Artificial Intelligence (AI) is changing healthcare in so many ways, but there’s one opportunity that many are missing – employer benefits plans. With real-time answers to health questions and predictive insights, AI and machine learning will change the way employers approach healthcare, writes Rod Reasen is the CEO of Springbuk, and Anne Fischer, Senior Director of Data Science and Methods, Springbuk.
Because of deferred care during COVID-19, healthcare costs are expected to jump next year – up to 5% beyond pre-pandemic growth projections. But 57% of employers sayOpens a new window they are not going to mitigate these increases by shifting the cost to their employees. That’s up a full 10% from last year. As employers rightly put their people’s needs first, they’ll need to find other creative ways to manage their costs and improve benefit strategies. The answer? Artificial intelligence (AI).
AI and machine learning are already being used in so many aspects of healthcare. In radiology, we can use AI to detect minute changes in an image. In telehealth, AI can help physicians identify possible disease activity in patients in remote locations. Yet, AI and machine learning are vastly overlooked when it comes to employer healthcare strategies and improving benefits plans.
This is shocking because employers have the greatest ability – and incentive – to put these health intelligence insights to work for the good of their people. Organizations have access to a robust set of health data, from plans and carriers to health interventions and wellness solutions. But what does it take to actually put this data to work to improve outcomes and manage costs?
Let’s look at some ways AI and machine learning can help benefit teams get ahead.
Real-Time Answers To Health Questions and Scenarios
With the advancement of natural language processing (NLP), asking a computer a question and getting a quick answer is no longer a thing of the future. Think about your digital personal assistant applications like Siri or Alexa – these tools make it easy to ask common or complex questions and get answers immediately.
This is possible in health benefits management too. By leveraging AI and machine learning built by industry-leading healthcare data scientists and clinical professionals, benefits leaders can quickly get answers to key business questions.
For instance, answering the question, “Is our wellness program actually making an impact?†no longer takes hours of data collection and sifting. This gives time back to benefits administrators so they can focus their attention on developing programs that will create a positive impact on the organization.
Or perhaps you want to ask, “Who is most at risk for becoming diabetic or suffering long-term effects of COVID-19?†With this data right at your fingertips, you’ll be able to forecast potential health problems and create strategies to mitigate risk factors.
Finally, AI and machine learning can make it easy and intuitive to monitor any slice of your organization — whether a specific department, region, age range, or combination of attributes. Knowing these answers gives you the information you need to create strategies for wellness programs and cut costs in the future.
Automated Predictive Insights
The best part about AI and machine learning is that it doesn’t just help you find answers to the questions on your mind, but even those that aren’t. Employers can receive proactive insights that alert them to important trends, patterns, and risk factors for serious conditions such as thyroid disease, stroke, or opioid dependence. Then, AI can also deliver action steps to help leaders intervene with effective treatments, disease management resources, and risk mitigation strategies.
These insights can also make it possible to create plans for care efficiency, identifying drug savings, and helping members avoid unnecessary procedures. AI-supported health benefits planning means leaders can build the right programs for the needs of their business and employees – enabling their organization to drive the most competitive and effective health plans in the market.
Of course, as with any kind of artificial intelligence, it’s important to have human experts who can ensure transparency and actionability of the results.
HR professionals should seek out healthcare analytics vendors who provide not only AI-powered technology, but a team of data scientists who have a deep understanding of the algorithms’ integrity and accuracy.
With the right application of AI and machine learning, employers can build more-targeted health programs to reduce costs and improve productivity and population health across their employees and dependents.
It’s Time To Let AI Take Employee Health To the Next Level
Healthcare costs have been out of control for years, but our workforce isn’t getting any healthier. Employers have the ability, and responsibility, to do something about it. By using true AI and machine learning to make sense of the wealth of employee health data, employers can drive real predictive insights that actually improve their workforce’s health. It’s time to harness the latest tools in AI and machine learning to improve employee healthcare. The future of health, and work, depends on it.
Do you think the future of employee healthcare depends on AI? Tell us why on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window .
CO-Author
Anne Fischer, Senior Director of Data Science and Methods, Springbuk
Anne FischerOpens a new window , Senior Director of Data Science and Methods, Springbuk.
With over 20 years of experience, Anne Fischer has held multiple leadership roles in the Healthcare Information Technology industry and garnered extensive healthcare analytics experience. Before joining Springbuk, Anne led the transition of a former Truven Analytics team of data scientists, business analysts, and clinicians to create the new Watson Health Value-Based Care Emerging Analytics team. In the winter of 2019, Anne joined the Springbuk Health Intelligence team as the Sr. Director of Data Science and Methods to help develop analytic methodologies and drive value to healthcare payers and providers.