Amazon Web Services, Inc. (AWS) has announced Amazon HealthLake, a HIPAA-eligible service for healthcare and life sciences organisations. Amazon HealthLake aggregates an organisation’s complete data across various silos and disparate formats into a centralised AWS data lake and automatically normalises this information using machine learning.
The service identifies each piece of clinical information, tags, and indexes events in a timeline view with standardised labels so it can be easily searched, and structures all of the data into the Fast Healthcare Interoperability Resources (FHIR) industry standard format for a complete view of the health of individual patients and entire populations. As a result, Amazon HealthLake makes it easier for customers to query, perform analytics, and run machine learning to derive meaningful value from the newly normalised data.
organisations such as healthcare systems, pharmaceutical companies, clinical researchers, health insurers, and more can use Amazon HealthLake to help spot trends and anomalies in health data so they can make much more precise predictions about the progression of diseases, the efficacy of clinical trials, the accuracy of insurance premiums, and many other applications.
As machine learning becomes more mainstream, companies across every vertical business are trying to apply it to their data to deliver meaningful business value. Healthcare is applying machine learning to improve operations and patient care, with AWS customers like 3M, Anthem, AstraZeneca, Bristol Myers Squibb, Cerner, the Fred Hutchinson Cancer Research Center, GE Healthcare, Infor, Pfizer, and Philips embracing the cloud and machine learning to get more value out of their vast data troves.
From family history and clinical observations to diagnoses and medications, healthcare organisations are creating huge volumes of patient information every day with the goal of getting a full view of a patient’s health and applying analytics and machine learning to improve care, analyse population health trends, and improve operational efficiency. However, clinical data is complex and renowned for being siloed, incomplete, incompatible, and stored in on-premises systems spread across multiple locations. Getting all this information aggregated and in the FHIR format is a start toward the goal of standardising structured data, but the majority of data remains unstructured and still needs to be tagged, indexed, and structured in chronological order to make all of the data understandable and able to query.
Some healthcare organisations build rule-based tools to automate the process of transforming unstructured data (e.g., medical histories, physician notes, and medical imaging reports) and tagging clinical information (e.g., diagnoses, medications, and procedures), but these solutions often fail because the data needs to be normalised across disparate systems and because the tools can’t account for every possible variation in spelling, unintended typos, and grammatical errors.
Other organisations use general-purpose optical character recognition (OCR) software to process data sources, but these tools lack the medical expertise to be effective and so organisations resort to manual data entry by medical professionals which adds expense to the digitisation process. Even if organisations are able to aggregate and structure their data, they still need to build their own analytics and machine learning applications to uncover relationships in the data, discover trends, and make precise predictions. The cost and operational complexity of doing all this work is prohibitive to most organisations; and as a result, the vast majority of organisations end up missing out on the untapped potential to use their data to improve the health of patients and communities.
Amazon HealthLake offers medical providers, health insurers, and pharmaceutical companies a service that brings together and makes sense of all their patient data, so healthcare organisations can make more precise predictions about the health of patients and populations. The new HIPAA-eligible service enables organisations to store, tag, index, standardise, query, and apply machine learning to analyse data at petabyte scale in the cloud.
Amazon HealthLake allows organisations to easily copy health data from on-premises systems to a secure data lake in the cloud and normalise every patient record across disparate formats automatically. Upon ingestion, Amazon HealthLake uses machine learning trained to understand medical terminology to identify and tag each piece of clinical information, index events into a timeline view, and enrich the data with standardised labels (e.g., medications, conditions, diagnoses, procedures, etc.) so all this information can be easily searched. For example, organisations can quickly and accurately find answers to their questions like, “How has the use of cholesterol-lowering medications helped our patients with high blood pressure last year?” To do this, customers can create a list of patients by selecting “High Cholesterol” from a standard list of medical conditions, “Oral Drugs” from a menu of treatments, and blood pressure values from the “Blood Pressure” structured field – and then they can further refine the list by choosing attributes like time frame, gender, and age.
Because Amazon HealthLake also automatically structures all of a healthcare organisation’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.
“There has been an explosion of digitised health data in recent years with the advent of electronic medical records, but organisations are telling us that unlocking the value from this information using technology like machine learning is still challenging and riddled with barriers,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “With Amazon HealthLake, healthcare organisations can reduce the time it takes to transform health data in the cloud from weeks to minutes so that it can be analysed securely, even at petabyte scale. This completely reinvents what’s possible with healthcare and brings us that much closer to everyone’s goal of providing patients with more personalised and predictive treatment for individuals and across entire populations.”
By aggregating, labeling, indexing, and structuring all their data, Amazon HealthLake makes it easy for customers to query, analyse, and use machine learning to make sense of their data. Customers can use other AWS analytics and machine learning services with Amazon HealthLake like Amazon QuickSight for interactive dashboards and Amazon SageMaker for easily building, training, and deploying custom machine learning models. For example, healthcare organisations can use Jupyter Notebook templates in Amazon SageMaker to quickly and easily run analysis for common tasks like diagnosis predictions, hospital re-admittance probability, and operating room utilisation forecasts. Healthcare and life science organisations can use Amazon HealthLake to get a complete view of patient and population health, derive insights using analytics and machine learning, and discover previously obscured relationships and trends.