Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Appendicectomy pathway: Insights from electronic medical records of a local health district in Australia

BACKGROUND

This study aims to identify the common pathways of appendicectomy, the most common emergency surgery in Australia's public hospitals and any variations within a regional public health district in New South Wales, Australia.

METHODS

We analyzed the electronic medical records of 3,943 patients who underwent appendicectomy between January 2014 and July 2020 at 2 hospitals in the Illawarra Shoalhaven Local Health District, New South Wales, Australia, using the PM2 approach for surgical pathway identification and subsequent statistical analyses.

RESULTS

Among 3,943 patients, 3,606 (91.5%) followed an 11-step main pathway: (1) emergency department admission, (2) surgery booking, (3) anesthesia start, (4) operating room entry, (5) surgery start, (6) surgery end, (7) anesthesia end, (8) operating room discharge, (9) postanesthesia care unit admission, (10) postanesthesia care unit discharge, and (11) hospital discharge. The median length of stay was 48.13 hours (interquartile range 32.74). The main pathway differed from either variation 1 (n = 246, 6.2%) or variation 2 (n = 30, 0.8%) only in the timing and location of anesthesia administration or conclusion. Variation 3 (n = 26, 0.7%) included patients who underwent appendicectomy twice, whereas variation 4 (n = 25, 0.6%) included patients booked for surgery before emergency department admission through community doctor referrals. Thirteen exceptional cases experienced combinations of the aforementioned pathways. The length of stay and phase durations varied between the main pathway and these variations.

CONCLUSION

The appendicectomy pathway was largely standardized across the studied hospitals, with the location of anesthesia administration or conclusion affecting specific stages but not the overall length of stay. Only a complex 2-surgery pathway increased length of stay.

A Study on Decision-Making for Improving Service Efficiency in Hospitals

The provision of efficient healthcare services is essential, driven by the increasing demand for healthcare resources and the need to optimize hospital operations. In this context, the motivation to innovate and improve services while addressing urgent concerns is critical. Hospitals face challenges in managing internal dispatch services efficiently. Outsourcing such services can alleviate the burden on hospital staff, reduce costs, and introduce professional expertise. However, the pressing motivation lies in enhancing service quality, minimizing costs, and exploring innovative approaches. With the rising demand for healthcare services, there is an immediate need to streamline hospital operations. Delays in internal transportation services can have far-reaching implications for patient care, necessitating a prompt and effective solution. Drawing upon dispatch data from a healthcare center in Taiwan, this study constructed a decision-making model to optimize the allocation of hospital service resources. Employing simulation techniques, we closely examine how hospital services are currently organized and how they work. In our research, we utilized dispatch data gathered from a healthcare center in Taichung, Taiwan, spanning from January 2020 to December 2020. Our findings underscore the potential of an intelligent dispatch strategy combined with deployment restricted to the nearest available workers. Our study demonstrates that for cases requiring urgent attention, delay rates that previously ranged from 5% to 34% can be notably reduced to a much-improved 3% to 18%. However, it is important to recognize that the realm of worker dispatch remains subject to a multifaceted array of influencing factors. It becomes evident that a comprehensive dispatching mechanism must be established as part of a broader drive to enhance the efficiency of hospital service operations.

Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students

Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.

How to adjust the expected waiting time to improve patient's satisfaction?

BACKGROUND

Long waiting time in hospital leads to patient's low satisfaction. In addition to reducing the actual waiting time (AWT), we can also improve satisfaction by adjusting the expected waiting time (EWT). Then how much can the EWT be adjusted to attribute a higher satisfaction?

METHODS

This study was conducted though experimental with hypothetical scenarios. A total of 303 patients who were treated by the same doctor from August 2021 to April 2022 voluntarily participated in this study. The patients were randomly divided into six groups: a control group (n = 52) and five experimental groups (n = 245). In the control group, the patients were asked their satisfaction degree regarding a communicated EWT (T₀) and AWT (T_a) under a hypothetical situation. In the experimental groups, in addition to the same T₀ and T_a as the control group, the patients were also asked about their satisfaction degree with the extended communicated EWT (T₁). Patients in five experimental groups were given T₁ values with 70, 80, 90, 100, and 110 min respectively. Patients in both control and experiment groups were asked to indicate their initial EWT, after given unfavorable information (UI) in a hypothetical situation, the experiment groups were asked to indicate their extended EWT. Each participant only participated in filling out one hypothetical scenario. 297 valid hypothetical scenarios were obtained from the 303 hypothetical scenarios given.

