Analysts 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 like 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 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.
Many episodes in a dataset will not be complete; meaning there are still outstanding claims related to those episodes which 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 example, 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 an episode is complete if there are no incurred professional claims for that condition for a predefined number of days. Chronic conditions, like 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 are 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 you 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 dataset. The average cost for incomplete episodes is usually lower than the average cost for complete episodes.
Episodes that have atypically higher costs or atypically lower costs compared to 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 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 to 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 to others. A higher percent 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.
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.
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.
Hospitals have historically relied on surgeries contributing to their top service lines and providing a significant source of revenue generation for hospitals and surgeons. This has, in part, led to the overutilization of surgeries. In the area of spinal surgeries, as noted in the Modern Healthcare article from March 24, 2014, the mounting research evidence shows that too many Americans are undergoing unnecessary spinal procedures and experiencing mixed outcomes. According to the Agency for Healthcare Research and Quality, there were more than 465,000 spinal-fusion operations in the U.S. in 2011, compared with 252,400 in 2001. The estimated cost of spinal-fusion procedures was more than $12.8 billion in 2011, according to AHRQ. Hospital costs alone for a spine procedure average $27,568 and total costs can hit six figures for major spinal-fusion procedures, experts say.
Isolating Spinal Surgeries
Spinal surgeries can occur in both inpatient and outpatient settings. Through the use of the Major Diagnostic Categories (MDC), the MDC related to musculoskeletal diseases has the highest cost (as measured by the total allowed amount) we will focus on the inpatient facility component for these surgeries. From here, drilling to the individual DRGs within this MDC will present the DRGs on which targeted analysis can be conducted.
There are several types of spinal fusion surgery DRGs but DRG460 has the highest total cost and the highest volume.
There are several options to continue the analysis. Using Milliman Benchmarks validates the opportunity based upon the total cost as well as the cost per admit. While the total allowed amount is higher than the benchmarks, the allowed per admit is significantly higher.
Understanding the elements driving this utilization and associated costs can include a demographic analysis to evaluate the variances within the population. Below, we show the breakout between gender by basic age bands, showing a higher surgical incidence among females across almost all age groups.
Another level of analysis can take a more clinical focus and isolate the types of diagnoses driving these surgeries. The AHRQ Clinical Classification System (CCS) rolls up the detail of the ICD9 diagnoses for efficient drilling for analysis, allowing you to quickly get to detailed diagnosis information.
Additional analysis could include:
- Provider level drills evaluating for specific patterns. This would include a look at what facilities these surgeries are occurring as well the individual surgeons, to compare the cost and utilization at each of these levels.
- Assessment of pre-operative activities – Evaluating outpatient modalities focused on non-surgical interventions to determine what activities occurred prior to the decision to perform surgery.
With the growing consensus about overutilization of surgery, more organizations are developing centers of care that promote a more coordinated approach to care. These centers offer multiple alternatives to surgery using a variety of treatment modalities and resources in a coordinated manner to reduce or avoid unnecessary surgeries while maintaining good patient outcomes.
Being able to analyze and understand the utilization of spinal surgeries is an important component in developing strategies to provide alternatives to surgery to help reduce utilization, curb the costs and better align with changing financial incentives.
Milliman MedInsight publishes this blog as a forum for meaningful discussion of day to day use of healthcare data to address issues and challenges encountered by healthcare organizations. Our consultants offer their expertise on innovative approaches and processes to leverage data to identify root causes of changes in cost and utilization trend, clinical and quality initiatives, and more.
The following list highlights MedInsight’s top 5 blogs in 2013 based on total page views:
5. David Mirkin’s blog “Innovation in Heath Plan Medical Management Metrics” explores the characteristics an idea medical management ROI tool or tools would have.
4. The concurrent and prospective models of risk adjustment both offer different advantages. Barb Ward breaks down each type in her blog “Risk Adjustment and Provider Profiling – My Patients are Sicker.”
3. Individual and small group health insurance markets went through a dramatic change in 2013 driven by the exchange. Andrew Naugle highlights why monitoring administrative expenses through benchmarking is essential in his blog “The Importance of Administrative Cost Benchmarking.”
2. In his blog “Employer Group Reporting: Just checking the box or true data analytics,” Brian Studebaker answers these important questions: what do employer groups really want from employer group reporting and what do employers really do with these reports?
1. Al Prysunka provides insight for APCDs on the utilization of an EDI format in his blog “Implementing the PACDR Guides for APCD’s - EDI vs. ASCII.”
