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.

BackgroundImproved medical practice efficiency has been demonstrated by physicians using mobile device (mobile phones, tablets) electronic medical record (EMR) systems. However, the quantitative effects of these systems have not been adequately measured.ObjectiveThis study aimed to determine the effectiveness of near-field communication (NFC) integrated with a mobile EMR system regarding physician turnaround time in a hospital emergency department (ED).MethodsA simulation study was performed in a hospital ED. Twenty-five physicians working in the ED participated in 2 scenarios, using either a mobile device or personal computer (PC). Scenario A involved randomly locating designated patients in the ED. Scenario B consisted of accessing laboratory results of an ED patient at the bedside. After completing the scenarios, participants responded to 10 questions that were scored using a system usability scale (SUS). The primary metric was the turnaround time for each scenario. The secondary metric was the usability of the system, graded by the study participants.ResultsLocating patients from the ED entrance took a mean of 93.0 seconds (SD 34.4) using the mobile scenario. In contrast, it only required a mean of 57.3 seconds (SD 10.5) using the PC scenario (P<.001). Searching for laboratory results of the patients at the bedside required a mean of only 25.2 seconds (SD 5.3) with the mobile scenario, and a mean of 61.5 seconds (SD 11.6) using the PC scenario (P<.001). Sensitivity analysis comparing only the time for login and accessing the relevant information also determined mobile devices to be significantly faster. The mean SUS score of NFC-mobile EMR was 71.90 points.ConclusionsNFC integrated with mobile EMR provided for a more efficient physician practice with good usability.

Background Specialty consultation is a critical aspect of emergency department (ED) practice, and a delay in providing consultation might have a significant clinical effect and worsen ED overcrowding. Although mobile electronic medical records (EMR) are being increasingly used and are known to improve the workflow of health care providers, limited studies have evaluated their effectiveness in real-life clinical scenarios. Objective For this study, we aimed to determine the association between response duration to an ED specialty consultation request and the frequency of mobile EMR use. Methods This retrospective study was conducted in an academic ED in Seoul, South Korea. We analyzed EMR and mobile EMR data from May 2018 to December 2018. Timestamps of ED consultation requests were retrieved from a PC-based EMR, and the response interval was calculated. Doctors’ log frequencies were obtained from the mobile EMR, and we merged data using doctors’ deidentification numbers. Pearson’s product-moment correlation was performed to identify this association. The primary outcome was the relationship between the frequency of mobile EMR usage and the time interval from ED request to consultation completion by specialty doctors. The secondary outcome was the relationship between the frequency of specialty doctors’ mobile EMR usage and the response time to consultation requests. Results A total of 25,454 consultations requests were made for 15,555 patients, and 252 specialty doctors provided ED specialty consultations. Of the 742 doctors who used the mobile EMR, 208 doctors used it for the specialty consultation process. After excluding the cases lacking essential information, 21,885 consultations with 208 doctors were included for analysis. According to the mobile EMR usage pattern, the average usage frequency of all users was 13.3 logs/day, and the average duration of the completion of the specialty consultation was 51.7 minutes. There was a significant inverse relationship between the frequency of mobile EMR usage and time interval from ED request to consultation completion by specialty doctors (coefficient=–0.19; 95% CI –0.32 to –0.06; P=.005). Secondary analysis with the response time was done. There was also a significant inverse relationship between the frequency of specialty doctors’ mobile EMR usage and the response time to consultation requests (coefficient=–0.18; 95% CI –0.30 to –0.04; P=.009). Conclusions Our findings suggest that frequent mobile EMR usage is associated with quicker response time to ED consultation requests.

Purpose: For patients with time-critical acute coronary syndrome, reporting electrocardiogram (ECG) findings is the most important component of the treatment process. We aimed to develop and validate an automated Fast Healthcare Interoperability Resources (FHIR)-based 12-lead ECG mobile alert system for use in an emergency department (ED). Materials and Methods: An automated FHIR-based 12-lead ECG alert system was developed in the ED of an academic tertiary care hospital. The system was aimed at generating an alert for patients with suspected acute coronary syndrome based on interpretation by the legacy device. The alert is transmitted to physicians both via a mobile application and the patient's electronic medical record (EMR). The automated FHIR-based 12-lead ECG alert system processing interval was defined as the time from ED arrival and 12-lead ECG capture to the time when the FHIR-based notification was transmitted. Results: During the study period, 3812 emergency visits and 1581 12-lead ECGs were recorded. The FHIR system generated 155 alerts for 116 patients. The alerted patients were significantly older [mean (standard deviation): 68.1 (12.4) years vs. 59.6 (16.8) years, p<0.001], and the cardiac-related symptom rate was higher (34.5% vs. 19%, p<0.001). Among the 155 alerts, 146 (94%) were transmitted successfully within 5 minutes. The median interval from 12-lead ECG capture to FHIR notification was 2.7 min [interquartile range (IQR) 2.2-3.1 min] for the group with cardiac-related symptoms and 3.0 min (IQR 2.5-3.4 min) for the group with non-cardiac-related symptoms. Conclusion: An automated FHIR-based 12-lead ECG mobile alert system was successfully implemented in an ED.