Inpatient portal clusters: identifying user groups based on portal features

Abstract

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.

Exploring How Professionals Within Agile Health Care Informatics Perceive Visualizations of Log File Analyses: Observational Study Followed by a Focus Group Interview (Preprint)

“…We have found that analyzing log files seems to provide agile professionals valuable insights into users' behavior. This is in line with previous research related to health information technology, in which it was shown that researchers interpreted log file analyses into valuable insights into users' behavior [8,18,26]. The important innovation of this study is that researchers looked at what you can do with these log file analyses and professional agile team members were also asked about how they can use these in practice.…”

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia

“… 27 Then and now, hierarchical clustering methods have been a dominant approach. 12 , 27–30 Recently, a few studies have applied k-means and k-medoids algorithms to cluster clinical data. 31–33 Increasingly, studies have emerged comparing traditional hierarchical clustering approaches to k-means and k-medoids.…”

“… 11 Clustering has applications in health services, such as subtyping inpatient e-portal users to improve care delivery. 12 Some analyses of heterogeneous diseases discover subclasses that are fuzzy or not mutually exclusive, both in chronic conditions such as COPD 13 and acute sepsis. 14 To the best of our knowledge, this is the first application of clustering to clinical data in cancer.…”

An electronic health record (EHR) log analysis shows limited clinician engagement with unsolicited genetic test results

“…Clustering analysis, long recognized as an effective classification method for exploratory analysis of high-dimensional and heterogenous EHR log data, 23 , 24 was used to identify patterns of clinicians’ engagement with EHR-integrated genetic results. Variables extracted from access logs and participants’ charts included in the final dataset used for the clustering analysis were as follows: time of Genetics section access, EHR platform used to access iNYP, practice setting, clinician categorical data, type of genetic test result document opened and clinical context of Genetics section access.…”