Equity-promoting integer programming approaches for medical resident rotation scheduling.

At academic teaching hospitals around the country, the majority of clinical care is provided by resident physicians. During their training, medical residents often rotate through various hospitals and/or medical services to maximize their education. Depending on the size of the training program, manually constructing such a rotation schedule can be cumbersome and time consuming. Further, rules governing allowable duty hours for residents have grown more restrictive in recent years (ACGME 2011), making day-to-day shift scheduling of residents more difficult (Connors et al., J Thorac Cardiovasc Surg 137:710-713, 2009; McCoy et al., May Clin Proc 86(3):192, 2011; Willis et al., J Surg Edu 66(4):216-221, 2009). These rules limit lengths of duty periods, allowable duty hours in a week, and rest periods, to name a few. In this paper, we present two integer programming models (IPs) with the goals of (1) creating feasible assignments of residents to rotations over a one-year period, and (2) constructing night and weekend call-shift schedules for the individual rotations. These models capture various duty-hour rules and constraints, provide the ability to test multiple what-if scenarios, and largely automate the process of schedule generation, solving these scheduling problems more effectively and efficiently compared to manual methods. Applying our models on data from a surgical residency program, we highlight the infeasibilities created by increased duty-hour restrictions placed on residents in conjunction with current scheduling paradigms.

Residency program, a stage of medical postgraduate education, plays an important role in a physician’s professional competency. Each year a resident rotation schedule has to be created, and residents develop practical skills and receive faculty coaching in different rotations. The creation of rotation schedule is typically a manual process and thus falls into a sub-optimal solution due to many practical criteria such as the issues of burnout, professional development, and quality of care. This study is motivated by a resident rotation scheduling problem of Mayo Clinic Florida Categorical Internal Medicine residency program. By analyzing all requirements and relative importance, an optimization model is developed, aimed at the first draft of the resident rotation schedule. A desktop application is developed to provide program coordinators with a friendly user interface. Using the method and software proposed in this paper allows drafting the optimal resident rotation schedule to be achieved in seconds.

Scheduling is a very important step in high-level synthesis. Integer Programming (IP) approach was successfully used to solve the scheduling problem, but it suffers from its restricted mathematical model. Constraint Logic Programming (CLP) was also proposed as a solution technique. Although its flexible model, CLP suffers from high runtimes in large problems. The objective of this paper is to compare the two approaches and to present a hybrid approach that use complementary strengths of Integer Programming (IP) and Constraint Logic Programming (CLP). These approaches were applied to fifth order elliptic wave filter to solve scheduling with module selection problem and the results show the effectiveness of the proposed hybrid approach.

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

Aim:To evaluate Gurukkul, a commercially available software tool that can help residency programs maintain documentation and compliance with accreditation standards. It helps both the resident and residency program to effectively manage responsibilities. Materials: Gurukkul has different modules to track resident progress and help manage the program effectively.Administration: ‐Three levels of access: admin, mentor and resident; each with different rights to appropriately upload documents, edit and review information enabling the program compliance with accreditation standards. Management: ‐Configure, view and edit several modules to track and review resident progress such as: Rotation description, Rotation schedule, Course and Seminar management. ‐Checklists to evaluate resident competency in each rotation. ‐Alerts, reminders and notifications of upcoming deadlines, pending tasks and approvals.Performance: ‐View reports tracking individual and group performance throughout the program. ‐Enable accountability by tracking time of completion and approval of tasks. ‐Dashboard to view current activities and alerts Evaluation: ‐360° evaluations for residents, faculty and program tailored to institutional performance metrics. Self‐assessment: ‐Configure, schedule and administer testing materials linked to rotation topics. ‐Guide education through question‐based approach.Results:Advantages: Organized platform for managing residency program. Performance monitor and dashboard ensure timeliness of tasks. Self‐assessment modules better prepare residents for rotation exams. Reading and competency checklists help complete assigned tasks. Timelines were met after using this system unlike before where we used to miss reports and resident presentations. Administration and documentation time reduced significantly as they were better organized and readily available. Reduces administration/documentation time. Helpful during accreditation site visits. Disadvantages: Time‐consuming initial setup.Conclusion:This tool is multi‐functional as: residency program management, resident management, resident learning resource, resident and program evaluation. It relieves documentation and management time from faculty and residents that can be used towards actual learning. Dashboards keep everyone up‐to‐date. Specifically, it can help immensely during accreditation site visits.Dr. Rangaraj is the founder of Crux Quality Solutions LLC which markets Gurukkul.