RESULTS

The experimental groups had significant differences between the initial indicated EWT and extended indicated EWT under the effect of UI (20 [10, 30] vs. 30 [10, 50], Z = -4.086, P < 0.001). There was no significant difference in gender, age, education level and hospital visit history (χ² = 3.198, P = 0.270; χ² = 2.177, P = 0.903; χ² = 3.988, P = 0.678; χ² = 3.979, P = 0.264) in extended indicated EWT. As for patient's satisfaction, compared with the control group, significant differences were found when T₁ = 80 min (χ² = 13.511, P = 0.004), T₁ = 90 min (χ² = 12.207, P = 0.007) and T₁ = 100 min (χ² = 12.941, P = 0.005). When T₁ = 90 min, which is equal to the T_a, 69.4% (34/49) of the patients felt "very satisfied", this proportion is not only significantly higher than that of the control group (34/ 49 vs. 19/52, χ² = 10.916, P = 0.001), but also the highest among all groups. When T₁ = 100 min (10 min longer than T_a), 62.5% (30/48) of the patients felt "very satisfied", it is significantly higher than that of the control group (30/ 48 vs. 19/52, χ² = 6.732, P = 0.009). When T₁ = 80 min (10 min shorter than T_a), 64.8% (35/54) of the patients felt "satisfied", it is significantly higher than that of the control group (35/ 54 vs. 17/52, χ² = 10.938, P = 0.001). However, no significant difference was found when T₁ = 70 min (χ² = 7.747, P = 0.052) and T₁ = 110 min (χ² = 4.382, P = 0.223).

CONCLUSIONS

Providing UI prompts can extend the EWT. When the extended EWT is closer to the AWT, the patient's satisfaction level can be improved higher. Therefore, medical institutions can adjust the EWT of patient's through UI release according to the AWT of hospitals to improve patient's satisfaction.

A comparative analysis of outpatient nutrition clinic scheduling outcomes based on in-person and telehealth patient care delivery modalities☆

During the start of the global COVID-19 pandemic in March 2020, patient care modalities changed from in-person to telehealth to comply with physical distancing guidelines. Our study uniquely examines operations data from three distinct periods: before the transition to telehealth, early transition from in-person care to telehealth, and the eventual adoption of telehealth. We present a comparative analysis of outpatient nutrition clinic scheduling outcomes based on care delivery modality. We used descriptive statistics to report means and variance and frequencies. We used inferential statistics to make comparisons: categorical data were compared using chi- square analysis with post-hoc comparisons using a z-test with alpha at 0.05. Means of continuous variables were compared using ANOVA with Tukey HSD post-hoc analysis. We found patient demographics remained widely unchanged across the three distinct periods as the demand for telehealth visits increased, with a notable rise in return patient visits, signaling both adaptability across the patient population and acceptance of the telehealth modality. These analyses along with evidence from the included literature review point to many the benefits of telehealth, thus telehealth as a healthcare delivery modality is here to stay. Our work serves as a foundation for future studies in this field, provides information for decision-makers in telehealth-related strategic planning, and can be utilized in advocacy for the extension of telehealth coverage.

The Effect of Mobile Payment on Payment Waiting Time for Outpatients With Medical Insurance: Historically Controlled Study

BACKGROUND

Waiting for a long time to make payments in outpatient wards and long queues of insured patients at the checkout window are common in many hospitals across China. To alleviate the problem of long queues for payment, many hospitals in China have established various mobile apps that those without health insurance can use. However, medically insured outpatients are still required to pay manually at the checkout window. Therefore, it is urgent to use information technology to innovate and optimize the outpatient service process, implement mobile payment for medically insured outpatients, and shorten the waiting time for outpatients, especially in the context of the COVID-19 epidemic. Furthermore, smartphone-based mobile payment for outpatients with health insurance could be superior to on-site cashier billing.

OBJECTIVE

This study aimed to investigate the impact of smartphone-based mobile payment in relation to different aspects, such as waiting time, satisfaction with patients' waiting time, payment experience, the proportion of those dissatisfied with payment, total outpatient satisfaction, and outpatient volume, and compare mobile payment with on-site payment.

