The below is an abridged version. The complete article is available on-line at Information Management.
Rays of light but no ready cure as institutions wrangle with foundational requirements of business intelligence
Matching the practices of business intelligence (BI) to health care institutions is a process that has been steadily — if unevenly — taking place over the last two decades. Like any other industry, health care adheres to the topics and buzzwords that touch any BI undertaking which include data warehousing, data quality, data integration, metadata management, governance and analytics.
Unlike other industries, the clinical side of health care is based on interpretations of care practices that change over time. And while health care institutions compete for customers, there is no one set of product or service standards — as would come with a new refrigerator — because the patient is in fact the “product” shared among many providers.
Two Views of BI
Business intelligence can be described as a process that leads to better decision-making. In its original and ongoing mission, a business analyst selects and extracts historical data from one or many databases. The analyst then structures and loads the data into a single repository (a data mart or data warehouse). Analytical and visual tools are then applied to perform trending and comparative studies that, sliced in different ways, create reports to measure performance and uncover opportunities for improvement.
In contemporary form, BI also takes an operational approach that gathers near real-time data to support ongoing processes. These include sales, marketing and customer interactions. Operational BI is more process oriented than the data warehouse model and is associated with key performance indicators, dashboards and scorecards that support performance management.
In the clinical world, health care providers have tuned operational workflows to fit processes from admission and treatment to checkout with a flexibility other industries might envy. A visitor to an emergency room or someone admitted for an inpatient or outpatient treatment might well regard the event as orderly and procedural.
Whether or not this appearance is true, the actual management and reconciliation of data that flows through admissions, doctor notes, labs and pharmacies becomes a huge challenge to implementing BI. And data complexity provides only a partial explanation of why the industry is a relative late-comer to the BI strategies employed in other economic sectors.
While current budgets are tight everywhere, the health care sector has traditionally been an IT spending laggard. Without accounting for scale, a 2009 Gartner Research note predicts that the financial services industry will spend more than six dollars on IT for every equivalent dollar spent by health care this year. But according to figures from HIMSS, the Healthcare Information and Management Systems Society, this under spending may change. Spending, which now sits at approximately 2 percent of total revenue, is expected to grow at a compounded rate of 7.5 percent through 2014. A 2008 snapshot of health institutions conducted by IDC subsidiary Health Industry Insights found that less than 20 percent had instituted an enterprise data warehouse. The study also found that more than 30 percent were planning to do so.
Platforms and Standards
If the history of the ERP industry is any indication, EHR platforms present benefits and risks related to their relative maturity. Pre-integrated platforms lessen requirements for systems integration and help unify documentation for decision-support, BI and performance management. They help bridge workflows and processes formerly siloed in individual applications, and may come with uniform upgrades across application areas.
On the downside, platform investments cost many millions of dollars in a commitment that can lead to vendor lock-in. Product roadmaps don’t serve all interests and vendors offer products based on proprietary code that may or may not work well with newer technology practices. Some platforms are based on code written decades ago, which can make them more proprietary.
In the ERP world, platform vendors have grown more dominant and improved product lines steadily over the years. They have also acknowledged the need for specialized and heterogeneous technology infrastructures, a trend also proving true in health care.
“Under the very best of circumstances if you bought everything you could from the smallest number of vendors you would still have a substantial variety of information systems just because of the nature of health care,” says Vi Shaffer, an analyst with Gartner Research and 30-year veteran of the health care industry. “You’re always going to have interoperability and interface challenges; the standards bodies have done yeoman service over the years to help that along but we still have a long way ago.”
The risk of non-standardization also exists where inaccurate sharing of information might affect patient safety. An example arose years ago at Geisinger Health System where, in a pilot program, order entry and pharmacy data were mismatched and patients were sometimes ordered the wrong medications or doses. The error was corrected four years ago but a BusinessWeek article recounted the episode as recently as April under the gloomy title, “The Dubious Promise of Digital Medicine,” and quoted James Walker, Geisinger’s chief health information officer, as saying that providers are thinking, “Look, let’s slow down.”
Walker now says his quote was taken out of context and that safety issues around data quality are an ongoing priority in every aspect of hospital operations. “We’re deadly in earnest about making electronic health records safer but there is no question in my mind that the EHR has already made care safer than it was before.” In fact, he says, “once you get used to having all that information in a form that’s usable, it’s scary to take care of patients without it.”
Standards also change outside the four walls of databases and institutions. An issue at Geisinger and other facilities is the transition to ICD-10, the latest global coding standard for diagnoses and disease established by the World Health Organization. “ICD-9 does not translate to IDC-10, and we need to understand how we’re going to make that work for billing, for our clinicians, for our data warehouse and other systems with comparable data,” Walker says.
An exception to the single-vendor approach is work under way at the University of Pittsburgh Medical Center, a sprawling $8 billion network of 20 hospitals and 400 outpatient sites. UPMC had tested a platform product and found resistance to functionality that poorly served different roles. Despite its monolithic structure, the platform was also creating new silos of data, so UPMC embarked on a strategy of data translation and integration between applications to provide “semantic interoperability” with a (partly owned) partner called dbMotion.
For example, Cerner software dominates inpatient systems in UMPC’s hospitals, while a different system from Epic is used by nearly 800 physician offices. “We’re mapping data from both those systems to common fields for medications, allergies, immunizations and problem lists,” says Dan Martich, chief medical information officer at UPMC. “We’re taking on documentation and mapping lab information from inpatient and outpatient systems and making sure we get all the encounter and demographic information correct.”
Jim Ericson is editorial director of Information Management (formerly DM Review), a SourceMedia publication. You can reach him at Jim.Ericson@sourcemedia.com.