Tag Archives: Brian Studebaker

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 its 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 questions are, 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 its portfolio of standard reports a new “storybook” employer group report that focuses on meeting the needs of employers looking to understand their healthcare spending. Additionally, the report can serve as the foundation for data-driven discussions with brokers or sales agent about how to better tailor benefit design and healthcare investment to better serve 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 its historical healthcare spend.
2. Provide the employer data-driven insight into how it might wish to change its 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 modifiable at run time by the sales group to add comments and adjust report output.

Some of the analytic tactics employed in the report to achieve the goals for the first objective above 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 the second objective above 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 healthcare 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.

This article first appeared at Milliman MedInsight.

So you want to build your own healthcare data warehouse….

Many organizations struggle with the business decision of whether to build their own healthcare data warehouse and decision support solution or to license a solution from an organization that specializes in healthcare data analytics.

Most healthcare payor organizations have some form of an operational data store (ODS) that serves as a storage site for their claims adjudication system data. The temptation to expand the ODS data sources to include non-claims adjudication system data sources and layer data tools on top is strong. But is this the right course of action? Should the ODS be transformed into a full healthcare business intelligence (BI) solution? If not, what role should a third-party BI solution play?

In my opinion, the answer is a bit of both—an ODS for operational reporting and a third-party business intelligence tool for cutting edge business analytics. I postulate that the following structure optimizes the strengths of both models:

1. The payor organization builds and maintains an ODS that has frequent (or real-time) updates of claims adjudication system data. The data is subjected to little or no transformation or enhancement.
2. The ODS has a limited number of “operational” reports written against it. The defining metric on whether a report should be written against the ODS is the “currency” of the data. If you need a near real-time list of open claims, this is a report that should populate from the ODS.
3. Layer on top of the ODS a “best in class” healthcare decision support system. This system is typically characterized by periodic data updates, typically monthly, and a number of advanced data analytic enhancements. Enhancements include methods such as risk scores, service classification grouping, episodes of care, quality metrics, completion factors, attribution methods, benchmarks, etc.
4. Included as part of the decision support system are user tools such as dashboards, online analytical processing (OLAP) cubes, standard reports, and user portals. These different interfaces provide for access to a wide variety of users in the organization.
5. The decision support system also allows for a wide variant of different data sources to be combined together in a standardized format. By combining data from pharmacy benefit managers, third-party carve-outs such as vision or mental health, lab results, wellness programs, or even administrative data, powerful new analysis can be accomplished.

Where do you draw the analytic line between your ODS and BI solution?

Does the proximity of IT firms influence the cost of EMR?

The National Bureau of Economic Research published a new paper (payment may be required) examining the “relationship between the adoption of electronic medical records (EMR) and hospital operating costs at thousands of US hospitals between 1996 and 2009.”

Here is an excerpt from the study’s abstract:

We find evidence consistent with this approach, namely, that: (1) EMR adoption is initially associated with a rise in costs; (2) EMR adoption at hospitals in favorable conditions – such as urban locations – leads to a decrease in costs after three years; and (3) Hospitals in unfavorable conditions experience a sharp increase in costs even after six years.

“Unfavorable conditions” include greater distance from information technology hubs, meaning that rural hospitals are less likely to see a short-term return on their EMR/EHR investment.

Today’s most competitive healthcare organizations are data-driven. Brian Studebaker and Roger Connolly provide perspective into innovative business intelligence (BI)—the process of turning data into actionable information… Read their paper on getting a return on investment out of business intelligence here.

Click on electronic health records for more insights.

Get better ROI from your business intelligence

Business intelligence (BI)—the process of turning data into actionable information—is a critical capability in the healthcare industry. Business intelligence enables healthcare entities to measure, analyze, and improve across multiple areas of organizational performance.

Most healthcare entities have a business intelligence strategy, but many struggle to get the greatest value from their clinical, operational, and financial data. They may have robust data and the tools for analytic enhancement, but without an interface that facilitates data sharing across departments and job roles and an enterprise-wide commitment to integrating data with business process, they are in effect leaving money on the table. Today’s most competitive healthcare organizations are data-driven.

This paper provides insight into innovative business intelligence in healthcare and steps that an organization can take to get a better return on investment (ROI) from its data.