Without much fanfare, the Department of Health and Human Services (HHS) opened the Small Business Health Options Program (SHOP) marketplace in five states last week after a year-long delay. HHS did this soft launch as a test before rolling out the SHOP to most of the rest of the country by on November 15. Although the action was quiet, make no mistake: this is big news for small employers and the health plans that serve them. Employers are once again faced with the tough decision on whether to continue offering benefits. And health plans have much to gain or lose in this process.
When it comes to Pay or Play decisions, health plans are also at risk, because employer decisions about this Affordable Care Act (ACA) provision will have a far-reaching impact on their business. There are billions of premium dollars at stake, potential shifts in health status, and the significant challenge of managing the Medical Loss Ratio requirements.
Any Pay or Play decisions must be approached by measuring the impact of continuing to offer group health benefits and complying with legislative mandates (Play) or exiting group health and paying the noncompliance penalty (Pay). Modeling should project the effect of the ACA regulations on employer health plan costs for 2014-2020, as well as the influence of the Cadillac tax slated for 2018, transitional reinsurance, comparative effectiveness fees, and for small employers, the value of Small Business Healthcare Tax Credits.
Now is the time for employers to tap into the right resources to make an educated Pay or Play decision. Wise health plan executives will take the lead by supporting their employer partners in this process.
Anita Nair-Hartman, Vice President, Market Planning and Strategy
Bryan Briegel, Director, Operations
A recent CNBC article discussed Walmart’s announcement that it will spend far more than anticipated on employee health coverage and have to trim its earnings forecast for the year. The retailer expected more workers to seek coverage under the Affordable Care Act’s (ACA) mandated coverage requirement, but the actual number topped their projections. Although this news has gotten a lot of attention, National Business Group on Health research indicates that most employers aren’t expecting as large of a jump in healthcare costs as Walmart, and Truven Health research supports this. As the CNBC article points out, Walmart’s employee base has some unique characteristics -- including low-wage workers in states where Medicaid expansion didn’t occur, forcing them to chose Walmart (rather than Medicaid) coverage. These aren’t typical employer circumstances.
Nonetheless, after years of low healthcare inflation, employee benefit costs have grown this year, and Wall Street is going to be keeping an eye on the impact to every company’s bottom line. For employers, monitoring benefits spend and strategy is more critical than ever. Equally important will be engaging employees in healthcare decision making, improving health and productivity through wellness programs, and remaining vigilant on fraud and waste.
Vice President Market Planning and Strategy
Medicaid agencies have increasingly turned to managed care organizations (MCOs) to deal with the tremendous increase in enrollment driven by the Affordable Care Act (ACA). The Centers for Medicare and Medicaid Services (CMS) released an Encounter Data Toolkit in November of 2013 to assist states with the operational task of managing the data streams from their MCO contractors.
While most states are collecting encounter data, many face challenges in assessing the quality of data, and some still lack the confidence in their data to use it for rate setting, quality improvement, or public reporting. Over the past 15 years, Truven Health has helped nearly 20 states with their managed care programs and encounter data quality and completeness. We have assisted agencies with encounter data and managed care at all points of the encounter data process, including plan selection and evaluation, data collection, edit revisions, data quality improvement, and using data for plan management.
Most states choose to collect and process managed care data using their Medicaid management information systems (MMIS), for reasons that include the following:
However, processing managed care data through the MMIS can also have drawbacks. Other states have experienced such issues as:
- The state can leverage the electronic data collection and translation processes already used for fee-for-service (FFS) claims.
- The MMIS transaction system allows the state to process managed care data on a record-by-record basis, performing such tasks as editing and shadow pricing using procedures/protocols that are familiar because they are also used for FFS data.
- All data are maintained in the same system of record. The managed care data are housed with the FFS service data, which allows the Medicaid agency to incorporate all of the data, as needed and appropriate, in federal and state reports.
To avoid the above problems, states can either make appropriate adjustments to their MMIS systems and processes to fully accommodate encounter data, or consider other system options. States that are planning to re-procure their MMIS systems in the near future have the additional consideration of how much to invest in the existing MMIS system. This is particularly true for states that are moving to statewide, capitated managed care.
- Delays in implementing new processes for managed care data because of the competing demands from FFS claims processing and associated system change orders.
- Over-rejection of managed care encounters when edits designed for FFS claims processing are inappropriately applied to managed care records, which have already been adjudicated by the health plan.
- Delays in the ongoing processing of managed care encounter data because persistent data quality issues cause repeated edit failures. This problem can be exacerbated if processes for resubmitting rejected records aren’t well designed and/or well understood and followed by the plans.
- Inaccurate use or interpretation of managed care data in reporting and analysis because the nuances of encounter data are not accounted for in standard reports or communicated to users performing ad hoc analysis.
