Introduction
In the current world of AI, data serves as a pivotal force that propels various industries, and healthcare is no exception.
Research suggests that 30% of world data volume is generated by healthcare industry whereas around 47% of that volume is underutilized in making clinical and business decisions. Given the current data and analytics ecosystem, organizations are already experiencing a demand for real-time access to large cloud-based data while making sure that sharing this information across teams is more secure.
Federated, otherwise known as standardized, data is a critical component of creating a centralized data and analytics (D&A) center of excellence, which is increasingly becoming a key priority for organizations. Organizations can use a standardized approach for managing data to ensure that their data is consistent, accurate, and reliable. This approach helps make it easier for organizations to analyze and gain insights.
With higher adoption of cloud-based business intelligence and analytics tools, it's imperative that organizations work with a partner that has a trusted portfolio of security products that allow faster, data-driven decisions in a more secure manner.
Key data and analytics challenges in AI
Currently, healthcare organizations experience the following challenges with data and analytics:
Unstructured and inaccessible data - For most healthcare providers, a substantial portion of their data, ranging from 50% to 90%, is compartmentalized or siloed. This segregation impedes progress in the development of medical treatments and technologies, concurrently imposing constraints on the providers' ability to adhere to regulatory compliance standards.
Limited view into patient experiences - Organizations face the inability to anticipate emergencies, enhance diagnostics, and optimize treatment modalities based on clinical patterns, which persist as a significant limitation.
Difficulty accessing the power of insights - Healthcare providers consistently report wasting a substantial portion of their valuable analysis time, ranging from 60% to 70%, on processing the data.
Healthcare organization data
To address these challenges, organizations need to improve their ability to harness data so that they can discover clinical insights and deliver value-based care models. Healthcare organizations are required to harness certain types of data, including:
Clinical
Engagement
Imaging
Genomics
Conversational
Claims
Social determinants of health (SDOH)
Microsoft's approach for mitigating data challenges
Through the formulation and implementation of an effective data management strategy, healthcare organizations can harness the power of this data to glean invaluable insights into diverse realms, including:
Patient care
Clinical decision making
Research endeavors
Operational efficiency
An effective data management strategy can revolutionize and optimize various facets of the healthcare domain, contributing to enhanced outcomes and advancements within the sector.
Microsoft Cloud for Healthcare plays a pivotal role in unlocking distinctive technology considerations that are specific to the healthcare industry. Microsoft Cloud for Healthcare provides complete enterprise-ready, end-to-end workflows across data and AI workflows (Microsoft Azure data, AI, and machine learning), human workflows (Microsoft 365), and business process workflows (Microsoft Dynamics 365 and Microsoft Power Platform) to achieve the following outcomes:
Interoperability - Unify data from across your organization.
Compliance - Use the most comprehensive compliance capabilities in the industry.
Security and data protection - Access tools, products, and services for encryption, identity protection, and more.
Microsoft helps organizations achieve these outcomes by using the following strategic approach:
Connect - Use data connectors to create a new value by connecting patient and organization signals to operational systems.
Unify - Allow the unification and enrichment of the data by using clinical, operational, and analytical data stores and models.
Analyze - Use pretrained and extensively validated AI/machine learning models to support use cases, including predictive care guidance, resource optimization, and automation in healthcare.
Act - Use application templates to accelerate clinical and operational analytical use cases such as care management, patient engagement etc. and facilitate the determination of the next best action throughout the patient journey.
