Tag Archives: business intelligence

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.