Understanding the antecedents of patients’missed appointments:the perspective of attribution theory

The occurrence of missed appointments from online outpatient bookings significantly hinders the operational efficiency of outpatient services.This study aimed to investigate various factors influencing patients’missed appointments from online outpatient bookings.Drawing on attribution theory,an empirical analysis was conducted using 382,004 authentic online outpatient appointments.The empirical findings revealed that appointment lead-time,appointment time,weekday appointments,online doctor rating,appointment doctor’s expertise,patient distance,and previous outpatient visit experience significantly influenced patients’missed appointment behaviors from online outpatient bookings.Importantly,previous outpatient experience positively moderated the relationship between the appointment doctor’s expertise and patients’missed-appointment behavior.This study provides insights into the factors influencing patients’missed-appointment behavior from online outpatient bookings.It further offers a theoretical foundation for medical institutions in China to mitigate the likelihood and adverse effects of patients’missed-appointment behavior from online outpatient bookings.

Appointment scheduling with customer impatience based on operating cost model

An appointment scheduling problem is studied with the consideration of customer impatience.On the assumption that both the time of leaving queue and the time of service are exponentially distributed,in order to minimize the joint cost,the optimal appointment schedule of the fixed number of customers is studied.The joint cost function is composed of customers expected delay time and service availability time.The expected delay time of each customer in the queue is recursively computed in terms of customer interarrival time.Furthermore,the effect of impatience on the optimal schedule as well as the total operating cost is studied.The results show that as the impatience rate increases,the optimal interarrival time becomes shorter and the interarrival time of the last few customers gradually approaches that of the customers in the middle.In addition,impatient behaviors can increase the joint cost.

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies,all public tertiary hospitals in China began to use online outpatient appointment systems.However,the phenomenon of patient no-shows in online outpatient appointments is becoming more serious.The objective of this study is to design a prediction model for patient no-shows,thereby assisting hospitals in making relevant decisions,and reducing the probability of patient no-show behavior.We used 382,004 original online outpatient appointment records,and divided the data set into a training set(N_(1)=286,503),and a validation set(N_(2)=95,501).We used machine learning algorithms such as logistic regression,k-nearest neighbor(KNN),boosting,decision tree(DT),random forest(RF)and bagging to design prediction models for patient no-show in online outpatient appointments.The patient no-show rate of online outpatient appointment was 11.1%(N=42,224).From the validation set,bagging had the highest area under the ROC curve and AUC value,which was 0.990,followed by random forest and boosting models,which were 0.987 and 0.976,respectively.In contrast,compared with the previous prediction models,the area under ROC and AUC values of the logistic regression,decision tree,and k-nearest neighbors were lower at 0.597,0.499 and 0.843,respectively.This study demonstrates the possibility of using data from multiple sources to predict patient no-shows.The prediction model results can provide decision basis for hospitals to reduce medical resource waste,develop effective outpatient appointment policies,and optimize operations.

Appointment of Dr. Xue-Long JIANG as the Associate Editor-in-Chief of Zoological Research

We are very pleased to announce that we have invited Dr. Xue-Long JIANG to serve as the Associate Editor-in-Chief for Zoological Research （ZR）, effective from 1 March, 2016. Dr. JIANG, Professor and principle investigator from the Laboratory of Mammal Ecology and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, has worked with ZR since 2006 as a member of the editorial board and has played an active and important role in maintaining ZR as a respected academic publishing platform. Currently, he also works as the Associate Editor-in-Chief of Acta Thefiologica Sinica, Mammal Research and as a senior editorial board member of Biodiversity Science.

Appointment of Dr. Wai-Yee Chan as the Associate Editor-in-Chief of Zoological Research

We are very pleased to announce that we have invited Dr. Wai-Yee Chan, Professor and Director of School of Biomedical Sciences, The Chinese University of Hong Kong, to serve as the Associate Editor- in-Chief for Zoological Research （ZR）, effective 1 September, 2016.

Quantum Algorithm for Appointment Scheduling

Suppose a practical scene that when two or more parties want to schedule anappointment, they need to share their calendars with each other in order to make itpossible. According to the present result the whole communication cost to solve thisproblem should be their calendars’ length by using a classical algorithm. In this work, weinvestigate the appointment schedule issue made by N users and try to accomplish it inquantum information case. Our study shows that the total communication cost will bequadratic times smaller than the conventional case if we apply a quantum algorithm in theappointment-scheduling problem.

