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. 2023 Aug 2;14(3):600–608. doi: 10.1055/a-2091-1162

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The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ )

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.

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Fig. 3 — Co-occurrence of subset of pain care quality indicators describing pain assessment, organized by visit type. The height of each stacked bar represents proportion of notes with at least the specified lower bound number of pain care quality indicators. *Comparison data from primary care pain visit notes from Fodeh SJ, Finch D, Bouayad L, et al. Classifying clinical notes with pain assessment using machine learning. Med Biol Eng Comput 2018;56(7):1285–1292