It goes without question that the U.S. health insurance industry is in a state of flux. Americans are buying individual products through health insurance marketplaces, new insurance carriers have entered the market, and Medicaid has been expanded in 29 states and the District of Columbia. These market changes, in addition to other reform provisions already introduced and others just starting to take hold, have subjected the market to an unprecedented level of change.
It is said that insurers like risk but hate uncertainty. What is for certain today is that the old strategies of accepting good risks and repelling poor risks is no longer a recipe for success. To thrive in this new environment, health insurers must make smart decisions using data to keep ahead of the competition.
Within that context, here are five areas where Milliman clients are using data and analytics in innovative ways to bring some order to the chaos:
- Provider Network Optimization. Despite bending the cost curve, one of the great lessons of the HMO era was that consumers value choice. For years, PPOs competed on network size; employers cared more about network disruption affecting their employees than the cost/volume trade-off. In the face of cost pressures, employers and consumers are now starting to accept that smaller networks may be worth the disruption. To meet this need, plans are deploying sophisticated modeling that combines traditional network access and adequacy measures with reimbursement and quality analytics to develop new “smart” networks.
- Value-Based Incentive Programs. It’s widely accepted that fee-for-service reimbursement rewards volume over value. As a replacement for FFS, many payers are promoting value-based incentive strategies that shift reimbursement from fee schedules to bonus pools that pay additional incentives when quality and/ or cost targets are met. Analytics are key to selecting measures, setting thresholds, and assessing provider performance. They also aid providers trying to operate under these new risk arrangements, identifying gaps in care, and benchmarking peer performance.
- New Trend Dynamics. While predicting the actual numbers requires the proverbial “crystal ball,” the health insurance industry has a reasonably mature understanding of the drivers of health care cost trend. But things are getting more complicated as physician practice patterns change, populations age but live longer, millions of new consumers flood into the individual and Medicaid markets, and burgeoning innovation (e.g., telemedicine/ telehealth, wearables, smartphones, home visits, retail clinics, etc.) disrupts how and where care is provided. Analytics are key to understanding the “trends in trend” in this new world.
- Transparency. The healthcare market has earned a reputation for opaqueness. Consumers are more likely to rely on word-of-mouth when selecting a physician, the price of services depends on who’s paying and has little relationship with the actual cost of services, and information on outcomes and quality is kept locked away from prying eyes. Not so in a post-reform world; consumers can now shop on the basis of price and quality, they can go online and find out how much an appendectomy costs at hospital A or B and which one has a higher success rate, and health plan quality ratings are there for all to see when selecting an exchange plan. Big data and analytics make all of this possible.
- Care Management Efficiency. Gone are the days when health insurers had unlimited funding for care management programs. Today, plans must make judicious use of limited administrative dollars to meet medical loss ratio minimums while still managing complex and catastrophic cases. Analytics help plans optimize their care management programs, prospectively identifying those members most likely to benefit from care management, and then enrolling them in the right program.
With many of their traditional performance management tools neutralized by reform, health insurers have had to get smart about how they leverage data and information: they use analytics to design benefit plans, develop marketing strategies and consumer segmentations, select network providers, develop reimbursement strategies, improve clinical quality, and optimize their remaining cost and quality management tools. In today’s market, how a plan leverages analytics, turning data into actionable information, will make the difference between survival and demise.
One area of data management within the healthcare industry that is getting new emphasis and interest from regulators is a focus on encounter data. Today, the majority of Americans receiving healthcare services funded through the Medicaid program are enrolled in some form of managed care. Under this scheme, states contract with MCOs that take on responsibility for providing Medicaid services in exchange for a fixed capitation payment. This approach is in contrast to the traditional fee-for-service (FFS) program where providers submit claims directly to the state for payment.
It is widely recognized that the FFS approach to provider payment creates perverse incentives for delivery of unnecessary services and uncoordinated care. Medicaid managed care gets away from this by providing a fixed capitation amount to the Medicaid MCOs, giving them incentives to more effectively manage care. The MCOs often pass along those same incentives by paying certain providers using capitation as well.
Notwithstanding the undesirable incentives that FFS creates, one area where FFS excels is the collection of timely and complete data about the services rendered to each patient and information about the price of those services. Because FFS claims are essentially invoices, this approach offers strong incentives for providers to submit claims in a timely manner (for timely payment) and to ensure those claims are a complete reflection of services rendered (for complete payment).
Under capitation, the payment mechanism is decoupled from the data collection process. In lieu of claims, MCOs must collect encounter data from their providers and then submit that data to the states. Unfortunately, this takes away the direct financial incentives providers have for timely and complete data submission. As a result, many states have struggled to collect credible information about the services delivered under Medicaid managed care. Such data is essential for important activities such as rate setting and program management.
Viewing submission of encounter data as an MCO function, many states have implemented strict contractual requirements coupled with tough performance guarantees and financial penalties to motivate MCOs to improve the quality and timeliness of the encounter data they submit. Failure to perform can have significant consequences for MCOs including financial penalties, corrective action plans, bad press, and even contract termination. Based on our experience working with states and MCOs to help improve encounter data quality, we have identified a few things that can help improve the results:
- Evangelize the Importance of Encounter Data among Providers. Once decoupled from the payment process, it can be hard to convince providers of the importance of collecting and reporting encounter data. Ensuring that these constituents understand the value of encounter data, and are submitting complete and timely information is key for MCOs to meet their contractual obligations to the state. Regardless of how strong the processes are within the MCO to ensure complete and timely submission, if the source information coming from providers is incomplete, what goes to the State will also be incomplete. In addition to including encounter data requirements in their provider contracts, MCOs should include encounter data as a topic in their provider communication/education plans, and evangelize its importance whenever they can.
