Using Decision Science to predict and reduce (re)hospitalization

Using Decision Science to predict and reduce (re)hospitalization

Hospitalizations and readmissions negatively impact Long-Term Care (LTC) and Medicare Advantage (MA) plan providers as well as their policyholders. For policyholders, the financial and emotional costs of hospitalization and readmission can be significant. For providers, the impact on profitability is substantial, given sheer expense of institutional care and because many hospitalizations and re-hospitalizations are avoidable. 

Decision Science empowers providers with an opportunity to better understand the drivers of these events, improving intervention success, benefit design, and financial projections.

Hospitalization and readmission is enormously taxing on both carriers and policyholders

With hospitalization and readmission rates typically accounting for one third of all healthcare spending (over $1 trillion) reducing these numbers is a serious priority for providers. Not only are these high-cost events detrimental to providers’ financials and policyholders’ wellbeing, but readmissions slap providers with additional penalties from CMS. 

For the LTC insurance industry, hospitalizations increase the likelihood and severity of claims. An estimated 70 percent of Medicare beneficiaries will need more help and support after discharge than before. If this help is poorly designed or delivered, the risk of readmission increases, making the move from a cherished home to a nursing home far more likely. 

Despite the negative impact of high hospitalization and readmission rates, the LTC and health insurance industry has been slow to address them and transition to models where incentives favor preventative and proactive care. Many providers struggle with understanding how to best predict preventable, high-cost events and develop the products and intervention programs which are specific and effective enough to reduce them. 

Providers must gain a better understanding of what occurs in a policyholder’s life leading up to a hospitalization or readmission. What are the triggers?  What signals rising risk? Examining this at a more individualized level gives providers more opportunities to deliver the resources and support that can help policyholders improve their wellness and reduce their risk of an unplanned hospitalization. 

The key is in the data, particularly the data that providers are not currently leveraging when developing wellness intervention programs and designing improved products and benefits.

New data is needed to effectively reduce preventable hospitalizations and readmissions

While MA and health providers typically leverage medical data to gauge and prepare wellness interventions, social determinants of health data (SDoH) may be even more significant. According to the World Health Organization, over 50 percent of a person's health and wellness outcomes are directly related to SDoH factors, which influence their immediate surroundings, day-to-day functions and activities, social interactions, access to basic needs, and more.

“If you look at Medicare's annual budget, hospital and institutional care is about a quarter of all their spending, second only to MA plans payments,” explains Mike Hughes, expert on social determinants of health. “This is mostly concentrated in high-need, high-risk patient populations. Now consider that the vast majority of re-hospitalizations are not because clinical condition declines, instead they’re caused by social determinants of health which, when properly addressed, can reduce readmission risk considerably.” 

Providers must incorporate SDoH data into cost and risk propensity models to sharpen financial projections, improve product and benefit design, and run effective wellness intervention programs. Those who are successful in achieving these objectives will see a marked reduction in preventable hospitalizations. With medical costs on the rise and a rapidly growing aging population, the price of not reducing hospitalization and readmission rates is high for both providers and policyholders.

Decision Science incorporates medical and SDoH data into providers’ decision making

“Health insurers, health systems, and LTCi providers have very similar goals,” says Mike. “They want to reduce risks that put people into institutional care. Many of the same factors that influence hospitalization also influence the likelihood of needing to start nursing home care, and they’re grounded in social determinants of health.  This doesn’t just mean ‘macro’ level factors like food insecurity and access to quality preventative care, it's also the ‘micro’ factors like the safety of the home, a person’s functional ability and - importantly - the presence and quality of family caregivers. This is the data carriers need to identify, model, and address through targeted interventions.”

LTCi and MA providers have never been in a better position to leverage SDoH Data in their decision making than right now. Implemented properly, this can effectively reduce both hospitalization and readmission rates, leading to better outcomes for all parties. To do this, providers need access to not just the right data, but the right tools to analyze and understand it.

A Decision Science platform gives providers access to the holistic and clear data insights they need to better understand their policyholders and make the right calls when taking measures to proactively reduce hospitalizations. Decision Science delivers actionable insights that allow carriers to make informed decisions which improve policyholder wellness, reduce unnecessary claims, and keep people home and out of the hospital.

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