Effects of a comprehensive reservation service for non-emergency registration on appointment registration rate, patient waiting time, patient satisfaction and outpatient volume in a tertiary hospital in China

Xie, Wanhua; Yang, Xiufeng; Cao, Xiaojun; Liu, Peiying

doi:10.1186/s12913-019-4652-6

Cited by 42 publications

(38 citation statements)

References 24 publications

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“…The implementation of the comprehensive reservation service for non-emergency registration in the hospital shortened patient waiting time and improved patient satisfaction, and the outpatient volume was effectively controlled [4].…”

Section: Design Of Cloud Computing Outpatient Registration Model Thromentioning

confidence: 99%

Design of Cloud Computing Outpatient Registration Model Through SMS Messages at Hospitals using TOGAF ADM

Binsar*,

Legowo

2020

IJRTE

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The queue is one indicator of patient satisfaction at the hospital in addition to the service of getting treatment itself. Uncertainty, when patients will be served, will reduce patient satisfaction. Hospital outpatient poly enrollment services are the most numerous patient queue. Patients who come to the hospital may not necessarily get services directly because they only know the doctor's schedule and practice information after coming to the hospital. There are times when patients have to go home without getting treatment because they did not find the desired service schedule. The Ibnusina Indrapuri Aceh Hospital has a Hospital Management Information System (HMIS) application that is connected to the Health Social Security Administering Agency (Badan Penyelenggara Jaminan Sosial-BPJS) application to receive outpatient poly referral patients. The reception of outpatient registration has been done in a semi-manual way by officers where patients must come directly to the hospital. It takes a long time to input one patient's registration data, if there are many patient visits, the time for data input will be longer, causing long queues. So that registration can be done well and faster, it requires an easy way of registration that can be done by the patient himself from home without having to come directly to the hospital, which is using SMS messaging facilities. Patients will receive a reply message in the form of information on the availability of service schedules and queue numbers, patients do not need to come again to the hospital if the desired service schedule is not available. A registration model design is needed to illustrate the organization's management to be integrated into the HMIS application. The design of this registration model was created using the TOGAF framework. The use of the stages and phases contained in the TOGAF ADM framework shows results that are acceptable to the hospital management to design the architecture of the outpatient poly enrollment system at the hospital at an affordable cost and very feasible to use

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Section: Design Of Cloud Computing Outpatient Registration Model Thromentioning

confidence: 99%

Design of Cloud Computing Outpatient Registration Model Through SMS Messages at Hospitals using TOGAF ADM

Binsar*,

Legowo

2020

IJRTE

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“…There are two main reasons for this. First, most Chinese hospitals do not require patients to have a prescheduled appointment [6].…”

Section: Studymentioning

confidence: 99%

Artificial Intelligence-Assisted Reduction in Patients’ Waiting Time for Outpatient Procedures: A Matched Case–Control Study

Tian

et al. 2020

Preprint

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Background: Many studies indicate that patient satisfaction is significantly negatively correlated with waiting time. A well-designed healthcare system should not keep patients waiting too long for appointment and consultation. However, in China, patients spend considerable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less.Methods: We developed an artificial intelligence (AI)-assisted module that is embedded in hospital information systems. Through its use, outpatients were automatically recommended an imaging examination or a laboratory test based on their symptoms and chief complaint. Thus, they could get examined or tested before they went to see the doctor. People who saw a doctor in the traditional way were assigned to the conventional group, and those who used the AI-assisted system were assigned to the AI-assisted group. We conducted a 1:1 case–control study that applied propensity score matching to pair the data from patients in a pediatric tertiary hospital between August 1, 2019 and January 31, 2020. Waiting time was defined as the time from registration to preparation for a laboratory test or an imaging examination. The total cost included the registration fee, test fee, examination fee, and drug fee. The Wilcoxon rank-sum test was used to compare the differences in time and cost between the AI-assisted group and the conventional group. The statistical significance level was set at 0.05 for two sides.Results: A total of 12,342 visits were recruited for this study, consisting of 6,171 visits in the conventional group and 6,171 visits in the AI-assisted group. The median waiting time was 0.38 (inter-quartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05).Conclusions: Using AI can significantly reduce the waiting time of patients for outpatient procedures, and thus, enhance the outpatient process of hospitals.

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“…It has two dimensions: actual waiting time and perceived waiting time [3]. Some studies indicate that patient satisfaction is significantly negatively correlated with actual waiting time [2,[4][5][6][7]. While some studies believe the perception towards waiting time will affect overall satisfaction, but actual waiting time will not [3,8].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: a retrospective cohort study

Tian

et al. 2021

BMC Health Serv Res

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Background Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less. Methods We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw the doctor in the traditional way were allocated to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study from August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost. The statistical significance level was set at 0.05 for two sides. Results Twelve thousand and three hundred forty-two visits were recruited, consisting of 6171 visits in the conventional group and 6171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05). The total cost was 335.97 (interquartile range: 244.80, 437.60) CNY (Chinese Yuan) for the AI-assisted group and 364.58 (249.70, 497.76) CNY for the conventional group (p < 0.05). Conclusions Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.

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Effects of a comprehensive reservation service for non-emergency registration on appointment registration rate, patient waiting time, patient satisfaction and outpatient volume in a tertiary hospital in China

Cited by 42 publications

References 24 publications

Design of Cloud Computing Outpatient Registration Model Through SMS Messages at Hospitals using TOGAF ADM

Design of Cloud Computing Outpatient Registration Model Through SMS Messages at Hospitals using TOGAF ADM

Artificial Intelligence-Assisted Reduction in Patients’ Waiting Time for Outpatient Procedures: A Matched Case–Control Study

Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: a retrospective cohort study

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