Analytically Speaking: Analytics and PL loss

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Steven Littlehale
Steven Littlehale
New York Times reporter Paula Span penned an article last April based upon a research initiative headed by David G. Stevenson, Ph.D., a health policy analyst for the Harvard Medical School, based on legal claims against nursing homes. Stevenson had persuaded five of the long-term care industry's large chains (four commercial and one nonprofit, collectively operating 1,465 nursing homes in 48 states) to provide information on every claim brought against them over multiyear periods from 1998 to 2006.

It turns out plaintiffs filed 4,716 claims against the homes during those years, most commonly for injuries from falls and pressure ulcers. The data show that 61% of these claims resulted in a payment averaging approximately $200,000.

Analytics are most commonly used to capture knowledge on specific ways to improve performance across the provider operation; but providers are largely missing an opportunity to use analytics for risk management programs that minimize exposure, defend against professional liability claims, and compel performance improvement.

Not all PL risk is due to geography or bad luck; underlying risk is imbedded in the choices operators make in their daily processes. If a facility identifies modifiable risk factors — for example, the proper mix of RN/LPN/CNA staffing — it can incorporate best practices to decrease liability exposure. Even in high-risk areas of the country, key practices, such as obtaining advance directives, have a tremendous impact on outcomes.

When a claim occurs, an analytic-driven investigation builds a cost-effective defense strategy. Identifying intrinsic resident-level, non-modifiable risk factors offers invaluable insight that goes well beyond the typical standard chart review. For example, MDS predictive algorithms can identify the probability that the adverse outcome like a pressure ulcer would have occurred given the resident's co-morbidities, regardless of the care received. This type of analysis provides evidence that mitigates the perception of negligence.