What's Included in a Healthcare Data Platform?

In the vast ocean of healthcare data, a single hospital produces nearly 50 petabytes annually, yet an astounding 95% of this valuable resource remains untapped. This includes data from clinical notes, lab tests, medical images, and other operational and financial information. This underutilization highlights significant opportunities for healthcare organizations to leverage data more effectively to improve patient care and operational efficiency.(HealthTech Solutions) (Fierce Healthcare).

Health data platforms are pivotal in transforming healthcare delivery by centralizing massive amounts of diverse data, from patient records to clinical studies, enabling holistic, data-driven decision-making. They facilitate the integration of advanced analytics and artificial intelligence, improving outcomes through personalized and timely medical interventions.

Here's a bit about the typical components of a Healthcare Data Platform, although this may vary depending on the scale and use cases !

1. Connectivity Layer

The connectivity layer sets the groundwork for how data will be gathered, focusing on aligning this process with your business use cases. This dictates the required data sources, targeted data elements, and the extraction method's performance. Popular tools like MuleSoft and Apache NiFi are widely used for their robust data integration capabilities, facilitating efficient data flow from multiple sources into healthcare systems.

2. Data Lake

Once data is extracted, it is stored in a central repository known as a data lake. Data lakes support massive volumes of raw data, which must be processed for meaningful analytics. Amazon S3 and Azure Data Lake Storage are prominent examples, favored for their scalability and integration features that support various analytics and machine learning operations.

3. Analytics and Enrichment

This phase is crucial for transforming raw data into actionable insights through processes like data harmonization, quality measure calculation, and predictive analytics. IBM Watson and SAS Analytics are key players in this space, providing advanced analytics capabilities that help healthcare organizations derive meaningful insights for clinical decision-making and operational improvements.

4. Data Warehouse

Integrating and consolidating data into a harmonized, enriched data warehouse is essential. This structure facilitates more efficient and powerful analysis. Snowflake and Google BigQuery are extensively used due to their abilities to handle large datasets with quick processing times and provide insights through an SQL-like interface, making data analysis accessible to analysts.

5. Business Intelligence, Reporting, and Application Integration

Effective planning for business intelligence and reporting is integral before setting up your data pipeline. Ideal platforms simplify technical tasks and offer extensive out-of-the-box analytics content. Microsoft Power BI and Tableau are leading tools in this domain, known for their dynamic reporting capabilities and integrations with other data handling systems, empowering healthcare organizations to build comprehensive dashboards and reports.

Below are some interesting case studies related to healthcare data platforms and their use in various applications:

1. IBM Watson in Oncology: IBM Watson Health demonstrated significant advances in cancer care through AI at the American Society for Clinical Oncology 2019 annual meeting. Watson for Oncology was used to inform clinical decision changes in a significant percentage of cases by providing recent evidence and insights from genotypic and phenotypic data. It also helped in identifying clinically actionable genomic variants that had not been identified by manual interpretations, showcasing AI's potential in enhancing clinical decision support and reducing the cognitive burden on oncologists (IBM Newsroom).
2. IBM Watson and Boston Children's Hospital: This partnership focused on using Watson’s cognitive platform to help identify, diagnose, and treat rare pediatric diseases, starting with steroid-resistant nephrotic syndrome. The project utilized Watson's ability to analyze vast amounts of data to support faster and more accurate diagnoses and treatments, demonstrating AI's potential in addressing rare diseases (Medical Daily).
3. Power BI at H?pital Montfort: H?pital Montfort utilized Microsoft Power BI to improve healthcare services by empowering physicians with data-driven insights. This case study highlights how effective data visualization tools like Power BI can enhance the utilization of healthcare resources and improve patient care by providing evidence-based insights to physicians (Power BI).
4. Intermountain Healthcare and UPMC's Transition to Epic: Intermountain Healthcare and UPMC, major healthcare providers, have transitioned to Epic's EHR system. This move was driven by the potential for significant cost savings and improved system integration. Intermountain specifically noted improvements in provider satisfaction and received recognition as a highly innovative technology user. UPMC highlighted the transformative nature of moving to a single platform, emphasizing the extensive organizational impact and benefits from the integration (Healthcare IT News).
5. Geisinger and Cerner for Population Health Management: Geisinger Health System expanded its relationship with Cerner to use HealtheIntent? as the preferred data platform across its clinical enterprise and health plans. This collaboration focuses on enhancing patient care through advanced analytics and AI, leveraging Cerner’s capabilities to manage population health and improve outcomes. This initiative highlights the use of EHR-agnostic platforms to integrate various data sources and provide comprehensive health insights (Geisinger).
6. Epic's Role in Immunization Data Management: A case study with QuadMed illustrated Epic's effectiveness in managing immunization data through seamless integration with state registries. This capability helps prevent unnecessary vaccinations and tests, thereby saving costs and improving patient care management. Epic's interoperability features facilitated easy access to critical health data, enhancing the quality of care delivered by healthcare providers (Qvera).