Some states have recently asked Truven Health about collecting encounter data directly from their managed care organizations. States could use their data warehouse decision support system (DW/DSS) to collect and process encounter data as either an interim approach or as a longer term process independent of the MMIS. Factors in support of loading the data directly into the DW/DSS include:
Specifically, Truven Health’s managed care encounter data services, using the DW/DSS would include:
- The DW/DSS is designed to incorporate managed care data – the data model and analytic reporting applications already anticipate the inclusion of managed care data. The DW/DSS provides a single, integrated repository for FFS and managed care data, capable of supporting transformed Medicaid statistical information systems (T-MSIS) and other federal reporting, as well as state-specific reporting needs.
- By outsourcing this specialized function to a vendor like Truven Health that is highly experienced with encounter data, a state might help speed the availability of the quality data needed for performance monitoring, rate-setting, and public accountability.
- Our experience with the validation of managed care data will also help speed improvements in data integrity and increase credibility of the information.
As Medicaid agencies turn to MCOs to deal with the tremendous increase in enrollment driven by the ACA, they have a partner in their DW/DSS contractors to implement the best practices outlined in the Encounter Data Toolkit. For more information you can contact me at firstname.lastname@example.org.
- Receiving, processing, and translating managed care encounter data
- Editing encounter data and providing feedback reports to managed care plans for resubmission
- Storing encounter data and making it accessible for analysis alone or with FFS data
- Incorporating encounter data into select federal reports
- Validating and improving encounter data accuracy and completeness
- An annual in-depth study of the quality of encounter data and development of a Data Quality Improvement Plan with each managed care organization
Vice President, Market Planning & Strategy
In a recent article in Healthcare IT News, the author did an excellent job of summarizing several key components of a successful population health program, illustrated by a short case study about how finance leaders at Legacy Health in Portland, OR partnered with physicians to educate them on the financial impact of cost drivers. When discussing population health, I find it helpful to remember the Kindig and Stoddart definition of population health from 2003: “Health outcomes of a group of individuals, including the distribution of such outcomes within the group.” This really helps summarize any framework and takes into account the end result of health improvement – how to monitor variability and the associated cost.
In order to have streamlined reporting, you need data. This sounds easy, but is often complex when extracting information from various health information systems (HIS) within a hospital or physician group. Many health systems have different electronic health record systems and having the tools and software to provide interconnectivity is essential. The data extracted must also be reliable, not only for clinicians, but for any other end user in the system that has a role in managing population health. Within hospitals, having this data will be essential when trying to reduce cost and variability in one key aspect of population health – supply chain cost. In the article, the author mentioned reducing the use of more expensive implants in the operating room, but this is the tip of the iceberg. The continued streamlining of pharmaceuticals and other medical devices will be paramount in reducing overall cost.
As a physician, I believe partnering with physicians is essential. Some may call it being aligned, but I think calling it partnering is more collegial. Reducing physician variability requires reliable data that physicians can trust. Physicians are scientists and are often competitive, and if you provide them with trusted data, they will make improvements. However, it doesn’t just happen unless you provide physician leaders to guide them, and this requires investing in order to get a return. In other words, hospitals, health systems, and physician groups must continue to invest in physician leadership education and training to provide financially-astute leaders in the era of the Affordable Care Act.
Byron C. Scott, MD, MBA, FACPE
Medical Director, National Clinical Medical Leader
The recent article, “Seven Changes the Affordable Care Act will Likely Encourage in the Medical System,” discusses several new approaches to healthcare that rely on effective management of new data sources and data streams. The ripple effects of the Affordable Care Act will take on many dimensions, ranging from the operational work flow of a health network, to the revenue cycle of those entities going at risk, to the relationship between patients and providers, to the way providers prioritize and spend their time with patients. Given this level of dramatic change, Truven Health Analytics is developing development partnerships with select customers to focus on the required flow of information that will be needed to run at at-risk organization, and the types of analytics and decision support various roles throughout a health network will need to have.
At the center of these activities is the fundamental requirement to establish a single patient record that accumulates knowledge of the patient through each and every encounter. Furthermore, the data collected needs to be organized and acted upon given specific temporal requirements. There is data used for an initial encounter, data for diagnosing, data for monitoring treatment effectiveness, and data for determining overall quality and effectiveness over time. Each requirement has specific conditions, and potentially, limitations, based upon how robust the single patient record is or is not. For example, encounters with new patients where no background information exists will be treated differently than encounters where there is a rich patient history of information. Likewise, encounters with healthy patients may provide the opportunity to collect new data insights into behavioral measures that can be used to keep them healthy, whereas patients with chronic conditions will likely require insights collected related to improving compliance to care guidelines.
In many respects, we may see a future where each encounter has both a patient care and an information care component with it. In fact, patient care and the required work flow is intimately connected to the information gleaned from diagnosis and the eligibility and payment and risk requirements the encounter triggers.
Director for Strategy and Business Development