While healthcare traditionally focuses on addressing medical needs in clinical settings, the influence of external factors — such as the conditions in which people live, work, and age — on health outcomes cannot be ignored. In an age where providers increasingly recognize these social determinants of health (SDOH) as critical factors driving patient outcomes, providers must harness data to offer comprehensive care that addresses the root causes of health disparities.
Data aggregation, as Arcadia defines it, is the process of collecting and combining many different pieces of data in one place to create a single, unified data asset. Through aggregation, you can process large amounts of data and gain a holistic view of each patient, identifying social risk factors that may impact health outcomes and tailoring interventions as needed.
This article explores how providers can transform patient care by aggregating data on SDOH.
Identify social risk factors
Numerous conditions and circumstances influence an individual’s ability to achieve and maintain good health. To increase patients’ chances of experiencing positive outcomes, you must first identify the factors impacting their health and well-being.
For example, a patient’s medical history denotes a chronic condition, but not the patient’s housing conditions, which exacerbate the health concern. Using a healthcare data platform that integrates data from multiple systems, you can identify these social risk factors for a unified view of patient health and deeper insights into necessary treatment.
Social risk factors can include:
- Economic instability
- Educational barriers
- Limited access to food
- Living environment
These factors are interdependent, meaning social challenges can exacerbate health issues, and vice versa. Without identifying the root issue, patients can get caught in this cycle, which leads to increased healthcare costs, hospital readmissions, and poorer outcomes.
Aside from identifying social risk factors in individual patients, data aggregation highlights influences that are prevalent in specific populations. As a result, you can identify relevant trends or patterns and target interventions more effectively. This supports population health management and enables health professionals to close gaps in care.
Typically, data enrichment does not include SDOH data and requires working with a 3rd party, like Socially Determined, that can support more sophisticated efforts of data aggregation.
Personalize care plans
Understanding the SDOH factors prepares providers to address social needs alongside health concerns — but every patient is unique. Personalized care plans, enriched by aggregated data, are crucial for addressing the full spectrum of a patient’s needs.
The insights gleaned from aggregated data provide a roadmap for personalized care plans that address all the factors driving health outcomes. Components of a personalized care plan include:
- Medical treatments, such as specific treatments, medications, and follow-up appointments
- Social services referrals, which involve connecting patients with specific resources to address their health related social needs (HRSNs)
- Community support, including support groups, wellness programs, and other resources that offer ongoing assistance that contributes to the patient’s long-term health and well-being
For example, let’s say a 55-year-old patient named Marie was recently diagnosed with type 2 diabetes. Your aggregated dataset tells you that Marie lives in a food desert with limited access to community health centers and lacks reliable transportation.
In this case, a personalized care plan might include not only medication to manage Marie’s glucose levels but also telehealth consultations with a dietician. Additionally, Marie’s provider might refer her to a local food bank that provides healthy food options or a community-based exercise program to help improve her physical activity levels.
Improve health equity
SDOH and related social risks are key contributors to health disparities, as they create barriers to accessing quality care. As a result, various populations experience significant differences in health outcomes.
Along with mitigating social risk in individual patients, data aggregation supports more equitable care across populations by helping providers understand the factors contributing to health disparities. With this information, health professionals can identify and address specific inequities, such as a population’s limited access to preventive care or increased risk of certain chronic conditions.
To address the factors driving health disparities, applying predictive analytics to aggregated data is key. Predictive analytics allows you to not only identify specific health needs, but proactively develop programming to address them. This occurs in four stages:
- Data collection, which involves gathering data from multiple sources and data types
- Preprocessing, or the process of cleaning your database to ensure all information is accurate and relevant
- Modeling, where data algorithms make predictions and recommendations for specific patient populations
- Interpretation of results, where you use predictive insights to make informed decisions
With predictive analytics, you can anticipate which populations are at risk of specific health issues due to social factors and implement proactive measures to reduce the likelihood of adverse outcomes. This could include predicting patients’ follow-up needs to reduce hospital readmission rates or detecting disease outbreaks to identify groups with potential exposure.
Using our example from earlier, consider Marie’s neighborhood. Other individuals who live near Marie may face similar challenges, such as lack of access to healthcare and food insecurity. Addressing these issues collectively leads to broader improvements in health outcomes, strengthening the overall community’s health. Over time, the community as a whole benefits from fewer health issues and reduced care costs.
Data aggregation holds transformative potential in patient care — but a data platform is essential to unlock this potential. With a solution that collects and integrates data from a wide range of sources, providers can access the comprehensive view of patient care needed to make informed decisions. By embracing this technology, providers can better serve their patients and ensure that every individual achieves the best possible health.