Precision population health (PPH) is a sophisticated and predictive form of risk stratification that borrows concepts from precision medicine and population health management. PPH models provide nuanced risk profiles of patients who may or may not routinely access health care services. Providers can then proactively connect at-risk patients to targeted, high-impact health or social care interventions before the patients escalate.
Precision population health will help organizations target finite resources across an increasingly more complex patient population in two ways. First, it will pinpoint cohorts of patients with risk factors that can be reduced. Second, precision population health will make it easier to identify the clinical or non-clinical solutions that yield the biggest impact on health outcomes and total cost of care.
Many patients are deferring care and adopting unhealthy behaviors amid the Covid-19 pandemic. Doing so exacerbates preexisting conditions and creates blind spots in providers’ knowledge of their populations’ health needs. Health care organizations are also facing unprecedented financial pressures, putting a premium on health care solutions that lower total cost of care.
Precision population health requires substantial investment in data repositories, technology, staff, and infrastructure. Many providers may not have these assets in-house and may lack the funds to purchase them. Further, precision population health requires a high level of coordination across all downstream sites, social care, and local payers to ensure that patients are connected to the most impactful clinical and non-clinical interventions. This level of coordination is historically uncommon.
Precision population health (PPH) is a sophisticated and predictive form of risk stratification that borrows concepts from precision medicine and population health management.
Precision medicine and PPH each identify patients’ specific health care needs that are otherwise difficult to uncover and provide a set of targeted interventions to address those specific needs. The main difference between precision medicine and PPH is scope. Using expansive clinical, psychosocial, and public and proprietary consumer data sets, PPH proactively identifies patient cohorts with similar risk profiles that may escalate to high-cost care. Through PPH, health systems can then proactively connect these cohorts to the specific clinical and non-clinical interventions that will yield both the greatest benefit to the cohort’s health outcomes and biggest cost reductions to the system. In contrast, precision medicine targets individual patients for personalized treatments using genomic medicine.
PPH also builds on existing population health management frameworks to target a narrower cross-section of patients. Traditional risk stratification frameworks categorize patients as low-, moderate-, and high-risk based on disease burden, utilization, and cost. PPH is more precise in calculating patient risk, as well as identifying and grouping patients into cohorts. Additionally, due to their expansive data sets and computing power, precision population health models can identify any patient in an organization’s network who is at risk for a clinical or non-clinical need, not just those with whom providers have frequent interaction.
Precision population health is emerging as systems are becoming more open to value-based payment models to curb the rising costs of care. While we’ve yet to see the full extent of how Covid-19 will impact the shift to value-based care, precision population health accomplishes many of the same goals: improving health outcomes, reducing total cost of care, providing proactive care, reducing inappropriate demand for treatment, and uncovering latent or undiagnosed illnesses.
Precision population health has only recently become possible because of broader access to clinical and consumer data, as well as recent advancements in computing power.
Over the past decade, large data repositories (“big data”) have become far easier and cheaper to access. With this shift, population health management models have become more sophisticated as progressive, value-leaning organizations have invested in and incorporated more nontraditional data sets (e.g., credit score, purchasing habits, internet history and access) into their risk analyses. Layering on this data allows providers to better capture root causes of patient risk and more accurately inform resource allocation to solve for them.
Further, recent advancements in computing power have allowed these organizations to gain sharper and more actionable insights from the large swaths of data available. Cloud-based computing is more available than ever, which in turn has led to an increase in powerful and marketable risk products that can blend hundreds of otherwise disconnected indicators into a holistic patient risk profile.
While achieving true precision population health is not possible for many organizations today, it's important to note that organizations have already been building toward it.
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