At Magellan Rx Management, we passionately roll up our sleeves and dig in to solve complex pharmacy problems. One of the ways we do that is by harnessing the power of data for predictive analytics. Predictive analytics play a very important role in the healthcare industry today. It is critical to gain valuable insights about the overall health of population as well as each individual patient. The ability of predictive analytics to take vast amounts of data and condense it down to targeted insights at the individual patient level can elevate care received in both general and specialized healthcare settings. Leveraging predictive analytics can help reduce healthcare costs and improve outcomes.
The guiding principles to build a data driven organization include three data strategies: data centralization to bring all data together; data curation to explain the data; and data utilization to ensure business needs are met. Data centralization prepares an organization to grow and evolve new business models. Data curation and data models prepare an organization to scale to support a defined business model. Data utilization provides thoughtful management of resources for data centralization and curation.
As a company that harnesses the power of quality data, we know the cleaner the data, the more accurate and valuable insights will be as a result. Once data is collected, it is organized into a model that accurately represents the business and its needs, however, businesses evolve and transform, so the approach to creating the model needs to be flexible and adaptable.
There are different kinds of predictive modeling approaches that can be used. In order to optimize drug spend, it’s important to focus on improving overall health of the population. To gain valuable insights about health outcomes through data, a variety of data attributes need to be considered, as they all play an important role in helping create better health outcomes for patients. These involve demographic factors (gender, age, geography), medical conditions (disease category, brand, or generic drug utilization), and social determinants of health (caregiver support availability, transportation, food, nutrition, economic health of patient etc.).
Predictive analytics in healthcare usually falls into two goal categories: improve operational efficiencies or enhance clinical outcomes. Operational efficiency business needs usually include utilization management, provider management and claims management, so data to improve operational efficiency will come from sources like claims data, member demographics, and call center data. Predictive analytics can improve operational efficiency by connecting clinical staff with patients and reducing administrative burden of authorizations – all by leveraging intelligent algorithms!
As for the goal of enhancing clinical outcomes, this may include upgrading a current business model or creating a new one to fulfill a need. For these purposes, it’s important to have a variety of data in order to successfully accomplish the goal of improving outcomes. For example, early identification of high-risk patients and ensuring they have an appropriate care plan in place can avoid adverse health outcomes.
Predictive analytics leverages the power of data to improve healthcare outcomes, for the customer and its members. By predicting at-risk patients, costly health outcomes can be avoided, and members stay healthier. The possibilities are endless when it comes to the power of prediction – identify tomorrow’s challenges today!