Advanced Analytics: Addressing Social Determinants of Health

The social determinants of health (SDOH) can have a significant influence on our overall well-being, from physical to mental health. In fact, up to 80% of an individual’s health and longevity is in many ways predicated upon factors associated with the environment in which they live and work.

Fortunately, advanced analytics are now being used to address these disparate factors in ways that will ultimately lead to more equitable outcomes across the many and varied subsections of our communities. The impact of health disparities extends beyond the marginalized individual, it extends to their families, and into the larger social fabric. The repercussions are palpable, visible, and costly. In response, social scientists and health systems are increasingly relying upon descriptive, predictive and prescriptive analytics in efforts to effectively tackle disparities in areas such as access to care, allocation of resources, and healthcare services as they relate to SDOH. These analytical tools can bring focus to not only which patients are at risk, but the precise reasons why.

So let’s dive right into how analytics is revolutionizing population management today!

Key Takeaways

1. Advanced analytics such as descriptive, predictive, and prescriptive analytics can be used to effectively address social determinants of health to provide better care access and quality for all.
2. Descriptive analytics helps to detect abnormalities in large datasets by analyzing historical patient information such as demographics, diagnosis records, prescriptions etc., and it can be used to target high-risk areas or populations that require additional resources or interventions.
3. Predictive analysis utilizes historical data combined with machine learning algorithms to forecast future outcomes related to social determinants of health; enabling solutions that are proactive rather than reactive in terms of addressing disparities across the healthcare ecosystem.
4. Prescriptive analysis provides valuable insights derived from aggregated data used in the design of customized interventions aimed at optimizing conditions among vulnerable groups while avoiding disease progression through early identification of signs and symptoms.

Descriptive Analytics: Understanding Social Determinants of Health

Descriptive analytics can be used to identify patterns and examine relationships between variables in data to better understand SDOH.

Definition of descriptive analytics

Descriptive analytics is a process of collecting and examining data to identify trends, relationships, or patterns. It helps to detect abnormalities in large datasets by analyzing historical patient information such as demographics, diagnoses, prescriptions, lab results etc. The insights generated from descriptive analytics allow us to gain an understanding of the impact of social determinants on an individual’s health outcomes.

Descriptive analytics also assists in identifying areas of improvement and in developing healthcare policies and strategies which can help eliminate social disparities. The effective use of this approach helps in the development of specialized interventions that focus on vulnerable and marginalized communities for prompt action against the rise of health inequities caused by SDOH.

Using descriptive analytics to analyze social determinants of health

Descriptive analytics is the process of assessing healthcare data to identify and understand trends, relationships, and correlations in patient care. It can be used to analyze social determinants of health such as education and income, access to nutrition, safe housing, and transportation, which are important factors that tend to affect a person’s overall health.

This type of analysis provides a better understanding of how social factors impact an individual’s access to timely quality healthcare and help shape their medical outcomes.

Using descriptive analytics to examine social determinants helps healthcare providers identify high-risk populations so they can target interventions more effectively. Such insights could include determining which neighborhoods require additional resources or identifying disparities between males/females or young/old patients with specific conditions that require unique preventive measures based on gender or age groupings.

Examples of descriptive analytics in addressing social determinants of health

1. Social media analytics can be used to identify correlations between social media presence and health outcomes related to social factors. For example, a study of depression levels in California found an association between higher numbers of Twitter posts on mental health topics and higher prevalence rates for depression.
2. Descriptive analytics can be used to assess gaps in geographic or demographic categories related to access to resources. A study of access to safe drinking water, for example, could use descriptive analytics methods to analyze certain ZIP codes or regions and identify areas where access is limited.
3. Healthcare providers can use descriptive analytics to track patient engagement with healthcare services related to chronic illnesses like diabetes, hypertension, asthma, or heart disease. This data can provide insight into patients’ health literacy and the extent of adherence so that providers can customize care plans around their needs and preferences.
4. Descriptive analytics may be used to identify at risk populations, and groups who are most vulnerable due to existing disparities in accessing resources or services such as nutrition education, housing assistance programs, preventative care, etc. Organizations conducting clinical trials may also use descriptive methods for patient recruitment and selection by analyzing data points (age, gender, race/ethnicity) from eligible participants versus blind reliance on recruiting goals set forth by the research teams.
5. Descriptive analytics can help teams visualize inequities across commonly examined social determinants such as educational level, income level, employment status and more so that they can create strategies targeting perceived gaps in care delivery among those populations. For instance, a team may look at distribution maps of poverty areas versus uninsured rates indicating which communities could benefit from increased access and outreach initiatives.

Predictive Analytics: Forecasting Health Outcomes based on Social Determinants

Predictive analytics utilizes historical data to create a model that evaluates and predicts potential health outcomes related to social determinants of health.

