March 1, 2021
Common wisdom holds that data is the critical component of population health. The key to success, however, comes from identifying relationships and meaning within the data that affect both broad populations and individuals within those populations. This deep understanding of non-intuitive correlations connects macro level population trends to the various determinants and drivers that affect health. That knowledge can then drive individualized programs designed to make it easy for patients to adopt and follow.
That’s how population health goals transform into high quality outcomes. Quality comes a comprehensive approach to data analytics that keeps the patient at the center never forgets that the end goal remains improved health and well-being for each and every patient.
Let’s break down the key areas where initiatives succeed or fail to deliver quality results:
Any population health solution must hold fast to the fact that populations are people, and people are complicated. And yet, too many population health datasets aren’t as comprehensive as they need to be. The deeper and broader the datasets, the higher the quality of the recommendations that underpin a pop health initiative. Data must come from sources that are as diverse as the people being served.
Naturally, clinical and medication records matter. But we must also account for socioeconomics, physical environment, health behaviors, lifestyle habits, healthcare access, and support from peers, friends, family. No single source of information will deliver it all. Some of it can be found in census, data.gov and other public data sources. Some of it will come from claim information and assessments.
Beyond that, however, it’s essential to have a powerful analytics engine, built around the kind of artificial intelligence (AI) that can deliver the right correlations between massive, disparate datasets and connect these insights to specific population health initiatives – from individual health recommendations to workflow to tracking and, ultimately, results.
That’s what we deliver with ZeOmega’s CareIntel AI-based population health solution. We don’t just process the data – we reach across multiple types of population and treatment information to enable a truly comprehensive solution that works at both the macro and the individual level. The result – an end-to-end solution that drives better outcomes that other products cannot match.
While no individual dataset delivers that “single source of truth,” CareIntel provides the linkages and insights within population and health data that achieves the same result. CareIntel’s AI connects all the factors that impact an individual’s health and willingness to follow a treatment regimen within a single population health management system, including:
Identification of individual risk validation and intervention opportunities within broad populations
Member outreach, enrollment, encouragement, and support through focused partnerships and community services
Identify individuals needing intervention or treatment assessment to improve adherence
Results tracking over time, which delivers ongoing additional data for future recommendations
In short, CareIntel improves population health solutions outcomes by delivering a full lifecycle management system for population health programs.
If you play a role in any part of the population health management process for your organization, you know that a significant part of the work is exceptionally tedious. That leads to two critical challenges: end user experience and operational efficiency. CareIntel’s built-in workflow support and intuitive interface remove the drudgery from the care management process, dramatically accelerating the scale and the scope of what pop health professionals can do in any given day. That improves outcomes through:
More time for patient outreach and follow up
Person-centered care plans and interventions
Greater job satisfaction through better outcomes and results
Here’s an example of what happens when we apply the ZeOmega’s CareIntel and its explainable AI to diabetes, one of the most common chronic health issues. By analyzing census and socioeconomic data, CareIntel helps the care team quickly identify geographical and socioeconomic areas where diabetes is most likely to be undertreated. It then cross-references this content with clinical and payer information to identify individuals most at risk.
Population health program managers use these insights to generate customized treatment and adherence regimens, taking into account access to treatment, transportation, primary care physicians, dietary needs, and specialty services (i.e., vascular care, dialysis, ophthalmology, etc.). These insights also help address dietary needs, including social services that ensure food quality, support organizations to encourage adherence, and incentives to stick with medication and nutrition goals.
CareIntel’s automated workflows take the chore out of tracking and managing these processes, which lets caregivers focus on treatment, not paperwork or data analysis. The result? Better outcomes for chronic conditions through earlier recognition and intervention, and ongoing follow up and support. All through an intuitive, AI-driven solution that connects broad data analyses with individual treatment regimens to deliver more effective population health on a human scale and with a human touch.
To learn more about how ZeOmega’s CareIntel AI solution improves engagement and experience for payers, providers, and patients — contact us at email@example.com or 214.618.9880.