RESEARCH & RESOURCES

Q&A with the Experts

A business intelligence or data warehouse implementation can be a formidable undertaking. In this section, leading business intelligence and data warehousing solution providers share their answers to the following question:

Q: What’s the biggest obstacle to success with business intelligence (BI) and analytics in healthcare, and what do you recommend that organizations do to overcome it?

Information Builders

A. Lack of executive involvement is often the biggest obstacle when it comes to the successful implementation of BI and analytics for healthcare performance management. Historically, management has focused its attention on the analysis of financial results, such as margins or reimbursements, while clinicians used reporting to oversee the execution and outcome of treatment activities.

Today, quality of care and profitability go hand in hand, and executives must guide the organization to find balance between the two. In order to optimize performance organization wide, both from a clinical and financial perspective, management must also closely monitor metrics related to care quality, patient satisfaction, and so on. For example, doctors and nurses don’t want to deliver subpar care. But if they aren’t aware of their own treatment pattern variances derived from evidence-based medical best practices, and if they aren’t held accountable, doctors and nurses have little motivation to change the status quo. Physicians who know that executives are tracking mortality rates, post-surgical infections, and readmissions will take extra care to ensure the best possible outcomes. On the flip side, when executives are fully aware of potential problems in clinical management, they can implement swift corrective action before care quality or profitability are negatively impacted.

Melissa Data

A. Poor data quality. Data streams in from multiple touch points and channels. Data entry errors can occur during patient registration, or data can go stale as patients move, physicians change practices, and so on. The most critical part of BI implementation is to capture and maintain timely, relevant, and trusted data. To do this, it’s important to deploy a data quality firewall that prevents bad data from entering your database from the start, and then enrich accurate data for a deeper understanding of your patient information.

First, verify, correct, update, and standardize your data. Having clean, consistent, and standardized contact data will facilitate data aggregation, analysis, data mining, and duplicate detection. Next, identify and prevent duplicate data. Finally, enrich your data to fill in the gaps, such as adding missing e-mail addresses and phone numbers, or updating records with current address information. Enrichment can also provide greater insight into your contact data by appending additional data such as demographic and geographic information.

With the appropriate attention to good quality data, your BI efforts and analytics will improve immensely, resulting in better decision making, maximized cost savings, and increased operational efficiencies.

SAP

A. Successful BI and analytics aren’t as dependent on specific software as they are on these two fundamentals: (1) the cultural readiness of the organization and (2) data readiness.

Cultural readiness refers to an organization’s ability to make tough decisions using available information. In the past, hospital managers had the luxury of extended periods of time and diminished accountability for operations. Management feedback often came weeks or months after intervention was needed. With modern business intelligence, fact-based decision making is greatly accelerated with higher levels of transparency. Outliers can be spotted immediately. Without the organizational fortitude to change based on the data, progress may be impeded.

The second biggest obstacle is the presence of high-quality data, which serves as the basis for a rationally managed hospital. Modern hospitals churn increasing volumes of data with no end in sight. Without data integrity, which comes from data ownership and data governance, hospitals may continue to be managed in obsolete ways, resulting in decisions based on intuition, gut feel, or simple guesswork. The operational staff must decide which vital trends and data points need attention and ignore the nonessential operational information.

This article originally appeared in the issue of .

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