A Fresh Perspective on Employer Group Reporting

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 its selection process related to health benefits reporting. However, employer groups have a wide range of reporting needs depending on their size, industry, funding arrangements and corporate philosophies related to employee benefits. As a result, there is no single “one-size-fits-all” solution for health plans to generate employer reports.

Milliman experts, Doug Bates and Brian Studebaker presented an employer group reporting webinar on May 15, 2013. Doug is Manager of Business Analytics with extensive experience in the healthcare industry with an emphasis in employer solutions. Brian is a Principal and Healthcare Technology Consultant specializing in healthcare information technology.  The webinar focused on employer group reporting needs and the reporting and analytic options, all of which are available within Milliman’s MedInsight enterprise-wide analytic reporting platform.

If you missed the webinar and would like to view it, you can access a recording at: A Fresh Perspective on Employer Group Reporting. To listen to the webinar, click on the “Register” button, enter the requested information, and click “Complete Registration.” The webinar will then automatically stream to your computer.

You may also be interested in our past employer group reporting blog posts:

• Employer Group Reporting: a top focus of many health plans
• Employer Group Reporting: Just checking the box or true data analytics...

Are your claims paid properly?

Claims audit reporting can help you decide.                 

Healthcare plan managers sometimes overlook the value of auditing members’ actual medical claims and payments. However, making sure that a healthcare plan is set up properly and performing well is a way of checking the work of a third-party administrator (TPA) and a wise step toward preventing future errors.

Large volumes of claims practically ensure that there will be errors such as duplicate billing, pricing mistakes, or mistakes in member eligibility. For this reason, it’s advisable to audit your claims data to assure compliance with contractual guarantees. Milliman MedInsight can help you analyze your data using audit testing and reporting.  Using several standard reports within MedInsight, you can estimate how accurately your claims are being paid and determine whether you may benefit from a full claims audit.   It’s important to note that only a thorough claims audit will validate the audit results such as those found in the MedInsight analytics, and that no electronic audit can account for all possible payment situations.

Today’s computerized audit testing in MedInsight makes it possible to examine 100% of a plan’s data. This is far preferable to the earlier practice of manually reviewing only a sample of data. Because of the various ways a medical claim can be paid – such as based on whether it is medically necessary, preventative, experimental - as well as claim limitations, copayments, membership status, and other factors, there are a great many potential payment issues.  The difficult task for a health plan is to know where potential issues indicate actual claims processing deficiencies, and a continuous claims audit reporting ability can help.

Electronic tests.  The following are only a few of the tests MedInsight runs on a complete claims-data set:

• Member not eligible: Identifies claims paid when the member was not eligible at the time of service based on data in the supplied enrollment file.
• Duplicate procedures: Identifies paid duplicate charges where the patient, procedure, date, and provider are the same.
• Outlier physician charges: Identifies procedures that have been paid at a rate of more than two standard deviations above the mean rate for that procedure.  This edit often identifies adjudication mistakes and out-of-network contracting opportunities.
• Possible COB opportunities: Identifies claims that have no COB recovery even though the patients have COB recovery history and/or COB indicators on their enrollment records.

Using the MedInsight standard reporting tool, the MedInsight Claims Audit Summary report shows the results of the following audit tests. 

 

For each test, the report shows the number of claims that were found to fail the test, the billed and paid amount of claims how much should have been paid and savings potential metrics.

The most important aspect of auditing focuses on pricing—verifying that the benefits spelled out in the contract are in fact being provided for eligible members and at the price levels contracted for. This is the first step toward correcting administrative practices, cleaning up enrollment, improving the claims data, or renegotiating a TPA contract.

Claim audits used to be a “once-every-three-years” due diligence procedure, but more plan sponsors and brokers are now turning to annual audits.  MedInsight can be used to identify specific audit opportunities or as a monthly screening method to keep tabs on large volumes of claims.

