October 15, 2021

Analytics to the rescue: How maximizing data’s potential can advance care delivery

Tanya Travers

Analytics to the rescue: How maximizing data’s potential can advance care delivery
By Mihir Mullick

The U.S. has established a sophisticated and advanced healthcare industry, but it is humbled by an inability to control cost. Providers, as well as payors, recognize that the current focus on volume and disease treatment needs to pivot to disease prevention and patient wellness.

Healthcare organizations increasingly need timely predictive and prescriptive insights to improve engagement, quality and consistency at each phase in the patient journey. And bending the cost curve is essential for the long-term sustainability of health systems.

The value-based healthcare model places the patient at the core of the equation. Data collection, analysis and sharing must occur across every touchpoint in the patient journey, within and outside the walls of the care facility. In other words, providers must understand patients’ emotional, mental, spiritual, social and financial perspective to optimize care. These factors have a high correlation with improved clinical outcomes and can significantly enhance the patient experience and overall patient satisfaction.

As technology and devices help care teams make interventions at an individual level, there is an opportunity for analytics to measure quality and predict outcomes for both individuals and populations. Analytics that take advantage of multiple sources of data (EHR, lab, pharmacy, claims) can more substantially measure quality and predict outcomes.

Different approaches to place the patient at the center include segmentation strategies for proactive, high-value interventions to utilization studies of disease management or coordinated care models to care models that consider financial risk and clinical effectiveness. Analytics can also be leveraged to predict likelihood of hospital readmission, calculate a risk score and uncover utilization patterns. Predictive models that can automatically be trained and tuned can support activities like timely intervention. All need advanced analytics to make insightful predictions of success.

Combining the data requires heavy lifting from a data science and data engineering perspective. It means ensuring the data is as clean and complete as possible. Our CoreTechs® platform resolves this key challenge with rapid data intake, mastery and preparation regardless of source or format. CoreTechs® supports smart data processes, policies and management to enable safe practices and data-driven decision-making.

Learn more from our eBook.