Completing a residency program is a requirement for medical students before they can practice medicine independently. Residency programs in internal medicine must undergo a series of supervised rotations in elective, inpatient, and ambulatory units. Typically, a team of chief residents is charged to develop a yearly rotational schedule. This process is complex, as it needs to consider academic, managerial, regulatory, and legal restrictions while also facilitating the provision of patient care, ensuring a diverse educational experience, balancing the workload, and improving resident satisfaction. This study proposes (1) a multi-stage multi-objective optimization approach for generating yearlong weekly resident rotation schedules and (2) the use of Analytical Hierarchy Process (AHP) to compare schedules across multiple criteria to select those that are more equitable and hence to facilitate their adoption and implementation. Furthermore, the proposed approach allows the scheduling of periodic clinic rotation schemes that are commonly used to facilitate continuity of care, such as "4+1" or the "8+2" policies. In the "4+1" policy residents rotate for four consecutive weeks in different units prior to return for a week to a predetermined clinical post. Similarly, in the "8+2" policy, residents rotate eight weeks across multiple units before doing a two week rotation at a predetermined clinic.

Family medicine faculty and residents have observed that continuity clinic is often unsatisfying, attributed to a lack of patient and team continuity and erratic clinic schedules pieced together after the prioritization of hospital service and rotation schedules. In 2019, a 3-year Clinic First project, called Clinic as Curriculum (CaC), was launched across the 4 family medicine residencies of the Department of Family Medicine and Community Health, University of Minnesota Medical School. The department began publishing quarterly CaC dashboard data. Each clinic completed a baseline assessment of their performance on the 13 Building Blocks of High-Performing Primary Care. Using their baseline data, each clinic identified which block or blocks, in addition to the blocks on continuity of care and resident scheduling, to focus on. The plan is to collaboratively implement the overall and local goals using dashboard data and iterative process improvement over 3 years. At baseline, clinics functioned quite well with respect to the 13 building blocks, but CaC dashboard data varied across the 4 clinics, with large variation between clinics on how frequently faculty were scheduled in the clinic and the proportion of total clinic visits seen by faculty. Resident continuity rates were low (range, 38%-47%). Level loading (consistent physician availability to meet patient demand) rates ranged from 1 to 11 days a month. Regarding resident schedules, 2 programs are moving from 4-week to 2-week inpatient blocks, and 2 programs are exploring longitudinal scheduling. One clinic will assign faculty and residents to specific clinic days. Two clinics are implementing microteams of 1 faculty and 3-4 residents. The authors plan to analyze the dashboard data longitudinally; explore microteams, team continuity, and team scheduling adherence; and develop and implement resident scheduling changes over the next 3 years.

Generalised Assignment Problems (GAP), traditionally solved by Integer Programming techniques, are addressed in the light of current Constraint Programming methods. A scheduling application from manufacturing, based on a modified GAP, is used to examine the performance of each technique under a variety of problem characteristics. Experimental evidence showed that, for a set of assignment problems, Constraint Logic Programming (CLP) performed consistently better than Integer Programming (IP). Analysis of the CLP and IP processes identified ways in which the search was effective. The insight gained from the analysis led to an Integer Programming approach with significantly improved performance. Finally, the issue of collaboration between the two contrasting approaches is examined with respect to ways in which the solvers can be combined in an effective manner.

Low energy consumption is a critical issue in embedded systems design. As the technology feature sizes of SoC (systems on chip) become smaller and smaller, the percentage of leakage power consumption in total power consumption continues to grow. Traditional dynamic voltage scaling (DVS) fail to accurately address the impact of scaling on system power consumption as the leakage power increases exponentially. The combination of DVS and adaptive body biasing (ABB) is an effective technique to jointly optimize dynamic and leakage energy dissipation. In this paper, we propose an optimal soft real-time rotation scheduling and voltage assignment algorithm, RSVAME (rotation scheduling and voltage assignment to minimize energy), to minimize both dynamic and leakage energy via DVS and ABB. Voltage transition overhead has been considered in our approach. We conduct experiments on a set of DSP benchmarks based on the power model of 70 nm technology. The experimental results show that our approach achieves significant energy saving than that of ILP (integer linear programming) approach.