Moving from Patient to Population-Centric Analytics in an ACO

As accountable care organization (ACO) contracts gain in popularity among health care systems, the need for a different type of data analytics is growing for provider organizations.  Most health care systems are usually very adept at mining their electronic medical record (EMR) systems to support quality improvement and care management programs.  Reporting of this nature is patient-centric and can be supported by EMR data that typically includes only a partial picture of the medical encounters a person has.  However, the reporting demands of most successful ACO contracts require population-centric metrics.  Many provider-based decision support systems face a number of challenges to meet population-based analytic demands. These include: 

• EMR systems lack a complete medical encounter history (e.g. system leakage)
• Patient enrollment data lacks a complete enrollment/eligibility history
• Pharmacy data is incomplete or not available
• Analytic solutions such as population risk adjusters and episode of care groupers are not available

 Get the Most from Your EMR Data

There are several methods that ACOs can employ to maximize the insight and resluting value they can achieve from their EMR data:

• Develop a member month proxy table based on the patient information included in the EMR.  This method will require gap filling techniques to span periods of no member activity. 
• Leverage all EMR data sources within the system.  Of particular importance is mining the data included in office and clinic settings of primary care practitioners.
• Utilize risk adjustment software that is based on diagnosis only and avoid risk adjustment methodologies that require procedural information as an input.
• Explore the accessibility of All Payer Claims Databases (APCD) in your region/state.  APCDs, while typically member de-identified, can provide a complete episode of care for members who seek services within your system.  Issues such as efficiency of care by episode and system leakage can be identified - highly valuable pieces of information.

 The Ideal Solution

Rather than rely entirely on EMR data, the ideal solution is to deploy and leverage an analytic infrastructure that is designed specifically for population-based healthcare analytics.  Several key characteristics of a successful solution of this nature include: 

• Detailed enrollment information from each ACO contractor.
• Detailed claim information, including allowed amounts, from each ACO contractor.  If allowed amount is not available, encounter data is still useful as RVUs can be used as a proxy for allowed amount.
• Pharmacy data for all members, including NDC and member ID.
• Provider and member matching logic to link data between the various data sources and the systems EMR data.
• Provider attribution logic to assign members to a PCP as well as specialty and facility attribution for episodes of care.
• Classification systems such as DRG assignment, service and utilization count assignment, episodes of care assignment and member risk score assignment.
• Evidence-Based Measures to calculate industry standard quality and care metrics (e.g. HEDIS, PQI, NYU ED Algorithm, etc.)
• Benchmark information from either an APCD or other external source.

 Open the Door to Success

As your organization forms, and then begins executing, its plan for the development of a population-centric decision support system, the first and most important step is to make sure your ACO contracts entitle you to receive detailed enrollment and claim information for the populations you will be taking risk for.  Solve this challenge, and the other elements of a population-centric decision support system will fall into place.

Employer Group Reporting: a top focus of many health plans

We reported on this topic in February 2013. It continues to be part of many conversations Milliman is having with current and prospective clients. Health plans are focusing on this topic as evidenced by the number of RFPs released the past few months seeking analytic software to support this use case. The MedInsight team will be focusing on employer group reporting in May by presenting a webinar on May 15th, as well as two white papers on best practices.

The remaining part of this article is from our February 22nd blog article.

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.

 

Tier Analysis - Current Year (Paid & Allowed)

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.

 

Top 10 Medical CCHGs

 

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. Also watch for announcements about our May 15th webinar.

Using Administrative Claims Data for Quality Reporting

Developing health care quality metrics based on administrative claims data has become increasingly common over the past several years.  NCQA’s (National Committee for Quality Assurance) HEDIS (Healthcare Effectiveness Data and Information Set) measures have been a standard for health plan quality reporting for over two decades, and more recently, newer programs such as the CMS Pioneer ACO (Accountable Care Organization) program and Oregon CCO (Coordinated Care Organization) program have included claims based quality measures as requirements for program participation.

Most claims-based measures are process based, evaluating if appropriate services are provided for specified groups of patients, or identifying potential over-utilization of services, but claims data are not the sole source of quality measurement.  Survey data are often used for patient satisfaction and operational measures, and there is increasing use of lab results and EMR (electronic medical record) data to expand the clinical components of quality that can be measured (a topic for another posting).

