User experience of a hepatitis c population management dashboard in the Department of Veterans Affairs

PLOS ONE(2023)

引用 0|浏览8
暂无评分
摘要
BackgroundThe Veterans Health Administration (VA) is the largest integrated healthcare organization in the US and cares for the largest cohort of individuals with hepatitis C (HCV). A national HCV population management dashboard enabled rapid identification and treatment uptake with direct acting antiviral agents across VA hospitals. We describe the HCV dashboard (HCVDB) and evaluate its use and user experience. MethodsA user-centered design approach created the HCVDB to include reports based on the HCV care continuum: 1) 1945-1965 birth cohort high-risk screening, 2) linkage to care and treatment of chronic HCV, 3) treatment monitoring, 4) post-treatment to confirm cure (i.e., sustained virologic response), and 5) special populations of unstably housed Veterans. We evaluated frequency of usage and user experience with the System Usability Score (SUS) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) instruments. ResultsBetween November 2016 and July 2021, 1302 unique users accessed the HCVDB a total of 163,836 times. The linkage report was used most frequently (71%), followed by screening (13%), sustained virologic response (11%), on-treatment (4%), and special populations (<1%). Based on user feedback (n = 105), the mean SUS score was 73 +/- 16, indicating a good user experience. Overall acceptability was high with the following UTAUT2 rated from highest to least: Price Value, Performance Expectancy, Social Influence, and Facilitating Conditions. ConclusionsThe HCVDB had rapid and widespread uptake, met provider needs, and scored highly on user experience measures. Collaboration between clinicians, clinical informatics, and population health experts was essential for dashboard design and sustained use. Population health management tools have the potential for large-scale impacts on care timeliness and efficiency.
更多
查看译文
关键词
veterans affairs,experience
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要