Big data, when smartly analyzed, offers opportunities for employers to identify strategies that work specifically for the members they cover.
Throughout this five-part series, we have looked at how employers can leverage medical and pharmacy claims data to:
- Identify wellness strategies on programs that address unmet needs
- Focus wellness programs on gaps in preventive care
- Identify utilization patterns that might warrant additional strategies
- Justify additional strategies that promote better utilization of health care services
- Identify excesses in high-cost and high-complexity claims
- Focus targeted strategies on the relatively few members who account for the majority of a plans spending.
Doing the Math on Medical Costs
A basic understanding of health care spending for a large group is that not all people are alike. Recognizing that a relatively small minority of individuals drives the overwhelming majority of costs is fundamental to understanding how medical services are consumed.
Taking this principle further, we often divide a population into three groups, based on cost-stratification. What is remarkable is that the cost stratification of 1,000 members of a health plan for an automobile parts manufacturer in Kenosha, Wisconsin shows remarkable similarity to a similar analysis for the same size population of a department store in Abilene, Texas.
In fact, we can depend on the fact that any population of health plan members falls into three predictable cohorts, pictured in the chart above.
70% of the Population Drive 7% of the Costs
The first cohort comprises the 70% of a population that typically drives only 7% of the overall health care costs. Most members spend relatively few dollars (typically, 15-18% of the members of a medical plan will spend zero dollars in a given calendar year).
The 70% in the first cohort tend to be relatively younger, low-risk individuals. In that group, however, are also individuals who have delayed receiving health care or who have not adequately addressed significant health risks. Lapses in critical preventive screening may exist in this cohort. Therefore, it’s important to focus on wellness, prevention and primary care to maintain the low costs of this generally health population.
25% of the Population Drive 35% of the Costs
On the other hand, 25% of a population drives a disproportionate share (35%) of the costs.
This 25% are often individuals who have higher risks and are actively engaged in treatment. A portion of this group has acute conditions that result in relatively limited medical spending. Individuals with chronic conditions who seek medical care are also in this group. Understanding this group and following cost trends, utilization trends, risk stratification and gaps in care for a population are a key population health strategy.
5% of the Population Drive 58% of the Costs
Perhaps most importantly, 5% of a population drives, on average, 58% of the medical coasts for the population, according to Verisk Health Analytics normative determinations.
So who are these individuals, and what conditions account for these expenditures? A recent analysis of Willis health care data reveals that the majority of these expenditures are attributed to the following conditions:
- Pregnancy complications
- Intervertebral disc disorders
- Breast cancer
- Coronary artery disease
- Joint derangement
- Complicated gastrointestinal disorders
- Cancer therapies
- Procedure complications
- Gynecological disorders
- Demyelinating diseases
- Myocardial infarction
- Back pain
- Musculoskeletal disorders
- Lower GI disorders
- Rheumatoid arthritis
- Diabetes mellitus
- Gall bladder diseases
- Renal failure
- Newborn care
It’s also important to note that pharmacy costs, in particular specialty pharmacy costs, are emerging as powerful drivers of higher medical costs in a population. Look for the specialty pharmacy category to play an even greater role in the highest cost cohort in the coming years.
So What Does This Mean?
What a medical plan sponsor can depend upon is that a small number of people will drive the majority of its costs. Understanding who those individuals are and what drives those costs may be the most powerful way to reduce medical trends in the short-term. It is important to appreciate, however, that individuals migrate from one cohort to another from year to year.
A recent analysis of high utilizers of hospital services published in Health Affairs found that only 28% of these high cost utilizers were still high utilizers one year later. In other words, the individuals who make up this cohort “turns over” significantly from year to year. Whereas health care costs were on average $113,522 for someone in this cohort: costs for individuals in this group dropped by 60% within 2 years.
In other words, the ability to predict (or pinpoint) who might be in that most expensive 5% cohort has powerful implications. It can help you lower costs, improve health outcomes and powerfully improve peoples’ lives.
So What Should You Do?
The solution is in the data. “Big Data” health care analytics can help employer plan sponsors understand what has happened, but also understand where the next wave of expenditures might come from.
Employers are starting to focus on understanding their high cost patterns; finding covered members with higher levels of risk; identifying members with high medical complexity, and pinpointing situations where high levels of medical uncertainty may drive higher costs.
Understanding the conditions that drive the highest costs, including orthopedic, cancer, cardiac, labor and delivery complications, and learning when and how to identify next year’s high cost claims this year requires doing the math. That’s where employers craft high-yield strategies that make a meaningful difference in their medical cost trend.