Despite the expansion of claims-based quality measures, some still question the merit of these measures.  Those citing concerns point out known limitations with claims data including:

• Potential errors or inconsistencies in coding
• Availability of required data sources may be constrained if components of benefits are administered by multiple sources.
• Lack of complete clinical information.
  • No diagnostic coding for blood pressure, laboratory results or pathology results
  • Clinical information is limited to conditions for which the patient was treated and submitted a claim.  A noncompliant diabetic may have no claim history of the disease.
  • Timeliness of data is impacted by claim lag

However, the advantages of claims data greatly outweigh the limitations noted above.  The advantages include:

• Data are commonly available and relatively inexpensive to analyze.
• Data are available for very large populations, allowing for more robust sample sizes.
• Coding accuracy has improved dramatically over the past 20 years, and
• For some types of measures, claims may produce a more accurate picture than even chart reviews.

An example of this last point would be measures focusing on patient compliance with medications.  A physician may regularly write refill prescriptions for a patient’s hypertension medication, and those refills may be well documented in the patient’s chart, but those data provide no real evidence that the patient filled those prescriptions.  Tracking actual claims for prescription refills is a much better measure.  Granted, submitting a claim for a hypertension medication does not prove that the patient actually took the medication at the appropriate frequency, but a regular, on-going refill pattern is a better proxy of medication adherence than chart review information.

Days supplied is commonly available on claims data making it easy to calculate “possession ratios” to monitor patient compliance from pharmacy claims.  A simplistic way (additional conditions can be added to the calculation) to measure possession ratios is demonstrated in table 1.  For patients continuously enrolled during a 180 day period and previously diagnosed with hypertension, the possession ratio for each patient is the sum of all days supplied on their prescriptions during the study period, divided by 180 days.

Although claims data are not perfect for clinical reporting, they will continue to be a valuable and important source of data for quality reporting for a selected set of metrics.

Recent Analyses of Interventions for Avoidable ED Visits

Over the past several years there has been substantial interest in reducing avoidable emergency department (ED) visits. A wide variety of strategies have been employed to achieve these reductions including:

1. Benefit design changes such as increasing visit copays or putting limits on the reimbursement of number of un-necessary ED visits by a single patient.
2. Provider incentives through programs, such as Patient Centered Medical Homes (PCMH) to reduce the avoidable ER rate.
3. Structural delivery system changes to emphasize Urgent Care Facilities and after hours primary care.

Many of these interventions rely on analytics based on NYU’s avoidable ED algorithm which uses a probabilistic algorithm based on primary diagnosis code to identify the likelihood of avoidable ED visits within populations. Several analyses have now been done that analyze the effectiveness and/or the safety of these interventions.

The first analysis was done by the Washington State Health Care Authority (HCA). It cites an over 10% decrease in ED utilization and ED PMPM costs in the first 6 months of a program instituting 7 best practices for Medicaid enrollees in the State. The best practices included the electronic exchange of information between emergency departments, patient education of ED utilizers, sharing of lists of frequent ED utilizers, development of ED care plans, guidelines and monitoring of narcotic prescribing and the periodic review of feedback reports. For more information on this program, read HCA's report, Emergency Department Utilization: Assumed Savings from Best Practices Implementation.

The second is a peer reviewed study by ED physicians, whose conclusion was that the NYU ED algorithm did a relatively poor job in identifying an individual patient’s need for an ED visit.  In this study they compared presenting complaint data with ED discharge diagnosis run through the NYU ED algorithm. They found that the presenting compliant predicted poorly whether the visit should have been avoided and that doing so could have safety consequences. While arguably the NYU ED algorithm wasn’t designed to guide individual patient decisions, the article is thought provoking and undoubtedly can be cited as an argument against ED Visit interventions. Read recent article in JAMA, Comparison of Presenting Compaint vs Discharge Diagnosis for Identifying "Nonemergency" Emergency Departement Visits for more information. 

I’d expect that many more articles to be published about these interventions in the coming months and years. It will be important for informatics to be aware of these evaluations.

