How the SDOH machine learning model improves patients’ health and your bottom line

Preventive care management—Transcending traditional ways

The healthcare paradigm is shifting from a reactive approach to a proactive and holistic model. Preventive care is important for staying healthy and identifying problems early before they lead to other complications or become more difficult to treat. While early intervention has proven instrumental in advancing diagnostics and treatments, a critical element has been missing until now: the incorporation of social determinants of health (SDOH). Recognizing that health outcomes are intricately woven into the fabric of our lives, the integration of SDOH into preventive care emerges as a transformative solution.

Beyond genetics and clinical data, social determinants encompass factors like socioeconomic status, living conditions, education, and access to nutritious food. By embedding these key influencers into preventive care, healthcare providers gain an unprecedented understanding of their patients’ lives, empowering them to offer personalized and proactive interventions.

Social Determinants of Health: Impact on healthcare outcomes

The non-medical elements that affect health outcomes are referred to as social determinants of health (SDOH). Socioeconomic position, education, physical environment and neighborhood, job, and social support systems are a few of these variables. SDOH has a major effect on health and can impact healthcare outcomes in a number of ways.

For example, a patient with a lower socioeconomic status is more likely to have chronic diseases, such as diabetes and heart ailment. By understanding this patient’s social determinants, a healthcare provider can recommend preventive care measures that are tailored to their needs, such as financial assistance for medication or enrolling them in wellness programs.

Patient 360: A holistic view of patient data

Patient 360 is a comprehensive view of a patient’s health information, including their medical history, social determinants, and other relevant data. By integrating SDOH into patient 360, healthcare providers can gain a better understanding of the factors that are affecting their patients’ health and make more informed decisions about preventive care.

Here are some of the benefits of leveraging SDOH parameters in the patient 360 framework:

Better patient care: Integrating SDOH elements into the patient 360 approach helps improve treatment efficiency by empowering physicians to address the factors that influence healthcare outcomes. This can save time and resources, which can be used to provide better care for patients.

Enhanced patient engagement: Addressing SDOH factors helps enhance patient engagement by giving patients more awareness of their health data. This can lead to patients being more involved in their care management and being more likely to follow treatment plans.

Clinical notes to actionable insights: Physician notes record important patient medical histories, symptoms, demographics, and clinical data. These observations provide a holistic picture of the patient’s health. SDOH factors are important predictors of preventive care needs, which is why it is important to include them in patient records.

The integration of SDOH into patient 360 is a promising way to improve preventive care and achieve better health outcomes for all patients.

Manual SDOH data extraction: Typical challenges in the current system

Manually extracting social determinants of health (SDOH) elements, poses numerous challenges that can hinder the efficiency and accuracy of the process. SDOH data is often embedded in unstructured sources such as physician notes, medical records, or social service assessments, making it laborious and time-consuming for healthcare professionals to extract relevant information. Here are some of the difficulties associated with manual data extraction for SDOH:

Unstructured data: SDOH elements are often scattered throughout free-text narratives, that lack a standardized format.

Human error: Human analysts are susceptible to making errors during data extraction, leading to inaccuracies in the collected information.

Incomplete data capture: Due to the sheer volume of information, manually extracting SDOH elements from various sources may result in incomplete data capture.

Limited scalability: As healthcare organizations grow and data volumes increase, manual data extraction becomes less scalable and impractical.

Cracking the code of health: Indium’s SDOH machine learning model 

Indium’s expertise in developing the SDOH ML model is based on two pillars: NLP technology and a deep understanding of the healthcare landscape. With a team of experts in data science, engineering, and healthcare, Indium is at the forefront of using AI to transform preventive care.

Indium’s journey began with a recognition of the importance of social factors in determining health outcomes. The company’s ML model is designed to identify and address these factors, which can help improve the health of individuals and communities. Recognizing that manually extracting these factors from unstructured physician notes is labor-intensive and prone to errors, Indium sought to create an efficient and accurate solution. Leveraging Natural Language Processing (NLP) techniques, the team precisely crafted a robust ML model that swiftly identifies key social determinants hidden within vast amounts of textual data.

The success of Indium’s SDOH ML model lies in its ability to provide healthcare providers and payers with invaluable insights. By seamlessly integrating social determinants into preventive care, the model empowers stakeholders to offer personalized preventive interventions, optimize patient care, and drive cost savings within the healthcare ecosystem.

SDOH ML model

ML techniques can be used to identify and extract SDOH from physician notes. These techniques can identify patterns in text, such as the presence of certain words or phrases that are associated with SDOH. For example, the phrase “food insecurity” might be associated with the SDOH of food insecurity. By using the SDOH ML model, healthcare providers can make right interventions to help improve healthcare outcomes and reduce costs.

Once SDOH have been identified and extracted from physician notes, they can be integrated into preventive care management. This information can be used to provide a more comprehensive understanding of the patient’s overall well-being and to develop a more personalized treatment plan.

The power of precision: Partner with Indium

As a leading healthcare service provider and a leader in the digital engineering space, Indium has developed the SDOH machine learning model. Understanding the profound influence that social factors have on health outcomes, and recognizing the value of this information is crucial to bring transformative advancements in patient care, the SDOH model is trained to accurately extract social factors from patient records. Beyond improving patient care, the integration of social determinants also serves as a strategic tool in reducing healthcare costs by proactively addressing health issues. Unlike the traditional method, our model is 90% accurate and can identify SDOH attributes from thousands of patient records in a matter of seconds.