BACKGROUND: Use of differential diagnosis (DDX) generators may reduce the incidence of misdiagnosis-related harm, but there is a paucity of research examining the use and impact of such systems in real-world settings. METHODS: In September 2012, the DDX generator VisualDx was made available across our entire academic healthcare system. We examined the use of VisualDx by month for the 18 months following its introduction. In addition, we compared the number of inpatient dermatology consults requested per month at the flagship hospital of our healthcare system for the 12 months before versus 18 months after VisualDx introduction. RESULTS: Across our entire academic healthcare system, there were a median of 474 (interquartile range 390-544) unique VisualDx sessions per month. VisualDx was accessed most frequently through mobile devices (35%) and the inpatient electronic health record (34%). Prior to VisualDx introduction, there was a non-significant increase in the number of inpatient dermatology consultations requested per month at the flagship hospital of our healthcare system (1.0 per month, 95% CI -2.5-4.6, p=0.54), which remained 1.0 consults per month (95% CI -0.9-2.9, p=0.27) following its introduction (p=0.99 comparing post- versus pre-introduction rates). CONCLUSIONS: The DDX generator VisualDx was regularly used, primarily on mobile devices and inpatient workstations, and was not associated with a change in inpatient dermatology consultation requests. Given the interest in DDX generators, it will be important to evaluate further the impact of such tools on the quality and value of care delivered.
BACKGROUND: Use of differential diagnosis (DDX) generators may reduce the incidence of misdiagnosis-related harm, but there is a paucity of research examining the use and impact of such systems in real-world settings. METHODS: In September 2012, the DDX generator VisualDx was made available across our entire academic healthcare system. We examined the use of VisualDx by month for the 18 months following its introduction. In addition, we compared the number of inpatient dermatology consults requested per month at the flagship hospital of our healthcare system for the 12 months before versus 18 months after VisualDx introduction. RESULTS: Across our entire academic healthcare system, there were a median of 474 (interquartile range 390-544) unique VisualDx sessions per month. VisualDx was accessed most frequently through mobile devices (35%) and the inpatient electronic health record (34%). Prior to VisualDx introduction, there was a non-significant increase in the number of inpatient dermatology consultations requested per month at the flagship hospital of our healthcare system (1.0 per month, 95% CI -2.5-4.6, p=0.54), which remained 1.0 consults per month (95% CI -0.9-2.9, p=0.27) following its introduction (p=0.99 comparing post- versus pre-introduction rates). CONCLUSIONS: The DDX generator VisualDx was regularly used, primarily on mobile devices and inpatient workstations, and was not associated with a change in inpatient dermatology consultation requests. Given the interest in DDX generators, it will be important to evaluate further the impact of such tools on the quality and value of care delivered.
Keywords:
decision making; decision support systems clinical; diagnostic errors; information technology; quality of health care
Authors: P Fritz; A Kleinhans; R Raoufi; A Sediqi; N Schmid; S Schricker; M Schanz; C Fritz-Kuisle; P Dalquen; H Firooz; G Stauch; M D Alscher Journal: BMC Med Inform Decis Mak Date: 2022-09-24 Impact factor: 3.298