Over the last few years one day hospital stays have been a focal point for medical management efforts to convert these to observation stays. In addition the 2014 Medicare’s Inpatient Prospective Payment System’s final rule on inpatient admission defines an “appropriate” inpatient admission as one that in the judgment of the admitting physician requires a hospital stay of at least two midnights or in medical management terms, a two day length of stay (LOS). Patients not meeting this criteria but needing inpatient hospital care lasting past one midnight but less than two will be classified as observation cases. The combination of these and other factors is leading to an increase in utilization for observation. So is this a positive outcome from a cost management perspective or is it problematic? This is becoming an important question for population management and one that does not have a simple answer.
A not uncommon situation is one where the reimbursement levels for inpatient hospital admissions and observation stays are not rationale. By this I mean that observations stays are paid more than if the patient were admitted as a regular inpatient admission. Avoiding an admission through the use of observation may be the right thing to do in this situation from a resource efficiency perspective but may actually cost a payer more than the hospital admission that is being avoided. Regardless, the overall trend is to move patients to observation status when this is appropriate clinically.
So how should observation status utilization be measured and what benchmarks should be used to monitor results? If we see an increase in observation utilization is it a desired outcome or should we be concerned? The typical practice of measuring billed units may not be the best approach since observation stays are billed using different units depending on how payer hospital contracts are designed so some observation services may be billed in hourly increments in some settings, in 24 hour increments in others and as a generic per observation episode in others. Our recommendation is to use “observation episodes” as the unit for measuring utilization. An observation episode is measured using the same logic as an inpatient stay, each midnight occurring during an observation stay counts as 1 observation unit with a minimum value of 1 for observation stay not spanning midnight. As examples, an observation stay starting at 4 AM and ending at 8 PM would be 1 observation unit, a stay starting at 4 AM and ending at 1 AM would also be 1 observation unit and finally a stay starting at 4 AM and ending at 1 AM after two midnight would be 2 observation units. This allows us to directly compare observation unit utilization with inpatient hospital utilization and one goal for directing patients towards observation is to reduce inpatient utilization.
Now for the question of how do we determine if our rate for observation utilization is positive or a problem we need to address. Since observation is a substitute for short stay hospital admissions we need to look both at observation utilization and short stay hospital utilization. We recommend combining observation episode with 1 day LOS hospital admissions to produce a combined utilization rate and then comparing this to either a historical target or a benchmark from a source such as Milliman. An example taken from a Milliman analytic tool (MedInsight Guideline Analytics) is shown below.
Using this example the target utilization is the “combined Obs + 1 Day LOS” or 27.7/1000. Our actual is 28.3 or a bit higher than we would like. In addition, our 1 Day LOS utilization is higher than the benchmark while our Obs utilization is less meaning we should have opportunity to shift more 1 day admits cases to Obs without causes excess utilization.
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.
Observation care continues to be a hot topic in the news for both Medicare and Commercial health plans in the United States today. One aspect of observation care is the lack of a clear definition of what constitutes an observation case. In the news the primary focus on observation case definition revolves around when an observation case should be considered an inpatient stay. For example, on July 30th, an investigation conducted by the Department of Health and Human Services Inspector General found both Medicare officials and hospitals are struggling to fully understand the difference between observation and inpatient status. In fact this report noted that the six of the top 10 reasons for observation care were also among the 10 most frequent reasons for a short inpatient hospital stay of one night or less, http://www.kaiserhealthnews.org/Stories/2013/July/30/IG-report-observation-care.aspx. In June of 2013 Premier requested the Centers for Medicare and Medicaid Services define observation care as inpatient after 72 hours of care, http://www.healthdatamanagement.com/news/hospital-long-term-inpatient-prospective-payment-system-46305-1.html.
Additionally, we find there to be a lack of uniformity in the definition for considering observation cases being distinct from an emergency room (ER) case or not. We feel this lack of clarity is important particularly when we have to consider the rising costs and rates of ER usage and the ancillary services being provided in that setting of care.
When Milliman researched how commercial health plans define observation care we found various models. For example we found some contracts to state that when ER services precede an observation stay, the ER services are considered to be incidental to the observation stay and are not separately reimbursed. For other contracts, ER and observation services can both be reimbursed separately. Currently, the Milliman Health Cost Guidelines (HCGs), embodied by an algorithm within the popular Milliman HCG Grouper, have ER services override observations services if they are billed together on the same claim, all costs and services are bundled into the ER case.
