The Use of Smartphone-Based Triage to Reduce the Rate of Outpatient Error Registration: Cross-Sectional Study

Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study

BACKGROUND

In emergency departments (EDs), triage nurses are under tremendous daily pressure to rapidly assess the acuity level of patients and log the collected information into computers. With self-service technologies, patients could complete data entry on their own, allowing nurses to focus on higher-order tasks. Kiosks are a popular working example of such self-service technologies; however, placing a sufficient number of unwieldy and fixed machines demands a spatial change in the greeting area and affects pretriage flow. Mobile technologies could offer a solution to these issues.

OBJECTIVE

The aim of this study was to investigate the use of mobile technologies to improve pretriage flow in EDs.

METHODS

The proposed stack of mobile technologies includes patient-carried smartphones and QR technology. The web address of the self-registration app is encoded into a QR code, which was posted directly outside the walk-in entrance to be seen by every ambulatory arrival. Registration is initiated immediately after patients or their proxies scan the code using their smartphones. Patients could complete data entry at any site on the way to the triage area. Upon completion, the result is saved locally on smartphones. At the triage area, the result is automatically decoded by a portable code reader and then loaded into the triage computer. This system was implemented in three busy metropolitan EDs in Shanghai, China. Both kiosks and smartphones were evaluated randomly while being used to direct pretriage patient flow. Data were collected during a 20-day period in each center. Timeliness and usability of medical students simulating ED arrivals were assessed with the After-Scenario Questionnaire. Usability was assessed by triage nurses with the Net Promoter Score (NPS). Observations made during system implementation were subject to qualitative thematic analysis.

RESULTS

Overall, 5928 of 8575 patients performed self-registration on kiosks, and 7330 of 8532 patients checked in on their smartphones. Referring effort was significantly reduced (43.7% vs 8.8%; P<.001) and mean pretriage waiting times were significantly reduced (4.4, SD 1.7 vs 2.9, SD 1.0 minutes; P<.001) with the use of smartphones compared to kiosks. There was a significant difference in mean usability scores for "ease of task completion" (4.4, SD 1.5 vs 6.7, SD 0.7; P<.001), "satisfaction with completion time" (4.5, SD 1.4 vs 6.8, SD 0.6; P<.001), and "satisfaction with support" (4.9, SD 1.9 vs 6.6, SD 1.2; P<.001). Triage nurses provided a higher NPS after implementation of mobile self-registration compared to the use of kiosks (13.3% vs 93.3%; P<.001). A modified queueing model was identified and qualitative findings were grouped by sequential steps.

CONCLUSIONS

This study suggests patient-carried smartphones as a useful tool for ED self-registration. With increased usability and a tailored queueing model, the proposed system is expected to minimize pretriage waiting for patients in the ED.

Assessing the utility of artificial intelligence throughout the triage outpatients: a prospective randomized controlled clinical study

Currently, there are still many patients who require outpatient triage assistance. ChatGPT, a natural language processing tool powered by artificial intelligence technology, is increasingly utilized in medicine. To facilitate and expedite patients' navigation to the appropriate department, we conducted an outpatient triage evaluation of ChatGPT. For this evaluation, we posed 30 highly representative and common outpatient questions to ChatGPT and scored its responses using a panel of five experienced doctors. The consistency of manual triage and ChatGPT triage was assessed by five experienced doctors, and statistical analysis was performed using the Chi-square test. The expert ratings of ChatGPT's answers to these 30 frequently asked questions revealed 17 responses earning very high scores (10 and 9.5 points), 7 earning high scores (9 points), and 6 receiving low scores (8 and 7 points). Additionally, we conducted a prospective cohort study in which 45 patients completed forms detailing gender, age, and symptoms. Triage was then performed by outpatient triage staff and ChatGPT. Among the 45 patients, we found a high level of agreement between manual triage and ChatGPT triage (consistency: 93.3-100%, p<0.0001). We were pleasantly surprised to observe that ChatGPT's responses were highly professional, comprehensive, and humanized. This innovation can help patients win more treatment time, improve patient diagnosis and cure rates, and alleviate the pressure of medical staff shortage.

