Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis
Abstract Background Antimicrobial stewardship programs (ASPs) are essential for optimizing antimicrobial use, but many medium-sized hospitals lack infectious disease (ID) specialists. Ward pharmacists can contribute to ASPs, but the qualitative changes in their practice patterns after ASP implementa...
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| Language: | English |
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BMC
2025-04-01
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| Series: | Journal of Pharmaceutical Health Care and Sciences |
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| Online Access: | https://doi.org/10.1186/s40780-025-00439-0 |
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| author | Keisuke Sawada Shuji Kono Ryo Inose Yuichi Muraki |
| author_facet | Keisuke Sawada Shuji Kono Ryo Inose Yuichi Muraki |
| author_sort | Keisuke Sawada |
| collection | DOAJ |
| description | Abstract Background Antimicrobial stewardship programs (ASPs) are essential for optimizing antimicrobial use, but many medium-sized hospitals lack infectious disease (ID) specialists. Ward pharmacists can contribute to ASPs, but the qualitative changes in their practice patterns after ASP implementation remains unclear. We aimed to explore the potential of text mining as a novel methodology to evaluate changes in ward pharmacist antimicrobial management practices after ASP implementation in a medium-sized hospital without ID physicians. Methods We conducted a retrospective observational analysis of data documented in clinical records by ward pharmacists in a 313-bed community hospital from April 2014 to March 2022. The ASP team conducted weekly reviews of targeted patients, provided feedback to physicians, and shared recommendations with ward pharmacists who then collaborated to optimize antimicrobial therapy. Using Python-based text mining with standardized technical terms and compound word extraction, we performed morphological analysis, co-occurrence network analysis, and hierarchical clustering to evaluate documentation patterns before and after ASP implementation in April 2018. Co-occurrence relationships were assessed using Dice coefficients (threshold, ≥ 0.3), and communities were detected using the Louvain algorithm. Changes in documentation patterns were compared using Fisher’s exact test. Results The analysis included 1,353 pre-ASP and 5,155 post-ASP clinical records containing antimicrobial-related terms, which increased from 3.12 to 7.81% of the total pharmacy records. New strong co-occurrence relationships emerged in the post-ASP period for several laboratory parameters (c-reactive protein, 0.646; estimated glomerular filtration rate, 0.594; and white blood cell count, 0.582). Network analysis revealed a shift from medication-focused communities (Medication Review, Prescription Verification, and Patient Education) to infection-focused communities (Infection Assessment, Microbiological Review, and Severe Infection Management). Although Antimicrobial Management was consistently used in both periods (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.38–1.20), cross-tabulation analysis increased significantly in Laboratory Monitoring (OR: 1.58, 95% CI: 1.39–1.78) and Infection Assessment (OR: 2.09, 95% CI: 1.85–2.36). Conclusions This pilot application of text mining demonstrated potential as a novel methodology for objectively evaluating qualitative changes in clinical practice patterns following ASP implementation, successfully identifying shifts in pharmacists’ documentation focus and providing a foundation for future multi-center validation studies across diverse healthcare settings. |
| format | Article |
| id | doaj-art-5047285fe7bd47daba20d6563518c2c7 |
| institution | DOAJ |
| issn | 2055-0294 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Pharmaceutical Health Care and Sciences |
| spelling | doaj-art-5047285fe7bd47daba20d6563518c2c72025-08-20T03:15:09ZengBMCJournal of Pharmaceutical Health Care and Sciences2055-02942025-04-0111111110.1186/s40780-025-00439-0Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysisKeisuke Sawada0Shuji Kono1Ryo Inose2Yuichi Muraki3Department of Pharmacy, Federation of National Public Service Personnel Mutual Aid Associations Hirakata Kohsai HospitalDepartment of Pharmacy, Federation of National Public Service Personnel Mutual Aid Associations Hirakata Kohsai HospitalLaboratory of Clinical Pharmacoepidemiology, Kyoto Pharmaceutical UniversityLaboratory of Clinical Pharmacoepidemiology, Kyoto Pharmaceutical UniversityAbstract Background Antimicrobial stewardship programs (ASPs) are essential for optimizing antimicrobial use, but many medium-sized hospitals lack infectious disease (ID) specialists. Ward pharmacists can contribute to ASPs, but the qualitative changes in their practice patterns after ASP implementation remains unclear. We aimed to explore the potential of text mining as a novel methodology to evaluate changes in ward pharmacist antimicrobial management practices after ASP implementation in a medium-sized hospital without ID physicians. Methods We conducted a retrospective observational analysis of data documented in clinical records by ward pharmacists in a 313-bed community hospital from April 2014 to March 2022. The ASP team conducted weekly reviews of targeted patients, provided feedback to physicians, and shared recommendations with ward pharmacists who then collaborated to optimize antimicrobial therapy. Using Python-based text mining with standardized technical terms and compound word extraction, we performed morphological analysis, co-occurrence network analysis, and hierarchical clustering to evaluate documentation patterns before and after ASP implementation in April 2018. Co-occurrence relationships were assessed using Dice coefficients (threshold, ≥ 0.3), and communities were detected using the Louvain algorithm. Changes in documentation patterns were compared using Fisher’s exact test. Results The analysis included 1,353 pre-ASP and 5,155 post-ASP clinical records containing antimicrobial-related terms, which increased from 3.12 to 7.81% of the total pharmacy records. New strong co-occurrence relationships emerged in the post-ASP period for several laboratory parameters (c-reactive protein, 0.646; estimated glomerular filtration rate, 0.594; and white blood cell count, 0.582). Network analysis revealed a shift from medication-focused communities (Medication Review, Prescription Verification, and Patient Education) to infection-focused communities (Infection Assessment, Microbiological Review, and Severe Infection Management). Although Antimicrobial Management was consistently used in both periods (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.38–1.20), cross-tabulation analysis increased significantly in Laboratory Monitoring (OR: 1.58, 95% CI: 1.39–1.78) and Infection Assessment (OR: 2.09, 95% CI: 1.85–2.36). Conclusions This pilot application of text mining demonstrated potential as a novel methodology for objectively evaluating qualitative changes in clinical practice patterns following ASP implementation, successfully identifying shifts in pharmacists’ documentation focus and providing a foundation for future multi-center validation studies across diverse healthcare settings.https://doi.org/10.1186/s40780-025-00439-0Antimicrobial stewardship programWard pharmacistText miningCo-occurrence network analysisMedical recordsInfectious disease |
| spellingShingle | Keisuke Sawada Shuji Kono Ryo Inose Yuichi Muraki Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis Journal of Pharmaceutical Health Care and Sciences Antimicrobial stewardship program Ward pharmacist Text mining Co-occurrence network analysis Medical records Infectious disease |
| title | Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis |
| title_full | Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis |
| title_fullStr | Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis |
| title_full_unstemmed | Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis |
| title_short | Qualitative changes in clinical records after implementation of pharmacist-led antimicrobial stewardship program: a text mining analysis |
| title_sort | qualitative changes in clinical records after implementation of pharmacist led antimicrobial stewardship program a text mining analysis |
| topic | Antimicrobial stewardship program Ward pharmacist Text mining Co-occurrence network analysis Medical records Infectious disease |
| url | https://doi.org/10.1186/s40780-025-00439-0 |
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