It is an understatement to say that analytics is essential in today’s healthcare environment. Virtually every healthcare organization is undertaking some level of analytics to help respond to the demands facing today’s healthcare industry. Whether it is improving clinical performance and outcomes, risk stratification, managing costs to remain competitive in your market, or participating in emerging value-based care initiatives, data is driving evolution in healthcare.
"Meaningful insights delivered to clinicians–via an actionable, user-friendly interface embedded into existing workflows at the point-of-care–is key to closing clinical gaps and increasing value"
Widespread electronic health record (EHR) adoption in the past decade has generated massive amounts of data from across health systems and countries worldwide. Unfortunately, a fragmented approach to data management, competing priorities, poorly defined governance and lack of buy-in from end users can prevent analytics from making meaningful impact.
Large-scale data necessitates new infrastructure tools to aggregate, manage, process and analyze data. In the retail sector, these tools have been applied for decades, but it is only recently that we are applying these techniques to population health. This has facilitated addressing old problems in new ways, yielding insights that can be leveraged to better identify and proactively manage patients with an increasingly personalized approach to care. No matter where your organization is in its analytics journey, it’s important to continuously evaluate the ways analytics can help improve health in your community.
The tailored approach to care depends on understanding different patterns of disease within populations. To see the full picture of patient health, we need to dig in deeper, going beyond traditional health data to understand the complex social and environmental factors that impact patients’ lives.
Factors associated with health outcomes in networks of patients that may be similar in age, race, residential neighbourhoods, lab and other clinical data and can facilitate a precision medicine approach to care. Insights from these networks can help identify and optimize which individuals can achieve success with evidence-based treatment plans or preventive lifestyle interventions, and thus be leveraged to overcome barriers and improve outcomes in chronic disease management.
Combining Disparate Data
Both payers and providers today are facing the challenge of improving the quality of patient care while reducing healthcare costs. In the shift from fee-for-service to value-based models, providers are now tasked with early identification and intervention for chronic diseases with emphasis on preventing disease progression and complications, maximize cost-savings, and take advantage of bundled payment opportunities.
The ability to aggregate, normalize and process disparate data from both core clinical and financial systems, as well the ability to integrate community data from health and non-health networks enables a more holistic picture of patient health, proactively facilitates management of gaps in care and ensures compliance with value-based program metrics.
Executing the Analytics Strategy
Meaningful insights delivered to clinicians– via an actionable, user-friendly interface embedded into existing workflows at the point-of-care–is key to closing clinical gaps and increasing value. Ideally, an integrated analytics solution provides quality metrics and dashboards most applicable to the user’s context, while delivering insights that matter.
Emphasis on a team-based approach across the continuum of care, including community centers, tertiary hospitals and rehabilitation facilities, can not only improve individual patient outcomes but can facilitate integration of care networks, reduce waste and enhance support for data-driven change.
As you develop and refine your analytics strategy, you’ll need an approach that addresses the priorities specific to your organization. With so many tools and solutions available, it’s important to remember analytics is not one-size fits all.