Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 24, 2019
Open Peer Review Period: May 27, 2019 - Jul 22, 2019
Date Accepted: Nov 18, 2020
(closed for review but you can still tweet)
How do patients use hospital websites? A Bayesian Network-Based Browsing Model for Patients Seeking Radiology-Related Information
ABSTRACT
Background:
Today, patients use information obtained from hospital websites to select hospitals. Therefore, it is important that hospitals provide information to meet patient’s needs, on their websites. However, few studies have investigated whether hospital websites meet the needs of patients and conducted on the usefulness of hospital websites for patients, especially patients seeking radiology-related information.
Objective:
The purpose of this study is to visualize and propose methods of enhancing the radiology-related information provision on hospital websites by analyzing access logs and building browsing models of a radiology-related contents based on a Bayesian network.
Methods:
First, we followed the website access log of Hokkaido University Hospital which collected from September 1st, 2016, to August 31st, 2017 invoked from Google Analytics modules. Second, we specified the access records related to radiology from visitor browsing pages and keywords. Third, using these resources, we built three Bayesian network models based on specific patient needs: radiotherapy, nuclear medicine examination, and radiological diagnosis. Analyzing each model, we considered why some visitors could not reach their desired page and improvements.
Results:
The radiotherapy model showed that, 74% (accuracy=0.86, F-measure=0.92) of target viewers could reach their request page, but that of 2.7% could reach the Center page where inspection information, one of their request pages, is posted.
Conclusions:
The results of our study indicated that the radiology-related information provided on hospital websites meets the most of patients needs. We proposed the potential solutions of patient web-browsing accessibility based on Bayesian network.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.