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 outpati...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.展开更多
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 op...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.展开更多
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 ap...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.展开更多
基金National Natural Science Foundation Program of China[No.71971092],[No.71671073]and[71810107003].
文摘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.
文摘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.
基金This paper was financially supported by National Natural Science Foundation of China(71021061,61273204).
文摘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.
