Overview of data estate strategy

Integrating healthcare and life sciences data from various systems and applications is a costly and time-intensive undertaking. To address this problem, implementing a data estate strategy becomes crucial as it establishes uniform standards for organizations to efficiently manage all their data, regardless of its storage location or format.

Data estate strategy refers to a comprehensive and structured approach adopted by organizations to manage their entire data ecosystem effectively. It involves developing a well-defined plan and guidelines for acquiring, storing, processing, securing, and utilizing data across various sources, systems, and applications within and across an organization. As healthcare and life sciences institutions handle diverse data, effective data management becomes crucial for maintaining confidentiality, regulatory compliance, and gaining a competitive edge to provide effective care to patients.

Data management in healthcare and life sciences

As the healthcare industry transitions to a value-based care model with an emphasis on patient-centered care, the volume of patient data generated through immersive experiences grows significantly. The exponential growth of healthcare data at various touchpoints necessitates a robust data management strategy to effectively manage and utilize this data to generate actionable insights that can improve the overall health of members and patients.

Data management challenges

Healthcare and life sciences industries are complex and dynamic environments that require a high degree of integration and interoperability to function effectively. A primary challenge with this industry is that the data is traditionally siloed, with different providers and organizations that use different systems and technologies. The absence of integration and interoperability between these systems and technologies lead to inefficiencies, errors, and a lack of continuity in care for patients. These common data management challenges include:

• Data silos: The lack of data sharing between different systems leads to data silos. Healthcare providers have difficulty accessing and sharing patient data, which can lead to a lack of continuity of care.
• Lack of standardization: Healthcare organizations and life sciences companies use different systems and technologies, making it difficult to communicate and exchange data seamlessly.
• Complexity of systems: Healthcare and life sciences systems are increasingly complex and a challenge to integrate and interoperate data effectively. This challenge leads to increased costs and delays in care delivery.
• Malformed or missing data: Malformed or missing data compromise the accuracy and reliability of insights derived from this data.
• Security and privacy concerns: The security and privacy of patient data is critical to healthcare providers and life sciences companies. Sharing data across different systems can increase the risk of data breaches and compromise patient privacy.
• Industry regulations: Healthcare and life sciences industries have the most stringent industry regulations on data handling that makes data sharing and access difficult.
• De-identification of non-uniform data: De-identification of data is often required by law which is difficult and time-consuming.
• Geographically unique datasets: It's difficult to transform geographically unique datasets, such as population health data, for research.

Data management stages

Managing large data effectively involves various stages. Each stage is equally important to generate high quality, actionable insights using the underlying data. The following sections outline the major stages.

Discovery

Data discovery in healthcare and life sciences involves identifying data sources, data formats (such as structured and unstructured), and accessing them. Real-world data and real-world evidence are a few ways to discover data. Real-world data refers to data routinely collected from various sources outside of traditional clinical trials. These sources include electronic health records, claims and billing activities, prescription data, data from wearables, and data collected via patient surveys, or other patient-generated methods. The following image illustrates the most common health care data based on their taxonomy and data standards:

Ingestion

Ingestion is the process of connecting, collecting, and controlling the flow of information from various data sources identified in the discovery stage. The following images illustrate Microsoft's data source options, such as Azure Functions, Azure Logic Apps, and Azure Data Factory, for ingesting different types of information.

• Ingestion pipeline to ingest IoT data from medical devices such as smart wearables.

  

• Medical device generated data might not always be in a standard format. Therefore, the data is first normalized and then stored in a FHIR server as a FHIR Observation resource. The MedTech service in Azure Health Data Services automatically performs the steps shown in the following diagram:

  

• Ingestion pipeline to work with clinical data, DICOM (digital imaging and communications in medicine) data, unstructured data, and SDOH (social determinants of health).

  

Persistence

Storing ingested data on permanent storage is crucial. It allows other applications, such as a machine learning pipeline, to use and generate insights on the data. It also enables Power BI to visualize the distribution of data. Microsoft provides various data persistence platforms, such as Azure Data Lake storage, Azure Health Data Services, and Dataverse, to store your healthcare data. Microsoft also provides APIs such as FHIR service API, DICOM service, MedTech service, and Dataverse healthcare APIs to ingest data into the provided platform.

Integration

Integration refers to the process of unifying different systems, technologies, data sources, and processes to create a cohesive experience for patients, healthcare providers, and other stakeholders. Microsoft Cloud for Healthcare offers ready-to-use tools to integrate the Dataverse data repository with various healthcare data sources such as Azure Health Data Services and non-Microsoft FHIR servers. The following diagram illustrates the integration of FHIR data to Azure Databricks Delta Lake in Azure Health Data Services. For more information, see Connect FHIR Data to Azure Databricks Delta Lake in Azure Health Data Services.

Intelligence

Intelligence refers to the process of adding intelligence to data to draw deeper insights. Microsoft provides tools such as Azure Machine Learning, cognitive services, Azure Databricks, and Azure Synapse Analytics to add intelligence to healthcare data.

Analytics

Analytics involves analyzing healthcare data to uncover trends and patterns. You can use tools such as Power BI to visualize trends and patterns in healthcare data to improve clinical decision support and enhance operational efficacy.

The following image displays the complete lifecycle of healthcare data:

The following image illustrates the MedTech data lifecycle:

Data management solutions offered by Microsoft

Microsoft offers a wide range of tools to handle and manage healthcare data. The following table provides a comprehensive list of tools to manage healthcare data. For more information, follow the reference link corresponding to each tool.

Related information

There are several commonly used architectures on the Microsoft Cloud for Healthcare platform for working with healthcare data. You can use these architectures as references to customize your solutions for managing healthcare data. For more information, see Microsoft Cloud for Healthcare reference architectures..