The CitiusTech team has extensive experience in healthcare and lifescience and understands digital innovation plays a critical role in enabling the future by transforming business processes, engaging patients more effectively, and enhancing member experiences to drive growth and assisting organizations in navigating the intricacies of an industry that is rapidly converging.
To carry out this journey, we partnered with Amazon Web Services to create Amazon HealthLake strategies, identifying different data sources and formats, and integrating streaming and batch data from providers, payers, pharmaceutical companies, patients, and their families into the Fast Healthcare Interoperability Resources (FHIR) standard and storing them securely. As part of our partnership with AWS, we are helping our customers develop a solid analytics platform for patient 360 and consumer analytics, using Amazon HealthLake and AWS Lake Formation as managed repositories for FHIR and non-FHIR data, applying machine learning technology, and improving care for patients.
Amazon HealthLake and its benefits
Amazon HealthLake is a HIPAA-eligible service offering healthcare and life sciences companies a chronological view of individual or patient population health data store, query and perform analytics at scale. CitiusTech as AWS HealthLake launch partner can get organizations start quickly with a range of Amazon HealthLake applications and services for the data that is Fast Healthcare Interoperability Resources (FHIR) standard or otherwise to make profound sense of the health data in following ways.
- Ingest variety of data: Ingest streaming or bulk import on-premises Fast Healthcare Interoperability Resources (FHIR) files, including clinical notes, lab reports, insurance claims, and more.
- Data Security and Compliance: Store your data in the AWS Cloud in a secure, HIPAA-eligible manner that can be audited
- Extract insights from Health data: It has built in Natural Language Processing (NLP) to automatically extract insights from the unstructured data like clinical notes, procedures, and diagnosis instantly. Also, to have catalogue, search and query using AWS Services like Glue and Athena.
- Support to Interoperability Standards: Capture complete medical lifecycle of a patient and structure it in the Fast Healthcare Interoperability Resources (FHIR) standard to facilitate exchange of information to upstream and downstream applications.
- Analyse data and make predictions: Help to build ML models using AWS Sagemaker and run analytics using Amazon Quicksight to understand relationships, identify trends, make predictions with the Healthcare data, and share data securely.
- Use the FHIR REST API operations – HealthLake supports using the FHIR REST API operations to perform CRUD operations on your data store. FHIR search is also supported.
CitiusTech Patient 360 vision with AWS HealthLake
We are working hand in hand with AWS for catering to patient 360 using AWS HealthLake with a clear objective to provide single managed repository with cost-saving storage, simplified patient data transformation, patient intelligence, using batch and real-time data for healthcare and life sciences organizations to deliver comprehensive patient success and optimum care with greater efficiency. Patients 360 to provide data-driven insights that allow healthcare organizations to make informed decisions to cater to the best care while conserving resources.
Challenges we foresee:
Patients see various healthcare providers at various locations, and not all EHR systems are interoperable. This leads to data fragmentation and isolation, making it difficult to keep track of patient information. Patient matching is also challenging due to multiple records across different care sites.
To make informed decisions, access to a patient's complete health history is crucial, but it raises privacy and data ownership concerns. The lack of standardization across the industry adds to the challenge, with different data formats used by various providers.
The presence of duplicate data hinders interoperability and increases unnecessary healthcare costs and denied insurance claims. To overcome these challenges, a common patient repository for analytics and machine learning models could provide new insights and improve the quality of care.
Joint Solution offering:
- Provide AWS Healthlake FHIR standards-based platform that can integrate disparate systems, aggregate information, and harmonize data to enable access to patient 360 by all stakeholders.
- Built and tested accelerators for data ingestion of FIHR and HL7 data post transformation into the AWS HealthLake and AWS Lake formation including validations and quality checks.
- Developed a clearly defined minimum set of data for identification of patients to help educate all providers to the importance of the information.
- Rely on an enterprise master patient index to ensure accurate consolidation of individual records into a longitudinal record.
- Flattened the nested resources into views to search and query using AWS Glue and AWS Athena.
- Defined the standard terminologies that will be used for aggregated data and create appropriate mappings between different terminologies.
- Implemented record updates in the “space” between all providers to minimize workflow interruptions.
- Built intuitive Patient 360 dashboards using Amazon Quicksight to understand relationships, identify trends, make ML Models and predictions leveraging AWS Sagemaker.
- Tested variety, volume and performance to advice best practices that helps organization to onboard to AWS Healthlake and provide best in class patient care.
In healthcare, patient 360 analytics and predictive decision support using ML models are vast and complex fields for improving patient outcomes. Innovations, technology, and strong domain expertise can help us improve human lives. Several challenges exist, including nonstandard & variety of data, data privacy & protection laws, and complex infrastructure. Healthcare centric cloud-based solutions like AWS Healthlake, Quicksight, Sagemaker, Lake formation and other AI/ML services are helping to extract patients, payers & providers information from structured and unstructured data quickly, building various predictive decision support systems and visualizing the insight for patients, clinicians, doctors, payers, and other medical professionals & Stakeholders.