Tag Archives: benchmarking

Indian health insurers can benefits from benchmarking administrative costs

Health insurance is the fastest growing segment in India’s nonlife insurance sector. Health insurance costs are also increasing quickly. According to Milliman’s Lalit Baveja, insurers in the market should consider the benefits of administrative savings as a larger part of a cost containment strategy.

Administrative costs, customer acquisition costs and benefit payment (in the form of claims payouts) are the three key expense areas for insurers. Going forward, the importance of managing administrative expenses will increase as competition continues to put pressure on overall premiums. In line with other markets, the Indian regulator also restricts the percentage of premium income that can be used as management expenses to promote efficiency and the availability of funds for benefit payments after a defined inception period. Insurers themselves have a vested interest in keeping these costs manageable. Topline focus must be complemented with cost containment in both benefits and administrative costs to achieve desired profitability and sustainability. While claims cost containment requires effective provider contracting and optimal utilisation management (and is reliant on multiple providers and other intermediaries), acquisition costs are dictated by market forces. Administrative efficiency within internal operations is one area where an insurance company can effectuate changes more directly. Tracking and managing these administrative costs can be a challenge, and identification of areas where there is opportunity to optimise administrative spending can be an even greater challenge.

Lalit discusses how benchmarking is an effective tool that can help health insurers manage their administrative efficiencies and expenses. To learn more, read his article “Administrative benchmarks for health insurance in India.”

Benchmarking provider cost using Medicare allowed

There are many reliable research statistics from the private sector and the federal agencies that support the evidence that medical costs are rising and the current pace is unsustainable. Medical cost trend has two primary components, the number of services provided to patients (utilization) and the cost of each of those services (unit cost). While utilization management can be important for achieving cost savings, some employers are now giving further attention to the significant price variation in unit cost. Chart 1 below provides an example of the price variation using the average reimbursement as a percentage of Medicare in Buffalo, New York; Indianapolis, Indiana; Ventura, California; and nationwide. As shown, going from Buffalo to Indianapolis reflects an 80% increase in cost, based on unit price alone.

Benchmarking provider cost using Medicare allowed_Figure 1

We regularly encounter employers who don’t fully understand the impact of provider reimbursement variation on their medical plans’ financial performances. This comes as no surprise, given the limited transparency and complexity of current provider reimbursements.

Limited transparency of provider reimbursement (allowed charges)
For employers, the industry standard technique of benchmarking commercial allowable charges has historically been traditional discount analyses, which compare discounts to billed charges. However, these approaches do not provide the required rigor and precision to understand medical service reimbursement analysis—both across markets and within a given market. This is because billed charges are not standardized across providers or different services. As a result, the exact same discount could mean very different things, depending on the provider and service—in some cases, price differences of over 300%. In addition, providers often optimize their billed charges to enhance reimbursement on contracts based on billed charges.

Complexity
Employers generally have had a difficult time measuring unit cost, which is solely due to the complexity of various medical procedures. There is a large amount of price variation within each inpatient diagnosis-related group (DRG) and outpatient type of service. Chart 2 below provides a powerful illustration of how reimbursement can vary significantly across even a single inpatient DRG or outpatient service category. The chart compares the commercial reimbursement for inpatient joint replacement and an outpatient MRI in three different metropolitan areas with what the government would pay under Medicare allowable. The variation in inpatient joint replacements, a large bundle of complicated services, is much lower than outpatient MRIs, which reflects a specific service that generally has little variation in intensity compared with a joint replacement.

Benchmarking provider cost using Medicare allowed_Figure 2

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Benchmarking analyses improve health plan cost management

A benchmarking analysis helps employers review their health plan data so they can find solutions to manage costs without shifting them to employees. It can also help employers mitigate the potential effects of the excise tax in 2018. In this article, Milliman consultant Marcella Giorgou identifies areas an employer can address so the plan can operate more efficiently.

Milliman launches new features in Hospital Performance Index

Milliman today announced that its Hospital Performance Index (HPI) software, a benchmarking tool for payers and providers that uses statistical methods to identify opportunities for increased efficiency in patient populations, has added two new capabilities.

The first new feature involves observation status benchmarks, which have become more important over the last two years with the institution of the 72-hour rule. HPI can now produce national and regional averages for observation statistics, allowing for quick comparison of individual facilities to national, regional, and state norms. Custom reporting is also available. With such wide discrepancy in rates regarding observation status by facility throughout the United States, this tool can now shed light on the relative performance of every facility in the country.

The second new feature involves readmission rate benchmarks by diagnosis-related group (DRG). HPI now provides data on the relative readmission rate performance of each hospital in the country, information that is crucial for both hospitals and payers. This new feature will enhance the existing functionality around potentially avoidable admissions and potentially avoidable inpatient days.

“These additions to HPI were very much driven by market demand,” said John Cookson, a principal at Milliman. “Hospitals and payers want more and better data so that they can make smarter decisions, improve efficiency, and contain costs. Now that HPI has information on observation status and readmissions rates, they are in a much better position to accomplish those goals.”

