Implementing your health equity strategy is a necessary first step, but long-term success requires you to optimize it.
In ACO REACH, continued financial success over time relies not only on implementing your health equity strategy, but meeting and even exceeding your goals in a cost-effective manner.
Do you have the right approach to ensure success?
Ultimately, the success of your health equity strategy will depend on how effectively you:
To give your Health Equity Plan the best chance of success, you will need the ability to leverage all available data—including individual social needs assessments as required in ACO REACH—to generate highly accurate risk predictions at the individual level.
But accurate predictive capabilities are not enough. Clinicians may not trust predictions if they are unaccompanied by clear detail on the specific factors contributing to an individual’s risk, leading to poor adoption. Clinical adoption is a key component of any health equity strategy.
Predictive accuracy is not the only concern when considering risk stratification approaches. Explainability and trust are key to maximizing the value of predictive insights. AI/ML tools have advanced to be more accessible to end-users than ever, enabling healthcare organizations of any size and data science expertise to empower clinicians with accurate, explainable, and actionable risk forecasts customized to organizations’ populations, data sources, and goals.
As you implement your health equity strategy, make sure that you choose the tools that enable clinicians to optimize health outcomes.
With CMS’ introduction of a new strategic approach and program requirements, now is an ideal time to evaluate the potential benefits of incorporating explainable AI/ML technology into your health equity strategy for ACO REACH.
