Patient 360 entails a holistic and comprehensive view of a patient's health data, encompassing not only their medical history but also vital lifestyle and social factors. According to the World Health Organization (WHO), these social determinants significantly influence 35–50 percent of health outcomes, making them essential components to consider in healthcare decision-making.
To achieve this comprehensive view, physicians diligently capture and maintain electronic health records (EHRs). However, within these records lies a wealth of unstructured text, including essential social elements, which can be challenging to extract manually. This process is not only time-consuming but also prone to errors, limiting the valuable insights that could otherwise be derived from the data.
Here at Indium, we propose an innovative machine learning solution that utilizes state-of-the-art Natural Language Processing (NLP) techniques to identify and extract key social determinants of health (SDOH) factors from the unstructured text found in physician notes. By automating this crucial task, we empower healthcare providers and payers to harness the full potential of patient 360 and provide targeted interventions saving huge treatment costs.
The goal of this whitepaper is to understand the importance of social factors in healthcare and how it can be leveraged using our SDOH ML model to improve patient care and reduce claim filings.
Understand patient 360 and the vital role of SDOH in healthcare
Unlock the secrets of SDOH from the physician notes for better care.
SDOH ML facilitates personalized care and optimal healthcare outcomes.