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Azar, M., Carrasco, R. A., & Mondschein, S. (2022). Dealing with Uncertain Surgery Times in Operating Room Scheduling. Eur. J. Oper. Res., 299(1), 377–394.
Abstract: The operating theater is one of the most expensive units in the hospital, representing up to 40% of the total expenses. Because of its importance, the operating room scheduling problem has been addressed from many different perspectives since the early 1960s. One of the main difficulties that
has reduced the applicability of the current results is the high variability in surgery duration, making schedule recommendations hard to implement.
In this work, we propose a time-indexed scheduling formulation to solve the operational problem. Our main contribution is that we propose the use of chance constraints related to the surgery duration's probability distribution for each surgeon to improve the scheduling performance. We show how to implement these chance constraints as linear ones in our time-indexed formulation, enhancing the performance of the resulting schedules significantly.
Through data analysis of real historical instances, we develop specific constraints that improve the schedule, reducing the need for overtime without affecting the utilization significantly. Furthermore, these constraints give the operating room manager the possibility of balancing overtime and utilization through a tunning parameter in our formulation. Finally, through simulations and the use of real instances, we report the performance for four different metrics, showing the importance of using historical data to get the right balance between the utilization and overtime.
Keywords: Scheduling, OR in health services, Operating Room Scheduling, Scheduling under Uncertainty
Barrera, J., Carrasco, R. A., Mondschein, S., Canessa, G., & Rojas-Zalazar, D. (2020). Operating room scheduling under waiting time constraints: the Chilean GES plan. Ann. Oper. Res., 286(1-2), 501–527.
Abstract: In 2000, Chile introduced profound health reforms to achieve a more equitable and fairer system (GES plan). The reforms established a maximum waiting time between diagnosis and treatment for a set of diseases, described as an opportunity guarantee within the reform. If the maximum waiting time is exceeded, the patient is referred to another (private) facility and receives a voucher to cover the additional expenses. This voucher is paid by the health provider that had to do the procedure, which generally is a public hospital. In general, this reform has improved the service for patients with GES pathologies at the expense of patients with non-GES pathologies. These new conditions create a complicated planning scenario for hospitals, in which the hospital's OR Manager must balance the fulfillment of these opportunity guarantees and the timely service of patients not covered by the guarantee. With the collaboration of the Instituto de Neurocirugia, in Santiago, Chile, we developed a mathematical model based on stochastic dynamic programming to schedule surgeries in order to minimize the cost of referrals to the private sector. Given the large size of the state space, we developed an heuristic to compute good solutions in reasonable time and analyzed its performance. Our experimental results, with both simulated and real data, show that our algorithm performs close to optimum and improves upon the current practice. When we compared the results of our heuristic against those obtained by the hospital's OR manager in a simulation setting with real data, we reduced the overtime from occurring 21% of the time to zero, and the non-GES average waiting list's length from 71 to 58 patients, without worsening the average throughput.
Keywords: Scheduling; Operating theater; Operating room scheduling
Bitran, G., & Mondschein, S. (2015). Why individualized contact policies are critical in the mass affluent market. Acad.-Rev. Latinoam. Adm., 28(2), 251–272.
Abstract: Purpose – The purpose of this paper is to study the optimal contact policies for customers that belong to the mass affluent market. Design/methodology/approach – The authors formulate a stochastic dynamic programming model to determine the optimal frequency of contacts in order to maximize the expected return of the company. Findings – The authors show that personalized marketing strategies provide a competitive advantage to companies that contact their customers directly through, for example, phone calls or meetings. The authors show that a threshold policy is only optimal for customers with increasing sensitivity to contact. In all other cases, optimal policies might have a less intuitive structure. The authors also study the importance of the size of the customer database and determine the optimal maximum recency when maintenance costs are present. Practical implications – Contact policies should be tailored for each company/industry individually, due to their sensitivity to customers' purchasing behavior.
Garmendia, M. L., Matus, O., Mondschein, S., & Kusanovic, J. P. (2018). Gestational weight gain recommendations for Chilean women: a mathematical optimization approach. Public Health, 163, 80–86.
