Identifying the social determinants of health (SDoH) hidden within clinical notes and other patient data enables providers and payers to build a complete picture of their patients and members.
In the United States and most western countries, medical care (in a hospital or clinic) accounts for only 20% of health outcomes. By contrast, 40% is attributed to socio-economic factors, 30% to health behaviors (e.g. obesity, tobacco use or alcohol abuse) and 10% to the physical environment.
Collectively, these factors represent the Social Determinants of Health (SDoH) defined by the World Health Organization (WHO) as “conditions in which people are born, grow, live, work and age”.
This definition recognizes that social determinants of health such as ambulatory issues, food insecurities, transportation issues, living conditions and cultural beliefs and habits due to race and/or ethnicity have a profound influence on patients’ lives long before they enter the healthcare system.
There are numerous determinants of health that can be considered “social”, for example, average life expectancy is reduced by 15-20 years for people living in low-income communities due to the increased risk of chronic conditions such as Diabetes and heart disease. Poverty can limit access to healthy food, limit the ability to afford safe neighborhoods (where they may be more likely to be victims of crime and/or violence, restrict healthy living conditions and promote transportation issues.
Most recently, organizations such as the Center for Disease Control and Prevention (CDC) have been attempting to measure the impact of health and social inequalities on rates of COVID-19 infection among racial and ethnic minorities.
The US healthcare industry spent over $2.5 billion on SDoH programs between 2017 and 2019, primarily focused on housing interventions, employment, education, food security, social and community context and transportation.
Recognition of the role played by social determinants in health outcomes is now prompting payers to become even more proactive:-
Social determinants of health are typically divided into several categories:-
Healthcare organizations have traditionally relied on the structured data in Electronic Health Records (EHRs) and insurance claims to analyze the health of patients or while making clinical decisions. This approach was based on long-standing fee-for-service compensation models.
Today, the government and private payers are shifting to alternative pay-for-value models that offer healthcare providers financial incentives for proactively monitoring the health of their patients, achieving quality clinical outcomes and controlling the cost of care.
However, to succeed with value-based payment models, providers and payers need to be able to identify social determinants within their healthcare data.
Structured data is valuable, but 70% of the clinical data stored in EHRs is in an "unstructured" form such as clinical notes, call center transcripts, diagnostic reports (ie. pathology, radiology) and discharge reports.
Patient narratives such as patient-reported information (PRI) such as patient portal messages and patient-reported outcomes (PRO) are also largely unstructured but can be vital in understanding social context and delivering successful healthcare outcomes.
While a challenge to analyze, this unstructured data contains a wealth of information on the social context surrounding the treatment of a patient (see table below).
|SDOH Factor||Unstructured Text Content|
…referred to social services for food insecurity
…makes use of the food bank every week…complains that there is not enough food in the house
...and they had become homeless in South Carolina
...has been attending the homeless clinic since discharge from hospital...sleeping on a friend's sofa
...spoke with the patient via an interpreter
...with the help of a Spanish interpreter, I explained......information was received through a translator as the patient is Spanish
The patient lives alone
Patient is widowed and lives alone
He lives alone, single and has no children
Patient has impaired mobility
Patient has a power wheelchair
Patient uses a multi-point cane
Artificial Intelligence (AI) platforms like Linguamatics Natural Language Processing (Linguamatics NLP) allow providers to unlock social determinants of health, giving them a far more complete picture of each patient’s circumstances.
The same sophisticated tools can also enable payers to analyze member-supplied data, including sources such as online chats between patient and nurse. NLP can even be used to review social media posts and provide relevant insights about exercise routines, diet and behavior.
Lack of insight into social determinants of health such as ambulatory status, food insecurity and social isolation can have a major impact on making the right decision for a member, and on their satisfaction with the health plan.
Beyond basic sentiment analysis, a sophisticated NLP platform is needed to extract key concepts and relationships, for example: to identify social isolation issues, transport problems and cultural factors, which can be used to improve understanding and customer satisfaction.
For both providers and payers, the volume of available data is increasing exponentially, and so is the need to analyze unstructured patient data in real-time. By deploying sophisticated, predictive clinical models driven (in part) by social determinants, providers can identify which patients are at higher risk and act accordingly.
Applying natural language processing enables the capture of information from unstructured patient data in a timely manner and facilitates its use for analytical purposes. Unlike earlier systems, tools such as Linguamatics NLP enable open and flexible development of queries and are not as reliant on expensive data sets manually annotated by clinicians.
Social determinants of health data can feed analytics tools, like machine learning algorithms, predictive analytics and risk stratification models, to forecast future health outcomes. Using these models, providers and payers can discover if patients are at risk of hospital re-admission or failing to take their medication and take appropriate action to mitigate that risk.
Healthcare organizations able to harness the power of predictive analytics can estimate the likelihood of future outcomes based on patterns in the historical data. This data can also be used to identify resource gaps so new care programs and interventions can be developed.
Coupled with predictive analytics, healthcare companies that can track interventions and their correlations to better health outcomes, can achieve greater savings and better outcomes - especially when an intervention makes a positive impact on a patient’s social needs.
By automating the analysis of social determinants of health and delivering results at the point of care, predictive analytics solutions can help care teams identify patients that need special attention and give them a head start on the types of issues to expect.
In some cases, these tools can even tie into care team workflows and automatically generate preliminary plans of care that specifically address non-medical risk factors.
Predictive analytics using social determinants of health therefore hold enormous promise for reducing costs and improving outcomes, but success depends on the quality of the data employed. For example, the more geographically precise and patient-specific the data, the more accurate the predictive model.
However, for both providers and payers, predictive analytics is often complicated by the heterogeneous nature of patient-related data. The ability to automatically extract precise data from unstructured text is invaluable for organizations participating in value-based payment models. By leveraging NLP, providers can look at both the structured and unstructured data for a complete picture of each patient's lifestyle.
Discusses the impact of COVID-19 and the use of NLP workflows to identify key social determinants of health.
Illustrates how payers can increase their own competitiveness by using unstructured data to stratify populations more effectively and improve the health outcomes of their members.
Explains how the Medical University of South Carolina (MUSC) used Natural Language Processing to identify social determinents of health and thereby improve clinical care.
Learn how one large payer extracted member-related data to improve their analysis of Congestive Heart Failure (CHF) populations from a mixture of unstructured formats held in a data lake.
Learn how Atrius Health employed Natural Language Processing to extract critical clinical information hidden in clinical notes and data to meet Accountable Care Organization (ACO) reporting requirements, and advance their quality care initiatives.
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Learn more about Asynchronous Messaging Pipeline (AMP), a component of the Linguamatics platform that provides workflow management for high-throughput, fault tolerant, real-time document or record processing.