In 2010 we highlighted an electronic health records (EHR) analysis conducted by Software Advice. The company has recently updated their EHR market share statistics.
According to the report, 115,918 eligible professionals (EPs)—physicians and other health professionals—have successfully qualified for Stage 1 Meaningful Use EHR incentives according to the criteria of the Centers for Medicare and Medicaid Services (CMS).
Also, the Office of the National Coordinator for Health Information Technology (ONC-HIT) lists 623 software vendors that provide solutions to EPs that can be eligible for incentives under the American Recovery and Reinvestment Act. However, the report shows only 387 of those vendors were found in the CMS data.
The entire report can be accessed here.
For Milliman’s perspective on EHR, click here.
With the interest in establishing all payor claims databases (APCDs) continuing to grow (currently APCDs exist, are being developed, or are being contemplated in over 25 states), the need to standardize the data collection component of APCDs is now much greater. In this context, the term standardization means that states and substate entities collecting APCD data would do so in the same manner (i.e., identical file structures, data elements, data type, positioning, lengths, and code sets). Also, because individual states and substate entities will need to collect some data elements that are unique, standardization must also include a uniform process, which involves both data submitters and data collectors, for modifying the accepted file structure.
While the states of Maine, Massachusetts, Minnesota, New Hampshire, Tennessee, and Vermont have very similar data collection requirements, differences do exist, and Maryland, Utah, and Oregon, even with many of the data elements collected identical to those collected by the six states listed above, have considerably different data file formats. This nonuniform approach to developing APCDs has resulted in increased costs to all stakeholders: states and other substate entities using “one-off” data collection systems cannot easily adopt or leverage the advancements made by those employing a more standardized format, resulting in increased costs. Because different extracts must be created for each data collection entity, costs for payors submitting data, especially for payors operating in multiple states, are significantly higher than the ideal. The ability of data users (including federal and state government agencies) to share analyses and applications across states is more complicated, resulting in additional analytic costs to normalize the data.
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?
The U.S. Department of Health and Human Services (HHS) announced the next steps in the president’s effort to help doctors and hospitals use electronic health records. Here is an excerpt from the HHS’ news release:
Today, HHS’ Centers for Medicare & Medicaid Services and HHS’ Office of the National Coordinator for Health IT released final requirements for stage 2 that hospitals and health care providers must meet in order to qualify for incentives during the second stage of the program, and criteria that electronic health records must meet to achieve certification.
The requirements announced today:
• Make clear that stage two of the program will begin as early as 2014. No providers will be required to follow the Stage 2 requirements outlined today before 2014.
• Outline the certification criteria for the certification of EHR technology, so eligible professionals and hospitals may be assured that the systems they use will work, help them meaningfully use health information technology, and qualify for incentive payments.
• Modify the certification program to cut red tape and make the certification process more efficient.
• Allow current “2011 Edition Certified EHR Technology” to be used until 2014.
For Milliman’s perspective on electronic health records, click here.
The high and sustained growth rate of healthcare in the United States over the past two decades has created significant financial pressures for both government payors (Medicare/Medicaid/VA) and private sector employers who offer health insurance to their employees. Additionally, while per capita healthcare expenditures have increased at an alarming rate, many believe that the overall quality of healthcare in the United States is not commensurate with the expenditures and that significant improvements in health outcomes for the general populace have not been realized.
In order to address the cost and quality issues in the U.S. healthcare system, both government policy makers and employers have found it critical that a comprehensive and timely source of data be available to better define the problems and to set forth proposed solutions. The data set that has emerged to meet those requirements is the all-payor claims database (APCD). This paper provides an overview of the structure and key considerations for planning, launching, and operating an APCD, and lists a number of potential uses for the data, once collected.
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.