Creating proactive solutions from advanced analytics and connected data across the care spectrum to engage members,
improve outcomes, and reduce costs.
Musculoskeletal conditions are painful and pricey for everyone.
More than 50% of Americans aged 18 and over1 suffer aches and pains due to bone, joint, or muscle conditions, also known as musculoskeletal (MSK) conditions. But care for these conditions is fragmented, resulting in frustrating patient experiences, ineffective treatments, and unnecessary spend.
Through predictive models, personalized guidance, and navigation tools, our fully-connected MSK solutions empower members to take charge of their health — and enable health plans, employers, and members to avoid unnecessary spend.
1. The Burden of Musculoskeletal Diseases in the U.S.: Prevalence, Societal and Economic Costs (BMUS). www.boneandjointburden.org.
MSK conditions are the most costly chronic conditions.
of plan spending on MSK conditions is driven by treating pain due to wear and tear2
2. National Health Interview Survey (NHIS)_Adult sample, 2015. http://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm. July 23, 2016
years of productive life is lost in the U.S. every year due to back pain3
3. Evernorth Research Institute. MSK in the USA Report. 2022.
estimated costs to the US health care system for MSK conditions4
4. Payers Have Room for Improvement in Delivering Pain Care, Study Says. (2018). https://www.ajmc.com/view/payers-have-room-for-improvement-in-delivering-pain-care-study-says
Our Member-Centric Approach
Members are at the center of our approach to care, and we are with them every step of their MSK journey.
Our advanced predictive models allow us to identify members at elevated risk for MSK surgeries up to a year in advance, so we can engage and guide them to the right care at the right time for their unique conditions and goals. Whether it's connecting them to in-person or digital physical therapy, in-network behavioral health providers, or support before and after surgery, we are there to help improve health outcomes and lower costs.
Advanced MSK Predictive Modeling
Through our proprietary analytic toolset, we use a series of machine learning, predictive models that allow us to identify and engage at-risk members as early as possible and align them to the most appropriate clinical steps—when and where our health advocacy services and tools can have the greatest impact.
Our models continually review member data to predict a likely health-related occurrence, such as surgery, with a high degree of accuracy. For example, members at elevated risk for back, knee or hip surgeries can be identified up to a year in advance with 75%-93% accuracy, so we can help them take action to avoid surgery.5
5. Accuracy metric is an “overall model performance measure” where the model ranks people in the right order 80% of the time. Depending on the use case, we would set the model to a certain True Positive threshold that would determine how much of the target population we predict and how many false positives we would identify. Some conditions have higher “False Positive” members, for example knee surgery or spine surgery, but the member would still benefit from coaching/steerage. The model helps to identify “high risk pain/osteo” customers rather than just targeting only members who will go on to surgery, especially for the further out timeframes.