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Behavioral Analysis of Visitors to a Medical Institution's Website Using Markov Chain Monte Carlo Methods

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Title: Behavioral Analysis of Visitors to a Medical Institution's Website Using Markov Chain Monte Carlo Methods
Authors: Suzuki, Teppei Browse this author
Tani, Yuji Browse this author
Ogasawara, Katsuhiko Browse this author →KAKEN DB
Keywords: information-seeking behavior
Bayesian analysis
Web marketing
Issue Date: Jul-2016
Publisher: Journal of Medical Internet Research(JMIR)
Journal Title: Journal of medical internet research
Volume: 18
Issue: 7
Start Page: e199
Publisher DOI: 10.2196/jmir.5139
PMID: 27457537
Abstract: Background: Consistent with the "attention, interest, desire, memory, action" (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. Objective: We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. Methods: We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. Results: In the case of the keyword "clinic name," the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the keyword "clinic name and regional name," the probability for a repeated visit to the website and the mammography screening page was negative. In the case of the keyword "clinic name + medical examination," the visit probability to the website was positive, and the visit probability to the information page was negative. When visitors referred to the keywords "mammography screening," the visit probability to the mammography screening page was positive (95% highest posterior density interval = 3.38-26.66). Conclusions: Further analysis for not only the clinic website but also various other medical institution websites is necessary to build a general inspection model for medical institution websites; we want to consider this in future research. Additionally, we hope to use the results obtained in this study as a prior distribution for future work to conduct higher-precision analysis.
Type: article
Appears in Collections:保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小笠原 克彦

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