Amazon Web Services has announced the launch of HealthLake, a new service that aims to allow healthcare organizations to store, transform, and analyze all of their data in the cloud.
According to AWS officials who made a Dec. 9 announcement, “Amazon HealthLake aggregates an organization’s complete data across various silos and disparate formats into a centralized AWS data lake and automatically normalizes this information using machine learning. The service identifies each piece of clinical information, tags, and indexes events in a timeline view with standardized 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 such, they added, Amazon HealthLake aims to make it easier for customers to query, perform analytics, and run machine learning to derive meaningful value from the newly normalized data. Organizations from healthcare systems to pharmaceutical companies to clinical researchers and health insurers 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 disease, the efficacy of clinical trials, the accuracy of insurance premiums, and many other applications,” officials said.
Speaking to the industry-wide need for the solution, AWS leaders noted that from family history and clinical observations to diagnoses and medications, healthcare organizations 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, analyze population health trends, and improve operational efficiency. The challenge here, they believe, is that 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 standardizing 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,” AWS officials contend.
To combat this problem, some healthcare organizations build rule-based tools to automate the process of transforming unstructured data such as medical histories, physician notes, and medical imaging reports, and tagging clinical information like diagnoses, medications, and procedures. According to AWS officials, these solutions “often fail because the data needs to be normalized across disparate systems and because the tools can’t account for every possible variation in spelling, unintended typos, and grammatical errors.”
Other organizations use general-purpose optical character recognition (OCR) software to process data sources, but AWS leaders again contend that “these tools lack the medical expertise to be effective, so organizations resort to manual data entry by medical professionals which adds expense to the digitization process. Even if organizations 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,” they stated.
Comparatively, according to officials, “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 organizations can make more precise predictions about the health of patients and populations. The new HIPAA-eligible service enables organizations to store, tag, index, standardize, query, and apply machine learning to analyze data at petabyte scale in the cloud,” they added.
One example of how HealthLake could provide value, officials offer, is by enabling organizations to 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 organization’s data into the FHIR industry format, the information can be shared between health systems and with third-party applications, according to company officials.
“There has been an explosion of digitized health data in recent years with the advent of electronic medical records, but organizations are telling us that unlocking the value from this information using technology like machine learning is still challenging and riddled with barriers,” Swami Sivasubramanian, vice president of Amazon machine learning for AWS, said in a statement. “With Amazon HealthLake, healthcare organizations can reduce the time it takes to transform health data in the cloud from weeks to minutes so that it can be analyzed 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 personalized and predictive treatment for individuals and across entire populations.”