A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage

Large hospitals can be complex, with numerous discipline and subspecialty settings. Patients may have limited medical knowledge, making it difficult for them to determine which department to visit. As a result, visits to the wrong departments and unnecessary appointments are common. To address this issue, modern hospitals require a remote system capable of performing intelligent triage, enabling patients to perform self-service triage. To address the challenges outlined above, this study presents an intelligent triage system based on transfer learning, capable of processing multilabel neurological medical texts. The system predicts a diagnosis and corresponding department based on the patient’s input. It utilizes the triage priority (TP) method to label diagnostic combinations found in medical records, converting a multilabel problem into a single-label one. The system considers disease severity and reduces the “class overlapping” of the dataset. The BERT model classifies the chief complaint text, predicting a primary diagnosis corresponding to the complaint. To address data imbalance, a composite loss function based on cost-sensitive learning is added to the BERT architecture. The study results indicate that the TP method achieves a classification accuracy of 87.47% on medical record text, outperforming other problem transformation methods. By incorporating the composite loss function, the system’s accuracy rate improves to 88.38% surpassing other loss functions. Compared to traditional methods, this system does not introduce significant complexity, yet substantially improves triage accuracy, reduces patient input confusion, and enhances hospital triage capabilities, ultimately improving the patient’s medical experience. The findings could provide a reference for intelligent triage development.

Reliability of smartphone measurements of peripheral oxygen saturation and heart rate in hypotensive patients measurement of vital signs with smartphones

Objective

With the increasing use of wearable technologies (smartphones and smartwatches), it has become possible to measure vital signs outside healthcare institutions without the need for an additional medical device. With the advancement in technologies, the accuracy of vital signs measured by smartphones and smartwatches has also increased. In this study, the accuracy of smart devices in the measurement of heart rate and saturation, which are two vital signs that are difficult to detect in conditions such as hypotension were investigated.

Materials and methods

The study was prospectively conducted in a tertiary healthcare center. In hypotensive patients who presented to the emergency department (ED) and required an arterial blood gas evaluation, oxygen saturation and heart rate values measured by a smartphone, those measured with a vital signs monitor (VSM) at the time of admission to the ED and oxygen saturation values measured by a blood gas analyzer (BGA) were compared.

Results

A total of 200 patients, 117 women and 83 men, were included in the study. It was determined that the correlation coefficients of the heart rate values measured by the vital signs monitor and smartphone were in a high statistical agreement. When the saturation values measured by the vital signs monitor, smartphone, and blood gas analyzer were compared, it was found that the intra-class correlation coefficients of the saturation values measured by the smartphone with reference to the blood gas analyzer and vital signs monitor were 0.957 and 0.949, respectively, indicating an excellent agreement.

Conclusion

Smartphones have as high efficiency as reference devices in measuring heart rate and saturation in hypotensive patients. In this way, hypotensive patients who need medical help can also have the opportunity to measure their vital parameters with their smartphones, without the need for any other medical device, before applying to the hospital or emergency health system. This may contribute to the improvement of the quality of life of the patients and the early and accurate information of the health care providers about the patient's health parameters.

The Association between mHealth App Use and Healthcare Satisfaction among Clients at Outpatient Clinics: A Cross-Sectional Study in Inner Mongolia, China

Mobile health (mHealth) applications (apps) have been developed in hospital settings to allocate and manage medical care services, which is one of the national strategies to improve health care in China. Little is known about the comprehensive effects of hospital-based mHealth app use on client satisfaction. The aim of this study was to determine the relationship between the full range of mHealth app use and satisfaction domains among clients attending outpatient clinics. A cross-sectional survey was conducted from January to February 2021 in twelve tertiary hospitals in Inner Mongolia. After the construction of the mHealth app use, structural equation modeling was used for data analysis. Of 1889 participants, the standardized coefficients β on environment/convenience, health information, and medical service fees were 0.11 (p < 0.001), 0.06 (p = 0.039), and 0.08 (p = 0.004), respectively. However, app use was not significantly associated with satisfaction of doctor−patient communication (β = 0.05, p = 0.069), short-term outcomes (β = 0.05, p = 0.054), and general satisfaction (β = 0.02, p = 0.429). Clients of the study hospitals were satisfied with the services, but their satisfaction was not much associated with mHealth use. The limitation of the mHealth system should be improved to enhance communication and engagement among clients, doctors, and healthcare givers, as well as to pay more attention to health outcomes and satisfaction of clients.

