Chicago-based Aon Risk Services announced that it has entered into an agreement with LMS Medical Systems, a Toronto health-care technology company and developer of the CALM system (Computer Assisted Labor Management), to assist hospitals to improve their risk management profile in obstetrics.
Obstetrics is well recognized as a highly complex specialty where medical professionals are required to make repeated timely and precise interpretation of changing physiological markers for both mother and baby as labor evolves. Failing to identify and or respond to any number of factors during labor and delivery can negatively impact outcomes. In addition, the absence of standardization of interpretation and care has led to the current overuse, under-use and misuse of clinical resources and interventions, all of which have placed a significant financial burden on medical staff and payors alike.
Over the past decade, obstetrics has become one of the most litigated areas in healthcare in the United States and elsewhere. It is therefore no surprise that hospitals, clinicians and risk managers are looking to improve outcomes, increase patient safety and enhance their overall risk profile.
Aon Healthcare works with health care facilities across the United States to help them maximize their “return on risk” by analyzing their risk profiles, providing front-end tools and delivering process-driven products that support better access and affordability of insurance for institutions and practitioners alike. Given the often-direct correlation between the quality of medicine in an organization and the cost of risk, providers can no longer afford to treat the two as unrelated.
LMS specializes in risk management tools for obstetrics. These tools analyze the dynamics of physiological indicators during childbirth for mother and baby. CALM Curve provides for the consistent and objective assessment of the progress of labor. Better labor assessment may lead to a more timely and appropriate use of cesarean sections. CALM Patterns provides objective, real-time detection, classification and labeling of fetal heart rate patterns. Better assessment of fetal distress may reduce the incidence and severity of birth related brain damage.
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