Why population health needs a new data strategy
‘Non-traditional’ information gain importance in treating the whole patient.
With significant shifts in quality and payment underway in healthcare, population health initiatives that focus on improving health outcomes have become core to the mission of many provider organizations.
Population-level approaches focus on improving the health of a whole population along a broad spectrum of physical, social, cultural, behavioral and environmental constructs. Naturally, this focus has emphasized the role and importance of primary care and community medicine as the frontlines to managing the care needs of specific populations.
With this focus also comes the need for new tools to understand which interventions stand to make the greatest impact for a given population. Unsurprisingly, leveraging the right data is critical.
Most providers—primary care clinicians or otherwise—have traditionally relied on data captured in their electronic health record (EHR) systems to understand their patients and determine effective interventions. Unlike a narrow focus on data traditionally stored in EHRs, providers increasingly incorporate insights from non-EHR environments to augment clinical interventions to optimize longitudinal health, as well as for the overall health of a specific population they are increasingly serving.
Many population-level approaches leverage data around core social determinants of health—such as income, education, housing and social services—that are not captured in EHRs. For example, in certain underserved areas, children with frequent acute asthma exacerbations may also be predisposed to chronic absenteeism in schools. Combining data from education and healthcare will enable us to develop new creative interventions (for example, an asthma counseling initiative as part of an after-school program) to tackle problems that address the whole child.
In short, we are moving in a direction where data sources—and not healthcare-only sources—are converging in such a way that enables us to provide better health services proactively, with the goal to optimize the value of population health.
As another example of this shift, there are more patient-facing mobile applications that enable patients to enter their own health, wellness and other personal data that they may wish to share with their physicians. But these applications are typically not connected to any EHRs. Capturing that data is critical for healthcare providers to better understand the barriers that their patients face.
As an ever-widening array of “non-traditional” data sources receive new importance across the care continuum, providers’ data strategy has begun to shift. Rather than a heavy focus on EHR systems, which do not contain all relevant data for most population health initiatives, providers must increasingly evaluate their entire technology stack against current and future data needs. Three key concerns should guide providers in evaluating their current architectures.
First, effective population health initiatives require significantly greater volumes of data than most legacy architectures are equipped to handle. Providers must ask whether their current architectures exhibit the scalability and flexibility to integrate heavy data volumes on an ongoing basis to adapt to evolving population and care needs.
Second, because many sources of population-level data remain unstructured, providers must evaluate whether legacy architectures can manage these key sources without undue resource expenditures. The architectures of many provider organizations still rely on relational technologies, which struggle to easily integrate and leverage unstructured data.
Finally, this confluence of data must be made readily available in real-time. This last point is critical because it differentiates a “retrospective” reporting system from a “prospective” actionable opportunity that may enable clinical providers to link patients to the appropriate interventions with the ultimate goal of impacting outcomes.
As population-level approaches continue to proliferate in primary care and community medicine, provider organizations must ensure that they have the right data strategy to facilitate improved health outcomes across a population.
Population-level approaches focus on improving the health of a whole population along a broad spectrum of physical, social, cultural, behavioral and environmental constructs. Naturally, this focus has emphasized the role and importance of primary care and community medicine as the frontlines to managing the care needs of specific populations.
With this focus also comes the need for new tools to understand which interventions stand to make the greatest impact for a given population. Unsurprisingly, leveraging the right data is critical.
Most providers—primary care clinicians or otherwise—have traditionally relied on data captured in their electronic health record (EHR) systems to understand their patients and determine effective interventions. Unlike a narrow focus on data traditionally stored in EHRs, providers increasingly incorporate insights from non-EHR environments to augment clinical interventions to optimize longitudinal health, as well as for the overall health of a specific population they are increasingly serving.
Many population-level approaches leverage data around core social determinants of health—such as income, education, housing and social services—that are not captured in EHRs. For example, in certain underserved areas, children with frequent acute asthma exacerbations may also be predisposed to chronic absenteeism in schools. Combining data from education and healthcare will enable us to develop new creative interventions (for example, an asthma counseling initiative as part of an after-school program) to tackle problems that address the whole child.In short, we are moving in a direction where data sources—and not healthcare-only sources—are converging in such a way that enables us to provide better health services proactively, with the goal to optimize the value of population health.
As another example of this shift, there are more patient-facing mobile applications that enable patients to enter their own health, wellness and other personal data that they may wish to share with their physicians. But these applications are typically not connected to any EHRs. Capturing that data is critical for healthcare providers to better understand the barriers that their patients face.
As an ever-widening array of “non-traditional” data sources receive new importance across the care continuum, providers’ data strategy has begun to shift. Rather than a heavy focus on EHR systems, which do not contain all relevant data for most population health initiatives, providers must increasingly evaluate their entire technology stack against current and future data needs. Three key concerns should guide providers in evaluating their current architectures.
First, effective population health initiatives require significantly greater volumes of data than most legacy architectures are equipped to handle. Providers must ask whether their current architectures exhibit the scalability and flexibility to integrate heavy data volumes on an ongoing basis to adapt to evolving population and care needs.
Second, because many sources of population-level data remain unstructured, providers must evaluate whether legacy architectures can manage these key sources without undue resource expenditures. The architectures of many provider organizations still rely on relational technologies, which struggle to easily integrate and leverage unstructured data.
Finally, this confluence of data must be made readily available in real-time. This last point is critical because it differentiates a “retrospective” reporting system from a “prospective” actionable opportunity that may enable clinical providers to link patients to the appropriate interventions with the ultimate goal of impacting outcomes.
As population-level approaches continue to proliferate in primary care and community medicine, provider organizations must ensure that they have the right data strategy to facilitate improved health outcomes across a population.
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