 

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Use of Analytic Tools to Meet Quality Compliance Requirements

As health plans and other organizations prepare for Health Reform - including meeting Health Exchange requirements and preparing and competing for the expansion in government program business - there is a renewed focus on meeting accreditation and quality measurement requirements.  These requirements are promulgated by organizations such as CMS and state regulators and administered by the National Committee on Quality Assurance (NCQA), URAC and other auditors that are certified by these government agencies.

For experienced health plans with generously staffed Quality Management departments this is old hat to them. To others - newer health plans, provider-sponsored health plans and community-based organizations - the language of the requirements can be foreign and the work required to meet them can seem ominous.

There tends to be theme songs to some of the key quality regulations and requirements. What data do you have that shows your knowledge of the population? How do you use the data to identify opportunities in the population? And how do you measure that your initiatives have had any impact?

Tools like MedInsight, with its clinical analytic and the risk scoring capabilities can be very effective in addressing the needs of some of these requirements. Most organizations are using these tools to identify preventive care gaps and address those gaps but many are missing opportunities to address other critical accreditation and quality improvement areas.

Some of these opportunities include:

• NCQA Health Plan Quality Improvement Standard 7 – requires the ability to identify members with complex illnesses and comorbidities, establish identification methods and conduct an annual population evaluation to determine the continued validity of those methods.
• NCQA Health Plan Quality Improvement Standard 8 – requires the ability to identify condition populations for disease management and implement a stratification approach for selective intervention.
• CMS Special Needs Plan (SNP) Model of Care (MOC)- requires the ability to define the needs of the population, identify frail and high need members and provide interventions based on analysis of population needs.

MedInsight and the Milliman Advanced Risk Adjuster (MARA) provide the ability to efficiently and consistently identify candidates for case and disease management, identify comorbidities and stratify members for targeted intervention. As quality managers and care management leaders gain access to this information these teams will find strong evidence of their ability to meet these accreditation requirements and a solid source of qualified members who will benefit from their programs.

Access to my data – The Provider Portal

The advent of changing reimbursement frameworks moving from fee for service and volume based incentives to budget-based and shared savings methodologies has intensified the need for physicians to access meaningful, timely physician profile reporting.

There are many variations in what gets presented in physician profile reporting as well as how and when it is delivered. In July 2012, the American Medical Association launched their Physician Reporting Guidelines in an effort to provide a physician perspective in profile reporting. The guidelines called for the need to be easy to understand, easy to access and provide an opportunity for review of detailed data. While the AMA reporting guidelines also called for standardized reporting, that remains a significant work in progress.

Technology and data availability continue to improve and have given rise to sharing information through provider or physician portals. There is a great deal of work on the front end to gather data, select measures and validate all this data before release.  Today, we scratch the surface of physician portals and share some examples and considerations in the design of the portal.

The critical elements in the design and implementation of a physician portal require early inclusion of physicians in the planning and design process. In accordance with the AMA Reporting Guidelines, it should also address ease of use, availability of actionable information and drill to their member detail.  

Dashboards are a popular method for sharing information through a portal. They provide for an accessible, visual and portable look for physicians. This type of display can begin at a higher level such as the medical group and then allow for drill to individual providers and then to their members. Depending on the tool and data sources, the provider portal can include claims and EMR data reporting. Retrospective data is very valuable in providing insights into opportunities for change.  The examples below are claims based and allow for review of the retrospective performance as well as provide transparency and insight into prospective issues, using risk assessment results and other mainstream methodologies.

 

Lastly, providing the option to submit feedback is a critical element for provider portals. Throughthe portal, the physician can be given access to submit requests to update or revise the data, and related results, based on additional information. The workflow of this process should be a significant consideration in the planning process to define standard reasons.

There is a great deal of planning involved in determining the specific metrics and the data visualization option but with early physician engagement and thorough data governance rules, the physician portal is a valuable tool in broadening the engagement of physicians.

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:

• What is driving Emergency Room costs?
• Claims-Based Analytics to Identify Potentially Avoidable ER Visits
• Potentially Avoidable Events - The Link to Care Coordination