Seamlessly use social determinants of health data in healthcare data solutions in Microsoft Fabric
Social determinants of health are the social conditions that contribute to an individual’s or a population group’s health outcomes, like place of birth, median household income, and access to transportation. Research and real-world evidence have established that SDOH information can complement medical information. This helps healthcare organizations understand their patients’ health profile more comprehensively and facilitate tailored care interventions. However, a fundamental challenge in leveraging SDOH data arises due to the lack of a standard data collection and exchange mechanism. To simplify this process, we are thrilled to announce the public preview of SDOH datasets- transformations (SDOH) in healthcare data solutions in Microsoft Fabric. It fuels large-scale analytics by enabling the unification of social determinants of health data with core healthcare domains like clinical & claims. Key features SDOH information can be seen in two forms- Public datasets that contain social determinant details aggregated at a geographic level, and patient-level SDOH data that depict those characteristics of an individual that might pose health risks. This release focuses on the public SDOH datasets, which comes with, A simple and intuitive data preparation mechanism to ready the datasets for ingestion into healthcare data solutions. The supported data formats are .csv and .xlsx. A set of powerful pipelines and notebooks that allow effortless transformation of the datasets into tabular shapes. Eight sample datasets across various SDOH domains that you can readily leverage for your use cases. As the data progresses through the medallion Lakehouse, it gets persisted within a robust data model, custom-built for the SDOH modality. This eases the process of combining SDOH data with other modalities, unlocking use cases such as Care management analytics, Risk stratification, and Population health. How it works The SDOH capability follows three simple steps to transform the disparate datasets into a unified data model, Data preparation and ingestion- As there are no established standards to collect and exchange the information captured in these datasets, it is necessary to unify them into a common shape before they can be ingested. This step requires you to add three sheets in your original dataset to capture key details like publisher information, description of the data columns, and location information. The shipped sample datasets are pre-populated with all the necessary information. Landing zone to bronze- Once the datasets are prepared, they can be uploaded into the landing zone. The bronze notebook will then populate all the key details in the bronze lake in delta table format. Bronze to silver- This notebook normalizes the data from the bronze lake into the custom SDOH data model in the silver lake by creating dedicated tables and establishing relationships between them. It preserves the context of the source tables to help you easily identify or query the data. You can trigger the SDOH pipeline to run all the steps after data preparation at one go and thereafter utilize the normalized silver lake data to build your analytical scenarios. Get started today The SDOH public preview is available in healthcare data solutions for teams to start using today. For a more detailed overview of the capability and the necessary configurations needed to deploy it, please check out the official documentation. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
