Nonattendance to medical specialists’ appointments and its relation to regional environmental and socioeconomic indicators in the Chilean public health system

Rebolledo, Elizabeth Angélica Salinas; Mesía, Rolando De la Cruz; Silva, Gabriel Bastías

doi:10.5867/medwave.2014.09.6023

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Some examples of absenteeism studies in Latin America also estimated variable rates: 11% in Costa Rica, 26 17% in Mexico, three studies from Argentina with nonattendance of 14% in Rosario, 27 22.7% in HIBA, 28 30% in the Mandirola study, 29 25.57% in Sao Paulo Brazil, 30 and between 8% and 20% in Chile's public system stratified by specialty and region. 31 These nonattendance rates present similar variability and heterogeneity between specialities, settings, and populations studied in concordance with other geographic areas estimations. Our estimations presented in this study are consistently within this range of variability.…”

Section: Discussionsupporting

confidence: 86%

“…Our nonattendance rates are similar to those reported in other systems with a public payer predominance, such as the Chile study. 31 Because of the consistent variability of nonattendance in different settings, it is strongly recommended that local estimates are used in the design of effective interventions to improve adherence with outpatient healthcare scheduled appointments.…”

Section: Discussionmentioning

confidence: 99%

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Nonattendance rates of scheduled outpatient appointments in a university general hospital

Giunta

Serena

2019

Health Planning & Management

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Summary Objective We aimed to estimate nonattendance to scheduled medical ambulatory appointments rates globally and by subgroups. Design and participants We designed a retrospective cohort of all adult outpatients over 18 years old who requested at least one scheduled ambulatory medical appointment from 1 January 2015 to 31 December 2016. Setting Hospital Italiano de Buenos Aires is a university general hospital in the Autonomous City of Buenos Aires, Argentina. It includes an integrated health care network that is formed by two high complexity hospitals and 22 primary care centers. Results The age median was 60.4 years, and 31.33% of the appointments were scheduled by men; 2 526 549 appointments fulfilled selection criteria, belonging to 348 420 patients. The global nonattendance rate was 27.84% (95% CI, 27.79‐27.9). The nonattendance rate to general practitioner appointments was 25.53% (95% CI, 25.42‐25.63); to clinical specialties, 27.78% (95% CI, 27.68‐27.87); and to surgical specialties, 29.31% (95% CI, 29.23‐29.4). Conclusions Because of the consistent variability of nonattendance in different settings, it is strongly recommended that local estimates are used in the design of effective interventions to improve adherence with outpatient healthcare scheduled appointments.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Nonattendance rates of scheduled outpatient appointments in a university general hospital

Giunta

Serena

2019

Health Planning & Management

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“…Applying Poisson regression models with random components, they analyzed the relationship between no-shows and environmental and socioeconomic regional indicators. They found that socioeconomic factors and healthcare resources management could influence no-shows more than environmental factors [35]. Samorani et al (2022) studied racial disparity in the context of predicting no-shows using a machine learning algorithm.…”

Section: Predicting No-showsmentioning

confidence: 99%

Ensemble Learning for Addressing Class Imbalance in Cardiology Appointment Scheduling and Overbooking

Aghaeifar,

Servis,

Khasawneh

2023

Preprint

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Purpose Outpatient appointment scheduling is a critical aspect of healthcare services; however, the unpredictable nature of patient behavior poses challenges. This study focuses on predicting patient behavior in cardiology appointment scheduling in an outpatient cardiology practice within the Mount Sinai Health System (MSHS) to optimize overbooking strategies. Methods By reviewing the literature and conducting exploratory data analysis, significant features influencing patient behavior were identified. An ensemble learning model for an imbalanced class was developed to accurately predict the likelihood of no-show appointments and enable strategic overbooking decisions. Results The findings demonstrate that an increased lead time is associated with a greater probability of appointment no-shows. Patient-initiated rescheduling and cancellations were identified as the primary reasons for appointment changes. Moreover, while average wait time increases with age, younger individuals tend to have longer appointments. A stacking ensemble model for imbalanced classes and three machine-learning approaches were evaluated. The stacking ensemble model outperformed traditional techniques with an impressive F1 score of 92.3% and an AUC of 91%. Conclusion The proposed model enables allocating appointments based on patient preferences and characteristics, optimizing resource use, and reducing the number of cancellations and no-shows.

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“…b) Predicting patients’ behaviour. To this end, researchers have used diverse statistical methods, including logistic regression [ 5 , 20 , 22 , 40 ], generalised additive models [ 43 ], multivariate [ 5 ], hybrid methods with Bayesian updating [ 1 ], Poisson regression [ 41 ], decision trees [ 12 , 13 ], ensembles [ 14 , 37 ], and stacking methods [ 46 ]. Their efficiency depends on the ability of predictors to compute the probability of no-show for a given patient and appointment.…”

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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Nonattendance to medical specialists’ appointments and its relation to regional environmental and socioeconomic indicators in the Chilean public health system

Abstract: In Chile, socioeconomic factors and the management of healthcare resources have greater influence on the nonattendance of patients to medical specialists appointments than environmental factors; therefore, this phenomenon may be avoidable.

Cited by 6 publications

References 19 publications

Nonattendance rates of scheduled outpatient appointments in a university general hospital

Nonattendance rates of scheduled outpatient appointments in a university general hospital

Ensemble Learning for Addressing Class Imbalance in Cardiology Appointment Scheduling and Overbooking

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

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Nonattendance to medical specialists’ appointments and its relation to regional environmental and socioeconomic indicators in the Chilean public health system

Abstract: In Chile, socioeconomic factors and the management of healthcare resources have greater influence on the nonattendance of patients to medical specialists appointments than environmental factors; therefore, this phenomenon may be avoidable.

Cited by 6 publications

References 19 publications

Nonattendance rates of scheduled outpatient appointments in a university general hospital

Nonattendance rates of scheduled outpatient appointments in a university general hospital

Ensemble Learning for Addressing Class Imbalance in Cardiology Appointment Scheduling and Overbooking

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

Contact Info

Product

Resources

About

Abstract: In Chile, socioeconomic factors and the management of healthcare resources have greater influence on the nonattendance of patients to medical specialists appointments than environmental factors; therefore, this phenomenon may be avoidable.