The impact of out-of-pocket expenditures on missed appointments at HIV r care and treatment centers in Northern Tanzania

Background:Missed clinic appointments negatively impact clinic patient flow and health outcomes of people living with HIV(PLHIV).PLHIV likelihood of missing clinic appointments is associated with direct and indirect expenditures made while accessing HIV care.The objective of this study was to examine the relationship between out-of-pocket(OOP)health expenditures and the likelihood of missing appointments.Method:Totally 618 PLHIV older than 18 years attending two HIV care and treatment centres(CTC)in Northern Tanzania were enrolled in the study.Clinic attendance and clinical characteristics were abstracted from medical records.Information on OOP health expenditures,demographics,and socio-economic factors were self-reported by the participants.We used a hurdle model.The first part of the hurdle model assessed the marginal effect of a one Tanzanian Shillings(TZS)increase in OOP health expenditure on the probability of having a missed appointment and the second part assessed the probability of having missed appointments for those who had missed an appointment over the study period.Results:Among these 618 participants,242(39%)had at least one missed clinic appointment in the past year.OOP expenditure was not significantly associated with the number of missed clinic appointments.The median amount of OOP paid was 5100 TZS per visit,about 7%of the median monthly income.Participants who were separated from their partners(adjusted odds ratio[AOR]=1.83,95%confidence interval[CZ]:1.11-8.03)and those aged above 50 years(AOR=2.85,95%CI:1.01-8.03)were significantly associated with missing an appointment.For those who had at least one missed appointment over the study period,the probability of missing a clinic appointment was significantly associated with seeking care in a public CTC(P=0.49,95%CI:0.88-0.09)and aged between>25-35 years(P=0.90,95%CI:0.11-1.69).Conclusion:Interventions focused on improving compliance to clinic appointments should target public CTCs,PLHIV aged between>25-35 years,above 50 years of age and those who are separated from their partners.

Design and Implementation of Clinic Appointment Registration System

Clinic Appointment Registration System is an important way to see a doctor, and it’s also a preliminary tool for storage and management of clinic medical records. This new system was developed using Visual Studio 2008 and C#.NET as the development environment and tools and Microsoft Access 2003 as the database to store the medical data based on Browse/Server (B/S) model. The system consists of several data operation functions including appointment registration, data management (e.g. addition, deletion and searching), data backup and recovery, etc., thus, achieve key research ob-jectives.

An Extended Bailey-welch Rule with No-show and Walk-in For Outpatient Appointment Scheduling

In clinic＇s appointment scheduling system no-shows have been a significant and confirmed issue with a bad influence on patient accessibility and clinic efficiency. The problem of walk-in has often been seen as the opposite of no-show problem. In this work we revisit a walk-in admitting based approach to mitigate the bad influence of no-show without overbooking. First we establish a model which utilizes marginal benefit objective function to balance the interests of the clinic, the patient and the doctor, we prove that no-show and walk-in cancels out each other straightly has a bad property. Then we propose a new rule which is an extension of the well-known Bailey - Welch rule, the simulation results show that our rule has an improvement comparing with the common rule that cancels them out straightly.

Visual analysis of shared medical appointments based on CiteSpace

Objective:Analyze the research hotspots and frontiers of shared outpatient service,and provide a reference for researchers in this field to carry out follow-up research.Methods:Search the Web of Science core collection database until 2022 and visually analyzekeywords in this field through CiteSpace5.8.R3 software,Results:A total of 261 iteratures were included.The research focuses mainly on advanced care planning,diabetes care,andgroup prenatal care,The research trend tends to be telemedicine and nursing provided by specialized nurses.Conclusion:Scholars can learn from the research hotspots of foreign shared medical appointments,pay attention to the research trend,expand its application scope in combination with China's national conditions,and further promote thedevelopment of shared medical appointments in China.