Abstract: Objectives: We examined if the guidelines for gestational weight gain (GWG) proposed by the Institute of Medicine (IOM) are the most suitable for Chilean women. Study design: Secondary analysis of records of single full-term births at the Dr. Sotero del Rio Hospital, Santiago, Chile, during 2003-2012 (n = 62,579). Methods: From clinical records, we obtained data regarding maternal age, height, prepregnancy and at delivery weights, pathologies during pregnancy such as gestational diabetes (GDM) and pre-eclampsia, gestational age at delivery, and number of infants born small for gestational age (SGA) and large for gestational age (LGA). We formulated a mathematical model (MM) to determine the GWG range that maximizes the likelihood of a healthy pregnancy (HP) if the recommendation is followed. We defined an HP as one where the mother has no complications such as pre-eclampsia, GDM, SGA, or LGA. Results: Forty-six percent of women had prepregnancy overweight or obesity. The prevalence of GDM, pre-eclampsia, SGA, and LGA were 3%, 1.2%, 9%, and 12%, respectively. An HP was present in 76% of pregnancies, 79% in the underweight group, 79% in normal weight group, 74% in the overweight group, and 67% in obese women. The GWG recommendations given by the MM (14-20 kg for underweight, 6-20 kg for normal weight, 9 -11 kg for overweight, and 6-7 kg for obese) led to higher probabilities of achieving an HP than the ones obtained with the IOM recommendations. Conclusion: The adoption of GWG recommendations based on characteristics of the Chilean population might lead to better short- and long-term health results for pregnant women. (C) 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Keywords: Pregnancy; Weight gain; Obesity; Pregnancy high risk; Chile
Garmendia, M. L., Mondschein, S., Matus, O., Murrugarra, R., & Uauy, R. (2017). Predictors of gestational weight gain among Chilean pregnant women: The Chilean Maternal and Infant Nutrition Cohort study. Health Care Women Int., 38(8), 892–904.
Abstract: We identified factors associated with gestational weight gain (GWG) in 1,654 Chilean pregnant women with full-term pregnancies. At baseline, we collected information about sociodemographic, gyneco-obstetric, anthropometric, and health-care-related factors. We found that prepregnancy nutritional body mass index was the most important factor related to GWG above recommendations (overweight: ratio of relative risks [RRR] = 2.31, 95% confidence interval [CI, 1.73, 3.09] and obesity: RRR = 2.90, 95% CI [2.08, 4.03]). We believe that women who are overweight/obese at the beginning of pregnancy should be identified because of their higher risk, and that adequate strategies should be designed and implemented to help them achieve a healthy GWG.
Garmendia, M. L., Mondschein, S., Montiel, B., & Kusanovic, J. P. (2020). Trend and predictors of gestational diabetes mellitus in Chile. Int. J. Gynecol. Obstet., 148(2), 210–218.
To examine the temporal trends in gestational diabetes mellitus (GDM) prevalence in Chile, and to determine the main predictors of GDM.
A secondary analysis was conducted of all birth records at Hospital Dr. Sótero del Río, Chile, from January 1, 2002, to December 31, 2015. We excluded those women with pre‐existing type 2 diabetes, those with missing data, and those with unlikely data. GDM was defined as fasting glucose levels >5.55 mmol/L [>100 mg/dL] or >7.77 mmol/L [>140 mg/dL] 2 hours after glucose load in the oral glucose tolerance test. Potential predictors were selected based on prior research and ease of evaluation.
From the original database of 100 758 records, 86 362 women were included in the final cohort. The mean GDM prevalence was 7.6% (95% CI [confidence interval] 7.5%�7.8%), increasing from 4.4% (95% CI 4.0%�4.9%) in 2002 to 13.0% (95% CI 12.0%�13.9%) in 2015. Age, education, marital status, parity, family history of type 2 diabetes, personal history of GDM, hypertension and pre‐eclampsia, alcohol consumption, smoking, and pre‐gestational nutritional status performed well in the prediction of GDM.
One out of eight Chilean pregnant women of medium‐ to low socio‐economic status were found to develop GDM. We identified a set of easy‐to‐capture predictors in the primary health care system that may allow for the early identification of women at high‐risk for the development of GDM.
