An expert in artificial intelligence-based healthcare recruiting technology explains how e-commerce sales techniques can be used to better solve staffing problems.
What healthcare can learn from e-commerce when filling shifts
An expert in artificial intelligence-based healthcare staffing technology explains how e-commerce sales techniques can be used to solve staffing problems more effectively.
In e-commerce, Amazon and Wayfairs have mastered the art of using data to show the right product to the right customer at the right time.
With dramatic staffing cuts due to the COVID-19 pandemic, some healthcare organizations are turning to e-commerce strategies to improve their own staffing and scheduling processes – to show the right shift to the right per diem worker at the right time.
Whether people are online shopping for a couch or browsing for available shifts, they will only tolerate scrolling on their phones for so long, so data science must offer users the options they are most likely to use.
In health care, of course, the stakes are much higher. If a consumer doesn’t buy a couch, another may do so; but if the shift goes unfilled, the healthcare organization may not meet established staffing standards – and the quality of care may suffer.
Ike Nnah, co-founder and CTO of IntelyCare, a provider of healthcare staffing and scheduling technology, discusses with Healthcare IT News the draw of e-commerce strategies, how healthcare can use those strategies, what role artificial intelligence plays and some examples of it all in action.
Q. Why is healthcare turning to e-commerce for help with data? What is the appeal?
A. E-commerce has already undergone the kind of digital transformation that healthcare is now undergoing. The e-commerce field has a wealth of talent, expertise and knowledge that the healthcare staffing industry can tap into.
There are similarities in the world of e-commerce and healthcare staffing, namely how a consumer buys a product and how a healthcare professional chooses a shift. But there are also many differences that need to be considered as well.
For example, geography is critical in medical staffing, whereas in e-commerce, prices are usually set nationally. And medical staff product (free shifts) has a short shelf life compared to products on sites such as Wayfair or Amazon.
However, medical organizations can succeed in applying data science methods if they can leverage the expertise of e-commerce models, apply them thoughtfully, given their similarities, but tailoring their operations to the needs of providers and institutions.
This does not apply to bricks-and-mortar staffing agencies. It only matters for those companies that allow vendors to choose which shifts they want. When it comes more to buying shifts, e-commerce is a natural place to look for ideas.
As healthcare staffing becomes more digital and gives providers more freedom to choose which shifts they want to work, the staffing experience begins to resemble the shopping experience in many ways.
Only instead of buying books, providers are choosing which shifts they want to work. But the key differences are this: shifts are more ephemeral than books or mattresses, and suppliers choose shifts only from nearby institutions, not from a global catalog.
Q. What are a few data science strategies and practices from e-commerce that healthcare staffing can use?
A. Personalization is one example. We know that consumers live in their phones, and providers are no exception to that rule. They like being able to create their schedules and pick up shifts from their phones. It’s easy and natural for them.
However, sometimes there are thousands of shifts to choose from, and they can’t view them all on their mobile device. Therefore, health care providers need to be selective about how they display shifts. If the best shifts aren’t on the first page, providers won’t see them.
Making shift recommendations based on provider parameters and behavior can streamline the process of filling shifts. This makes it much easier for agencies to fill shifts and provides a better experience for suppliers in finding the perfect schedule.
Dynamic pricing provides the ability to change prices frequently, constantly using all available data from purchase history to get the best prices in the future.
While this happens all the time on e-commerce sites, it is not fully utilized in the medical staffing industry. Dynamic pricing allows medical staffing providers to raise prices on the hardest shifts to fill and lower prices on the easiest shifts to fill, effectively increasing the chances of filling both shifts.
Ultimately, this strategy benefits the provider, the facility, and the patient. Providers optimize their earning potential, facilities optimize their schedules, and in turn, floors are well staffed and patients get the care they deserve.
Q. Where does artificial intelligence apply here? What role does it play?
A. As the algorithms for pricing and personalization become more sophisticated, they begin to move into the realm of true artificial intelligence. These algorithms can respond to patterns that companies may not be aware of.
When properly designed, these algorithms essentially become another member of the team. They make business decisions just like any other employee-just limited to the decisions the business allows them to make.
So if the company sets appropriate boundaries for these algorithms, they can work for the company around the clock, they can be scalable, and they can yield positive results for the business and for the future of healthcare staffing.
Q. Name an example or two of the principles of behavioral economics that can help develop algorithms to more effectively “sell” finite, time-limited resources such as shifts?
A. The first is loss aversion. In some cases, instilling the fear of losing something (such as a product or shift ratio) can be more motivating than simply presenting good products, savings, or bonuses.
How you present the price of something to the consumer also makes a huge difference. For example, you can advertise a shift as $16 an hour or you can advertise it as $108 a shift. Although these are the same payments, consumers end up responding differently to their design – so it’s important to consider how you communicate the reward.
Finally, the timing of the promotion is important. Data science can create a practical exercise in determining what motivates a consumer or, in this case, a supplier more – the promise of larger, future rewards versus smaller, instant rewards.