- Develop Clear Submission Requirements, Definitions, and Data Specifications. In any situation where data is being submitted to a third party, lack of agreement and understanding of the actual submission requirements, definitions, and data specifications is a set-up for downstream conflicts. Among states, the requirements for submission often vary, and sometimes change during the term of a contract. States and MCOs should engage in a collaborative process to ensure that all parties are working from the same guidance and interpretation of the submission requirements.
- Establish an Inter-Disciplinary Team. Many MCOs view encounter data submission as one department’s responsibility (typically, finance, operations, or information systems). In reality, it takes skilled and knowledgeable resources from throughout the organization to drive a high quality process and result. MCOs should establish an inter-disciplinary team that brings together the experts and makes everyone involved accountable for the outcome. That team may be led by someone from one of those primary departments, but requires support from others.
- Implement Automated Data Validation and Reconciliation Processes. Submitting encounter data typically involves collecting data from multiple sources, transforming it into a new format, and then submitting it to the State. There are many opportunities for errors along the way. To provide an early warning of potential errors and facilitate root cause assessment when errors are identified, MCOs should implement data validation and reconciliation processes that run parallel with the encounter submission preparation process. Often states will have internal processes they run on the data when they receive it; mirroring these processes can proactively reduce error rates. In addition, where possible, it makes sense to reconcile financial information against financial systems such as the general ledger, not just what’s in the claim system.
- Track and Resolve Errors Timely and Completely. When submitting encounter data, some errors are unavoidable and thus both MCOs and states must have resources and processes in place to support timely and complete resolution of those errors. Errors can be caused by changes in the way data is collected within the MCO, changes in the state’s internal validation processes, or simply anomalies in the data. Regardless of the reason, each error should be tracked and resolved to completion. Root causes should also be identified to allow for error prevention.
As states have made the transition from FFS to managed Medicaid, the quality of utilization and cost data they receive has eroded. With the majority of Medicaid beneficiaries today receiving healthcare services through managed care plans, this makes it more difficult for states to perform effective oversight of these programs. Multiple encounter data improvement initiatives have emerged as this has become an area of focus for both states and the Federal government. MCOs should expect this scrutiny to continue, but should also recognize that through a systematic approach to managing and reconciling data, and a collaborative posture with their state partners, many of these challenges can be overcome.
As we are all aware, the Affordable Care Act contained specific regulations that govern payers’ Medical Loss Ratio or MLR. These rules set minimums for the amount of the premium dollar that plans must spend on benefits. If we think of a premium as having three components: Benefits, Administrative cost, and profit or surplus, by setting a minimum for the size of the benefit component, the ACA essentially set maximums for administrative expense and profit.
These restrictions created new pressure for plans to manage their administrative expense as this is the primary opportunity for increasing profit or surplus. In reality, changing a payer’s administrative cost structure can be a challenge: It takes a disciplined approach; it may actually require increased spending through investments in technology and other efficiency improvements; and it doesn’t happen overnight.
Regardless of these challenges, organizations must find ways to manage their administrative expenses. To help organizations, we have identified five best practice approaches that organizations can use to support this work.
- Develop a defensible and accurate way to allocate administrative costs. Organizations must ensure that they are appropriately allocating administrative costs among lines of business. Not all product lines are subject to MLR reporting requirements, and thus it is important to ensure that costs are appropriately allocated to the right products based on cost-generating activities. Best practice organizations use a cost allocation model that uses quantifiable data to allocate costs and generate line of business financial reports.
- Employ an enterprise effort. Administrative cost management isn’t just finance’s problem—it requires an enterprise focus from managers and front-line staff throughout the organization. Efficiency improvements can come from anywhere within the organization. Any administrative cost management project requires leadership and stakeholder engagement, organizational understanding and buy-in, and transparency.
- Use benchmarking to set targets and understand what is possible. Benchmarking helps organizations understand how their own costs and performance stack up against the competition. Use of benchmarks can help identify opportunities for process or organizational renovation, estimate the potential savings from specific initiatives based on efficiency improvements, or even identify areas where additional investment is appropriate.
- Track and trend improvements over time. At the beginning of any cost management project, leadership should establish targets (benchmarks can give targets credibility). These targets should include both the overall goals (e.g., departmental administrative cost levels) as well as metrics related to drivers of cost reduction (e.g., efficiency, production, and quality). Over the course of the project, the organization should monitor changes and report progress so that participants can see progress being made. Best practice organizations use systems and tools such as dashboards, to report information throughout the organization.
- Ensure incentives are aligned to achieve desired outcomes. Many organizations recognize that what gets rewarded is what gets done. Incentive alignment strategies include: using benchmark data to set annual operating budgets; empowering cost center managers to negotiate trade-offs within benchmark budget targets; and tying budget performance into incentive compensation.
Administrative cost pressure is a reality for all payers. These five best practice approaches can provide the foundation on which organizations can more effectively manage their administrative performance and achieve long-term goals for organizational success.
To learn more about administrative cost and MLR, click the following link to access our most recent webinar: Managing the Other Side of the Medical Loss Ratio.