Development of mobile CAT for patient feedback on pediatric consultations based on Rasch analysis of online techniques.

The Rasch Rating Scale Model (RSM) is widely used in questionnaire analysis, providing insights into how individuals respond to item-level stimuli. Existing software for Rasch RSM parameter estimation, while powerful, often presents a steep learning curve. An accessible online tool can greatly benefit novice users, particularly students and clinicians, by simplifying the analytical process. This study introduces an online tool, an intuitive online RSM analysis tool designed to facilitate questionnaire data analysis for applied researchers, students, and clinicians. The online tool employs the joint maximum likelihood method for estimation, yielding estimates, standard errors (SE), and fit statistics iteratively. A unique feature of the tool is its ability to visualize estimates on Google Maps with an opacity setting of 0, enhancing data interpretation through a user-friendly interface. This study outlines the estimation process and key features, employing data from 200 proxy participants who answered 20 5-point questions regarding doctor-patient and doctor-family interactions in pediatric consultations. Mobile computerized adaptive testing (CAT) was employed. The online tool offers 5 essential visual displays often utilized in Rasch analyses, including the Wright Map, KIDMAP, category probability curve, performance plot, and differential item functioning (DIF) graph. DIF analysis revealed that 2 items, concerning the doctor attentiveness and empathy toward the child illness, exhibited differences in female proxy participants' responses, indicating lower satisfaction with pediatricians. The online tool emerges as a user-friendly and efficient RSM analysis tool with notable advantages for newcomers, improving data visualization and comprehension. Its capacity to pinpoint key areas of concern, such as gender-related satisfaction disparities among proxy participants, enhances its utility in questionnaire analysis. The online tool holds promise as a valuable resource for researchers, students, and clinicians seeking accessible Rasch analysis solutions.

Visualizing the features of inflection point shown on a temporal bar graph using the data of COVID-19 pandemic.

BACKGROUND: Exponential-like infection growth leading to peaks (denoted by inflection points [IP] or turning points) is usually the hallmark of infectious disease outbreaks, including coronaviruses. To determine the IPs of the novel coronavirus (COVID-19), we applied the item response theory model to detect phase transitions for each country/region and characterize the IP feature on the temporal bar graph (TBG). METHODS: The IP (using the item difficulty parameter to locate) was verified by the differential equation in calculus and interpreted by the TBG with 2 virtual and real empirical data (i.e., from Collatz conjecture and COVID-19 pandemic in 2020). Comparisons of IPs, R2, and burst strength [BS = ln() denoted by the infection number at IP(Nip) and the item slope parameter(a) in item response theory were made for countries/regions and continents on the choropleth map and the forest plot. RESULTS: We found that the evolution of COVID-19 on the TBG makes the data clear and easy to understand, the shorter IP (=53.9) was in China and the longest (=247.3) was in Europe, and the highest R2 (as the variance explained by the model) was in the US, with a mean R2 of 0.98. We successfully estimated the IPs for countries/regions on COVID-19 in 2020 and presented them on the TBG. CONCLUSION: Temporal visualization is recommended for researchers in future relevant studies (e.g., the evolution of keywords in a specific discipline) and is not merely limited to the IP search in COVID-19 pandemics as we did in this study.