The three-dimensional cutting problem is one of the major problems faced in the marble factories. Huge marble blocks coming from marble quarries are first cut into planes according to their depths. Then these planes are sized according to the length and width of the demanded products. In this study, the cutting problem of a marble processing factory is handled. Two different integer programming (IP) approaches are developed. In the first one, an IP model is built for each of the two cutting problems, which are cutting marble blocks into planes and sizing these planes. In the second approach, these two IP models are integrated together to handle the two different cutting operations together. The objective of both models is to minimize total spoilage of marble while finding the cutting designs of the marble blocks and marble planes. A number of real life problems are solved using both approaches. Computational results are compared statistically. Results showed that both approaches decrease the wasted amount of marble compared to the industry’s average.

Test-suite minimization is one key technique for optimizing the software testing process. Due to the need to balance multiple factors, multi-criteria test-suite minimization (MCTSM) becomes a popular research topic in the recent decade. The MCTSM problem is typically modeled as integer linear programming (ILP) problem and solved with weighted-sum single objective approach. However, there is no existing approach that can generate sound (i.e., being Pareto-optimal) and complete (i.e., covering the entire Pareto front) Pareto-optimal solution set, to the knowledge of the authors. In this work, we first prove that the ILP formulation can accurately model the MCTSM problem and then propose the multi-objective integer programming (MOIP) approaches to solve it. We apply our MOIP approaches on three specific MCTSM problems and compare the results with those of the cutting-edge methods, namely, NonlinearFormulation_LinearSolver (NF_LS) and two Multi-Objective Evolutionary Algorithms (MOEAs). The results show that our MOIP approaches can always find sound and complete solutions on five subject programs, using similar or significantly less time than NF_LS and two MOEAs do. The current experimental results are quite promising, and our approaches have the potential to be applied for other similar search-based software engineering problems.

Software pipelining can have an enormous impact on the clock cycle count and hence on the performance of a real-time signal processing design. Because it pays off to invest CPU time in the optimal software pipelining of time-critical parts of a design, an integer programming approach is proposed for simultaneous scheduling and software pipelining. The integer programming techniques in the literature do not support cyclic (repetitive) signal flow, graphs, and/or do not allow optimization of the storage cost of delay lines during software pipelining. The new contributions in this paper are the full integration of software pipelining and scheduling, based on a new timing model that supports cyclic signal flow, graphs and optimization of delay line storage costs. Experiments with several real-time signal processing applications have shown the practical applicability of the approach. >

INTRODUCTION: Program websites are the most common online resource applicants use to access information about residency programs. During the COVID-19 pandemic, online resources have had to take place of site visits when interviews transitioned to the virtual setting. At this time, the Accreditation Council for Graduate Medical Education does not have a plan for a transition back to in-person interviews. The objective of this study is to assess applicant use, and information that is sought for development of high-quality residency websites. METHODS: A survey was shared to interviewees on their interview day with Creighton University–Phoenix obstetrics and gynecology residency during the 2022–2023 application cycle. To remove the possibility of influence or coercion, the survey was kept anonymous. This was found to be IRB exempt. RESULTS: Of the 88 survey respondents, 100% marked they visited the program website. Only 32 of the 88 respondents indicated they did not find the information they were looking. The most-sought information by respondents was current resident profiles (75% of respondents), hospital/training site information (68%), rotation schedule (63%), and faculty profiles (60%). Other notable categories respondents were hoping to find information about included the following: surgical training, family planning training, and benefits offered by the program. CONCLUSION: Program websites are highly utilized by residency applicants. Information traditionally shared on interview day is now found through online resources; most-sought information included resident and faculty profiles, rotation schedules, and training site information.

Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element–based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.

In this case study, we describe an integer programming (IP) approach, which has been implemented at the School of Economics and Management at Hannover University, Germany, to create the complete timetable of all courses for a term. Approximately 150 different weekly lectures, tutorials and seminars ranging from 5 to 650 students are taught by about 100 teachers. The decision problem is to assign these teaching groups to time slots and rooms so that several soft and hard constraints are met. It is modeled as an assignment problem with numerous types of constraints and about 100,000 binary or integer variables. An open source mixed-integer solver can be used to solve the problem to optimality within minutes whereas the commercial CPLEX solver takes only seconds. We also describe the implementation process and report results from an anonymous satisfaction survey among the faculty with respect to the new planning approach.