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ead the news, and healthcare IT is at the heart of it. The HI-TECH Act, meaningful use and other mea- sures have created the perfect storm for investment in healthcare data capture, storage and analytics. Business intelligence (BI) for healthcare is not new. Payers,
especially, have leveraged BI for years, typically for fi nancial reporting efforts. The CFO traditionally drove these projects, and most early healthcare work paralleled BI initiatives in manufacturing, banking and retail. Yet healthcare BI is fundamentally different than BI in other industries, and a program founded with fi nance at its core or modeled on the BI templates of other sectors is ill suited to the complex demands of healthcare information management and reporting. This is especially true with the recent uptick in adoption of EHRs, and based on the enormity, complexity and subjectivity of clinical data. Today’s organizations need to leverage a new and distinct approach to data, one confi gured for the ever-changing landscape of healthcare.
Mainstream vs. healthcare BI Any discussion of healthcare BI begins with a common
defi nition. BI describes an organizational mindset where data drives decision making at all levels. The BI program establishes the platform to collect and standardize the organization’s data across multiple sources. It sets the rules and processes that govern the data and develops the applications, reports and
Laura Madsen is Lancet Software practice lead. For more on
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dashboards that present the information to business leaders in an insightful, understandable way. In short, BI provides a single worldview into the organization’s past, present and future.
Five hallmarks of healthcare BI Regardless of where it’s practiced, BI is all about extracting meaning from data. BI in healthcare, however, differs from other industries in fi ve primary ways: Regulation: Few, if any, other industries face the same level of regulatory fl ux as healthcare. From patient privacy and Medicare reimbursement policies to state-by-state mandates and meaningful-use compliance, healthcare leaders in provider, payer and life-science environments must address a dizzying list of requirements – each with its own analytical provisions. Pro- viders, especially, face signifi cant pressures to collect, measure and report on their operations. A recent count by one provider
12 November 2011
Five reasons healthcare business intelligence differs from other industries. By Laura Madsen
The power of BI: Dynamic dashboards, such as this scorecard from the Lancet Meaningful Use Reporting System, transform large amounts of complex data into easy-to-read and actionable charts and interfaces.
showed more than 1,200 regulatory reporting requirements per year. Another AHIP report tallied 18 agencies that health plans must account to. Risk: Every industry views its work as “mission critical,” but it’s hard to debate healthcare’s impact. When a data issue occurs in retail, it may delay a product shipment or limit a promotional opportunity. The result in healthcare? Even minor discrepancies in analytics or data misinterpretations have the potential to impact not only an organization’s bottom line, but more importantly, real-world, life-or-death decision making. Relationships: Most businesses operate in a linear workfl ow, moving goods from manufacturing through distribution to customer. In healthcare, the supply chain involves numerous entities operating at multiple levels. It’s a complicated process to replicate these relationships and interactions in the BI data model. What connects a physician, an insurer, a patient, a facility, a provider, an encounter and an event? Even pre-built templates for healthcare need customization. Non-standard data: Financial data, whether for auto deal- erships or telecommunications fi rms, exists in standard sets and arrives at regular intervals. Even claims data is typically quantitative and repeatable. Turn to the heartbeat of healthcare, however – clinical data – and dramatic differences appear. Self- reported information about how someone “feels” – a physician’s interpretation of how someone looks or a nurse’s notes on a patient visit – all represent data types that are qualitative and idiosyncratic. Gleaning value requires context, and makes the
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