Variability in Doctors’ Usage Paths of Mobile Electronic Health Records Across Specialties: Comprehensive Analysis of Log Data

Abstract

BackgroundWith the emergence of mobile devices, mobile electronic health record (mEHR) systems have been utilized by health care professionals (HCPs), including doctors, nurses, and other practitioners, to improve efficiency at the point of care. Although several studies on mEHR systems were conducted, including those analyzing their effects and HCPs’ usage frequency, only a few considered the specific workflows of doctors based on their specialties in which the work process differs greatly.ObjectiveThis study aimed to investigate the differences in mEHR usage paths across clinical specialties.MethodsWe collected the log data of 974 doctors who worked from August 2016 to August 2017 and used the mEHR system at the Samsung Medical Center, one of the biggest hospitals in South Korea. The doctors were classified into 3 groups based on their specialty: the physician, the surgeon, and other hospital-based physician (OHBP) groups. We used various descriptive and visualization methods to understand and compare doctors’ usage paths of mEHRs. First, the average numbers of log-ins per day and features used per log-in were examined over different specialties and positions. Second, the number of features used by each doctor was visualized via a heat map to provide an overview of mEHR usage across feature types and doctors’ specialties. Third, we conducted a path analysis via a Sankey diagram to describe main usage paths and association rule mining to find frequent paths in mEHR usage.ResultsThe physician group logged on most frequently, whereas the OHBP group logged on least frequently. In fact, the number of log-ins per day of residents in the physician group was 4.4 times higher than that of staff members in the other groups. The heat map visualization showed a visible difference among specialty groups. The physician group used more consultation-related features, whereas the surgeon group used more surgery-related features. Generally, 50% of the doctors spent about 15 seconds at a time when using mEHRs. In the Sankey diagram, the physician group showed diverse usage patterns with higher complexity compared with the other 2 groups; in particular, their paths contained more loops, which reflected repetitive checks on multiple patients. The most frequent path included inpatient summary, which means that most users stopped at the point of summary and did not proceed to view more details.ConclusionsThe usage paths of mEHRs showed considerable differences among the specialty groups. Such differences can be accommodated into an mEHR design to enhance the efficiency of care.

Physician Stress During Electronic Health Record Inbox Work: In Situ Measurement With Wearable Sensors

“…In this study, we also used detailed EHR and inbox usage characteristics such as window switching, inbox work batching, the time per message, message types, and the time distribution between work and nonwork hours. Our finding that the window switching rate was positively associated with stress could reflect the complexity and repetitiveness of physicians’ EHR interactions, as indicated in prior work [ 56 ], and the efficiency issues often associated with physicians’ satisfaction with EHRs [ 57 ]. Another study on EHR inbox burden [ 8 ] also reported that excessive steps were needed to process messages and that physicians recommended reducing the number of mouse clicks necessary to process messages.…”

“…Most studies use basic measures to characterize EHR usage, such as the duration of time [ 14 , 15 , 55 ]. In one study, researchers used more complex measures to characterize mobile EHR usage, such as the number of log-ins and features used and usage paths (ie, the frequency and complexity of consecutive actions) [ 56 ]. They compared doctors across medical specialties and found that physicians other than surgeons had more diverse mobile EHR usage patterns with higher complexity and repetitive loops compared to surgeons [ 56 ].…”

“…In one study, researchers used more complex measures to characterize mobile EHR usage, such as the number of log-ins and features used and usage paths (ie, the frequency and complexity of consecutive actions) [ 56 ]. They compared doctors across medical specialties and found that physicians other than surgeons had more diverse mobile EHR usage patterns with higher complexity and repetitive loops compared to surgeons [ 56 ]. In this study, we also used detailed EHR and inbox usage characteristics such as window switching, inbox work batching, the time per message, message types, and the time distribution between work and nonwork hours.…”

A Scoping Review of the Use of Log Data for Evaluating Mobile Apps: Exploring Implications for mHealth Apps

“…The users are classified according to benchmarks or threshold set for each group and App, which varies between research studies. Lastly, the user groups can be identified by categorizing or clustering users according to their: demographics, such as their age or gender [37,45]; their occupations or specialties [29,36,46]; or their device specifications such as the operating system [38], mobile platforms [37] or network [36]. User groups are identified and can be used to generate specific insights per user group.…”

N6-Methyladenosine RNA Methylation Regulator-Related Alternative Splicing (AS) Gene Signature Predicts Non–Small Cell Lung Cancer Prognosis

“…According to the hazard ratio (HR) values after the multivariate Cox regression analysis, we classified the m6A-related prognostic AS events into protective/risky AS events (HR > 1 as a risk factor; HR < 1 as a protective factor), and showed the Sankey diagram that plotted is by "ggalluvial, dplyr, and ggplot2" R packages (Graedel, 2019;Soh et al, 2019). According to the median value of the risk score of the signature of each cohort, we divided the LUAD and LUSC cohorts into two subgroups, that is, the high-and low-risk groups.…”