Background:Health behavior is an action taken by a person to maintain, attain, or regain good health and to prevent illness. As such, health behavior reflects a person's health beliefs and attracts many published papers in academics. However, who is the most influential author (MIA) remains unknown. Objective: The purpose of this study is to apply the algorithm of between centrality(BC) in social network analysis (SNA) to select the MIA on the topic of health behavior using the visual displays on Google Maps. Methods: We obtained 3,593 abstracts from Medline based on the keywords of (health [Title]) and (behavior [Title] or behaviour [Title]) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were applied to (1) select the MIA using SNA; (2) display the countries/areas distributed for the 1st author in geography, (3) discover the author clusters dispersed on Google Maps, and (4) investigate the keywords dispersed for the cluster related to the MIA on a dashboard. Pajek software was performed to yield the BC for each entity (or say node). Results: We found that the MIA is Spring, Bonnie (US). All visual representations that are the form of a dashboard can be easily displayed on Google Maps. The most influential country and the keywords are the US and health behavior. Readers are suggested to manipulate them on their own on Google Maps. Conclusion: Social network analysis provides wide and deep insight into the relationships with the pattern of international author collaborations. If incorporated with Google Maps, the dashboard can release much more information regarding our interesting topics for us in academics. The research approach using the BC to identify the same author names can be applied to other bibliometric analyses in the future.

Background: There are many ways to gather and assess patient expectations, experience, and satisfaction, but few use visual representations to report their results on the Google maps and help patients select the best hospitals for consultation. Objectives: To analyze 10-dimensional scores of states in the USA about inpatients' perceptions of their hospitalization experience to develop a method for analyzing data on the Google maps and getting feedback on the smartphones. Methods: We downloaded HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Services) data from the 2007 to 2014 summary of survey results to study (1) whether the 10 dimensional scores can be combined to determine its unidimensionality using Rasch continuous item responses, (2) what type of trends about inpatient perception on hospitalization experience that can be reported with an individual and an overall base, (3) what an online dashboard that can be designed using the Google maps for comparing results of each US state, and (4) how to demonstrate an online assessment that uses smartphones for gathering perceptions of their hospitalization experience in the future. Results: The ten core dimensional scores of each US state about inpatient hospitalization experience reported by the HCAHPS can be unidimensional. The improvement was evident of inpatient perception on hospitalization experience in the historical series. Online visual representation of the Google maps can be easy to build and allows for real-time identification in comparison with the performance of each state. A smartphone app was designed to get feedback directly from patients. Conclusions: We verified that the 10-dimensional scores reporting patient satisfaction in US states could be a unidimensional scale and use Rasch continuous item responses to show results on the Google maps.Keywords: Smart phone, unidimensionality, HCAHPS, hospitalized experience perception, the Google maps SUDPAR.Psychometrically, we are interested in identifying aberrant survey respondents and selecting the best and worst respondents [12]. A method for distinguishing the best and worst clusters is required using the multiple variables of the assessed dimensional scores for an investigation.