Effects of a smartphone application named "Shared Decision Making Assistant" for informed patients with primary liver cancer in decision-making in China: a quasi-experimental study

BACKGROUND

It is well known that decision aids can promote patients' participation in decision-making, increase patients' decision preparation and reduce decision conflict. The goal of this study is to explore the effects of a "Shared Decision Making Assistant" smartphone application on the decision-making of informed patients with Primary Liver Cancer (PLC) in China.

METHODS

In this quasi-experimental study , 180 PLC patients who knew their real diagnoses in the Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China, from April to December 2020 were randomly assigned to a control group and an intervention group. Patients in the intervention group had an access to the "Shared Decision Making Assistant" application in decision-making, which included primary liver cancer treatment knowledge, decision aids path, continuing nursing care video clips, latest information browsing and interactive platforms. The study used decision conflict scores to evaluate the primary outcome, and the data of decision preparation, decision self-efficacy, decision satisfaction and regret, and knowledge of PLC treatment for secondary outcomes. Then, the data were entered into the SPSS 22.0 software and were analyzed by descriptive statistics, Chi-square, independent t-test, paired t-test, and Mann-Whitney tests.

RESULTS

Informed PLC patients in the intervention group ("SDM Assistant" group) had significantly lower decision conflict scores than those in the control group. ("SDM Assistant" group: 16.89 ± 8.80 vs. control group: 26.75 ± 9.79, P < 0.05). Meanwhile, the decision preparation score (80.73 ± 8.16), decision self-efficacy score (87.75 ± 6.87), decision satisfaction score (25.68 ± 2.10) and knowledge of PLC treatment score (14.52 ± 1.91) of the intervention group were significantly higher than those of the control group patients (P < 0.05) at the end of the study. However, the scores of "regret of decision making" between the two groups had no statistical significance after 3 months (P > 0.05).

CONCLUSIONS

Access to the "Shared Decision Making Assistant" enhanced the PLC patients' performance and improved their quality of decision making in the areas of decision conflict, decision preparation, decision self-efficacy, knowledge of PLC treatment and satisfaction. Therefore, we recommend promoting and updating the "Shared Decision Making Assistant" in clinical employment and future studies.

Socio-demographic digital divide in mHealth app use among clients at outpatient departments in Inner Mongolia, China: a cross-sectional study (Preprint)

Background

Mobile health (mHealth) apps have become part of the infrastructure for access to health care in hospitals, especially during the COVID-19 pandemic. However, little is known about the effects of sociodemographic characteristics on the digital divide regarding the use of hospital-based mHealth apps and their benefits to patients and caregivers.

Objective

The aim of this study was to document the cascade of potential influences from digital access to digital use and then to mHealth use, as well as the potential influence of sociodemographic variables on elements of the cascade.

Methods

A cross-sectional survey was conducted from January to February 2021 among adult clients at outpatient departments in 12 tertiary hospitals of Inner Mongolia, China. Structural equation modeling was conducted after the construct comprising digital access, digital use, and mHealth use was validated.

Results

Of 2115 participants, the β coefficients (95% CI) of potential influence of digital access on digital use, and potential influence of digital use on mHealth use, were 0.28 (95% CI 0.22-0.34) and 0.51 (95% CI 0.38-0.64), respectively. Older adults were disadvantaged with regard to mHealth access and use (β=–0.38 and β=–0.41), as were less educated subgroups (β=–0.24 and β=–0.27), and these two factors had nonsignificant direct effects on mHealth use.

Conclusions

To overcome the mHealth use divide, it is important to improve digital access and digital use among older adults and less educated groups.

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.