Definition of predictive analytics

Predictive analytics is an advanced technological tool that utilizes historical data and advanced statistical techniques to forecast future events or outcomes. This area of analytics involves the collecting, storing, analyzing, reporting, and interpreting of data to accurately predict trends and develop strategies to decrease risks associated with specific variables.

The use of predictive analytics has become an essential feature of initiatives aimed at addressing barriers to social health barriers. In addition to predicting risks associated with SDOH it also enables the rapid and highly accurate identification of the most significant factors driving health risks. Furthermore, this technological tool can be employed to recognize patterns and trends from health data and can then be used to forecast adverse health outcomes related to social determinants such as poverty or inadequate access to healthcare resources. Examples include predicting hospital readmission rates and length of stays based on past trends as well as utilizing AI-powered digital technologies for population management strategies targeting high-risk individuals who face barriers due to marginalization or lack of early intervention services.

Applying predictive analytics to predict health outcomes related to social determinants

Predictive analytics in healthcare is an increasingly powerful force for understanding and managing the influence of social determinants on overall health. It utilizes large datasets, machine learning algorithms, as well as historical and current data about population characteristics to forecast future outcomes.

Predictive analytics can be used to identify individuals at risk for different unfavorable health outcomes related to social determinants such as adverse childhood experiences and lifestyle choices by utilizing various factors including gender, race/ethnicity, age group, location of residence or place of work/worship etc. This tool can be effectively implemented to monitor potential high-risk populations who are also statistically the most vulnerable; moreover, it can provide medical institutions with foresight to manage their resources for the appropriate and prompt care of community members based upon their real time needs.

Predictive analytics combined with other advanced digital technology solutions like artificial intelligence (AI), natural language processing (NLP) and machine learning techniques have already proven successful in predicting which interventions are more likely to improve treatment compliance rates with treatment plans among certain patient groups.

Predictive analytics use case in addressing social determinants of health

· NorthShore–Edward Elmhurst emergency department (ED) utilized a rule-based NLP tool to identify patients with unmet SDOH needs. By partnering with Linguamatics, the health system used the tool to uncover and classify key subjects, or SDOH relational entities in text. Social workers were then alerted to help navigate those patients toward the most appropriate interventions and resources.

Prescriptive Analytics: Recommending Interventions for Social Determinants of Health

Prescriptive analytics can provide recommendations to address social determinants of health which can lead to better patient outcomes.

Definition of prescriptive analytics

Prescriptive analytics is an advanced form of data analysis that leverages big data, business rules, machine learning algorithms, and artificial intelligence to determine the optimal course of action.

It utilizes statistical algorithms and predictive models to make recommendations for improving business processes or patient outcomes based on current performance metrics and historical trends. Its goal is to provide invaluable insights into decision-making leading to maximized efficiency and optimized resource utilization.

As it relates to addressing social determinants of health, prescriptive analytics greatly enhances our ability to evaluate inequities in access or utilization due to a wide range of risk factors thus allowing organizations to develop targeted interventions from early intervention strategies.

Utilizing prescriptive analytics to provide recommendations for addressing social determinants of health

Prescriptive analytics is a powerful tool that allows data managers and decision makers to develop strategies for mitigating risks, predicting outcomes, and identifying possible issues related to population management.

It aims to reduce the impact of the complex interplay between factors such as the environment, economics, education, and healthcare services provided in communities on an individual’s health outcomes. For example, prescriptive analytics can identify communities at risk for high readmissions due to adverse childhood experiences or lack of access to quality healthcare services and design intervention programs tailored for these vulnerable populations.

By employing prescriptive analytics decision makers and policy makers have the chance to make fully informed choices regarding interventions that target those health disparities driven by inequities and derived from social determinants that have a profound impact on healthcare costs, delivery, and outcomes.

Prescriptive analytics use case in addressing social determinants of health

· In the face of significant geographical and logistical challenges Dijon University Hospital Centre in France partnered with IBM to improve intra-hospital patient transports. A prescriptive analytics tool was applied to “ever-changing hospital and transport data” helping dispatchers plan, manage and execute hundreds of daily transport requests in real time, drastically improving punctuality, reducing patient wait times, and carrier walk times.

The Power of Analytics in Addressing Social Determinants of Health

Data analytics is playing an increasingly pivotal role in healthcare, and it can be an invaluable asset in the struggle to address SDOH. By utilizing advanced analytics healthcare practitioners are able to gain insights into the current state of the key factors shaping a person’s overall wellness and quality of life.

Healthcare organizations can leverage artificial intelligence (AI) coupled with machine learning (ML) technology to process huge volumes of patient data quickly and easily. With AI-driven data analysis methods like NLP being more accessible now than ever before, automated systems have made it possible for clinicians — and even patients — to make highly accurate data-based decisions rather than relying exclusively on guesswork or intuition.

Smart use of digital technologies does not include abandoning human discernment. Like any other tool or technology its role is to augment, not replace human input which includes rigorous validation of results and outputs.

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