The process of structuring pulmonary function data into a standardized, computable format involves converting diverse measurements like Forced Expiratory Volume (FEV1), Forced Vital Capacity (FVC), and diffusing capacity of the lungs for carbon monoxide (DLCO) into discrete, analyzable units. This structured data representation facilitates automated interpretation and integration with electronic health records. For example, converting raw spirometry readings into standardized Z-scores allows for comparisons across different patient populations and simplifies the identification of abnormalities.
Standardizing and organizing such data is crucial for enhancing diagnostic accuracy, facilitating research, and improving patient care. Historical reliance on manual interpretation of test results was time-consuming and prone to variability. Structured data enables the application of sophisticated algorithms for detecting subtle patterns, predicting disease progression, and personalizing treatment strategies. Furthermore, it promotes interoperability between healthcare systems, enabling large-scale data analysis for epidemiological studies and the development of improved diagnostic criteria.
