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By Thomas H. Davenport
Author, professor, lecturer, consultant
Organizations have invested millions of dollars in systems that capture data from every conceivable source. Enterprise resource planning, customer relationship management, point-of-sale and other systems ensure that no transaction or other significant exchange occurs without leaving a mark. But to compete on that information, your organization must present it in standard formats, integrate it, store it in a data warehouse, and make it easily accessible to anyone and everyone. The popularity of competing on analytics is partly in response to the emergence of integrated packages of these tools. Using them, your organization can become an "analytics competitor" that is able to wring every last drop of value from your data.
To identify characteristics shared by analytics competitors, I and two of my colleagues studied 32 organizations that have made a commitment to quantitative, fact-based analysis. We found three key attributes:
1. Widespread use of modeling and optimization. Any
company can generate simple statistics about its business.
Analytics competitors profile their customers, optimize their
supply chains and create complex models of how their
operational costs relate to their financial performance.
2. An enterprise approach. In traditional companies, "business intelligence" is generally managed by departments, each of
which selects its own tools (often error-prone spreadsheets),
controls its own data warehouses and trains its own people.
Analytics competitors field centralized groups to ensure that
critical data and other resources are well managed and that
different parts of the organization can share data easily, with
consistent formats, definitions and standards.
3. Senior executive advocates. A companywide embrace of analytics impels changes in culture, processes, behavior and
skills for many employees. Like any major transition, it requires leadership from executives at the very top who have a passion
for the quantitative approach. A background in statistics isn't necessary, but leaders must understand the theory behind
various quantitative methods so that they recognize those
methods' limitations — which factors are being weighed and
which ones aren't. As Gary Loveman, CEO of Harrah's,
frequently puts it, "Do we think this is true? Or do we know?"
How Healthcare Organizations Can Use Metrics
Healthcare organizations should follow the casino-giant's litmus test for making decisions. One healthcare organization, Cardinal Health System in East Central Indiana was faced with soaring costs, diminished reimbursement and vigorous industry competition. To fast-track improvements, the health system aggregated data from disparate sources and presented it in customized, metric-driven scorecards that were used by its service line managers for strategic planning and in guiding daily decisions. Within a year the organization improved clinical quality, patient volumes, revenues, and patient and employee satisfaction while also narrowing budget variances and lowering operating expenses.
By identifying root causes of potential problems and immediately addressing them, managers can exert more control over their service lines. The scorecards also include volume and charge information, which helps managers understand trends over time and keeps everyone in sync with performance improvement goals.
More than Simple Number-Crunching
Certainly, analytics competitors apply technology. But they also direct their energies toward finding the right focus and hiring the right people to make optimal use of the data. Generally, they pick several functions or initiatives that together serve an overarching strategy. They also hire analytical people who have the ability to express complex ideas in simple terms and the relationship skills to interact well with decision makers. Existing employees, meanwhile, will require extensive training. They need to know what data are available and all the ways the information can be analyzed; and they must learn to recognize such shortcomings as missing data, duplication and quality problems.
Thomas H. Davenport is the President's Distinguished Professor of Information Technology and Management at Babson College in Babson Park, Mass., the director of research at Babson Executive Education, and a fellow at Accenture. His latest book is Competing on Analytics: The New Science of Winning (Harvard Business School Press, 2007).
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Efforts to meet public and private payor demands for quality information are currently hindered by a lack of standards and methods for defining, measuring, extracting and reporting such data electronically. McKesson regularly participates in the Collaborative for Performance Measure Integration with EHR Systems (the Collaborative), which is addressing this issue by identifying the data and standards necessary for electronic performance measure reporting.
Co-sponsored by the American Medical Association, National Committee for Quality Assurance and the Centers for Medicare and Medicaid Services, the Collaborative is focused on addressing "performance measurement functionality and integration with EHRs — based on clinical and technical specifications provided by measure developers — in order to facilitate integration, calculation and reporting within vendor products." Initial work is focused on ambulatory settings. Efforts are being coordinated with other national entities such as the National Quality Forum and the Ambulatory Care Quality Alliance, which have been instrumental in consolidating standards introduced by multiple organizations.
McKesson content specialist Jon Blackman, MD, business analyst Andrea Mitchell, RN, Ph.D., and Deborah Bulger, vice president of performance management, sit on the workgroup charged with a) specifying how performance measures should be delivered to EHR vendors to facilitate their automation and b) standardizing how performance measure data should be exported to CMS and other reporting entities.
"It's important for McKesson to be at the table to represent our customers' best interests and to help ensure that what's meaningful from a quality perspective is also possible from a technology perspective," says Bulger. |
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