2.1 Study Setting
This retrospective study was conducted at Chung-Ang University Gwangmyeong Hospital in Gyeonggi-do, South Korea. The general hospital has 692 beds and began operating in March 2022. As of March 2023, it operated 380 patient rooms, including 23 intensive care units. This study was reviewed and approved by the Institutional Review Board of Chung-Ang University Gwangmyeong Hospital (approval number CAUGH 2304-077-041). This satisfies the requirements specified in the Ministerial Decree of Health and Welfare passed by the National Bioethics Committee.
2.2 Hospital Admission Process
Traditionally, patients prescribed for hospitalisation arrive at the hospital on their scheduled admission date and proceed to the administrative department. In the administrative department, patients go through the process of identification and complete various consent forms, such as the ‘Admission Contract’, with the assistance of hospital administrative staff. Subsequently, patients proceed to the admission guide booth to provide their clinical information to the nurse, receive information about hospital life and safety, and sign the ‘Hospitalisation Guide’ form as an acknowledgement of understanding. They then proceed to their assigned wards, where the nurse confirms their arrival and records the timestamp data.
The hospital developed and launched a mobile admissions system on August 1, 2022. The mobile admission process enables hospital administrative procedures to be performed using the patient’s mobile phone on the morning of admission. All patients or caregivers are informed in advance about the mobile admission process and hospitalisation instructions. The hospital’s administrative department sends each patient the ‘Admission Contract’ form and any necessary consent forms via SMS or mobile messenger applications, such as Kakao Talk, 24 h before the day of admission. Patients who complete and submit the forms do not need to visit the administrative department. Instead, they can proceed directly to the admission guide booth to give their basic clinical information, complete the ‘Hospitalisation Guide’ form, and proceed to their assigned wards.
2.3 Study Participants
We included patients admitted to the hospital during the weekdays with a doctor’s admission prescription from the outpatient department between August 2022 and January 2023. Patients lacking a timestamp owing to missing data resulting from an abnormal flow, such as patients not visiting the administration department or the absence of consent forms owing to repeated admissions, such as for chemotherapy treatments, were excluded. In addition, as patients admitted through the emergency room (ER) undergo the admission process and transition within the ER, which differ significantly from the outpatient flow, ER admissions were excluded. Furthermore, patients who underwent the admission process on weekends were excluded, as the weekend flow differs from that of weekdays. We sorted patients into two age groups: older adults (≥ 65) and adults (< 65). We named the age group 65 years or less the adult group because paediatric patients should be with adults as caregivers to be admitted to the hospital. The admission departments were categorised according to whether surgery was performed. The surgery section comprised the general surgery, neurosurgery, thoracic surgery, otolaryngology, ophthalmology, obstetrics/gynaecology, orthopaedic surgery, plastic surgery, dentistry, and urology departments. The remaining cases were categorised as nonsurgical.
2.4 Data Collection
Chung-Ang University Gwangmyeong Hospital employs electronic signatures using tablets or other devices for all consent forms within the hospital. These electronic signatures are seamlessly integrated with the electronic medical record system.
Hospital timestamp data were extracted and analysed based on the flow of patients or caregivers during the admission process. Timestamps were extracted from electronic medical record log data, which are created when (1) patients or caregivers fill out and sign the ‘Admission Contract’ form at the administration counter, (2) patients or caregivers sign the ‘Hospitalisation Guide’ form at the admission guide booth, and (3) patients arrive at the ward, as recorded by nurses. Time intervals between timestamp data were calculated. Figure 1 summarises the walk-in and mobile admission processes.
2.5 Survey
Mobile admissions users voluntarily responded to 10 questions on a system usability scale (SUS) during the study period. The SUS is a 5-point Likert scale rated from 1 (strongly disagree) to 5 (strongly agree) and evaluates the usability of the mobile admission process [14]. Quantitative research was conducted retrospectively on the target population within a specific period. Based on Denzin and Lincoln [15], the justification for determining the sample size in qualitative research is typically until the point of informational saturation, where no new themes or information emerge. Previous qualitative research suggests a sample size of approximately 10 to 20 participants, where saturation is expected. Accordingly, in this study, only responses received within a specific period from the start of the research were considered.
2.6 Outcome Measures and Sensitivity Analysis
The primary outcome was the turnaround time for each admission method. We also analysed the factors associated with the usability of the mobile admission process among groups sorted by sex, age (adults and older adults), and admission departments (surgery and non-surgery). Subsequently, the SUS questionnaires were collected and analysed.
2.7 Statistical Analysis
Continuous variables are expressed as averages and standard deviations with 95% confidence intervals (CIs), whereas categorical variables are expressed as frequencies and percentages. The frequency difference between the two groups was examined using Pearson’s χ2 analysis and Fisher’s exact test. The average difference was examined using the Student’s t-test. Statistical significance was set at P < 0.05. A linear regression analysis was performed to investigate the impact of each factor on the time interval. We conducted a univariate analysis to assess the relationship between the time interval and each factor, followed by a multiple linear regression analysis to obtain the adjusted time interval. R version 4.2.0 (2022-04-22; R Foundation, Vienna, Austria) was used for all statistical analyses.