Following a surge of cesarean section (C-section) operations for expecting mothers, doctors in California must now reduce the number of C-sections performed to 23.9 percent for low-risk births. The state’s health insurance marketplace under the Affordable Care Act, Covered California, wants to curb this rate to improve patient safety and quality. Any hospital that doesn’t meet these metrics runs the risk of being removed from the state’s health insurance marketplace as an in-network provider. Currently, most hospitals in California deliver around 40 percent of their babies via C-section.
The medical rationale for reducing the rate of C-sections is strong. C-sections can expose a woman to unnecessary risks such as infection, hemorrhage or even death. Studies also show babies born by C-section have a higher risk of complications and spending more time in the neonatal intensive care unit. We talked to Bruce Carver, Associate Vice President of payer services at MedeAnalytics, about this new mandate and how data analytics can be leveraged to address it.
How could this approach impact hospitals in California?
There are some factors that would be beneficial for Covered California to keep in mind. Reaching the goal ultimately falls on the provider, who is giving patients that care. But hospitals should be examined on a case by case basis if they are not meeting the metric, because removing them from the insurance marketplace could have a huge impact on providers and members, especially in rural areas. 1.4 million people in California purchase their health insurance through 11 insurers on the state exchange. Additionally, there could be valid, uncontrollable reasons why a hospital might miss the metric. But, of the 243 maternity hospitals in the state, 40 percent have already met this target.
How can Analytics Help Solve Healthcare’s Biggest RCM Issues?
Revenue cycle management (RCM) is a critical part of any healthcare operation and with rising healthcare costs it’s even more difficult to manage. However, to better handle costs and overall revenue, healthcare organizations need to prioritize and develop an RCM strategy. The strategy itself needs to address various revenue pain points, including: cash collections, bad debt, denials, productivity and overall business strategy. We recently connected with Tom Schaal, director of product management at MedeAnalytics, to better understand what’s holding healthcare organizations back from addressing these pain points and what to look for in an analytics partner.
- The Need to Do More with Less – Margins are shrinking across the board and a lot is changing, especially with value-based reimbursement expected to represent 83 percent of provider revenue by 2020. Provider organizations are struggling to meet quality metrics, not to mention, the added reporting required under MACRA and MIPS. With physician burnout and overall healthcare costs at an all-time high, organizations are looking to cut back anywhere they can.
- Disparate Solutions – According to a recent survey, almost 69 percent of healthcare organizations are using more than one vendor solution for revenue cycle management. This means different numbers, screens, reports, siloed data and inefficiencies.
- Lack of Actionable Insights – Many solutions aim to consolidate data, but significant challenges still exist when it comes to sharing that data. Some providers are leveraging data visualization tools to bring information together, but issues arise when it comes time to draw actionable and timely insights from it and hinders organizations from understanding how to adjust their RCM operations.
Payer and Provider Collaboration Ensures the Industry is Tracking Towards Value
Collaboration between payers and providers is an important asset to improving quality of care across the healthcare ecosystem, while simultaneously keeping costs down. For example, payers have access to a significant number of claims data that creates a holistic view of patients’ information, while providers typically have discrete clinical data information. By working together to exchange this information, payers and providers can stay on top of patients and ensure they are getting quality care and avoiding high-cost events, such as visits to the emergency room. Also, with value-based care top of mind, payers and providers need to work together to leverage data and analytics to reach that end goal. We talked to Bruce Carver, Associate Vice President of payer services at MedeAnalytics, for his insight on the current challenges that prevent payer-provider collaboration, best practices to achieve it and why it’s useful.
Challenges that can arise within collaboration
When it comes to payers exchanging information with providers, storing clinical data received from an electronic medical record (EMR) is very expensive. There is no standard format across the U.S. for providers to implement that information simultaneously. Oftentimes, payers don’t want to add another system to store that information, instead integrating it through an EMR that can be sent out from a provider’s system and to a payer. In addition, providers have access to more timely data as well, enabling immediate outreach to patients. Ultimately, collaboration is needed so both the payer and provider are on the same page when it comes to treating patients. If either doesn’t have the complete information, it can result in gaps in care and ineffective treatment.
How analytics can help
Analytics can provide additional ways that a provider and payer can exchange that clinical information. In an EMR, there are hundreds of different measures and metrics that are collected within that provider’s system but oftentimes only a small portion of that is necessary to be able to collaborate effectively for the value-based programs that are in place. MedeAnalytics has enabled external use of its platform by a provider to be able to attach and upload that information straight into an analytic tool that is used to measure results of the program, which makes for a more cost-effective solution.
Managing high-cost members with analytics
As the health demands of patients expand, payers are increasingly focused on a small segment of their member population that is driving the largest share of healthcare spending: high-cost members. According to the American Health Policy Institute, these high-need, high-cost individuals can cost $100,000 every year, which accounts for one-third of total healthcare spending.
As a result, payers are looking for new ways to identify and engage with these individuals using analytic tools. We recently spoke with Diane Gerdes, payer product marketing manager at MedeAnalytics, for her input on the value of using data analytics to address high-cost members’ needs and costs.
What challenges are payers facing when it comes to understanding high-cost members?
For many organizations, the biggest challenge is identifying individuals before they become a high-cost member. These individuals are dealing with acute and chronic conditions and possibly taking expensive prescription medications. Typically, it isn’t until a costly event like a hospitalization that problems finally come to the surface. Adding to this challenge is predicting who will continue to be a high-cost member in the long-term. That’s where advanced analytics can help by targeting individuals who are high-cost or at-risk and giving payers the insight to design proactive solutions that keep members healthy and minimize costly adverse health events.
What pain points do payers face around integrating and analyzing large amounts of data?
General pain points center around the overall process for collecting, integrating and analyzing data. Because data is typically housed in various operational and clinical systems, it takes an extraordinary amount of time and effort to organize and mine the data. By the time the data is ready for analysis, it is no longer timely, and payers miss critical opportunities to intervene and engage with members.
As costs go up, how can the industry adapt?
Healthcare costs continue to rise across the industry. According to a study published in the Journal of the American Medical Association, money spent on healthcare in the U.S. is nearly twice as high as 10 high-income countries. Not only does the U.S. have the highest percentage of obese adults but it also has the lowest life expectancy and the highest infant mortality rates compared to 10 high-income counties. What’s driving these costs? According to Harvard researchers, prescription drug prices and administrative costs are the main culprits.
Prescription Drug Prices
Prescription drug costs are high, not only for payers, but also for the patients that need these life-saving drugs. Unfortunately, treatment for some diseases can cost nearly a million dollars as one Wall Street Journal article explores. The administration is looking to put policies in place to cut costs, but what can be done at the health system level to help these efforts?
St. Joseph Hospital, part of Covenant Health, leverages MedeAnalytics to analyze their pharmaceutical spend and identify cost saving opportunities. The health system found that an extremely high percentage of their pharma spend came from specialty drugs. They were able to identify $100,000 in potential savings, just from generic drug substitution in three therapeutic classes where generic equivalents were available. Additionally, pharmaceutical data revealed one patient with a single drug at a special dosage cost $700 for a 30-day supply. Using analytics, the organization confirmed the cost was correct but also found other patients who were taking the same drug in a smaller dosage two or three times a day, had a significantly lower cost. By simply changing the dosage, they reduced the cost from $700 to just $9 a month. Download our case study to learn more on how St. Joseph Hospital cut costs around their pharma spend.