Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness

Abstract

Healthcare is increasingly dependent upon information technology (IT), but the accumulation of data has outpaced our capacity to use it to improve operating efficiency, clinical quality, and financial effectiveness. Moreover, hospitals have lagged in adopting thoughtful analytic approaches that would allow operational leaders and providers to capitalize upon existing data stores. In this manuscript, we propose a fundamental re-evaluation of strategic IT investments in healthcare, with the goal of increasing efficiency, reducing costs, and improving outcomes through the targeted application of health analytics. We also present three case studies that illustrate the use of health analytics to leverage pre-existing data resources to support improvements in patient safety and quality of care, to increase the accuracy of billing and collection, and support emerging health issues. We believe that such active investment in health analytics will prove essential to realizing the full promise of investments in electronic clinical systems.

Figure 1

Key functionalities of a business intelligence application in the healthcare environment.

Figure 2

Duke Health Safety Dashboard. Clinicians can use the interface to identify a patient cohort of interest (a), display census-corrected aggregate safety statistics (b, c), and ‘drill down’ on displayed data to show encounter-specific or event-specific detail (d).

Figure 3

Clostridium difficile cases per 100 admissions at Duke University Hospital and Durham Regional Hospital, 2005–2007. Adapted from: Kilbridge PM, Campbell UC, Cozart HB, Mojarrad MG. Automated surveillance for adverse drug events at a community hospital and an academic medical center. J Am Med Inform Assoc 2006;13:372-7; reproduced with permission from BMJ Publishing Group.

Figure 4

One-way analysis of birth weight by diagnostic related group (DRG). Circle indicates single account identified as having been properly paid.

Figure 5

Bivariate fit of account payments plotted against modeled revenue (original revenue model).

Figure 6

Difference between actual and modeled revenues according to base payments and outlier payments.

Figure 7

Bivariate fit of account payments plotted against modeled revenue (revised revenue model).

Figure 8

Results from continued monitoring of the revised revenue model. † indicates an increase in model error in 2003 that was attributable to out-of-cycle adjustments to the Medicaid payment table.