Making an Appointment

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Keeping an Appointment With Death The Final Contribution of a Woman Doct

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Sources appointment and health risks of PM_(2.5)-bound trace elements in a coastal city of southeastern China

To gain a comprehensive understanding of sources and health risks of trace elements in an area of China with high population densities and low PM_(2.5)concentrations,15 trace elements(Al,K,Ca,Ti,V,Cr,Mn,Fe,Ni,Cu,Zn,As,Sn,Ba,Pb)in PM_(2.5)were monitored from December 2020 to November 2021 in a representative city,Xiamen.The concentrations of trace elements in Xiamen displayed an obvious seasonal variation and were dominated by K,Fe,Al,Ca and Zn.Based on Positive Matrix Factorization analysis,source appointment revealed that the major sources of trace elements in Xiamen were traffic,dust,biomass and firework combustion,industrial manufacture and shipping emission.According to health risk assessment combined with the source appointment results,it indicated that the average noncarcinogenic risk was below the threshold and cancer risk of four hazardous metals(Cr,Ni,As,Pb)exceeded the threshold(10^(-6)).Traffic-related source had almost half amount of contribution to the health risk induced by PM_(2.5)-bound trace elements.During the dust transport period or Spring Festival period,the health risks exceeded an acceptable threshold even an order of magnitude higher,suggesting that the serious health risks still existed in low PM_(2.5)environment at certain times.Health risk assessment reminded that the health risk reduction in PM_(2.5)at southeastern China should prioritize traffic-related hazardous trace elements and highlighted the importance of controlling vehicles emissions in the future.

Factors associated with non-adherence to scheduled medical follow-up appointments among Cameroonian children requiring HIV care: a case-control analysis of the usual-care group in the MORE CARE trial

Background:A better understanding of why HIV-exposed/infected children fail to attend their scheduled follow-up medical appointments for HIV-related care would allow for interventions to enhance the delivery of care.The aim of this study was to determine characteristics of the caregiver-child dyad(CCD)associated with children’s non-adherence to scheduled follow-up medical appointments in HIV programs in Cameroon.Methods:We conducted a case-control analysis of the usual-care group of CCDs from the MORE CARE trial,in which the effect of mobile phone reminders for HIV-exposed/infected children in attending follow-up appointments was assessed from January to March 2013.For this study,the absence of a child at their appointment was considered a case and the presence of a child at their appointment was defined as a control.We used three multivariate binary logistic regression analyses.The best-fit model was the one which had the smallest chi-square value with the Hosmer-Lemeshow test(HLχ^(2)).Magnitudes of associations were expressed by odds ratio(OR),with a p-value<0.05 considered as statistically significant.Results:We included 30 cases and 31 controls.Our best-fit model which considered the sex of the adults and children separately(HLχ^(2)=3.5)showed that missing scheduled medical appointments was associated with:lack of formal education of the caregiver(OR 29.1,95%CI 1.1-777.0;p=0.044),prolonged time to the next appointment/follow-up(OR[1 week increase]1.4,95%CI 1.03-2.0;p=0.032),and being a female child(OR 5.2,95%CI 1.2-23.1;p=0.032).One model(HLχ^(2)=10.5)revealed that woman-boy pairs adhered less to medical appointments compared to woman-girl pairs(OR 4.9,95%CI 1.05-22.9;p=0.044).Another model(HLχ^(2)=11.1)revealed that man-boy pairs were more likely to attend appointments compared to woman-girl pairs(OR 0.23,95%CI 0.06-0.93;p=0.039).There were no statistical associations for the ages of the children or the caregivers,the study sites,or the HIV status(confirmed vs.suspected)of the children.Conclusion:The profile of children who would not attend follow-up medical appointments in an HIV program was:a female,with a caregiver who has had no formal education,and with a longer follow-up appointment interval.There is a possibility that female children are favored by female caregivers and that male children are favored by male caregivers when they come to medical care.