Garmendia, M. L., Mondschein, S., Montiel, B., & Kusanovic, J. P. (2021). Trends and predictors of birth weight in Chilean children. Public Health, 193, 61–68.
Abstract: Objectives: Birth weight is an important public health indicator that reflects fetal health conditions and predicts future health. Identifying the most important factors related to birth weight would help defining preventive health strategies for both mothers and children. The objectives of this study are i. to describe, using a large birth database from a Chilean hospital, the trend of birth weight during 2002-2015, and ii. to determine factors during prenatal care associated with low and high birth weight.
Study design: This study is a secondary analysis of all single birth records at a Chilean Hospital in the southeast district of Santiago, Chile, during 2002-2015 (N = 78,931).
Methods: Sociodemographic information, clinical and obstetric history, lifestyle, and anthropometric variables were evaluated as potential predictors. Birth weight was categorized into five groups as per percentiles of weight as per gestational age. Data were extracted from clinical records. We used classification and regression tree methodology and logistic regression.
Results: The average birth weight for the period was 3316 g (SD 566), with little variation across time. Preterm births increased from 7% in 2002 to 10% in 2015, and births >40 weeks decreased from 10.7% in 2002 to 4.4% in 2015. The percentages of small and large for gestational age changed from 10.9% and 12.7% in 2002 to 9.9% and 13.9% in 2015, respectively. The predictors included in the optimal tree were body mass index, gestational weight gain, pre-eclampsia, and gestational diabetes. We found that women with a pregestational body mass index <28 kg/m(2), gestational weight gain <17 kg, and pre-eclampsia had a probability of 41% of having a small for gestational age neonate. Conversely, women with a body mass index similar to 28 kg/m(2), gestational weight gain similar to 17 kg, and gestational diabetes had a probability of 44% of having a large for gestational age neonate.
Conclusions: This study showed that the most important variables explaining birth weight are those related to maternal nutritional status. Thus, the strategies to promote a normal birth weight should aim for a normal maternal weight at the beginning of pregnancy, gestational weight gain within the recommendations, and prevention of gestational diabetes and pre-eclampsia. (C) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Keywords: PREPREGNANCY WEIGHT; PREGNANCY; CONSEQUENCES; PREECLAMPSIA; HYPERTENSION; ASSOCIATION; VALIDITY; HISTORY; DISEASE; PARITY
Kong, Q. X., Mondschein, S., & Pereira, A. (2018). Effectiveness of breast cancer screening policies in countries with medium-low incidence rates. Rev. Saude Publica, 52, 9 pp.
Abstract: Chile has lower breast cancer incidence rates compared to those in developed countries. Our public health system aims to perform 10 biennial screening mammograms in the age group of 50 to 69 years by 2020. Using a dynamic programming model, we have found the optimal ages to perform 10 screening mammograms that lead to the lowest lifetime death rate and we have evaluated a set of fixed inter-screening interval policies. The optimal ages for the 10 mammograms are 43, 47, 51, 54, 57, 61, 65, 68, 72, and 76 years, and the most effective fixed inter-screening is every four years after the 40 years. Both policies respectively reduce lifetime death rate in 6.4% and 5.7% and the cost of saving one life in 17% and 9.3% compared to the 2020 Chilean policy. Our findings show that two-year inter-screening interval policies are less effective in countries with lower breast cancer incidence; thus we recommend screening policies with a wider age range and larger inter-screening intervals for Chile.
Keywords: Breast Neoplasms, epidemiology; Early Detection of Cancer; Mammography; Mass Screening; Preventive Health Services; Health Policy
Mondschein, S., Quinteros, M., & Yankovic, N. (2020). Gender bias in the Chilean public health system: Do we all wait the same? Plos One, 15(9), e0239445.
In 2002, Chile introduced a major health reform, designed to level out inequities in healthcare coverage, access and opportunities. In particular, the opportunity guarantees ensure a maximum time to receive the appropriate diagnosis and treatment, and thus, gender bias should not be observed.
To explore the existence of differences in the timeliness of treatment between women and men under the Chilean public health insurance system. We controlled by other observable variables, including age, insurance holder status, provider complexity and health district.