In a study of the Milliman normative databases, commercial health plan data in the United States using incurred medial claim data from 2010 and 2011, we found total observation care cases add up to be 40% of all emergency room cases, see the table below.
Furthermore the average allowed unit cost for ER cases with observation services included in the same claim was $3,891.34.
These data points are just the beginning to our exploration to re-evaluate if and when a claim or set of claims should be labeled as observation care or ER. For example should care after 8 hours in the ER shift to becoming an observation case? Are the service units encoded on claims data credible enough to use as hours of observation care in all situations? Again we must note that included in these costs are ancillary services which need to be attributed to either an ER or an observation case. See an earlier blog post on this related topic as well, http://info.medinsight.milliman.com/bid/305155/What-is-driving-Emergency-Room-costs, since the increases in services over time have been considered to be a leading driver of cost trends in the ER.
We are now being driven by these questions to work in the coming summer months of 2013, with our clients input, to hopefully add transparency in the process of defining observation care. We look forward to hearing any and all feedback so please email us or add comments to this blog post for the community to discuss.
A review of Milliman’s normative healthcare database indicates that nearly all of the recent increase in the cost of emergency rooms (ER) is due to significant increase in the prevalence of supporting services (laboratory, radiology, drugs, supplies, etc.) provided during while in the ER.
My study included claim and enrollment data for commercial (employer-sponsored and individual) coverage from 2007 through 2011. It revealed an annual rate of increase in allowed per member per month costs for ER of slightly over 10%, which is substantially above the inflation rate during that same period of time. Interestingly, the actual number of ER cases per 1,000 covered members decreased very slightly during that period of time, while the unit costs for the individual services provided as part of a typical ER case only increased 1% annually.
So, what caused the double-digit annual total cost increase?
It appears to be almost solely caused by an increase in the number of services provided during an ER case. My study shows that a patient is likely to receive 50% more services, as part of their ER visit, in 2011 than in 2007. 93% of these additional services are related to lab, drugs, IV therapy and radiology. Lab is most prevalent, with a 79% total increase in use from 2007 to 2011, but the unit cost of lab services decreased by about 25% during that same period. Combined drug and IV therapy prevalence rose 166% during the study period, but in this case, also experienced a marginal increase in unit cost. As a result, drug and IV therapy services explained nearly 35% of the total increase in ER costs. Finally, radiological services increased in prevalence per case by 22% during the period, and contributed 17% of the total increase in ER costs.
Further study might be necessary to understand the efficacy and value of the increase in these supporting ER services, and whether ER costs are continuing to increase in 2012 and in the future.
For all healthcare payors, managing administrative costs is increasingly important under the medical loss ratio requirements of the Affordable Care Act (ACA). Under the ACA, health insurers must utilize 80% of premiums on benefits in the individual and small group markets and 85% in the large group market. Payors need to efficiently and effectively manage these administrative costs in order to maximize the limited funds available for these operational activities.
For a healthcare payor organization, administrative expenses include those costs associated with operational activities such as claims adjudication, agent commissions, marketing, call centers, software licenses and more. Tracking and managing these administrative costs can be a challenge. And identification of areas where there is opportunity to optimize administrative spending can be an even greater challenge.
Benchmarking is an effective practice used by payor organizations to compare the cost and utilization in the delivery of medical benefits. Likewise, Operational Benchmarking of administrative activities enables healthcare organization to understand the performance of their internal and vendor provided administrative services. Types of administrative benchmarking measurement categories include:
- Efficiency benchmarks – the level of resource required for the completion of a defined number of transactions or members
- Quality benchmarks – both the level of consistency applied to similar transactions against recognized standards and the relative level of value of those services.
The purpose of establishing operational activities performance benchmarks is to define a vision for what is possible in “Best Practice” operations. Healthcare organizations utilize these benchmarks to identify strengths and weaknesses in their own operations, and to support operational improvement initiatives.
Administrative performance benchmarking allows a payor organization to:
- Analyze the efficiency of the health plan operational areas including claims, medical management, customer service, and administration
- Compare how the organization’s resource allocations compare to peers and competitors
- Evaluate whether resources are allocated correctly and whether additional staffing is warranted
- Measure administrative cost of the operations
- Target areas for improving customer service
Milliman has developed Operational Benchmarks which include measures for all medical administrative functions. They establish the Worst, Median, and Best Practice levels of cost, efficiency, and quality for administrative functions such as processed claims per 1,000 members per processor, reversed and adjusted claims, and call center abandonment rates. Milliman’s Operational Benchmarks have been collected from more than 100 payor organizations representing the full spectrum of the healthcare industry, ranging from small single-line carriers to large national carriers supporting a full suite of commercial products, as well as government programs and self-funded employer groups.