METHODS

This was a historically controlled study. This study analyzed the outpatients' waiting time to make a medical insurance payment, their satisfaction with the waiting time and payment experience, the proportion of those dissatisfied with payment, and the outpatient volume of patients at Guangzhou Women and Children's Medical Center 1 year before and after the implementation of mobile payment for medical insurance in January 2021. An independent sample 2-tailed t test was used to compare waiting time, satisfaction with waiting time, and overall satisfaction. Paired sample 2-tailed t test was used to compare monthly outpatient visits. The chi-square test was used to compare the percentages of patients dissatisfied with payment.

RESULTS

After the implementation of mobile payment for medical insurance outpatients, the patients' payment waiting time was significantly shortened (mean 45.28, SD 10.35 min vs mean 1.02, SD 0.25 min; t₉₀₁₄=53.396; P<.001), and satisfaction with waiting time and payment experience were significantly improved (mean 82.08, SD 3.17 vs mean 90.36, SD 3.45; t₉₀₁₄=-118.65; P<.001). Dissatisfaction with payment significantly decreased (10.27%, SD 2.18% vs 1.19% vs SD 0.30%; P<.001). The total satisfaction of outpatients significantly improved (mean 86.91, SD 3.23 vs mean 89.98, SD 3.31; t₉₀₁₄=-44.57; P<.001), and the outpatient volume increased (248,105.58, SD 89,280.76 vs 303,194.75, SD 53,773.12; t₁₁=2.414; P=.03). Furthermore, payment efficiency improved, and the number of the on-site cashiers substantially decreased.

CONCLUSIONS

Mobile payment for health insurance significantly shortened patients' payment waiting time; improved patient satisfaction on waiting time and payment experience and overall satisfaction; reduced the proportion of patients who were dissatisfied with payment and the cashier at the hospital; and increased monthly outpatient volume. This approach was effective and thus worthy of promoting.

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention

Assessment of attention is of great importance as one of human cognitive abilities. Although neuropsychological tests have been developed and used to evaluate the ability to pay attention, their validity and reliability have been reduced due to some limitations such as the presence of intervention factors, including human factors, limited range of languages, and cultural influences. Therefore, direct outputs of the brain system, represented by event-related potentials (ERPs), and the analysis of its function in cognitive activities have become very important as a complementary tool to assess various types of attention. This research tries to assess 4 types of attention including sustained, alternative, selective, and divided, using an integrated visual-auditory test and brain signals simultaneously. Thus, the electroencephalogram (EEG) data were recorded using 19 channels, and the integrated visual and auditory (IVA-AE) test was simultaneously performed on twenty-eight healthy volunteers including 22 male and 6 female subjects with the average age of 27 ± 5.3 years. Then ERPs related to auditory and visual stimuli with synchronous averaging technique were extracted. A topographic brain mapping (topo-map) was plotted for each frame of stimulation. Next, an optical flow method was implemented on different topo-maps to obtain motion vectors from one map to another. After obtaining the overall brain graph of an individual, some features were extracted and used as measures of local and global connectivity. The extracted features were consequently evaluated along with the parameters of the IVA test by support vector machine regression (SVM-R). The volume of attention was then quantified by combining the IVA parameters. Ultimately, estimation accuracy of each type of attention including focused attention (86.1%), sustained attention (83.4%), selective attention (80.9%), and divided attention (79.9%) was obtained. According to the present study, there is a significant relationship between response control and attention indicators of the IVA test as well as ERP brain signals.

Evaluating Different Strategies on the Blood Collection Counter Settings to Improve Patient Waiting Time in Outpatient Units

Long patient waiting time is one of the major problems in the healthcare system and it would decrease patient satisfaction. Previous studies usually investigated how to improve the treatment flow in order to reduce patient waiting time or length of stay. The studies on blood collection counters have received less attention. Therefore, the objective of this study is to reduce the patient waiting time at outpatient clinics for metabolism and nephrology outpatients. A discrete-event simulation is used to analyze the four different strategies for blood collection counter resource allocation. Through analyzing four different strategic settings, the experimental results revealed that the maximum number of patients waiting before the outpatient clinics was reduced from 41 to 33 (20%); the maximum patient waiti-ng time at the outpatient clinics was decreased from 201.6 minutes to 83 minutes (59%). In this study, we found that adjusting the settings of blood collection counters would be beneficial. Assigning one exclusive blood collection counter from 8 to 10 am is the most suitable option with the least impact on the operational process for hospital staff. The results provide managerial insight regarding the cost-effective strategy selection for the hospital operational strategy.