Category Archives: Electronic Health Records

Developing a population health management program: Considerations for population segmentation

Predictive analytics can improve medical outcomes by identifying patients needing medical interventions. Population segmentation is a common method used to identify such patients. Individuals are grouped into cohorts to help improve the quality of their care. In this paper, Milliman’s Jordan Paulus and Nick Creten explore four common methods for population segmentation: cost cohort segmentation, condition cohort segmentation, utilization cohort segmentation, and social cohort segmentation.

Regulatory roundup

More healthcare-related regulatory news for plan sponsors, including links to detailed information.

DOL releases annual report to Congress on self-insured group health plans
The Department of Labor (DOL) has released “Annual report to Congress on self-insured group health plans,” which provides detailed statistics currently available on self-insured group health plans filing a Form 5500 and on the sponsors of such plans that issue publicly traded equity or debt.

To download the entire report, click here.

CMS releases discussion paper for HHS-operated risk adjustment methodology meeting
The Center for Consumer Information and Insurance Oversight (CCIIO) of the Centers for Medicare and Medicaid Services (CMS) has released a discussion paper for a meeting to be held on March 31, 2016. The paper, “HHS-operated risk adjustment methodology,” discusses possible changes to the Patient Protection and Affordable Care Act (ACA) program for protecting insurers that cover sicker populations. Possible ideas covered by the discussion paper are how to include high-risk patients in risk adjustment payments made by plans that have healthier enrollees and including prescription drug information in the formula used to calculate payments.

To read the entire paper, click here.

Federal subsidies for health insurance coverage for people under age 65
The Congressional Budget Office (CBO) has released a report, “Federal subsidies for health insurance coverage for people under age 65: 2016 to 2026,” in conjunction with the Joint Committee on Taxation. The two agencies project that the federal subsidies, taxes, and penalties associated with health insurance coverage for people under age 65 will result in a net subsidy from the federal government of $660 billion in 2016.

To read the entire report, click here.

Excise tax on high-cost employer-sponsored health coverage
The Congressional Research Service has released the report “Excise tax on high-cost employer-sponsored health coverage: In brief.” The report provides an overview of how the excise tax, or the “Cadillac tax,” is to be implemented. The information in the report is based on statute and guidance issued by the Department of the Treasury and the Internal Revenue Service.

To read the entire report, click here.

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.”

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.

Top 10 worldwide Milliman publications of 2014

In 2014, Milliman published a range of articles and videos, covering issues including retirement ideas for Millennials, the pros and cons of catastrophe models, the value of enterprise risk management (ERM) programs, and the impact of the Patient Protection and Affordable Care Act (ACA) on financial statements. We also published on challenges related to healthcare costs and insurance and risk management issues—and about real insurance for fantasy football and insurance for ride sharing. To view this year’s 10 most viewed articles and reports, click here.

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.

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.

Medicaid population management

Moyer-RichardIn previous blogs, we’ve discussed population management concepts and given specific examples of pediatrics, accountable care organizations (ACOs), and clinical populations. In this blog we turn to the Medicaid population. Medicaid has unique characteristics because of the nature of the financing and because of the social demographics of the population served. This population is going through big changes because of Medicaid expansion and the advent of programs that may resemble some of the characteristics of Medicaid, such as subsidized rates through the federal and state exchanges.

A key to analyzing this population is to create homogeneous sub-populations. There are several ways to define the subpopulations:

• Program: Children’s Health Insurance Program (CHIP), Temporary Assistance for Needy Families (TANF)/Aid to Families with Dependent Children (AFDC), Aged, Blind, and Disabled (ABD) Medicaid (duals), waiver programs.
• Type of delivery model: Managed care and fee-for-service.
• County: Rates are typically defined at the county level for managed care and care delivery is organized around counties.
• Special populations: Pregnant women, children, mentally ill.

There are specific issues that drive the metrics and analysis for Medicaid recipients, including:

• High levels of emergency room (ER) use: This population has much higher ER usage rates, which often reflect access and sociodemographic issues.
• Maternity: This population has higher rates of maternity and has wider variability in maternity outcomes.
• Behavioral health: Managing behavioral health is a more important component of care.
• Community resource access: Population wellness is much more dependent on additional community resources such as case workers, food banks, and social workers.

It’s important to focus on the key issues that drive the results for the population and to create metrics that reflect these key issues and characteristics. Below are some sample metrics that should be tracked and improvement goals developed.

This article first appeared at Milliman MedInsight.

What data is needed to support population health management?

