Healthcare Data Lake: Unlocking Insights and Improving Patient Care

A healthcare data lake can unlock a wealth of insights, enabling healthcare organizations to make data-driven decisions and improve patient care.

By integrating various data sources, a healthcare data lake can provide a 360-degree view of patient information, including medical history, lab results, and treatment plans.

This comprehensive view can help identify patterns and trends that may not be immediately apparent, such as the correlation between certain medications and adverse reactions.

As a result, healthcare organizations can make more informed decisions, leading to better patient outcomes and improved overall quality of care.

A data lake is an architecture used to store high-volume, high-velocity, high-variety, as-is data in a centralized repository for Big Data and real-time analytics.

Data lakes can store vast amounts of data from various sources, including Internet of Things sensors, clickstream activity on a website, and log files.

Structured, semistructured, and unstructured data can be stored in a data lake, which allows for real-time analytics and processing.

Data can be ingested from online transaction processing (OLTP) systems, social media feeds, videos, and more.

Healthcare organizations can pull in data from anywhere, making it a powerful tool for real-time analytics and decision-making.

Data lakes present ripples as data enters and moves throughout an organization, ultimately resulting in better patient care, better treatments, and medication matching.

Employing a data lake strategy allows healthcare organizations to collect and standardize data from various sources, including claims, clinical information, patient registries, and electronic health records.

Data lakes enable quick analysis and experimentation, making it possible to load a variety of data types from multiple sources and engage in ad hoc analysis.

Data lakes can be used in conjunction with data warehouses, which are best for analyzing structured data quickly and with great accuracy.

Employing a data lake strategy means healthcare organizations can collect and standardize data, no matter how it's collected or enters the health system, and merge and analyze it to generate a holistic view of the patient.

Data lakes can aid in overall care coordination by providing quick access to data and insights, which can help with better outcomes, better economics, and improving medical decision-making and care quality.

Using efficient data processing, machine learning, and automation, data lakes can help develop a new care culture where insights are gleaned from acquired data and relevant care interventions are deployed quickly.

Regularly monitoring patient vital signs can deliver better care to each patient, and data collected by multiple monitors can be analyzed in real-time to issue notifications to providers.

A data lake provides a secure platform to store vast volumes of data with widely varying content.

Data can be sorted to make it more accessible and actionable, and data lakes may be transformed from storage pools to robust databases.

Data lakes eliminate the need for costly and insecure on-premise infrastructure to store data.

Utility-based cloud services are being used safely and effectively by providers, payers, and others to process, store, and transfer ePHI subject to HIPAA.

Healthcare organizations can have a secure and compliant solution with data lake storage, which includes administrative, physical, and technical measures to ensure confidentiality, integrity, and availability of ePHI.

A well-maintained data lake is crucial for healthcare organizations like New York's Montefiore Health System, which deals with large volumes of data.

To avoid a data lake turning into a swamp, it's essential to provide a catalog that makes data visible and accessible to both business and IT professionals.

A catalog helps ensure that relevant data can be surfaced for queries and analysis, making it easier for researchers to experiment and learn from the data.

Montefiore's multisourced, tagged data is linked to metadata and ontologies, supporting artificial intelligence and deep learning.

Consistent management of metadata, terminology management, and ontology management are all critical components of maintaining a healthy data lake.

Automated algorithms can efficiently use these resources to solve difficult problems, making data management more manageable.

It requires a team effort to ensure that a data lake is well-maintained, with IT, data-management professionals, and business stakeholders all working together.

Data Lake Tools play a crucial role in managing and processing the vast amounts of healthcare data stored in a data lake.

Apache Hadoop is a popular Data Lake Tool used in healthcare data lakes, providing scalable and flexible data processing capabilities.

With Data Lake Tools like Apache Spark, data can be processed in real-time, enabling healthcare organizations to make informed decisions quickly.

Data Lake Tools like Amazon S3 and Google Cloud Storage provide secure and reliable data storage, ensuring that sensitive patient data is protected.

Building a dashboard with Delta Lake is a straightforward process that involves loading data into Delta Lake tables, masking protected health information (PHI), and joining tables together to get the data representation we need for our downstream query.

