Making sense of your health data with Amazon HealthLake

 Making sense of your health data with Amazon HealthLake

We’re excited to announce Amazon HealthLake, a new HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud, at petabyte scale. HealthLake uses machine learning (ML) models trained to automatically understand and extract meaningful medical data from raw, disparate data, such as medications, procedures, and diagnoses. This revolutionizes a process that is traditionally manual, error-prone, and costly. HealthLake tags and indexes all the data and structures it in Fast Healthcare Interoperability Resources (FHIR) to provide a complete view of each patient and a consistent way to query and share the data. It integrates with services like Amazon QuickSight and Amazon SageMaker to visualize and understand relationships in the data, identify trends, and make predictions. Because HealthLake automatically structures all of a healthcare organization’s data into the FHIR industry format, the information can be easily and securely shared between health systems and with third-party applications, enabling providers to collaborate more effectively and allowing patients unfettered access to their medical information.

Every healthcare provider, payer, and life sciences company is trying to solve the problem of organizing and structuring their data in order to make better patient support decisions, design better clinical trials, operate more efficiently, understand population health trends, and share data securely. It all starts with making sense of health data.

Let’s look at one specific example—imagine you have a diabetic patient whom you’re trying to manage, and 2 months later their glucose level is still not responding to the treatment that you prescribed. With HealthLake, you can easily create a cohort of diabetic patients and their demographics, treatments, blood glucose readings, tests, and clinical observations and export this data. You can then create an interactive dashboard with QuickSight and compare that patient to a population with similar treatment options to see what helped improve their health outcome. You can use SageMaker to train and tune the best ML models to help you identify which subset of these diabetic patients are at increased risk of complications like high blood pressure so you can intervene early and introduce a second line of medications in addition to preventive measures, like special diets.

Health data is complex

Healthcare organizations are doing some amazing things with ML today, but health data remains complex and difficult to work with (data is siloed, spread out across multiple systems in incompatible formats). Over the past decade, we’ve witnessed a digital transformation in healthcare, with organ


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