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MedInsight's Top 5 Blogs in 2013

 

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.”

Observation Status - A Practical Approach to Evaluating Utilization

 

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.

MedInsight Guideline Analytics example

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.

Administrative Expenses: Five Best Practices

 

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. 

  1. 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. 
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Defining and Benchmarking Observation Cases with Medical Claims Data

 

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. 

Observation (1) 8.3.13

Furthermore the average allowed unit cost for ER cases with observation services included in the same claim was $3,891.34.

Observation (2) 8.3.13

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. 

What is driving Emergency Room costs?

 

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.

Managing Administrative Expenses with Operational Benchmarking

 

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.

Administrative Benchmarking

Emergency Room Cost – A Hot Topic For Data Analytics

 

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:

 

Guideline Analytics – Supports Clinical Category Based Claims Grouping, Benchmarking

 

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

Guideline Analytics example

Employer Group Reporting: Just checking the box or true data analytics...

 

Employer group reporting is a requirement for most health plans and third party administrators in today’s healthcare environment.  Almost every large group employer seeking healthcare coverage has some requirement in their selection process related to reporting on membership and claim experience.  These requirements can vary significantly depending on the analytic sophistication of the employer and or broker working with the group.  The real question is, what do employer groups really want from employer group reporting and what do employers really do with these reports?

Milliman MedInsight has recently added to their portfolio of standard reports a new “storybook” employer group report that focuses on meeting the needs of employers looking to understand their healthcare spend.  Additionally, the report can serve as the foundation for data-driven discussions with their broker or sales agent about how to better tailor their benefit design and healthcare investment to better serve their employees’ needs and the employer’s investment.

The report development process was a collaborative effort between several Milliman clients and MedInsight data analytics consultants.  At the start of the project the development team identified two primary objectives for the new storybook employer group report:

  1. Enable the employer to understand and reconcile their historic healthcare spend.
  2. Provide the employer data-driven insight into how they might wish to change their benefit offerings in the future – identify action items

As the report specification evolved further, several secondary requirements emerged: the report had to be easy to read and understand, the report had to have meaningful comparative benchmarks to help an employer put its experience in context, and the report needed to be able to be modifiable at runtime by the sales group so they could add comments and adjust report output.

Some of the analytic tactics employed in the report to achieve the goals for Objective One are:

  1. Analysis of both paid and allowed amounts by Milliman’s Health Cost Guidelines categories.
  2. Trend analysis between a definable current time period and prior time period.
  3. Benchmark comparatives between a similar block of business for the health plan and/or a set of benchmarks derived from Milliman’s research database.
  4. Reconciliation analysis of claims by paid date.
  5. Membership analysis by demographic and benefit design dimensions.
  6. Concurrent risk scores to measure the illness burden of the population between time periods.

Some of the analytic tactics employed in the report to achieve the goals for Objective Two are:

  1. Use of Milliman’s Chronic Condition Hierarchical Groupings (CCHGs) to identify medical condition prevalence when considering wellness program initiatives.
  2. Evidence based measures to identify gaps in preventive care that influence the long term health of the population.
  3. Predictive risk scores for the employer group and the other similar groups within the health plan to forecast how future health care expenditures might compare.
  4. Frequency of potentially avoidable emergency room use.
  5. Provider network utilization analysis.
  6. Pharmacy use analysis for mail order, generic use and specialty drug use.

Milliman’s design of the employer group report will continue to evolve as we present the report to more clients and get additional feedback from our user base.  If you’re interested in learning more about this new feature or would like to contribute your ideas to future versions of the report, please contact your Medinsight consultant or add a comment to this posting.

 

 

 

 

 

The Importance Of Administrative Cost Benchmarking

 

In the late 1990s, online travel agencies revolutionized the airline industry by publishing fares and allowing consumers to search for and purchase tickets.  No longer would consumers have to rely on an agent to filter and present options; travelers could search across all vendors and use their own criteria to evaluate their options and purchase a ticket.  The individual and small group health insurance markets are poised for the same sort of dramatic change, driven by the now familiar concept of the online marketplace, known in the health insurance industry as the exchange. 

Although the operation of a health insurance exchange is quite different from that of an online travel agency, these distribution channels are similar in their impact on price transparency.  Under the old travel agent model, consumers would first search for tickets based on convenience factors (e.g., travel dates and times, routes, etc.) and then use price to differentiate among a few options.  Likewise, in the individual and small group health insurance markets, price is often presented after the purchaser has already narrowed the options to a few that meet non-price criteria.  In both of these situations, price is applied as a deciding factor after the consumer has already narrowed the universe of choices to a subset of similarly appealing options; and the consumer lacks visibility to the prices of choices that were eliminated in that process.  Online markets on the other hand, allow consumers to see the prices of all or most options at the same time, making price a primary determining factor when making a purchase decision.  This new presentation format, which allows consumers to choose one product over another based on a small dollar price difference, discourages significant price variation among competitors for similar products.  

For most health insurance products, price is comprised of three primary components:  benefit expense, administrative expense, and risk margin.  Although benefit expense makes up the lion’s share of the premium or price, administrative cost differentials among health insurers can also materially contribute to premium differences.  These differences will become more pronounced and may affect consumer purchasing decisions as the benefit expense component of premium is constrained by the Affordable Care Act’s Medical Loss Ratio (MLR) requirements.  These rules effectively create a benefit expense floor, requiring that health insurers in the individual and small group markets spend no less than 80% of premium on benefits (85% in the large group market), or pay a rebate to policyholders.  It is likely that MLRs for individual and small group products will eventually settle around the 80% level or higher.  In this new world, the importance of managing administrative cost will increase as price competition puts pressure on overall premiums and the MLR rules force administrative cost and risk margin into a fixed share of the premium dollar.

Benchmarking is one of the most effective tools available to help health insurers manage their administrative expense.  For insurers working to achieve MLR targets through administrative cost reduction, a benchmarking assessment can offer a function-by-function comparison of administrative expenses and staffing levels versus competitors and peers.  Such an analysis can help organizations figure out where to target their cost reduction initiatives or determine what cost level is appropriate for a given department, cost center, or function. 

For insurers that have already achieved the MLR targets, administrative benchmarks combined with a dashboard view can allow for monitoring of administrative expense variation throughout the year.  Optimizing administrative cost is not something that can be achieved overnight; it takes time to plan and implement cost management initiatives, and months or years before the benefits accrue to the bottom line.  Thus a dashboard coupled with benchmarks can provide management the tools they need to effectively manage their price competitiveness in this new distribution paradigm.  

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