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
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
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
BACKGROUND Colonoscopy attendance is a key quality parameter in colorectal cancer population screening programmes.Within these programmes,educative interventions with bidirectional contact carried out by trained perso...BACKGROUND Colonoscopy attendance is a key quality parameter in colorectal cancer population screening programmes.Within these programmes,educative interventions with bidirectional contact carried out by trained personnel have been proved to be an important tool for colonoscopy attendance improvement,and because of its huge clinical and economic impact,they have been widely implemented.However,outside of this population programmes,educative measures to improve colonoscopy attendance have been poorly studied and no navigation interventions are usually performed.AIM To investigate the clinical and economic impacts of an educational telephone intervention on colonoscopy attendance outside colorectal cancer screening programmes.METHODS This randomized controlled trial included consecutive patients referred to colonoscopy from primary care centres from November 2017 to May 2018.The intervention group(IG)received a telephone intervention,while the control group(CG)did not.Patients assigned to the IG received an educational telephone call 7 d before the colonoscopy appointment.The intervention was carried out by two nurses with deep endoscopic knowledge who were previously trained for a telephone educational intervention for colonoscopy.The impact on patient compliance with preparedness protocols related to bowel cleansing,antithrombotic management,and sedation scheduling was also evaluated.A second call was conducted to assess patient satisfaction.Intention-to-treat(ITT)and perprotocol(PP)analyses were performed.RESULTS A total of 738 and 746 patients were finally included in the IG and CG respectively.Six hundred thirteen(83%)patients were contacted in the IG.The non-attendance rate was lower in the IG,both in the ITT analysis(IG 8.4%vs CG 14.3%,P<0.001)and in the PP analysis(4.4%vs 14.3%,P<0.001).In a multivariable analysis,belonging to the control group increased the risk of nonattendance in both,the ITT analysis(OR 1.81,95%CI:1.27 to 2.58,P=0.001)and the PP analysis(OR 3.56,95%CI:2.25 to 5.64,P<0.001).There was also a significant difference in compliance with preparedness protocols[bowel cleansing:IG 61.7%vs CG 52.6%(P=0.001),antithrombotic management:IG 92.5%vs CG 62.8%(P=0.001),and sedation scheduling:IG 78.8%vs CG 0%(P≤0.001)].We observed a net benefit of €55600/year after the intervention.The information given before the procedure was rated as excellent by 26%(CG)and 51%(IG)of patients,P≤0.001.CONCLUSION Educational telephone nurse intervention improves attendance,protocol compliance and patient satisfaction in the non-screening colonoscopy setting and has a large economic impact,which supports its imple-mentation and maintenance over time.展开更多
This paper focuses on an outpatient capacity allocation problem where the patient demand is quite higher than the supply. We study an adding capacity policy to mitigate the mismatch between supply and demand. Under th...This paper focuses on an outpatient capacity allocation problem where the patient demand is quite higher than the supply. We study an adding capacity policy to mitigate the mismatch between supply and demand. Under this policy, the doctor is allowed to add capacity if all regular capacity have been booked. A capacity allocation model is formulated for both possible no-show routine patients and all show-up same-day patients. The purpose is to determine the number of capacity can be added and how to allocate regular capacity among routine patients and same-day patients, towards maximizing the expected profit, which is composed of the expected income minus the cost of weighted expected doctor＇s overload work caused by the adding capacity policy and the cost of rejecting patients. To achieve the aims, we prove the expected profit monotonously decreases when the number of additional capacity exceeds a threshold, and present a two-tier enumeration search algorithm to fred the global optimal solution based on the proof. Numerical results indicate that the proposed policy performs well under different levels of demand higher than supply. The optimal number of the additional capacity is hardly affected by varying total expected patient demand. Additionally, under the change of no-show rate, the number of regular capacity allocated to routine patients becomes more stable, compared with the optimal scheme without considering adding capacity policy.展开更多
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.
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.
基金Supported by Hospital del Mar,Parc de Salut Mar.
文摘BACKGROUND Colonoscopy attendance is a key quality parameter in colorectal cancer population screening programmes.Within these programmes,educative interventions with bidirectional contact carried out by trained personnel have been proved to be an important tool for colonoscopy attendance improvement,and because of its huge clinical and economic impact,they have been widely implemented.However,outside of this population programmes,educative measures to improve colonoscopy attendance have been poorly studied and no navigation interventions are usually performed.AIM To investigate the clinical and economic impacts of an educational telephone intervention on colonoscopy attendance outside colorectal cancer screening programmes.METHODS This randomized controlled trial included consecutive patients referred to colonoscopy from primary care centres from November 2017 to May 2018.The intervention group(IG)received a telephone intervention,while the control group(CG)did not.Patients assigned to the IG received an educational telephone call 7 d before the colonoscopy appointment.The intervention was carried out by two nurses with deep endoscopic knowledge who were previously trained for a telephone educational intervention for colonoscopy.The impact on patient compliance with preparedness protocols related to bowel cleansing,antithrombotic management,and sedation scheduling was also evaluated.A second call was conducted to assess patient satisfaction.Intention-to-treat(ITT)and perprotocol(PP)analyses were performed.RESULTS A total of 738 and 746 patients were finally included in the IG and CG respectively.Six hundred thirteen(83%)patients were contacted in the IG.The non-attendance rate was lower in the IG,both in the ITT analysis(IG 8.4%vs CG 14.3%,P<0.001)and in the PP analysis(4.4%vs 14.3%,P<0.001).In a multivariable analysis,belonging to the control group increased the risk of nonattendance in both,the ITT analysis(OR 1.81,95%CI:1.27 to 2.58,P=0.001)and the PP analysis(OR 3.56,95%CI:2.25 to 5.64,P<0.001).There was also a significant difference in compliance with preparedness protocols[bowel cleansing:IG 61.7%vs CG 52.6%(P=0.001),antithrombotic management:IG 92.5%vs CG 62.8%(P=0.001),and sedation scheduling:IG 78.8%vs CG 0%(P≤0.001)].We observed a net benefit of €55600/year after the intervention.The information given before the procedure was rated as excellent by 26%(CG)and 51%(IG)of patients,P≤0.001.CONCLUSION Educational telephone nurse intervention improves attendance,protocol compliance and patient satisfaction in the non-screening colonoscopy setting and has a large economic impact,which supports its imple-mentation and maintenance over time.
基金This research is supported in part by the National Natural Science Foundation of China under Grant 71420107028, in part by Hong Kong Research Grant Council under Grant T32-102/14-N and in part by the National Natural Science Foundation of China under Grant 71501027.
文摘This paper focuses on an outpatient capacity allocation problem where the patient demand is quite higher than the supply. We study an adding capacity policy to mitigate the mismatch between supply and demand. Under this policy, the doctor is allowed to add capacity if all regular capacity have been booked. A capacity allocation model is formulated for both possible no-show routine patients and all show-up same-day patients. The purpose is to determine the number of capacity can be added and how to allocate regular capacity among routine patients and same-day patients, towards maximizing the expected profit, which is composed of the expected income minus the cost of weighted expected doctor＇s overload work caused by the adding capacity policy and the cost of rejecting patients. To achieve the aims, we prove the expected profit monotonously decreases when the number of additional capacity exceeds a threshold, and present a two-tier enumeration search algorithm to fred the global optimal solution based on the proof. Numerical results indicate that the proposed policy performs well under different levels of demand higher than supply. The optimal number of the additional capacity is hardly affected by varying total expected patient demand. Additionally, under the change of no-show rate, the number of regular capacity allocated to routine patients becomes more stable, compared with the optimal scheme without considering adding capacity policy.
