Details

Autor(en) / Beteiligte

• Campbell, Ian M
• Karavite, Dean J
• Mcmanus, Morgan L
• Cusick, Fred C
• Junod, David C
• Sheppard, Sarah E
• Lourie, Eli M
• Shelov, Eric D
• Hakonarson, Hakon
• Luberti, Anthony A
• Muthu, Naveen
• Grundmeier, Robert W

Titel

Clinical decision support with a comprehensive in-EHR patient tracking system improves genetic testing follow up

Ist Teil von

• Journal of the American Medical Informatics Association : JAMIA, 2023-06, Vol.30 (7), p.1274-1283

Ort / Verlag

England

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds. We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment, we assessed the system in 2 ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semistructured interviews with users to identify impact of the system on work. We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly sped review of results and significantly increased documentation of follow-up recommendations. Interviews with key system users identified a range of sociotechnical factors (ie, tools, tasks, collaboration) that contribute to safer and more efficient care. Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction. By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings.