The Association Health Plan Proposal is Making Big Waves – Here Are Our Predictions
Earlier this month, the Department of Labor (DoL) released a proposed rule that allows small business and employee groups to purchase association health plans (AHPs) instead of employee-sponsored or individual health insurance plans. This recent proposal was met with mixed views across the healthcare industry – some believing this would further complicate the insurance market or weaken consumer protections.
Our very own Bruce Carver, associate vice president of payer services at MedeAnalytics, offered his insight on the potential impact this proposal can have on the insurance market. His thoughts on benefits, risk and coverage are below.
What are the primary changes this rule would allow?
The Trump administration has proposed a new rule, based on an executive order by President Trump, that allows Association Health Plans (AHP’s) to expand the types of groups that can form an AHP. The two primary changes in the rule would allow AHP’s to be offered membership without regard to state lines, and allow self-employed individuals to take part in a large-group AHP.
What does this mean for essential benefits?
This proposal could allow insurers to sell plans that do not cover certain essential health benefits, like mental health, substance abuse treatment, maternity care and prescription drugs. This may cause a lot of confusion with members when they are treated by providers and any limitations in coverage will need to be clearly communicated between members and providers, in an already confusing market. Members will also need to consider if the plan benefits them based upon pre-existing conditions.
What about risk?
Any time you increase the number of people covered in a plan, you have the capability to diversify risk. The concept of “pooling” members in a region for covered benefits by putting small groups together into a single larger group is not new. Some states allow for this type of “pooling” under group rating programs for disability and workers’ compensation benefits.
How West Tennessee Healthcare Turned Bad Debt into Reclaimed Revenue
The healthcare reimbursement landscape is continuously changing, creating numerous challenges for healthcare organizations as they look to increase revenue. In fact, the revenue risk among not-for-profit and rural healthcare systems are even greater. The National Rural Hospital Association, estimated that 673 rural facilities were at risk of closure, out of over 2,000. West Tennessee Healthcare (West Tennessee), one of the largest, rural, public, not-for-profit healthcare systems in the U.S, acted proactively to combat this trend. In our recent case study, we examine how West Tennessee leveraged analytics to achieve financial success.
West Tennessee has four hospitals, two medical centers and offers 20 primary and specialty care centers. They service a population of 500,000 and with such a large rural patient base, needed guidance to address the following issues:
- 3:1 bad debt to charity ratio
- High percentage of accounts in arrears
- Long lead (30+ days) to denial and appeal process
Health IT Headlines for 2018
2018 is already off to a strong start with the J.P. Morgan Healthcare Conference and StartUp Health Festival making headlines last week. As 2018 progresses, we want to share the trends that we expect to make headlines this year:
- Mega merger and acquisition activity – 2017 was filled with notable M&A activity including CVS and Aetna, Humana and Kindred and Optum and AMGA, to name a few. We expect to see this activity continue throughout 2018 as payers, providers, pharmacies and more look for ways to innovate, meet consumer demands and ultimately improve the quality and cost of care. Bruce Carver, associate vice president of payer services at MedeAnalytics, shares additional thoughts on recent merger activity in our blog.
- Consumerization of healthcare – With consumer-facing companies outside of the traditional healthcare space making moves to enter the industry (like Amazon, Apple and Google), consumer focus is critical. In 2018, this focus will only increase as consumers continue to demand user-friendly and easy to use platforms and interfaces. With all the competition in the industry, healthcare organizations will have to ensure they are keeping the consumer top of mind to stay ahead.
- Emerging technologies, like AI, will take the stage – In 2017, the adoption of AI technology made headlines across all industries, healthcare included, as organizations looked for innovative ways to leverage this new tool. As we head into 2018, companies like Google will continue to lead the pack by working with startups that are focused on finding ways to leverage this technology to improve care. We recently sat down with our CTO, Tyler Downs, to discuss trends in AI in healthcare and to hear how companies can use AI to power data and analytics.
