Methods for identifying health status from routinely collected health data: An overview
The use of routinely collected health data (RCD) is currently helping to accelerate publications that evaluate the effectiveness and safety of medicines and medical devices. One fundamental step in using these data is developing algorithms to identify health status for use in observational studies....
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Integrative Medicine Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213422024000805 |
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| Summary: | The use of routinely collected health data (RCD) is currently helping to accelerate publications that evaluate the effectiveness and safety of medicines and medical devices. One fundamental step in using these data is developing algorithms to identify health status for use in observational studies. However, the processes and methodologies for determining health status using RCD remain insufficiently understood. While most current methods rely on the World Health Organization’s International Classification of Diseases (ICD) codes, they may not be universally applicable. Although machine learning methods are promising for more accurately identifying health status, they currently remain underutilized in RCD studies. To address these significant methodological gaps, we outline key steps and methodological considerations for identifying health statuses in observational studies using RCD. This review has the potential to reinforce the credibility of findings from observational studies that use RCD. |
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| ISSN: | 2213-4220 |