What if your data could generate predictions that made a material difference to your business: actuarial insights that are actionable, patient behavior, provider trends, predictions of new high-cost claimants?
Building predictive machine learning models requires significant infrastructure and capabilities, not to mention patience. As we talk to our customers, we find there are common hurdles that are slowing their Artificial Intelligence journey.
AI Journey: Where are you?
Infrastructure is expensive, centrally managed, time-consuming and
requires cross-functional executive approvals
Customers are constantly telling us that machine learning is a requirement — a must-have for a customer-centric and cost-effective business. The infrastructure required can be a challenge. It requires significant time and financial investment. Most of our customers have extensive annual project budget approval processes. And, the operational budget lift required to maintain the infrastructure is cumbersome. Then, you need the right team; trained engineers to build and maintain your models. You need hardware (virtual and physical) as well as software expertise. It can be very, very time and resource-consuming. And, if you can get the basic infrastructure right, the more precision and lift you want from your models, the more complex your models get. The more complex your models get, the more complicated your infrastructure becomes. The more data you have, the more infrastructure you need — it is a challenging cycle for any organization.
Required skillsets are nearly impossible to find
The talent crisis is global and hitting our healthcare customers hard. Hiring trained engineers and data scientists to build and maintain your models is not easy, “In the US, the share of jobs in AI-related topics increased from 0.26% of total jobs posted in 2010 to 1.32% in October 2019, with the highest share in Machine Learning (0.51% of total jobs). AI labor demand is growing especially in high-tech services and the manufacturing sector.”(1) The most qualified talent has a Ph.D. in a quantitative field, deep statistics and linear algebra skills, are highly adaptive and change-resilient, and, ideally, have health-care experience.
According to an article in the Financial Times, “Machine learning specialists topped its list of developers who said they were looking for a new job, at 14.3 percent. Data scientists were a close second, at 13.2 percent.” So, if you are lucky enough to hire qualified people, you want to keep them engaged and working “at the top of their license.” Again, according to the FT article, “People working in this field experience many frustrations… Bad data are one of the main ones: their employers cannot provide the essential raw material for them to obtain results.”
Lastly, patience is paramount to success
While healthcare is famous for moving slowly, most healthcare organizations we talked to want to move quickly. Building resilient, flexible infrastructure, hiring and keeping the right talent requires time and patience. Often you have to try hundreds of experiments to land on the one that works. Not all experiments yield good results and organizations can quickly lose patience and discontinue the appropriate investments needed for long-term solutions that solve the hardest problems. We hear this all the time, “funding was not approved.”
The solution, a partner who is dedicated to healthcare AI
Lumiata was founded specifically for the healthcare industry. We are completely focused on delivering solutions that seamlessly fit into healthcare’s existing workflow. We employ some of the nation’s leading data science and machine learning talent. Our AI Platform and tools, powered by over 100 million patient data records, are specifically designed to manage the challenges and risks that are unique to healthcare. Lumiata empowers your team to easily build, deploy, and monitor machine learning models — fast.
We’re offering a free trial of the Lumiata AI Platform. We’ll work with you and engage your teams to identify the solutions that address your unique challenges. Whether you’re struggling with infrastructure, the right talent and especially if you’re running out of patience — we can help.
AI Journey Graphic: Timo Elliott Blog
(1) Raymond Perrault, Yoav Shoham, Erik Brynjolfsson, Jack Clark, John Etchemendy, Barbara Grosz, Terah Lyons, James Manyika, Saurabh Mishra, and Juan Carlos Niebles, “The AI Index 2019 Annual Report”, AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, December 2019.