Tag Archives: MedInsight

Five healthcare analytic trends

Naugle-AndrewIt 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 are 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:

1. Provider network optimization. Despite bending the cost curve, one of the great lessons of the health maintenance organization (HMO) era was that consumers value choice. For years, preferred provider organizations (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.

2. Value-based incentive programs. It’s widely accepted that fee-for-service (FFS) 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.

3. 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 healthcare 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.

4. 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. Health plan quality ratings are there for all to see when selecting an exchange plan. Big data and analytics make all of this possible.

5. 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.

This article first appeared at Milliman MedInsight.

Health data analytics for identifying wasteful services

Zoelzer-NancyEliminating inefficient and unnecessary medical services improves overall healthcare efficiency while reducing costs. In 2009, the Institute of Medicine (IOM) identified $750 billion of wasted spending with unnecessary services accounting for $210 billion. The U.S. Congressional Budget Office (CBO) has estimated that 30% of medical care in the United States is unnecessary care. Removing this waste and unnecessary care from the system will reduce costs, and is an opportunity to improve quality and patient safety.

Health data analytics for identifying wasteful services

There are a number of use cases for analyzing health claims data to find wasteful and likely to be wasteful services.

• Quantify necessary vs. wasteful services
• Identify opportunities for cost savings
• Use provider profiling and pay for performance risk sharing reporting
• Use employer group reporting to convey the value of health plan services provided to employers

In a pilot study of wasteful services, Milliman looked at one health plan’s claims data for Medicare and commercial over a one-year period (November 2012 to October 2013). Observations from that study found that 21% of members had at least one wasteful service, 25% of all services provides were wasteful, and 2.12% of the total claims cost allowed dollars were wasteful. Further data analysis found that 80% of the wasteful dollars came from only four measures:

• Stress cardiac imaging or advanced noninvasive imaging (58%, $8,568,369)
• Annual EKGs or cardiac screening (12%, $1,779,260)
• Lower back pain image (6%, $940,363)
• ED CT Scans for Dizziness (4%, $533,876)

To assist in the identification of wasteful services Milliman, along with VBID Health, has developed the MedInsight Waste Calculator. This analytical tool provides actionable data to support healthcare quality, efficiency, and effectiveness reporting. The calculator brings together clinical expertise and powerful data analytics—allowing healthcare managers to target and reduce wasteful spending.

To learn more about the MedInsight Waste Calculator, click here.

This article first appeared at Milliman MedInsight.

Introduction to utilization health waste in the U.S. healthcare marketplace

The American healthcare system is experiencing rapid change, largely driven by the recognition by both public and private payors that the trajectory of healthcare spending growth must be slowed. Despite the recent slowdown in healthcare spending growth, which many attribute to the recession, efforts to transform benefit design and payment systems are proceeding rapidly. For example, public payors are both cutting payment rates and experimenting with bundled and global payment models. Private payors are adopting similar payment models and developing more sophisticated benefit designs that encourage patients to seek care from low-cost and maybe high-value providers, and to avoid expensive and maybe low-value services.

In this environment it is crucial to try to eliminate waste. The new payment models allow providers to share some of the savings if utilization of wasteful services can be curtailed. The challenge of course is identifying which services are wasteful. The fact that waste exists in the healthcare system is widely accepted. Berwick and Hackbarth (2012) estimate there is about $200 billion in waste that is due to overtreatment in the U.S. healthcare system, almost 10% of total spending.

But eliminating that waste may be a challenge. Like everything in healthcare, the waste is likely to vary across geography and, more importantly, across providers. Identifying which providers to focus on is a challenge. More fundamentally, aggregate measures of waste are not necessarily helpful to providers. Detailed, operational measures that can be applied to provider systems are needed. Fortunately, there has been a recent increase in effort to identify wasteful services. A number of lists exist. Perhaps the most prominent of these efforts is the Choosing Wisely campaign, sponsored by the American Board of Internal Medicine Foundation, which challenged specialty societies to identify wasteful practices. Other panels, such as the U.S. Preventive Services Task Force, have identified services that might be wasteful. Thus clinically meaningful knowledge of what is wasteful exists.

Translating the knowledge of what is wasteful into tools that can be applied to identify that waste at the system level is difficult. Claims data is not ideal in many cases to identify waste. Often the measures of waste depend on patient history. Clinical knowledge and information technology (IT) expertise are needed. Academic efforts to quantify waste using subsets of available measures are just beginning. For example, using a limited number of services, Schwartz et al. (2014) find that 0.6% to 2.7% of Medicare spending may be wasteful and between 25% and 40% of beneficiaries have received at least one low-value service. Moreover, they found that there was significant regional variation in spending on low-value services, suggesting some providers are more prone to use them than others. Finally, different measures of low-value services were correlated across regions, suggesting that measures of them based on a small number of services may be indicative of broader patterns of waste.

Commercial tools to quantify practice patterns will be crucial to many cost containment activities. Data can help focus efforts on reducing waste and thereby improve value. Such tools could be used to support payment reform, provider education, tiered benefits, or even value-based insurance designs. One way or another, spending growth must be contained. Our goal must be to do so in a way that improves value.

This article first appeared at Milliman MedInsight.

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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.