Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework

Srinivas, Sharan; Ravindran, A. Ravi

doi:10.1016/j.eswa.2018.02.022

Cited by 105 publications

(66 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Harris et al (2016) developed a regression-like model with sums of exponential functions for predicting patient no-shows. Srinivas and Ravindran (2018) incorporated the forecasted weather condition as a feature, in addition to other conventional features for predicting patient no-shows. They also proposed a stacking model by using logistic regression as the meta-classifiers and three algorithms of random forests, artificial neural networks, and stochastic gradient boosting as the base-learners.…”

Section: Review Of Patient No-show Research Papersmentioning

confidence: 99%

“…Ding et al (2018) considered cancellations on the day of the appointment as no-shows. Topuz et al (2018) and Srinivas and Ravindran (2018) considered a late cancellation as a missed appointment if the patient canceled the appointment within eight hours and 72 hours before the appointment date, respectively.…”

Section: Review Of Patient No-show Research Papersmentioning

confidence: 99%

“…Healthcare providers typically undertake three different non-analytical approaches to mitigate the effect of no-shows (Kheirkhah et al, 2016): (1) sending reminders to the patient, which has been shown to be ineffective in significantly reducing the rate of patient no-shows (Gurol-Urganci et al, 2013;Hixon et al, 1999;Leong et al, 2006;Satiani et al, 2009); (2) penalizing the patient through no-show fees, which is not an appropriate approach to deal with no-shows as this can adversely affect low-income patients in accessing the required care (Johnson et al, 2007); (3) incorporating overbooking into the scheduling plan (Srinivas and Ravindran, 2018). In order to efficiently utilize an overbooking strategy, an accurate prediction of the no-shows is essential; otherwise, patient wait time is likely to increase due to scheduling collisions (i.e., two patients show up at the same time slot) (Topuz et al, 2018), and costs will increase due to additional overtime (Daggy et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi

Garcia-Arce

Masel

et al. 2019

IISE Transactions on Healthcare Systems Engineering

View full text Add to dashboard Cite

Patient no-shows and late cancellations for an appointment are common problems in healthcare, which adversely affect the financial performance and quality of service of healthcare organizations. A high rate of patient no-show and late cancellation in a clinic can significantly limit access to healthcare. In general, hospitals create predictive models to assess risk of no-show, and then assign overbooking appointments utilizing those risks. In this paper, by incorporating machine learning and optimization techniques, we proposed a predictive model to assist with the overbooking decision. The model consists of two phases. First, we utilized a metaheuristic optimization technique to explore the best subset of featuresknown as feature selection problemthat can significantly contribute to the prediction outcomes. Second, using the output of the first stage, we proposed a stacking model to improve the prediction performances further. Our extensive computations and comparisons across different classifiers show that formulating the feature selection problem as a multi-objective problem instead of a single-objective problem using random forest classifier yields better results. The proposed model will improve the overbooking at clinics, by increasing the patient access to care. We introduced important new features to the literature that can describe the no-show and late cancellation behavior.

show abstract

Section: Review Of Patient No-show Research Papersmentioning

confidence: 99%

Section: Review Of Patient No-show Research Papersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Ahmadi

Garcia-Arce

Masel

et al. 2019

IISE Transactions on Healthcare Systems Engineering

View full text Add to dashboard Cite

show abstract

“…Developments in artificial intelligence (AI) for some aspects of tertiary care center management is predicted to lower costs. These may include machine learning algorithms in medical billing, supply chain management, scheduling efficiencies, virtual radiology (for image interpretation), and prevention of readmissions [71][72][73][74][75][76][77].…”

Section: Information Technology and Quality Benchmarksmentioning

confidence: 99%

Worldwide Network for Blood and Marrow Transplantation Recommendations for Establishing a Hematopoietic Stem Cell Transplantation Program in Countries with Limited Resources, Part II: Clinical, Technical, and Socioeconomic Considerations

Aljurf

Weisdorf

Hashmi

et al. 2019

Biology of Blood and Marrow Transplantation

View full text Add to dashboard Cite

A B S T R A C T The development of hematopoietic stem cell transplantation (HSCT) programs can face significant challenges in most developing countries because such endeavors must compete with other government health care priorities, including the delivery of basic services. Although this is may be a limiting factor, these countries should prioritize development of the needed expertise to offer state-of-the-art treatments, including transplantation, by providing financial, technological, legal, ethical, and other needed support. This would prove beneficial in providing successful programs customized to the needs of their population and potentially provide long-term cost savings by circumventing the need for their citizens to seek care abroad. The costs of establishing an HSCT program and the costs of the HSCT procedure itself can be substantial barriers in developing countries. In addition, socioeconomic factors intrinsic to specific countries can influence access to HSCT, patient eligibility for HSCT, and timely utilization of HSCT center capabilities. This report describes recommendations from the Worldwide Network for Blood and Marrow Transplantation for establishing HSCT programs, with a specific focus on developing countries, and identifies challenges and opportunities for providing this specialized procedure in resource-constrained settings.

show abstract

“…Previous literatures focus on solving the hospital planning and scheduling problems (Hulshof et al, 2012;Javid et al, 2017;Zhu et al, 2010), (Srinivas and Ravindran, 2018). However, these scheduling algorithms are focusing on providing a general solution for efficiency.…”

Section: Introductionmentioning

confidence: 99%

Psychological Acceptability Weighted Priority Scheduling: A Case Study of Animal Hospital

Chochiang

Choothong

Intarasombat

et al. 2019

Journal of Computer Science

View full text Add to dashboard Cite

A solution to the patient scheduling problem for a case study animal hospital called Mix+Factor scheduling is proposed in this work. Mix scheduling is based on the weighted sum of patient arrival order, the job type and the priority. The priority is based on the veterinarian opinion on the treatment type. Factor is an additional function based on the psychological acceptability of the patient owners in allowing some later jobs to be moved ahead in the waiting queue. The experimental results are conducted on three synthesis workloads to create a light-load, normal-load and high-load conditions. The synthesis workloads are created according to the mixture of jobs at the case study animal hospital. The results show that the Mix+Factor algorithm provides similar or better average waiting time performance in comparison with the currently used algorithms, namely First-Come-First-Served. In addition, Mix+Factor can also provide a better scheduling in order to reduce the waiting time for specific patients such as violence animals or patients with appointments. A study on potential benefit of opening an extra treatment room shows that an extra treatment room can significantly reduce the waiting time and better serve the specific patients than that of the currently used algorithm.

show abstract

Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework

Cited by 105 publications

References 29 publications

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Worldwide Network for Blood and Marrow Transplantation Recommendations for Establishing a Hematopoietic Stem Cell Transplantation Program in Countries with Limited Resources, Part II: Clinical, Technical, and Socioeconomic Considerations

Psychological Acceptability Weighted Priority Scheduling: A Case Study of Animal Hospital

Contact Info

Product

Resources

About