Paper Reference
R. J. Mieloszyk, J. I. Rosenbaum, P. Bhargava and C. S. Hall, "Predictive modeling to identify scheduled radiology appointments resulting in non-attendance in a hospital setting," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp. 2618-2621, doi: 10.1109/EMBC.2017.8037394.

Problem it tries to solve
No-show (did not turn up) pose a challenge to the Hospital Radiology Appointment System.

How it tries to solve
A feature specific data-driven no-show prediction model is proposed to be used by scheduler in automated scheduling policy.

Measurement
Data was evaluated based on Radiology Information System combined with Patient Income estimate. THe data is categorized into Patient, Exam and Scheduling. Model is developed using logistic regression to establish no-show probability.

Paper Reference
Y. Bai, J. Song and G. Zhang, "Design appointment in outpatient department with bulk service," 2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015, pp. 746-751, doi: 10.1109/CoASE.2015.7294170.
Problem it tries to solve
An appointment system include appointment rules and situation of cancelation affect the efficiency of the system.

How it tries to solve
Propose a design of appointment rule that improve the efficiency of the system using stochastic model.

Measurement
Numerical experiment is used to measure the system.

Paper Reference
S. F. Anvaryazdi, S. Venkatachalam and R. B. Chinnam, "Appointment Scheduling at Outpatient Clinics Using Two-Stage Stochastic Programming Approach," in IEEE Access, vol. 8, pp. 175297-175305, 2020, doi: 10.1109/ACCESS.2020.3025997.

Problem it tries to solve
Existing system is burdened with limited resources as well as system efficiency such as short wait time, duration, volume and no-show issues.

How it tries to solve
A two-stage stochastic mixed-integer linear programming model is being proposed to address the issue.

Measurement
Expected wait time.
Computational experiment is also used.

Literature Review with Respect to Mobile-apps based Appointment System for Hospital

A hospital appointment system faced the issue of no-show (R. J. Mieloszyk et. al, 2017), complicated rules, cancelation issue as well as limited resources and efficiency issue (Y. Bai et. al 2015),(S. F. Anvaryazdi et. al 2020). To address the issue several researchers attempted to model the pattern and propose a system that are able to predict the operation and ensure the smooth operation of the appointment system. R. J. Mieloszyk et. al (2017) for example proposed a feature specific data driven prediction model that focuses on no-show issue. The system hopes to be incorporated in automated scheduling policy. Y. Bai et. al (2015) on the other hand proposed prediction using stochastic model in effort to address the issue of complicated rules and cancelation situations. Meanwhile, S. F. Anvaryazdi et. al (2020) proposed an improvement by incorporating two-stage stochastic mixed-integer linear programming model to address the issue of limited resources,system efficiency such as short wait time, duration, volume and no-show issues. We proposed to address the issue by implementing mobile-based system. The proposed system will make use of Stochastic Model similar to R. J. Mieloszyk et. al (2017) in ensuring efficient appointment operation. The system hopes to address the issue of limited resources while improving system efficiency.