable sales channels dried up due to circumstances beyond their control.
Now that the market is beginning to open up, they need to proceed wisely to maximize financial gains while minimizing financial exposure. Sophisticated predictive and prescriptive analytics can help them determine where the best opportunities are—and the best strategies to take advantage of them.
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