Emergence of Healthcare Data Lake
The advancements of digital transformation have arrived at such a time when healthcare organizations are working their way around to improve the efficiency of their electronic health record (EHR). Thus, healthcare organizations need to discover new ways to detect at-risk patients, reduce adverse events, and use evidence-based medicine.
Huge amounts of structured, semi-structured and unstructured data from disparate sources are yet to be explored, all due to difficulty in accessing a variety of data. This makes it tough to migrate towards fast evolving expectations for preventive care and speedy diagnosis and treatment. With the huge amount of medical data available in the majority of healthcare organizations, such as payers, providers, pharmaceuticals and third-party vendors; there are exclusive prospects to harness these data sources for both clinical and business insights. Using these insights, they can improve quality of care, reduce costs, and prevent resource waste. However, the complexity of emerging data sources somehow presents new challenges in attaining strategic goals, and within the subsequent applications in the clinical environment.
This is where Data Lake comes into the picture. A data lake is more than just collection of big data; it is a pool of various data assets that can be kept in a Hadoop ecosystem native format. Using various tools researchers can apply “schema-on-read” to get access to information.
A healthcare data lake has a powerful architecture, which provides a singular repository for structured, semi-structured, variable format, internal, and external data. It enables data scientists and healthcare analysts to mine data that is scattered across data marts, data warehouses, operational systems, transactional systems, and external data sources.
The value of a data lake is recognized by its power to share data and support for rapid examination and discovery processes. Healthcare organizations can use analytics tools to uncover variables and patterns that better predict business performance and support decision making. A data lake enables predictive and prescriptive analytics necessary to support healthcare use cases and initiatives.
Using data lake approach, healthcare organizations can collect and analyze a wide range of data, such as claims and Rx data, clinical information, health survey, administrative data, patient registries, data from EHRs, and EMRs, drug research, digital images, weather, Geolocation, Email, and Machine Log. All these data can be combined together to create a comprehensive view of patient, assisting in a variety of use cases comprising better outcomes, cost reduction programs, medical decision-making, and quality improvement initiatives, etc. Using integrated data lake platform, healthcare providers can make decisions, which will lead to reduced in-hospital complications and lower readmission rate, promote use of personalized medicine, detect genetic markers, develop clinical trial safety, and much more.
Healthcare data lakes open up windows for extracting information from fitness devices, wearable, appliances built based on the Internet of Things (IoT) such as in-home intervention, for personalized, and timely care delivery. From keeping the value-based, patient-centric ratio and providing transparency, to filling gaps to have an overview of care services insightfully; Healthcare Data Lake has it all.
There is more to come as data lake gradually gets used for more use case including- addressing data accessibility and integration concern, creating scalable data lake roadmap, and how a data lake matures as it is used widely by the healthcare organizations. However, there is more to gain for the healthcare industry by using a data lake ecosystem that includes information governance, data security and patient record protection, data validation, and data management.
Read the White Paper – IMPROVING CLINICAL OUTCOMES: UNLEASHING THE POTENTIAL OF HEALTHCARE DATA LAKE
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