- 09/18/2024
With the rising volume of patient data and growing AI applications, healthcare organizations need robust data foundations to activate analytics at scale.
Healthcare data is rapidly growing in variety and volume. Every year, a typical patient generates nearly 80 MB of data in the form of radiological imaging, blood work, clinical notes, and prescriptions.[1] Therefore, unlike in other industries, healthcare data comprises both structured and unstructured data of differing formats.
At the same time, data is driving some of the most advanced use cases in healthcare technology today. From clinical decisioning to connected patient experiences, data is at the heart of large-scale care delivery transformation programs.
With falling costs of computation and the development of healthcare-specific AI applications, all care providers need to activate analytics at scale. Data lake technology is the answer to this pressing need in healthcare digital transformation.
Limitations of traditional data architectures
Despite significant leaps in AI and ML, healthcare organizations were limited by the data architectures that supported analytics over the last decade. Data warehouses were at the core of most architectural patterns, whereas structured data represented only a small fraction of healthcare data.
Moreover, data warehouses proved very costly for healthcare organizations: a 1 TB warehouse supporting 100,000 queries would cost north of $450,000 annually.[2] In addition, extensibility and scalability were a major limitation in on-prem models. Support for live data streams was difficult to implement, and pre-processing steps consumed a lot of time.
While cloud lowered the infrastructure costs, security and compliance were still a key concern for care providers. With these factors, healthcare organizations were expected to function like a technology company – a move that couldn’t be justified without proving RoI to senior leaders.
Why data lakes for healthcare analytics?
The challenges posed by data warehouses are no longer a limitation in healthcare analytics, thanks to the evolution of the data lake architecture.
What is a data lake?
Data lakes enable healthcare organizations to centralize the storage of structured and unstructured data, and unify the processing layer – thus enabling teams to consume analytics-ready data at scale. Because the schema of the data is not predefined, various use cases can be implemented over the data lake – like diagnostic decision support, remote patient monitoring, and so on.
Data lakes can be implemented on compliant cloud environments, where security operations can be handled centrally with Role or Policy-based access control (RBAC/PBAC).
How data lakes enhance patient outcomes
Data lakes are typically viewed from the perspective of data production and consumption. In a typical healthcare organization, data producers include:
- EHRs,
- medical device-generated data,
- admin and pharmacy data,
- files from radiology,
- data streams from wearables, and
- primary care data.
This data is unified and stored in its native format, allowing consumers – i.e., various analytics use cases, to manipulate it as needed. Data lakes are typically housed in low-cost storage tiers, enabling significant cost savings compared to data warehouses.
By making this data available in a central location, data lakes power complex analytics solutions. For instance, at the patient level, they can help with disease prediction, forecasting the trajectory of chronic conditions, and devising targeted treatment programs. This is facilitated by drawing inferences from various data sources at the same time. Moreover, hospitals can offer outpatient solutions like prescription adherence and continuous monitoring to drive better patient outcomes in the long run.
At the hospital level, data lakes can facilitate enhanced collaboration between physicians, and coordination with 3rd parties like payers and insurers.
To sum it up, these applications not only enhance the quality of care but also the patient experience at each stage of their journey – from the front desk to post-discharge care.
Next steps
How to modernize the data foundation at your healthcare organization
Building a data lake should begin with a thorough assessment of the use cases that your healthcare organization plans to implement. Based on this, data engineers devise an optimal architecture along with data governance mechanisms to support those use cases.
This is followed by configuration of the cloud environment, data integration, and cataloging. At first, such an initiative may seem daunting to hospitals with limited technical talent. However, data lakes and downstream analytics solutions can be easily implemented in collaboration with a technology partner that specializes in healthcare digital transformation. With trustworthy experts, the vision of connected, AI-enabled care is now within reach for healthcare organizations.
[1] https://publichealth.tulane.edu/blog/data-driven-decision-making/
[2]https://www.striim.com/blog/data-warehouse-vs-data-lake-vs-data-lakehouse-an-overview/
