Google Healthcare
Author: Ronald Fung
Creation Date: 9 June 2023
Next Modified Date: 9 June 2024
A. Introduction
Healthcare organizations often exist in a siloed data world that includes multiple systems, data types, and modalities. You can modernize your infrastructure by moving healthcare data from multiple on-premises sources into an analytics platform on Google Cloud, which lets you harmonize data, monitor data pipelines, run analytics, and create visualizations for provider insights and data-driven decision-making.
Through this cloud-based analytics platform, your environment scales automatically in response to spikes in workload. For example, new data sources can trigger an increase in analytical processes or require additional compute power for research. By using the healthcare analytics platform, you can also minimize the resources needed by health systems managers to prepare the KPIs and metrics that drive business decisions. In addition, the healthcare analytics platform provides a holistic view of the processes required to deliver insights to healthcare providers, including the following:
Harmonizing healthcare-specific data types.
Developing portable cohort definitions, clinical measurement. definitions, and dashboards.
Providing relevant datasets for integration into your analytics.
Secure sharing of aggregated data subsets with others.
B. How is it used at Seagen
Seagen can use Google Cloud Healthcare to store and manage their healthcare data, including electronic health records (EHRs), medical images, and clinical trial data. Here are some steps to get started with Google Cloud Healthcare:
Create a Google Cloud account: Seagen can create a Google Cloud account in the Google Cloud Console. This will give them access to Google Cloud Healthcare and other Google Cloud services.
Create a Healthcare project: Seagen can create a Healthcare project in the Google Cloud Console, which represents a workspace for healthcare data. They can specify the project name, region, and other project settings.
Import the healthcare data: Seagen can import the healthcare data into Google Cloud Healthcare, using a variety of data sources, such as HL7 messages, DICOM images, or FHIR resources. They can specify the data source location, format, and schema.
Store and manage the data: Seagen can store and manage the healthcare data using Google Cloud Healthcare’s powerful tools and services, such as Google Cloud Storage, Cloud SQL, or Cloud Bigtable. They can configure the data storage and access policies, set up data backups, and monitor the data usage.
Analyze the data: Seagen can analyze the healthcare data using other Google Cloud services, such as Google Cloud AI Platform or Google Cloud BigQuery. They can perform data analytics, machine learning, and predictive modeling on the healthcare data, to gain insights and drive business decisions.
Overall, by using Google Cloud Healthcare, Seagen can store and manage their healthcare data securely and efficiently, and ensure that the data is compliant with healthcare regulations, such as HIPAA. With its support for different healthcare data formats, powerful data management capabilities, and easy-to-use interface, Google Cloud Healthcare is an excellent choice for businesses and individuals who need to manage large amounts of healthcare data quickly and efficiently.
C. Features
The core features of the healthcare analytics platform are as follows:
A set of tools for harmonizing and enriching clinical and operational healthcare data.
Cohort and machine learning (ML) tools for portable research and analytics.
Ingestion of healthcare-specific data types—Fast Healthcare Interoperability Resources (FHIR), HL7v2, and Digital Imaging and Communications in Medicine (DICOM) in raw form, with no need for parsing, indexing, or data management.
Open source technology that ingests and harmonizes data on-premises, in multi-cloud environments, or on Google Cloud, with minimal change in code and support from the user community.
Fast, scalable analytics that support data queries with common analytics tools and that simplify the process of creating visualizations.
Data lineage and indexing automation, pipelines for standard data models, and scalability, which reduce the need for management and also reduce overhead.
Secure data storage enabled by data encryption at rest by default, the Healthcare Data Protection Suite, and other functionalities.
D. Where Implemented
E. How it is tested
Testing Google Cloud Healthcare involves ensuring that the healthcare data storage and management functions are working correctly, and that the data is properly secured and compliant with healthcare regulations. Here are some steps to test Google Cloud Healthcare:
Create a test healthcare data source: Create a test healthcare data source that mimics the production data source as closely as possible, including the data format, schema, and metadata.
Create a test Healthcare project: Create a test Healthcare project that mimics the production project as closely as possible, including the project configuration, region, and other settings.
Import the healthcare data: Import the test healthcare data into Google Cloud Healthcare, using the same data source location, format, and schema as the production data.
Store and manage the data: Store and manage the test healthcare data using Google Cloud Healthcare’s powerful tools and services, such as Google Cloud Storage, Cloud SQL, or Cloud Bigtable. Configure the data storage and access policies, set up data backups, and monitor the data usage.
Verify the data compliance: Verify that the test healthcare data is compliant with healthcare regulations, such as HIPAA. Ensure that the data is properly secured, encrypted, and accessible only to authorized users.
Analyze the data: Analyze the test healthcare data using other Google Cloud services, such as Google Cloud AI Platform or Google Cloud BigQuery. Perform data analytics, machine learning, and predictive modeling on the healthcare data, to gain insights and drive business decisions.
Repeat the process: Repeat the process as needed, creating additional test healthcare data sources and management scenarios to test different data formats or to simulate different healthcare data management scenarios.
Overall, by thoroughly testing Google Cloud Healthcare, users can ensure that their healthcare data storage and management pipeline is reliable, scalable, and compliant with healthcare regulations. Additionally, users can reach out to Google Cloud support for help with any technical challenges they may encounter.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
While Google Cloud Healthcare is a reliable and secure healthcare data management solution, there are some known issues that users may encounter. Here are some of the known issues for Google Cloud Healthcare:
Performance issues: Users may encounter performance issues with Google Cloud Healthcare, such as slow data processing times or high resource utilization. These issues can often be resolved by optimizing the project configuration, such as using the appropriate machine types or adjusting the project settings.
Data consistency issues: Users may encounter data consistency issues with Google Cloud Healthcare, such as data corruption or data loss. These issues can often be resolved by using the appropriate data sources, such as durable storage systems, and implementing data validation and error handling mechanisms.
Access control issues: Users may encounter access control issues with Google Cloud Healthcare, such as unauthorized access or data breaches. These issues can often be resolved by configuring the appropriate access control policies, such as role-based access control or attribute-based access control.
Compliance issues: Users may encounter compliance issues with Google Cloud Healthcare, such as non-compliance with healthcare regulations, such as HIPAA. These issues can often be resolved by implementing the appropriate security and compliance controls, such as encryption, auditing, and monitoring.
Integration issues: Users may encounter integration issues with Google Cloud Healthcare, such as interoperability issues or compatibility issues with other healthcare systems. These issues can often be resolved by using the appropriate integration standards, such as FHIR or HL7, and ensuring that the healthcare data is compatible with other healthcare systems.
Overall, while these issues may impact some users, Google Cloud Healthcare remains a reliable and secure healthcare data management solution that is widely used by healthcare providers, payers, and researchers. By monitoring their Google Cloud Healthcare usage and reviewing their usage reports and logs, users can ensure that their healthcare data is secure and accessible, and that they are complying with healthcare regulations and standards. Additionally, users can reach out to Google Cloud support for help with any known issues or other technical challenges they may encounter.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture