Industry executives from across the healthcare ecosystem, including payers, providers, pharmaceutical, health systems, technology, government agencies, device manufacturers and specialty pharmacy, gathered in Washington, D.C., for the 9th annual Inovalon Client Congress, October 1 – 3.
Many of us make important healthcare decisions with a surprising lack of rigor. Instead of conducting in-depth research one would expect with a potentially life-and-death decision, we often just ask those close to us for their advice or recommendations.
Why do we do this? Because when we start to peel the onion, we quickly experience a data avalanche of disparate and disconnected data points, a wide array of information that may or may not be relevant to our individual situation, but nothing resembling “the truth,” and often nothing valuable enough to help us make a decision. And we get overwhelmed. This isn’t unique to patient care decisions.
To deliver patient-centered care and improve long-term health outcomes, healthcare organizations are increasingly investing in efforts to address patients’ social needs, such as housing, employment, education, transportation and family support, in addition to their clinical needs, according to a new study by the Deloitte Center for Health Solutions.
According to the second annual study from Inovalon and Quest Diagnostics to gauge physician and health plan executive perspectives on the industry’s transition to value-based care, physicians still lack the tools needed to succeed in a value-based care system, with many indicating electronic health records (EHRs) have yet to realize their potential to improve patient outcomes. Last year’s survey revealed similar desire among physicians for tools to accelerate value-based care.
The U.S. healthcare system has been plagued by rising costs and inefficiencies attributed to the fragmented nature of care delivery and communication. As frameworks for interoperability and interconnectedness evolve to eliminate healthcare siloes, there will be opportunities to more quickly aggregate historically fragmented disparate data sources – which, when applying advanced parallel processing, the ability for real-time machine learning and NLP and other artificial intelligence (AI)-related approaches can be realized. The goal is to make that enhanced, patient-specific analytical derivative available to the healthcare delivery system so it can change how payers, providers, and other healthcare organizations engage with patients and drive better outcomes under value-based care.