Which Users Should Be the Focus of Mobile Personal Health Records? Analysis of User Characteristics Influencing Usage of a Tethered Mobile Personal Health Record

Telemed J E Health. 2016 May;22(5):419-28. doi: 10.1089/tmj.2015.0137. Epub 2015 Oct 8.

Abstract

Background: This study was conducted to analyze the usage pattern of a hospital-tethered mobile personal health records (m-PHRs) application named My Chart in My Hand (MCMH) and to identify user characteristics that influence m-PHR usage.

Materials and methods: Access logs to MCMH and its menus were collected for a total of 18 months, from August 2011 to January 2013. Usage patterns between users without a patient identification number (ID) and users with a patient ID were compared. Users with a patient ID were divided into light and heavy user groups by the median number of monthly access. Multiple linear regression models were used to assess MCMH usage pattern by characteristics of MCMH user with a patient ID.

Results: The total number of MCMH logins was 105,603, and the median number of accesses was 15 times. Users (n = 7,096) mostly accessed the "My Chart" menu, but "OPD [outpatient department] Service Support" and "Health Management" menus were also frequently used. Patients with chronic diseases, experience of hospital visits including emergency room and OPD, and age group of 0-19 years were more frequently found among users with a patient ID (n = 2,186) (p < 0.001). A similar trend was found in the heavy user group (n = 1,123). Submenus of laboratory result, online appointment, and medication lists that were accessed mostly by users with a patient ID were associated with OPD visit and chronic diseases.

Conclusions: This study showed that focuses on patients with chronic disease and more hospital visits and empowerment functions in a tethered m-PHR would be helpful to pursue the extensive use.

Keywords: chronic disease; consumer health information; empowerment; mobile health; personal health record.

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Electronic Health Records / statistics & numerical data*
  • Female
  • Hospitalization / statistics & numerical data
  • Humans
  • Linear Models
  • Male
  • Middle Aged
  • Mobile Applications / statistics & numerical data*
  • Residence Characteristics
  • Sex Factors
  • User-Computer Interface
  • Young Adult