We used an individual level database that includes all interactions for the diseases covered under the national plan from 2014 to 2019. We excluded from the analysis the diseases affecting only men, women, and infants. To study the waiting time differences between women and men, we first perform a Welch two sample t-test. Then, we used a multilevel hierarchical regression model to further explore the impact of gender in waiting time. At the individual level, we included gender, insurance holder status, age, and the interaction between gender and age. For the aggregate levels, we used the specific opportunity guarantee, the type of provider, and health district.
From the Welch two sample t-test, we found significant differences in waiting times between women and men, in seven opportunity guarantees. From the multilevel regression, the individual variables: holder status, ages between 35 and 49, and the interaction between gender and age for ages between 40 and 54 were statistically significant at 95% level. We remark that the major differences in waiting times between women and men were observed for individuals between ages from 40 to 54, with women waiting significantly longer.
Results show the existence of bias in the timeliness of treatment, proving that universal guarantees are not enough to reduce gender inequalities in health care.
Mondschein, S., Yankovic, N., & Matus, O. (2021). Age-dependent optimal policies for hepatitis C virus treatment. Int. Trans. Oper. Res., 28(6), 3303–3329.
Abstract: In recent years, highly effective treatments for hepatitis C virus (HCV) have become available. However, high prices of new treatments call for a careful policy evaluation when considering economic constraints. Although the current medical advice is to administer the new therapies to all patients, economic and capacity constraints require an efficient allocation of these scarce resources. We use stochastic dynamic programming to determine the optimal policy for prescribing the new treatment based on the age and disease progression of the patient. We show that, in a simplified version of the model, new drugs should be administered to patients at a given level of fibrosis if they are within prespecified age limits; otherwise, a conservative approach of closely monitoring the evolution of the patient should be followed. We use a cohort of Spanish patients to study the optimal policy regarding costs and health indicators. For this purpose, we compare the performance of the optimal policy against a liberal policy of treating all sick patients. In this analysis, we achieve similar results in terms of the number of transplants, HCV-related deaths, and quality of adjusted life years, with a significant reduction in overall expenditure. Furthermore, the budget required during the first year of implementation when using the proposed methodology is only 12% of that when administering the treatment to all patients at once. Finally, we propose a method to prioritize patients when there is a shortage (surplus) in the annual budget constraint and, therefore, some recommended treatments must be postponed (added).
Keywords: dynamic programming; public health; hepatitis C virus
Mondschein, S., Yankovic, N., & Matus, O. (2021). The Challenges of Administering a New Treatment: The Case of Direct -Acting Antivirals for Hepatitis C Virus. Public Health, 190, 116–122.
Abstract: Objectives: We develop a patient prioritization scheme for treating patients infected with hepatitis C virus (HCV) and study under which scenarios it outperforms the current practices in Spain and Chile.
Study design: We use simulation to evaluate the performance of prioritization rules under two HCV patient cohorts, constructed using secondary data of public records from Chile and Spain, during 2015-2016.
Methods: We use the results of a mathematical model, which determines individual optimal HCV treatment policies as an input for constructing a patient prioritization rule, when limited resources are present. The prioritization is based on marginal analysis on cost increases and health-outcome gains. We construct the Chilean and Spanish case studies and used Monte Carlo simulation to evaluate the performance of our methodology in these two scenarios.
Results: The resulting prioritizations for the Chilean and Spanish patients are similar, despite the significant differences of both countries, in terms of epidemiological profiles and cost structures. Furthermore, when resources are scarce compared with the number of patients in need of the new drug, our prioritization significantly outperforms current practices of treating sicker patients first, both in terms of cost and healthcare indicators: for the Chilean case, we have an increase in the quality-adjusted life years (QALYs) of 0.83 with a cost reduction of 8176 euros per patient, with a budget covering 2.5% of the patients in the cohort. This difference slowly decreases when increasing the available resources, converging to the performance indicators obtained when all patients are treated immediately: for the Spanish case, we have a decrease in the QALYs of 0.17 with a cost reduction of 1134 euros per patient, with a budget covering 20% of the patients in the cohort.
Conclusion: Decision science can provide useful analytical tools for designing efficient public policies that can excel in terms of quantitative health performance indicators.
Keywords: Hepatitis C virus; Patient prioritization; Heuristics