- Clarity around industry uncertainty and policy changes – With the Trump administration and new faces in prominent health IT positions, the industry saw major shifts in 2017. According to a recent poll, healthcare is the one topic keeping both Democrats and Republicans up at night. 2018 will hopefully bring some clarity to the shifting tide as policies get ironed out and the state of Obamacare is decided. Regardless of these policy changes, providing patients with the best quality care should remain the industry’s top priority.
With all the new trends, emerging innovations, policy changes and more, is your organization prepared? Check out our solutions page to learn how MedeAnalytics can help you find success in the new year or contact us for additional information.
A Year In Review – Our Top Blogs from 2017
As snow starts to fall across the country and temperatures decline, winter and 2018 are here! 2017 was a busy year for the healthcare industry, from the new administration, ground breaking treatment advancements, the potential of artificial intelligence and most recently, the CVS and Aetna health merger. 2017 has also been a busy year for us at MedeAnalytics with new hires, customer announcements, speaking sessions and more! As we gear up for the new year, we’re looking back and highlighting our top three blog posts from 2017:
Meet MedeAnalytics’ new CEO, Paul Kaiser – In May, we were excited to announce our new CEO, Paul Kaiser, and in June, we had a chance to connect with him to learn more about his background, his company observations thus far and his future plans for the company. Our blog outlines the opportunities our CEO sees for Mede clients and how we can continue to support them amidst the everchanging healthcare landscape.
Enterprise Analytics and Beyond: A Q&A with our Vice President of Healthcare Provider Solutions – In March, our blog explored top provider concerns and offered insight from a Mede executive on the challenges that lay ahead and continued importance of investing in an Enterprise Analytics (EA) strategy. The post outlined proactive approaches for providers to take to ensure a seamless EA strategy, including training and educating clinical leadership, establishing realistic goals and empowering self-service. The post concluded by outlining how analytics’ role in reporting and identifying care and cost improvement opportunities will only grow in importance in the years ahead.
Machine Learning: A Q&A with CTO Tyler Downs
We recently sat down with Tyler Downs, CTO of MedeAnalytics to discuss trends in machine learning in healthcare. He offered insights into how MedeAnalytics is using machine learning to power data analytics and what’s in store for the future of the technology.
Q: What exactly is machine learning?
A: In layman’s terms, machine learning is the application of artificial intelligence that enables computers to automatically learn and improve from experience without being explicitly programmed. It’s all about developing computer programs that can access data and learn from it themselves.
Machine learning is used every day in social networking, commuting apps, and online shopping. Think of Facebook. When you upload photos to Facebook, it automatically highlights faces and suggests friends to tag. Facebook also uses algorithms to personalize your newsfeed and ensure you’re seeing posts that interest you. Google Maps analyzes the speed of traffic at any given time. And on Amazon, you see recommendations for products you might be interested in, displaying “customers who bought this item also bought…”. These all use forms of machine learning.
Q: How is machine learning being used in healthcare?
A: Think about the huge volumes of data being generated in healthcare. Machine learning can be trained to look at structured and unstructured data (images, video, audio files), identify anomalies, and point to areas that need attention.
There are clinical, financial, and operational applications of machine learning in healthcare. In the clinical realm, Google has developed a machine learning algorithm to aid in tumor-detection on mammograms. Stanford is using algorithms to identify skin cancer.
In the revenue cycle, machine learning can be used to predict denials, improve point-of-service collections, and identify a patient’s propensity to pay their bills. This enables the health system to increase reimbursement, reduce the cost collect, reduce bad debt, and more.
For healthcare operations, machine learning enables providers to improve throughput in the OR and ED, assess the risk of no-shows, identify trends in patient volumes, determine the appropriate appointment length, and enable schedulers to allocate the right amount of time for procedures based on the individual patient and physician. These capabilities drive down costs, which is important considering the average cost of an OR can be as much as $60-80 per minute (Stanford).