Zoelzer-NancyIn our series of blogs on the topic of population health management, we have discussed how to define populations in “Analytics for population health management,” and we have looked at the types of analytic tools available for analysis of populations in “Identifying appropriate metrics for population management.” In this blog, we will focus on types of data, both traditional and nontraditional, that can assist in the performance of a robust population health analysis.

Many healthcare organizations have access to a wealth of data. However, this data has not historically been brought together in an organized manner. An emerging concept in data management is “big data,” an approach where all data relevant to an individual’s healthcare—including those data that may exist outside an individual organization’s walls—are gathered and linked for analysis.

To effectively gather and link these data for population health management, the enterprise data warehouse needs to be flexible enough to accept data from traditional and nontraditional sources. Data sources that can be accessed and linked for robust population health analysis include:

• Insurance administration data (e.g., claims, pharmacy, enrollment data): Insurance administration data is by far the most commonly used data for analysis.
• Clinical data (e.g., electronic medical records, lab results, registries): Clinical data increases the richness of data available about an individual and a population. This type of data is starting to make its way into the enterprise data warehouse.
• Medical management data (e.g., health risk assessment, authorization, disease/case management data): Most health organizations have medical management data readily available, but most have not yet brought this data into an enterprise database.
• Provider administrative data (e.g., physician practice management, hospital billing, admissions discharge, and transfers data): Provider administrative data is generally available in a more timely manner, allowing for data analysis that is nearer to real-time.
• Public data (e.g., state discharge data sets, immunization registries): Public data has not typically been accessible in an enterprise data warehouse, yet it can provide additional insight for population health analysis.

Why incorporate all these data?
Access to a wide variety of data allows for broader population health analysis, which can lead to new and earlier insights to help identify improvements, efficiencies, and effectiveness of the healthcare delivery system. A population health management strategy with a robust data warehouse can help answer the question, “Who will benefit from which programs?”

For more information download the paper entitled “Population health management concepts.”

This article first appeared at Milliman MedInsight.

Identifying appropriate metrics for population management

Zoelzer-NancyFor many reasons, including the advent of the Patient Protection and Affordable Care Act (ACA), population health management is gaining increased attention from healthcare organizations. A population health initiative has the promise to make care proactive, better coordinated, and more customized for the population. A previous MedInsight blog entitled “Analytics for population health management” outlined how populations are defined and also presented a sampling of metrics for measuring performance. In this blog article we dive deeper into the process of identifying the appropriate metrics for population health management.

One size does not fit all for population-based analytic methodologies. One reason to look at populations from a variety of perspectives is to analyze trend. What is driving trend? Is it something clinical? Is it geographic disparity? Or network variations? Differing metrics are needed in order to effectively analyze a population. For example, looking at compliance with recommended HbA1c lab testing is appropriate when looking at a population of diabetics, but the same measure is far less meaningful when looking at a population of cancer patients.

In identifying the appropriate analytic metrics one should consider the population to be analyzed, as well as the focus of the population analysis initiative. There are several categories of analytic tools for population health management, including measures, service categorization, clinical categorization, and risk adjustment.

Measures include:

• Primary prevention measures: Disease avoidance, immunization rates, and wellness program participants
• Secondary prevention measures: Early detection and treatment of disease, colon cancer screening, and breast cancer screening
• Tertiary prevention measures: Reducing the impact of disease and aspirin use in congestive heart failure (CHF) patients
• Quaternary prevention measures: Avoidance of unnecessary or unsafe interventions and avoidance of imaging for low back pain

Service categorization is a methodology used to analyze resource utilization and cost by service types. Categorization enables the creation of a cost model for population trending, benchmarking, and profiling. Milliman’s Health Cost GuidelinesTM is used to organize healthcare claims into service categories.

Clinical categorization, such as Milliman’s Chronic Condition Hierarchical Groups, helps organize populations by disease cohorts.

Risk adjustment is a methodology used to assess the overall health of a population or subpopulation. Risk adjustment enables comparison across populations by adjusting the health risk of the population. Additionally, individuals within a population can be stratified by risk. There are many risk adjustment methodologies available in the industry, including the Milliman Advanced Risk Adjuster (MARA).

Other analytic tools include resource efficiency tools (e.g., Dartmouth Atlas, NYU Avoidable ED Visits, and Prometheus), care management tools, and Milliman Global RVUs (a method to measure utilization across all categories of care).

For a robust population analysis, benchmarks are needed in addition to the analytic tools. Benchmarks enable the comparison of actual population cost and utilization with a benchmark.

Watch for upcoming blog posts that dive into the development of a population analysis for a specific population. To review an earlier post describing pediatric population analysis, click here.

Download Milliman’s white paper entitled “Population health management concepts” here.

This article first appeared at Milliman MedInsight.