Healthcare organizations, from hospitals to health tech companies, face a shared challenge: effectively making use of an influx of information from diverse sources. And navigating this flood becomes even trickier if data lakes are compromised – or dirty.
Often, patient data develops gaps as it flows through various electronic health records (EHRs) and health information systems, making it less reliable and usable. A missing lab result or unspecified diagnostic code here and there may seem insignificant, but these incidents can compound over time, undermining critical functions like revenue cycle management, complex analytics, and quality reporting.
In addition, manual efforts to standardize this data can drain valuable resources, and typically require advanced technologies like natural language processing (NLP) to accurately capture nuances in unstructured data.
So, how do you clean a dirty data lake? Well, it starts with a foundational clinical terminology and improves with the strategic use of NLP and normalization tools. Dive into our latest eBook for details.
EBOOK
Avoiding the downstream dangers of a dirty data lake:
The crucial roles of NLP and normalization
Only have time for an excerpt? Continue reading to learn how analytics suffers without complete and consistent data.
The need for reliable enterprise analytics
HAZARD: Incomplete patient data
No matter the use case, the need for effective, accurate analytics is a given. Predictive analytics are essential to forecast patient outcomes, disease progression, and the allocation of resources to optimize care. Benchmark analytics allow for the comparison of key metrics to drive operational efficiency and financial outcomes. And the ability to derive insights from clinical data lies at the heart of initiatives from clinical decision support to population health management to life science research. However, without complete and consistent data, the value of analytics is greatly diminished.
A number of factors contribute to poor data quality, including the aggregation of variable information from diverse sources and the need to keep data assets current despite frequent regulatory releases and standard code set updates. But manual efforts to clean and standardize clinical data are tedious and time-consuming, and divert the attention of staff from more meaningful, strategic work. This bottleneck not only slows down teams but delays important analytics and innovation.
While organizations can develop their own internal expertise to standardize data for analytics, it may not be the optimal (or most cost-effective) path. Specialized solutions – particularly those that leverage domain-specific NLP to normalize data and add standard codes – can take the burden off data scientists and analysts, freeing them to focus on more important projects.