Analytic basics: Completeness and outlier episode flags

Bates-DougAnalysts working with episode of care groupers for the first time often have questions about how to use the various value-added flags assigned to episodes. Episode of care groupers link together all of the claims that pertain to the treatment of a particular condition for a particular patient, to create a powerful unit of analysis. For example, a patient with a condition such as diabetes may receive multiple types of services from multiple providers and provider types for the treatment of their diabetes. An episode of care grouper will combine all of the individual claims from the different providers so that the full cost of treatment can be assessed.

Two of the value-added flags commonly assigned to episodes include completeness flags and outlier flags. Both of these flags enable analysts to filter out, or include, types of episodes to optimize their reporting. How to apply filters using these flags depends on the analysis being performed. A brief summary of these flags, and their use, is described below.

Completeness flags
Many episodes in a data set will not be complete, meaning there are still outstanding claims related to those episodes that were not available when the data were grouped. Typically, episodes that start toward the end of your grouping period are more likely to be incomplete. For instance, if you are grouping data incurred from January 2011 through December 2013 and an episode begins on December 19, 2013, there is a lower probability that all claims for this episode will be available in the data than if the episode had started in January 2013.

Episode groupers use different logic when assessing completeness for acute and chronic conditions. For acute conditions, most groupers determine that an episode is complete if there are no incurred professional claims for that condition for a predefined number of days. Chronic conditions such as diabetes are never cured, so technically those episodes never end, but in order to support analyses, chronic conditions are often divided into annual periods and may be defined as complete when a full year of data is available for the members with those episodes.

When comparing costs to benchmarks, incomplete episodes should be excluded because incomplete episodes are excluded from cost benchmarks.

If you are comparing the average length (days) or average cost of episodes across various populations or provider groups, then you should also exclude incomplete episodes. It is impossible to accurately assess average costs per episode if every claim for every episode is not included.

If the purpose of your analysis is to evaluate the prevalence of episode conditions, then include all episodes (complete and incomplete) in your reports.

Table 1 displays the distribution of complete and incomplete diabetes episodes from a sample data set. The average cost for incomplete episodes is usually lower than the average cost for complete episodes.

Table 1

Outlier flags
Episodes that have atypically higher costs or atypically lower costs compared with other episodes within the same class are flagged as high or low outliers. There are multiple methodologies for defining outlier episodes, but commonly the flags are based on statistical variance (i.e., a number of standard deviations from the mean). In and of themselves, outlier flags are not a measure of efficiency or quality, but the magnitude of the variance in their cost indicates there is something atypical about these cases.

When comparing with benchmarks, outliers should be excluded because most benchmarks will exclude outliers for consistency.

When comparing average costs across populations or provider groups, many analysts may choose to exclude all outliers, because a few outliers for a given group may skew their results. That being said, it is also important to assess if any given population of patients has significantly more episodes flagged as high outliers compared with others. A higher percentage of high outliers might warrant the need for further investigation.

For many episode classes, all that is needed to start an episode is a professional encounter with a primary diagnosis relevant to that episode class. In some cases, very short episodes may represent visits to rule out a specific diagnosis or other situations that don’t really represent full treatment for a condition. Excluding low outliers can help remove those types of episodes from your analysis.

Table 2 displays a sample of diabetes episodes by outlier status.

Table 2

Episode completeness and outlier flags can, of course, be used together. For most comparative analyses (to benchmarks or across populations), only complete non-outlier episodes are included. Table 3 displays the distribution of diabetes episodes when both flags are used as report dimensions.

Note that an analysis based solely on complete non-outlier episodes from these sample data would reduce the number of episodes from 57,193 to 34,250, removing 40% of the episodes from the analysis. When analyzing episode classes with a limited number of episodes, applying these filters may reduce your sample size to volumes that are too small to produce statistically significant results, so it is important to assess how many episodes are in your sample before you begin.

Table 3

Episodes of care provide a useful unit of analysis for evaluating healthcare utilization and cost. The episode completeness and outlier flags allow users to include, or exclude, types of episodes to further refine their analysis.

This article first appeared at Milliman MedInsight.

Milliman launches next generation Hospital Performance Index

Milliman has announced the launch of a next generation Hospital Performance Index software, a benchmarking tool for payors and providers that uses statistical methods to identify opportunities for increased efficiency in patient populations. The new product significantly enhances the granularity of hospital benchmarking available to payors and providers and fits the needs of accountable care organizations (ACOs) by identifying instances where care management may yield improved quality and increased efficiency.

“The move toward accountable care spurred by the Affordable Care Act requires payors and providers to constantly benchmark their populations in search of opportunities for increased efficiency,” said John Cookson, Milliman principal. “The enhanced Hospital Performance Index, which builds on technology with a 20-year track record, is optimized for today’s market and allows national, regional, and local benchmarking. This benchmarking can be performed using multiple variables, including diagnosis-related groups (DRGs), major disease category, readmissions, and avoidable inpatient days.”

The enhanced Hospital Performance Index includes an updated user interface that brings these benchmarks to payors and providers in an intuitive fashion. The benchmarks can be applied against Medicare, Medicaid, and commercial populations and allow for comparison with every hospital in the country. The updated tool can help payors and providers prioritize their care management efforts, highlight instances where better management could result in cost reduction, and aid in contract negotiations and in the creation of accountable care partnerships. For more information, click here.