It was a historic moment for physicists and humankind when the first image of a black hole was captured by researchers from the Event Horizon Telescope. The image of the phenomenon dubbed as the “point of no return” where nothing, not even light, can escape was the result of a years-long effort by dozens of researchers. The seemingly impossible became possible because of the hard work and determination of a group of believers.
While little else equates to the mystery of a black hole, when it comes to workforce practices many people are stuck working with inefficient processes because they can’t see better opportunities.
In our digital age, advanced technology is available to streamline workforce practices, such as staffing and scheduling of hospital staff. But it isn’t widely known. And worse, it isn’t always accepted as an accurate or helpful tool – at least initially.
Like other industries, predictive analytics came galloping into healthcare like a magical unicorn, turning the seemingly impossible into the possible. The potential of predictive analytics remains relatively untapped in multiple areas within healthcare. One area is the forecasting of staff needed to care for patients. The ability to accurately predict staffing needs months in advance of a shift is a win for all – it reduces the time managers spend on scheduling, decreases labor costs for the organization, and gives staff the balanced schedule they want and expect.
How does data science do this? With machine learning, of course!
While the terms AI and machine learning get thrown around a lot, I find that they are often not used in the correct way. Much of what is called AI is usually machine learning. This might be splitting hairs, as machine learning is a subset of AI, but there are pretty big differences. AI implies that the machine is able to sense, reason, and make decisions like a human would. Machine learning involves the use of algorithms to parse data, learn from it, and make a determination or prediction about something in the world.
Avantas uses machine learning in our predictions for hospital patient and staff demand to determine the best predictive models to deploy. As I said above, this helps hospitals staff for patients more efficiently, more affordably, and in ways that encourage greater staff and patient satisfaction.
So why isn’t everyone using machine learning and predictive analytics? While the science and technology exist, getting leaders and staff in provider organizations to trust it is another issue. For many, they’ve relied on their gut feeling for so long that they feel no one knows their unit or department better than them, and they schedule their staff according to their own intuition – even if it’s wrong.
It is a significant change to implement a technology that is based on data and statistics rather than a person’s gut feeling. And the reality is that organizations will likely fail to achieve their expected ROI if people are not willing to trust the predictions.
So how do you build trust around a solution that promises the unbelievable? It takes a lot of data and even more faith. The more data that is fed into the predictive model, the more accurate the predictions become. Healthcare leaders must learn how to give the predictive model some autonomy to work. This doesn’t mean that it’s a hands-off technology. It’s a collaborative effort between the users and the predictive model. Communication between what is happening on a unit and the system increases the accuracy of the predictions.
While not a black hole, an air of mystery surrounds predictive analytics and its potential in healthcare. Humanity can revel over the snapshot captured of a black hole – a feat that seemed light-years away. But having the right tools and data, and a hardworking group of believers brought the implausible dream into realty.