As shown below, Milliman Operational Benchmarks can be used to compare a client’s administrative costs by functional area. The results can be used to target specific areas for optimization or additional investment. Note that the information shown in the table below is for illustration purposes only. Milliman develops customized benchmarks for each client based on the client’s unique mix of business, plan size, location of operations, and administrative intensity.
We’ve blogged before about emergency room cost and utilization. ER cost and utilization – or rather the reduction of ER cost and utilization - is a frequent topic of discussion and data analysis for healthcare payer organizations. A recent Milliman study found an annual increase in the allowed per member per month costs for ER of slightly over 10% between 2007 and 2011, which is substantially above the inflation rate during that same period of time. It was also found that during that period of time, the actual number of ER cases per 1000 members decreased very slightly while the unit costs for the individual services provided during the ER cases on increased 1% annually.
Healthcare organizations turn to the data to identify potentially avoidable emergency room events, especially those that could have been provided in a less expensive care setting. In a patient centered medical home (PCMH) reporting project performed by Milliman, ER utilization was one of several utilization targets. The analysis performed identified opportunities for cost reduction, such as identification of members who visit the ER frequently. This analysis also provided insight into access and care management issues by tracking the day of the week ER visits were occurring.
Data analytics is becoming more and more valuable throughout all functions within healthcare payer organizations. Another Milliman study looks at how care coordinators use data analytics for analysis of preventable ER visits by line of business.
Further detail on each of the mentioned studies may be found at:
The CMS diagnostic related groups (DRGs) have undergone numerous refinements since first introduced in the early 1980s, but they remain essentially a tool to support the CMS prospective payment system. What would a grouper that focused on clinical categories rather than payment look like? For the answer, take a tour of our new product – Guideline Analytics.
Guideline Analytics uses standard claims data elements — diagnosis/procedure codes and patient demographics, for example — to assign each inpatient admission to an Optimal Recovery Guideline (ORG) or General Recovery Guideline (GRG) category drawn directly from our acute care content —Inpatient & Surgical Care and General Recovery Care. Each admission is categorized by a principal ORG or GRG code, subsidiary ORG codes, and a severity category based on the comorbidity methodology developed by CMS for its DRG system, complication/comorbidity (CC) and major CC.
The results are easy-to-manipulate data warehouse analytics and/or an Excel spreadsheet report that allows you to analyze performance against optimal outcomes, compare yourself to peer organizations, and identify clinical areas where you can provide care more effectively.
Why not simply use the MS-DRGs to accomplish this analysis? The short answer is that the MS-DRGs were built for a different purpose. For the long answer, let’s say you’re the medical director at a medium-sized health plan. (The data that follows is actual 2010 data from such an organization.) You’re trying to understand how your network is addressing obesity using surgical procedures. Specifically, you want to know how many surgeries are being performed on an inpatient basis that – under optimal conditions – could be performed on an outpatient basis. The DRGs provide three categories for Operating Room Procedures for Obesity. However, two of those categories are for cases with significant complications and comorbidities– unlikely candidates for outpatient surgery, and in practice, only about one-sixth of cases. That leaves one large category (DRG 621) for analysis. Not much granularity.
Guideline Analytics breaks DRG 621 into seven MCG™ guideline categories (see chart below), including S-515 – Gastric Restrictive Procedure without Gastric Bypass by Laparoscopy. Why is S-515 so interesting? According to current medical literature, given optimal conditions, patients can receive this procedure on an outpatient basis. Yet 28% of such procedures are being performed on an inpatient basis at an overall cost of $1.3 million. Is that a reasonable percentage? Should you dig deeper into the data? Guideline Analytics provides risk-adjusted benchmarks – covering different lines of business, different regions of the country, different degrees of medical management, and different delivery systems – so you can assess where that 28% places you compared to peer organizations. Guideline Analytics are linked to MCG™ guideline categories in terms of their foundation only, the Guideline Analytics are not dependent on a client having a license to MCG™ products. Due to the linkage of the Guideline Analytic categories to MCG™ we know that the recent medical evidence on this procedure, S-515, includes five published studies showing the safety of outpatient treatment and four articles describing situations in which inpatient care may be required. This unique combination of statistical data and medical evidence from peer-reviewed sources allows you to decide your next step with confidence.
2010 Inpatient Medical Claims Analysis for Medium-Sized US Health Plan