Inpatient portal clusters: Identifying user groups based on portal features

Naleef Fareed, Daniel Walker, Cynthia J. Sieck, Robert Taylor, Seth Scarborough, Timothy R. Huerta, Ann Scheck McAlearney

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Objective: Conduct a cluster analysis of inpatient portal (IPP) users from an academic medical center to improve understanding of who uses these portals and how. Methods: We used 18 months of data from audit log files, which recorded IPP user actions, of 2815 patient admissions. A hierarchical clustering algorithm was executed to group patient admissions on the basis of proportion of use for each of 10 IPP features. Post-hoc analyses were conducted to further understand IPP use. Results: Five cluster solutions were developed for the study sample. Our taxonomy included users with high levels of accessing features that were linked to reviewing schedules, results, tutorials, and ordering food. Patients tended to stay within their clusters over multiple admissions, and the clusters had differences based on patient and clinical characteristics. Discussion: Distinct groups of users exist among IPP users, suggesting that training on IPP use to enhance patient engagement could be tailored to patients. More exploration is also needed to understand why certain features were not used across all clusters. Conclusions: It is important to understand the specifics about how patients use IPPs to help them better engage with their healthcare. Our taxonomy enabled characterization of 5 groups of IPP users who demonstrated distinct preferences. These results may inform targeted improvements to IPP tools, could provide insights to improve patient training around portal use, and may help care team members effectively engage patients in the use of IPPs. We also discuss the implications of our findings for future research.

Original languageEnglish (US)
Pages (from-to)28-36
Number of pages9
JournalJournal of the American Medical Informatics Association
Issue number1
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Health Informatics


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