Demand for innovation designed to improve healthcare delivery—and resilience during difficult times such as the COVID-19 pandemic—reflects a number of key challenges: rising healthcare costs, suboptimal outcomes, deaths of despair, declining longevity and post-pandemic challenges to behavioral health.
Healthcare information technology and the power of providers, payers and government agencies sharing and analyzing data can illuminate the path to better care and improve outcomes for all.
Executives are pivotal to this transformation. They must lead data aggregation and assessment, distribute that information and communicate valuable, relevant and actionable insights, then offer ways to overcome the challenges created by siloed business models. When these data-driven initiatives are powered by professionals with track records of delivering meaningful change, every patient and organization wins.
To tackle problems at every level of often complex healthcare challenges, data must be gathered across a number of critical areas. One key lesson from the pandemic is that more individualized care can address the needs of underserved and often overlooked patient populations.
A system serving more than 2 million people in Georgia leveraged data to help them deliver, engage and personalize care at scale. Bringing together data from partner apps and services, the platform now gives the organization a shared view of a patient—including medical history, insurance, scheduled appointments, preferences—all in one place.
In another example, Mayo Clinic has undertaken a five-year program to develop, establish and populate a national limb loss and preservation registry as part of a competitive contract from the National Institutes of Health and the Defense Department. They joined forces with an organization that uses data to drive outcomes that ensure this growing field of research and treatment is based on reliable evidence.
These organizations understand that big-data analytics of medical information allows diagnostics, therapy and development of care plans and can lead to unprecedented, quality treatment while containing costs.
The data experts they employ provide innovative solutions to resolve today’s healthcare challenges while closing disparities in trust across public healthcare services. Such experts approach challenges holistically by looking at cost, patient engagement, compliance and social determinants of health, such as food insecurity, transportation and housing.
As partners, they serve as catalysts for change, pursuing targeted, consistent and intensive efforts applied in steady increments. These innovators ask the questions that matter and pay rigorous attention to key questions framed by diverse stakeholders and opinion leaders.
The best thought leadership and innovative partner does more than process big data. They create road maps to transformation by getting to the aspirational future, provide solutions through believable and practical evidence and ask generative questions. They also employ top-down and bottom-up strategies to achieve a new normal through culture, practice and strategy, working directly with those most affected by the issues.
Thought leadership and innovation organizations understand how to exploit data and technology for the good of everyone, determining the right tools that enable physicians and other healthcare providers to focus exclusively on output, patient health and outcomes—not medical bills and reimbursements.
To bring more recognition around thought leadership, April 2021 is the first national Healthcare Thought Leadership & Innovation Foundation Awareness Month in the Health Observances & Recognition Days calendar published by the Society for Healthcare Strategy & Market Development, part of the American Hospital Association.
Addressing the approximately 5% of patients that account for about 50% of all U.S. healthcare spending requires big data, which refers to the high volume, variety and potential for the rapid accumulation of data and analytics—the discovery and communication of patterns in the numbers.
Frequently, high-cost patients involve behavioral health problems or socioeconomic factors. Predictive analytics links data from multiple sources, including clinical, genetic and genomic, outcomes, claims and social data. Aggregating this data for the purpose of achieving clinical predictive analytics will require adoption of standards.
Success in these endeavors relies upon genuine and effective thought leadership that transcends the traditional “talking heads” approach. Leaders must build solutions with intent, clear processes, diligence, broadmindedness and creativity—with meaningful data that lays the groundwork for improvement.
