Healthcare organizations, including hospitals and health tech companies, face a common challenge: effectively managing the vast amounts of data flowing in from multiple sources. This task becomes even more difficult when the data in your system is incomplete or inconsistent—what’s known as a “dirty data lake.”
Patient data can lose critical details as it passes through different electronic health records (EHRs) and health information systems, creating gaps that make the data less reliable. A missing lab result or an incomplete diagnosis code might not seem like a big deal, but over time, these issues add up. They can disrupt essential processes like revenue cycle management, advanced analytics, and quality reporting.
Manual efforts to clean and standardize this data can be both inefficient and resource-draining. Instead, advanced solutions like natural language processing (NLP) are necessary to accurately interpret and structure unstructured data. And for those considering AI-powered tools like autonomous coding, the challenges posed by complex clinical language and inconsistent data become even more apparent.
The solution to cleaning a dirty data lake begins with relying on strong clinical terminology, combined with NLP and data normalization tools. Explore our latest eBook to learn how you can turn data into a valuable asset.
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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 discover why healthcare organizations are cautious about AI-powered coding and the importance of NLP in managing complex clinical language.
Hazard: Complex clinical language
AI-powered autonomous coding is gaining momentum in healthcare. In fact, in a recent survey, 60% of healthcare organizations said they are already using this technology or are planning to. Given the promise that it can streamline coding for improved efficiency, and help optimize initiatives like revenue cycle management, many are bullish on autonomous coding’s potential.
However, about a quarter of the survey respondents revealed they are uncertain if the coding should be trusted. Some of the hesitancy may stem from the fact that machines cannot readily understand the nuances of clinical language, such as misspellings, specialty- dependent terminology, and the relationships between clinical concepts. And while NLP models are being applied to do this complex work, without proper training, their effectiveness is limited, potentially leading to coding errors, inaccuracies, and the need for manual staff fixes to recoup lost reimbursement.
Autonomous coding software vendors can improve their offerings by leveraging existing tools for more efficient NLP annotation, model training, and pipeline development. Rather than build an internal team of NLP and clinical terminology experts – a costly and time-consuming endeavor – integrating third-party solutions can speed development time and increase coding accuracy. This, in turn, means cleaner data and happier, more decisive end users.
