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Next-Generation Analytics Drive Better Clinical Care Decisions

Douglas M. Moeller, MD, January 12, 2010

In the drive toward better cost and quality decisions, our industry long has been held back by what might be called an information gap.

Providers and payers each have plenty of information, but have poor mechanisms for sharing it in a consistent, meaningful, and timely way. New technologies now make it possible to gather and assimilate massive amounts of information, distill it into useful data streams, infuse it with clinical and business intelligence, and present it in actionable formats to those who need to use it.

Health plans are urgently seeking ways to improve quality while controlling costs. In the past, we were hampered by systems that took months to move data into a warehouse, could not interact with each other, and often contained only claims data. Now, we are seeing next-generation analytics that can draw from multiple sources to meet some of our greatest challenges. We are moving into an era of information that is specific and current.

Newer technologies integrate lab values and point-of-care clinical information—including data from electronic health records. By applying rich clinical rules in the form of evidence-based clinical knowledge and sophisticated algorithms, we can create a more complete view of the patient. Presenting the results to providers rapidly and in a useful format can lead to more effective care for individuals and across populations.

Triggers, prompts, and alerts offer value to both the plan and the provider—and will be embraced by physicians if this information helps them to meet quality benchmarks. For example, a diabetic who is overdue for a hemoglobin A1c test can be flagged for follow-up, so that lab results will be available for an upcoming visit. When a woman is in her primary care doctor's office, an alert might pop up on her chart because she hasn't had her annual mammogram; the interaction is an opportunity for education, and ideally, for scheduling the screening exam on the spot.

Analytics coupled with the ability to reference multiple guidelines can minimize time-consuming and manual interactions between plans and providers. With the appropriate clinical evidence and benchmarks in a utilization report, a plan administrator will be better able to evaluate requests for exceptions and appeals.

But next-generation analytics also can show when a provider consistently deviates from evidence-based care, enabling rapid intervention before cost and quality suffer significantly. For example, if a plan's medical director notices an unusually high rate of deep-vein thrombosis (DVT) in a population, sophisticated analytics would enable the medical director to drill down into the data to identify which providers, which procedures, and even which surgeons are associated with the higher-than-expected rate of this dangerous and costly complication. Sharing the information with the provider is an appropriate first step toward ensuring that preventive measures are taken.

The ideal systems offer configurable dashboards that present data in role-specific ways within the plan and can be pushed to providers via a portal. Systems will be most valuable if they are supported with clinical and business intelligence to produce highly relevant reports that include regional and specialty comparisons. Especially for providers, such reports must demonstrate that clinical knowledge has informed the selection, integration, analysis, and presentation of the information.

Information as a foundation of trust and reform

Physicians, trained as scientists, are skeptical of unfamiliar information. But data that can be shown to be reliable, consistent, and transparent will be accepted—and used as a springboard toward more evidence-based care. Particularly when they're involved in defining metrics, physicians do respond to objective performance data and incentives for providing high-quality, cost-effective care.

As providers become more invested in meeting the cost and quality benchmarks of various reform initiatives, analytics systems will need to support granular, extended clinical reporting. The best of these systems will quickly and accurately integrate appropriate outcomes data for metric-driven incentive programs.

This is especially relevant as payment systems become more complex. Under new and pilot payment models, analytics can help pinpoint those providers that are most suitable to participate, evaluating for such characteristics as patient volume, quality measures, and geographic area served.

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