Medical appointment overbooking and optimal scheduling: tradeoffs between schedule efficiency and accessibility to service

Kuo, Yong-Hong; Balasubramanian, Hari; Chen, Yan

doi:10.1007/s10696-019-09340-z

Cited by 19 publications

(9 citation statements)

References 37 publications

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“…From a practical viewpoint, the study was motivated by concerns from the radiology service managers with the idleness of CT resources due to the no-show of outpatients and, consequently, with the large average waiting time for exams (78 days). With that in view, no-show prediction and the analysis of overbooking rules to mitigate its adverse consequences, as proposed in this article, can reduce waste in the use of resources, increase productivity and reduce waiting times, as reported in similar studies in other healthcare settings [ 19 – 22 ]. That is relevant, especially in the Brazilian case, characterized by a unified public health system with growing demand and lack of investments to expand service capacity.…”

Section: Introductionmentioning

confidence: 84%

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Silva

Fogliatto

Krindges

et al. 2021

BMC Health Serv Res

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Background We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated. Methods The model’s main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal. Results The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation. Conclusions Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system’s cost is minimized.

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Section: Introductionmentioning

confidence: 84%

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Silva

Fogliatto

Krindges

et al. 2021

BMC Health Serv Res

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“…Outpatients and visiting groups pay special attention to the parking fee and accept a longer walking distance (Chen et al, 2017). Standard practice for outpatients is the denominated appointment overbooking that hospitals have regularly adopted to mitigate patients' no-shows and the adverse effects of unattended medical staff time slots (Kuo et al, 2020). This multi booking strategy aims to increase hospital competitiveness (Chams-Anturi et al, 2020).Although typical, this practice affects the parking area's efficiency since it alters the number of spaces occupied by outpatients.…”

Section: Hospital Parking Usersmentioning

confidence: 99%

A Discrete-Event Simulation Approach to Assess the Benefits of Parking Technologies in Hospitals

Soto-Ferrari¹,

Romero-Rodríguez²,

Escorcia-Caballero³

et al. 2021

International Journal of Operations and Quantitative Management

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As essential service providers, hospitals receive a large quantity of traffic every day. The deficiency in hospital parking facilities causes drivers to spend more time getting a parking spot, causing delays for patients and physicians. We develop and validate discrete-event simulation models upon other parking configurations in a Saudi Arabian hospital to assess smart parking benefits. We find that technology implementations are not advantageous for condensed parking areas. Our results indicate that the improvements in drivers' time will depend on the various factors of the parking configuration and aspects such as the car arrival rate, car flow, and parking visibility.

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“…In terms of implementing mitigation actions, overbooking can maintain an efficient use of resources, despite no-show [ 2 , 25 ]. However, there is a trade-off between efficiency and service quality.…”

Section: Introductionmentioning

confidence: 99%

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Dunstan

Villena

Sanchez

et al. 2023

Health Care Manag Sci

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The Chilean public health system serves 74% of the country’s population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients’ historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.

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Medical appointment overbooking and optimal scheduling: tradeoffs between schedule efficiency and accessibility to service

Cited by 19 publications

References 37 publications

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

A Discrete-Event Simulation Approach to Assess the Benefits of Parking Technologies in Hospitals

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

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