Optimal appointment scheduling with no-shows and exponential service time considering overtime work

Appointment systems are used by health clinics to manage access to service providers.In such systems,a specified number of patients are scheduled in advance,but certain patients may not arrive or‘show up’to their appointments.The existence of no-show behaviour influences both the operational cost of the clinics and the waiting time of the patients.In this paper,we determine an optimal schedule that takes no-show behaviour into account to determine the time intervals between patients under the framework of the individual-block/variableinterval rule for minimising the overall cost of the patient waiting time,the practitioner idle time and overtime.Under the condition that the service time of each patient is exponentially distributed,we compare the results with a schedule designed for the same expected number of patients in the absence of no-shows and analyse the effect on the system performance from the perspectives of day-length,expected workload,no-show probability,ratio of overtime costs and no-golf policy.We extend our results to an equally-spaced appointment system,which is commonly used in practice.Our results show that not only do no-shows greatly affect the system performance compared with an appointment system with the same expected workload without no-shows,but they also affect the optimal scheduling behaviours in the dome-shaped distribution.In addition,overtime cannot be eliminated completely even if the day length is adequate for all patients because of the stochastic characteristic of service time.

A Study on Decision of Appointment Problems Under Fuzzy Conditions and The Computer Management System

Starting from the classical appointment problems,this paper studies the appointment decision under fuzzy conditions,using the theory of fuzzy sets and the quantified approach for’soft’index,and tries to solve the problem of bow to synthetically handle the fuzzy massages and then get to the best appointment decision by the way of classical approach.In this paper,the question of fuzzy appointment decision.I also design the related computer management system in Turbo C language.This system can be used in rapid general decision and solve the problem of decision wit single or several elements under the fuzzy condition.

Saturday Appointment

SATURDAY Appointment is a sentimental TV programme combining interviews, arts displays, and also serves as a match-maker for singles. The programme, as well as its host Ni Lin, is drawing more and more attention. Five to six citizens out of every ten polled will mention it as their favourite out of the huge number of exciting shows on television nowadays in Shanghai. With the help of a friend, I happened to meet Ni Lin on a Saturday, which we said jokingly was another Saturday appointment. Much of her e...

Applications of Artificial Intelligence in Morocco’s Healthcare Sector: A Springboard to Medical Excellence

Artificial intelligence (AI) is revolutionizing the healthcare sector worldwide. In Morocco, several AI applications are being deployed in public and private healthcare establishments, improving appointment management, surgical operations, diagnostics, patient record tracking, biology and radiology, and OR organization. This article explores the main AI applications used in the Moroccan healthcare sector, their frequency of use, the types of establishments adopting them, as well as the main functionalities of each application and its contribution to the sector. The aim of this study is to analyze the impact of the main AI applications on quality of care and process efficiency in Moroccan healthcare facilities. This research focuses on several fundamental questions: Which AI applications are most frequently used? What types of establishments are adopting these technologies, and for which specific functionalities? What are the benefits and challenges of integrating AI into the Moroccan healthcare system, particularly in terms of territorial distribution and accessibility? The methodology is based on a quantitative analysis of data collected from selected healthcare establishments, combined with studies of reports from public health authorities and a sweep of their websites. The results show that 45% of hospitals use AI systems for appointment scheduling and 30% for medical diagnosis. The use of surgical robots, such as the Da Vinci system, increased by 30% between 2020 and 2024. Comparisons with other emerging countries highlight Morocco’s acceptable advances, while underlining the challenges, particularly in terms of the territorial distribution of these technological infrastructures generally centralized in the country’s major cities.

老年患者门诊预约挂号的影响因素:基于安德森健康行为模型的实证研究

目的基于安德森健康行为模型,研究老年患者门诊预约挂号的影响因素,为优化门诊预约挂号服务、推进老年患者预约挂号提供参考。方法以安德森健康行为模型为理论框架设计问卷,采用方便抽样法向上海某三级甲等综合医院门诊老年患者发放调查问卷。将各维度因素纳入二分类logistic回归模型,采用单因素分析和logistic回归分析探讨老年患者门诊预约挂号的影响因素。结果共回收282份有效问卷,预约挂号187人,占66.31%。曾使用过1~2种预约挂号服务、存在社会支持、距离医院路程80 min以上、收入为>5000~7500元、就诊科室预约率25.00%以上、有预约挂号使用意愿的老年患者更倾向于预约挂号。患慢性病数量在5种及以上的老年患者更不倾向于预约挂号。结论老年患者门诊预约需求较大且受多种因素影响。须关注老年人预约挂号“痛点”,关怀老年群体,共建支持网络,加强有效宣传,优化医疗资源配置,激发使用意愿。