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\n\n \n \n \n \n \n Impact of Collaborative Documentation on Completeness and Length of Clinical Notes in Behavioral Health Settings.\n \n \n \n\n\n \n Yoo, N.; Matthews, E.; Baslock, D.; and Stanhope, V.\n\n\n \n\n\n\n
Psychiatric Services, 75(2): 186–190. 2024.\n
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@article{yoo2024impact,\n title={Impact of Collaborative Documentation on Completeness and Length of Clinical Notes in Behavioral Health Settings},\n author={Yoo, Nari and Matthews, Elizabeth and Baslock, Daniel and Stanhope, Victoria},\n journal={Psychiatric Services},\n volume={75},\n number={2},\n pages={186--190},\n year={2024},\n publisher={American Psychiatric Association Washington, DC}\n}\n\n
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\n\n \n \n \n \n \n The unequal weight of COVID-19 pandemic: national trends in body mass index among Korean adolescents by immigrant-origin and gender from 2013 to 2022.\n \n \n \n\n\n \n Yoo, N.; Hong, Y.; and Choi, Y.\n\n\n \n\n\n\n
International Journal of Adolescence and Youth, 29(1): 2387070. 2024.\n
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@article{yoo2024unequal,\n title={The unequal weight of COVID-19 pandemic: national trends in body mass index among Korean adolescents by immigrant-origin and gender from 2013 to 2022},\n author={Yoo, Nari and Hong, Yumin and Choi, Yoonyoung},\n journal={International Journal of Adolescence and Youth},\n volume={29},\n number={1},\n pages={2387070},\n year={2024},\n publisher={Taylor \\& Francis}\n}\n\n
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\n\n \n \n \n \n \n The Impact of Collaborative Documentation on Person-Centered Care: A Textual Analysis of Clinical Notes.\n \n \n \n\n\n \n Stanhope, V.; Yoo, N.; Matthews, E.; Baslock, D.; and Hu, Y.\n\n\n \n\n\n\n
JMIR Med. Inform.. September 2024.\n
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@ARTICLE{Stanhope2024-ko,\n title = "The Impact of Collaborative Documentation on Person-Centered Care:\n A Textual Analysis of Clinical Notes",\n author = "Stanhope, Victoria and Yoo, Nari and Matthews, Elizabeth and\n Baslock, Daniel and Hu, Yuanyuan",\n journal = "JMIR Med. Inform.",\n abstract = "Background: Collaborative documentation is a behavioral health\n practice involving shared writing of clinical visit notes by\n providers and consumers. Despite widespread dissemination of\n collaborative documentation, research on its effectiveness or\n impact on person-centered care has been limited. Principles of\n person-centered care planning, a recovery-based approach to\n service planning that operationalizes person-centered care, can\n inform measurement of person-centeredness within clinical\n documentation. Objective: This study uses the clinical informatics\n approach of Natural Language Processing to examine the impact of\n collaborative documentation on person-centeredness in clinical\n visit notes. Using a dictionary-based approach, this study\n conducts textual analysis of clinic notes from a community mental\n health center before and after staff were trained in collaborative\n documentation. Methods: This study used visit notes (n = 1,981)\n from 10 providers in a community mental health center 6 months\n pre-and-post training in collaborative documentation. Linguistic\n Inquiry and Word Count (LWIC)-22 was used to assess all notes\n using the LWIC dictionary that categorizes over 5000 linguistic\n and psychological words. Twelve LWIC categories were selected and\n mapped onto person-centered care planning principles through\n consensus of three domain experts. The LIWC-22 contextualizer was\n used to extract sentence fragments from notes corresponding to\n LIWC categories. Then, fixed-effects modelling was used to\n identify differences in notes pre-and-post collaborative\n documentation training while accounting for nesting within\n provider. Results: Sentence fragments identified by the\n contextualizing process illustrated how visit notes demonstrated\n person-centered care. The fixed effects analysis found a\n significant positive shift towards person-centeredness was\n observed in six of the selected LIWC categories post collaborative\n documentation. Specifically, there was a notable increase in words\n associated with achievement (?= 0.774, p<.001), power (?= 0.831,\n p<.001), money (?= 0.204, p<.001), physical health (?= 0.427,\n p=.027), while leisure words decreased (?= -0.135, p<.01).\n Conclusions: By using a dictionary-based approach, the study\n identified how collaborative documentation might influence the\n integration of person-centered care principles within clinical\n notes. Although the results were mixed, the findings highlight the\n potential effectiveness of collaborative documentation in\n enhancing person-centeredness in clinic notes. By leveraging\n natural language processing techniques, this research illuminated\n the value of narrative clinical notes in assessing the quality of\n care in behavioral health contexts. These findings underscore the\n promise of NLP for quality assurance in healthcare settings and\n emphasize the need for refining algorithms to more accurately\n measure person-centered care.person-centered care, mental health\n services, collaborative documentation, clinical notes;\n collaborative documentation; natural language processing",\n month = sep,\n year = 2024,\n language = "en"\n}\n
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\n Background: Collaborative documentation is a behavioral health practice involving shared writing of clinical visit notes by providers and consumers. Despite widespread dissemination of collaborative documentation, research on its effectiveness or impact on person-centered care has been limited. Principles of person-centered care planning, a recovery-based approach to service planning that operationalizes person-centered care, can inform measurement of person-centeredness within clinical documentation. Objective: This study uses the clinical informatics approach of Natural Language Processing to examine the impact of collaborative documentation on person-centeredness in clinical visit notes. Using a dictionary-based approach, this study conducts textual analysis of clinic notes from a community mental health center before and after staff were trained in collaborative documentation. Methods: This study used visit notes (n = 1,981) from 10 providers in a community mental health center 6 months pre-and-post training in collaborative documentation. Linguistic Inquiry and Word Count (LWIC)-22 was used to assess all notes using the LWIC dictionary that categorizes over 5000 linguistic and psychological words. Twelve LWIC categories were selected and mapped onto person-centered care planning principles through consensus of three domain experts. The LIWC-22 contextualizer was used to extract sentence fragments from notes corresponding to LIWC categories. Then, fixed-effects modelling was used to identify differences in notes pre-and-post collaborative documentation training while accounting for nesting within provider. Results: Sentence fragments identified by the contextualizing process illustrated how visit notes demonstrated person-centered care. The fixed effects analysis found a significant positive shift towards person-centeredness was observed in six of the selected LIWC categories post collaborative documentation. Specifically, there was a notable increase in words associated with achievement (?= 0.774, p<.001), power (?= 0.831, p<.001), money (?= 0.204, p<.001), physical health (?= 0.427, p=.027), while leisure words decreased (?= -0.135, p<.01). Conclusions: By using a dictionary-based approach, the study identified how collaborative documentation might influence the integration of person-centered care principles within clinical notes. Although the results were mixed, the findings highlight the potential effectiveness of collaborative documentation in enhancing person-centeredness in clinic notes. By leveraging natural language processing techniques, this research illuminated the value of narrative clinical notes in assessing the quality of care in behavioral health contexts. These findings underscore the promise of NLP for quality assurance in healthcare settings and emphasize the need for refining algorithms to more accurately measure person-centered care.person-centered care, mental health services, collaborative documentation, clinical notes; collaborative documentation; natural language processing\n
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