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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Aug 26, 2018
Open Peer Review Period: Sep 11, 2018 - Sep 5, 2018
Date Accepted: Dec 9, 2018
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Soh JY, Jung SH, Cha WC, Kang M, Chang DK, Jung J, Lee J, Choi JS, Kim K

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

JMIR Mhealth Uhealth 2019;7(1):e12041

DOI: 10.2196/12041

PMID: 30664473

PMCID: 6354233

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

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

  • Ji Yeong Soh; 
  • Sang-Hyuk Jung; 
  • Won Chul Cha; 
  • Mira Kang; 
  • Dong Kyung Chang; 
  • Jaegon Jung; 
  • JeanHyoung Lee; 
  • Jong Soo Choi; 
  • Kyunga Kim

Background:

With 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.

Objective:

This study aimed to investigate the differences in mEHR usage paths across clinical specialties.

Methods:

We 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.

Results:

The 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.

Conclusions:

The 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.


 Citation

Please cite as:

Soh JY, Jung SH, Cha WC, Kang M, Chang DK, Jung J, Lee J, Choi JS, Kim K

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

JMIR Mhealth Uhealth 2019;7(1):e12041

DOI: 10.2196/12041

PMID: 30664473

PMCID: 6354233

Per the author's request the PDF is not available.

© 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.