Delta Lake's Z-ordering allows us to efficiently query data across multiple dimensions, which is critical for working with EHR data, such as slicing and dicing our data by patient, by date, by care facility, or by condition.

We can load our raw CSV files into Delta Lake tables with a single line of code per file using Apache Spark's native support for loading CSV. Spark does not have built-in support for masking PHI, but we can use Spark's rich support for user-defined functions (UDFs) to define an arbitrary function that deterministically masks fields with PHI or PII.

A Python function can be used to compute a SHA1 hash to mask PHI, and saving the data into Delta Lake is a single line of code. Once data has been loaded into Delta, we can optimize the tables by running a simple SQL command, such as using Delta Lake's Z-ordering command to optimize the table for rapid querying down either dimension.

By using built-in capabilities in Databricks, we can directly transform a notebook into a dashboard, allowing us to interactively explore and compute common health statistics on our dataset. This can be a powerful tool for understanding the risk of a patient's disease increasing in severity, identifying common medical coding issues that impact reimbursement, and gaining a deeper understanding of the function of a gene.

Importing data into a data lake is a crucial step in building a comprehensive healthcare data environment. FHIR files can be easily migrated from Amazon S3 to the HealthLake Data Store using its import API.

HealthLake supports the FHIR R4 industry standard, which is a widely adopted format for healthcare data. This means that you can convert your data to FHIR R4 using an Amazon Web Services (AWS) Partner, and then import it into HealthLake.

With HealthLake's import API, you can bring in data from various sources and create a unified view of your healthcare data. This is a game-changer for healthcare organizations looking to improve patient care and streamline their operations.

With a data lake in place, you can query and search your data with ease. HealthLake, for instance, enables powerful query and search capabilities, including the use of predefined filters.

You can even use FHIR Create/Read/Update/Delete* search operations, where data is hidden from search results, not deleted from the service. This is a key feature that allows you to maintain data integrity while still being able to query and analyze it.

Delta Lake makes it easier to work with large clinical datasets, and it can be used to build a dashboard that allows you to identify comorbid conditions across a population of patients. With Delta Lake, you can load CSV files, mask protected health information, and join tables together to get the data representation you need for your downstream query.

HealthLake also leverages technological advances in medical natural language processing (NLP) algorithms and machine learning (ML) to decode clinical jargon, abbreviations, and free-text. This transforms unstructured data into meaningful information that can be queried and analyzed.

Healthcare organizations face significant challenges when working with big data, particularly when it comes to building a healthcare data lake.

The variety of data sources is a major issue, with 80% of healthcare data being unstructured, such as clinical notes, medical imaging, and genomics.

This makes it difficult for traditional data warehouses to support unstructured data.

Healthcare teams need to run queries across patients, treatments, facilities, and time windows to build a holistic view of the patient experience, which can be compute intensive for legacy analytics platforms.

Here are the three main big data challenges for healthcare organizations:

Data lakes and data warehouses serve different purposes. Data lakes store raw data, while data warehouses store current and historical data in an organized fashion.

Data warehouses are ideal for analyzing structured data quickly and with great accuracy and transparency. This makes them perfect for managerial or regulatory purposes.

Data lakes, on the other hand, are great for experimentation. Organizations can load a variety of data types from multiple sources and quickly engage in ad hoc analysis.

In a traditional data warehouse, it's not possible to rapidly include new data sets with its data model-specific structures and constraints on adding new sources or targets.

Organizations may choose to use both data lakes and data warehouses, depending on their specific needs.

Organizations face significant challenges when working with data lakes. The sheer volume of data, often in the petabytes, can be overwhelming. Traditional query engines struggle to handle such large volumes, resulting in slow analysis times.

80% of healthcare data is unstructured, making it difficult to work with. This includes data like clinical notes, medical imaging, and genomics. Traditional data warehouses don't support unstructured data, creating a major obstacle.

Healthcare organizations need to run queries across patients, treatments, facilities, and time windows to build a comprehensive view of the patient experience. This requires a compute-intensive process that legacy analytics platforms often can't handle.

Here are the top challenges for organizations:

These challenges are compounded by the need for data scientists to run ad-hoc transformations and build predictive insights using machine learning techniques.