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Reed Group Will Debut New Predictive Modeling Tool at DMEC Conference

Companies whose disability case managers rely only on standard return-to-work durations may be throwing away millions of dollars, says Reed Group President, Guidelines, Jon Seymour, MD.

Westminster, CO (Vocus) July 16, 2009 -- Companies whose disability case managers rely only on standard return-to-work durations may be throwing away millions of dollars, says Reed Group President, Guidelines, Jon Seymour, MD.

At next week's Disability Management Employer Coalition (DMEC) Conference, Reed Group, provider of the MDGuidelines Web portal, will demonstrate a new industry-changing predictive tool that will allow much more accurate prediction of disability return-to-work durations. The tool can help employers save millions by guiding disability case managers toward shorter recovery times.

Employees on medical disability leave typically receive case management services from their employer, a third-party administrator or insurance company. Disability case managers work with physicians and other professionals to ensure that employees receive the treatment they need to return as quickly as possible to full productivity.

The caseworkers who manage these disability cases typically rely on standard guidelines for return-to-work durations. Some standard durations are simply statistical averages based on previous cases.

But employees are not averages, and many factors can influence the time away from the job that an individual will need to recover. Emotional stress can play a huge role in recovery times for physical injuries. Co-morbid conditions (e.g., diabetes) can play a large role. A patient's age and job classification also matter.

With its new Predictive Modeling Tool, MDGuidelines offers case managers two sets of return-to-work durations. One set of durations, a unique result of Reed Group's clinical consensus process, provides minimum, optimum and maximum durations for physiological recovery times, an idealized figure that "factors out" the non-medical complexities of real-world disability cases. The second set, achieved via the Predictive Modeling Tool, creates a benchmark that case managers can weigh against the physiological durations.

"Case managers now can have a benchmark--based on the patient's actual conditions--that they can use to gauge the influence of their intervention," says Seymour.

For example, MDGuidelines lists the optimum duration for physiological recovery time for a bimalleolar (closed) ankle fracture as 14 days. The Predictive Modeling Tool calculates that a 30-year-old male with a sedentary job class and a history of episodic mood disorders usually takes 48.5 days to return to work.

Using the two sets of durations, a case manager could easily see that making sure the worker has appropriate behavioral support early in the healing process could bring the absence duration closer to 14 days instead of 48.5 days.

"The difference between the physiological recovery time (e.g., the time it actually takes for a broken ankle bone to heal) and the normative predicted duration, aside from medical co-morbidities, can be termed the psychosocial part of the duration," says Seymour. "The better our tools can help case managers predict that psychosocial element, the more they can address issues early on in the case to get employees back to full productivity sooner. If an employer compares that difference in physiological recovery times versus normative recovery times, they can directly calculate how much money they can save with good case management."

Seymour says that while the Predictive Tool is directly available on the MDGuidelines Web site, the real cost-savings traction occurs when MDGuidelines data is directly integrated with employee information in case management or electronic medical records software.

He cites three examples:

  • A corporate disability manager visits the "Medical" tab in her case management software. The predicted return-to-work duration is contrasted with the appropriate physiological recovery time and a dramatic difference is noted. She can see the factors that are contributing to the long prediction, and is shown, via a quick calculation, that intervention can save thousands of dollars by managing to the optimum duration.

  • A physician treating an employee patient for a potential disability would automatically have the predictive and normative return-to-work durations available as part of the patient's electronic medical record. So, instead of dashing off the standard 'two-weeks off' prescription, the physician would have actual guidelines to inform the prescription for time off from work.

  • An actuary for a large insurance company reviews her case managers' case outcomes versus the integrated MDGuidelines benchmarking data, all of which is available at her fingertips within her on-site database. She identifies ten common disability conditions that have a high psychosocial factor (e.g., fibromyalgia). The disability manager asks her case managers to pay special attention to managing those ten disability types, making sure employees get the support they need to return to full productivity sooner.

"Static disability duration guidelines simply can't provide the cost benefits and employee health benefits that these new integrated and predictive duration tools can," says Seymour. "This predictive durations content should used at every point in the disability process to drive better outcomes, both for employers and their employees."

Reed Group will be at booth #102 at the DMEC conference, July 19-22. Employers and media are invited to stop by for a demonstration of the new MDGuidelines Predictive Modeling Tool.

About Reed Group
Reed Group is the world's most trusted source of return-to-work information, helping companies improve employee absence outcomes. Reed Group's data, tools, customized solutions, and case management services help reduce absence incidence and duration, and get employees back to normal, healthy lives and full productivity. Reed Group is headquartered in Westminster, Colorado. More information at and


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