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This article presents a new model for constructing annual schedules for medical residents based on the regulations of a German teaching hospital as well as the program restrictions of the German Medical Association. Since resident programs of physicians do not only vary between disciplines but also between countries, it is essential to evaluate the main characteristics of the program. The main difference between the already well-studied resident programs in the US and the one of this article is the task-related structure. Residents need to perform different interventions several times to become specialists. This study will focus on Germany since there was a judgement in 2015 that hospital management needs training schedules guaranteeing the success of the resident program in time. Therefore, a new formulation of a tactical resident scheduling problem is presented. The problem is formulated in two stages considering the total number of interventions, equal progress in training as well as continuity of care. As the second stage of our formulation is a quadratic program and even by linearization standard solvers are not able to generate high-quality solutions within 24 h, a genetic algorithm using standard crossovers is developed for the second stage constructing annual schedules for an existing stock of residents. We evaluate our algorithm by comparing the solutions of the genetic algorithm and standard software with a real-world situation of a German training hospital from 2016. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents a new model for constructing annual block schedules for family medicine residents based on the rules and procedures followed by the Family Medicine Department at the University of Texas Health Science Center in San Antonio (UTHSC-SA). Such residency programs provide 3 years of specialty training for recentmedical school graduates. At the beginning of each academic year, each trainee is given an annual block schedule that indicates his or her monthly assignments. These assignments are called rotations and include a variety of experiences, such as pediatric ambulatory care, the emergency room, and inpatient surgery. An important requirement associated with a subset of the rotations is that the residents spend multiple half-day sessions a week in a primary care clinic treating patients from the community. This is a key considerationwhen constructing the annual block schedules. In particular, one of the primary goals of most residencies is to ensure that the number of residents in clinic each day is approximately the same, so that the number of patients that can be seen each day is also the same. Uniformity provides for a more efficient use of supervisory and staff resources. The difficulty in achieving this goal is that not all rotations allow for clinic duty and that the number of patients that can be seen by a resident each session depends on his or her year of training. When constructing annual block schedules, two high-level sets of variables are available to the program coordinator. The first is the assignment of residents to rotations for each of the 12 blocks, and the second is the (partial) ability to adjust the days on which a resident has clinic duty during each rotation. In approaching the problem, our aim was to redesign the current rotations while giving all residents a 12-month schedule that concurrently (i) balances the number of patients that can be seen in the clinic during each half-day session and (ii) minimizes the number of adjustments necessary to achieve the first objective. The problem was formulated as a mixed-integer program; however, it proved too difficult to solve exactly. As an alternative, several optimization-based heuristics were developed that yielded good feasible solutions. The model and computations are illustrated with data provided by the Family Medicine Department at UTHSC-SA for a typical academic year. [ABSTRACT FROM AUTHOR]

This paper presents a network model with the objective of maximizing the number of interns and residents (collectively called housestaff ) who are assigned clinic duty each month during their training in internal medicine. A complexity analysis is provided that demonstrates that the basic problem can be modeled as a pure network. When team considerations, on-service, and other hard clinic constraints are taken into account, it is shown that the problem can be modeled as a network with gains. This result is unaffected when a series of soft constraints related to the number of clinic assignments per week per housestaff, the number of required faculty, the ratio of housestaff to faculty, and clinic assignment requirements during the month are taken into account. To evaluate the effectiveness of the model, a comparative study for the 2012–13 academic year was undertaken in conjunction with the Internal Medicine Department at the University of Texas Health Science Center in San Antonio. The results indicated that an average increase of 5.3% in the number of clinic sessions assigned over the year is possible when compared to those obtained manually by the Chief Resident. In addition, the quality of the solutions with respect to the soft constraints was notably better. [ABSTRACT FROM AUTHOR]

This article presents a new model for constructing monthly clinic schedules for interns and residents (i.e., housestaff) training in Internal Medicine. Clinical experiences during the three years of residency occur in inpatient and outpatient settings, and on generalist and specialist clinical services. These experiences include spending time in a primary care setting caring for an assigned group of patients over time. Housestaff rotate through different clinical experiences monthly, with their primary care clinic time overlaid longitudinally on these other clinical services. The exact amount of primary care time spent varies between clinical rotations. In fact, it is the variable clinic hour requirements that drive the scheduling process, and is what distinguishes our problem from most personnel scheduling problems. Typically, staff schedules are driven by shift or hourly demand and are designed to minimize some measure of cost. The objective in our work is to both maximize clinic utilization and minimize the number of violations of a prioritized set of goals while ensuring that certain clinic-level and individual constraints are satisfied. The corresponding problem is formulated as an integer goal program in which several of the hard constraints are temporarily allowed to be violated to avoid infeasibility.  To find solutions, a three-phase methodology is proposed. In the first phase (pre-processing step), clinic assignments for a subset of the housestaff are either fixed or excluded each month in light of restrictions imposed by their current rotation. In the second phase, tentative solutions are obtained with a commercial solver. In the final phase (post-processing step), all violations of the relaxed hard constraints are removed and an attempt is made to lexicographically reduce violations of the major goals. The effectiveness of the methodology is demonstrated by analyzing eight monthly rosters provided by the Internal Medicine Residency Program at the University of Texas Health Science Center in San Antonio. On average, we found that up to 7.62% more clinic sessions could be assigned each month using our methodology, and that the corresponding rosters admitted an average of 37% fewer violations for 9 out of the 11 soft constraints than did the actual schedules worked. [ABSTRACT FROM PUBLISHER]

This paper addresses the resident scheduling problem (RSP) at hospitals concerned with prescribing work-nights for residents while considering departmental staffing and skill requirements as well as residents' preferences. Three scenarios that represent most situations and account for various departmental requirements and needs are described. Although similar scheduling problems are considered in the literature, no analysis exists that adequately deals with the speciffic nature of this problem. The problem is modeled as a mixed-integer program and heuristic solution procedures are developed for the different identified scheduling scenarios. These procedures exploit the inherent network structure of the problem which is an important feature that enhances problem solvability. For the sake of comparison, the problem is also solved exactly via the CPLEX-MIP (version 6.0) package. The contribution of this work is important since many hospitals are still utilizing manual techniques in preparing their own schedules, expending considerable effort and time and yet contending with limited scheduling flexibility. [ABSTRACT FROM AUTHOR]

Background: Most postgraduate medical education occurs in hospitals in an apprenticeship model with actual patients. Creating a work shift schedule must account for complex factors, including hospital needs, work-hour restrictions, trainee qualifications, and case distribution in order to fairly allocate the resident workload. In this study, we report the first successful implementation of an equitable, computer-generated scheduling system for anesthesiology residents., Methods: A total of 24 residents at a single, urban training program were surveyed in 2015 to rank work shift difficulty. Shifts were categorized and translated into a weighted point system by program leadership based on the survey results. An automated and modifiable scheduling system was created to incorporate rule-based assignment of prerequisites and evenly distribute points throughout the academic year. Point values were retrospectively calculated in 2014, and prospectively calculated from 2015 to 2018. The equality of variance test was used to evaluate the variation of the SD of monthly average point distributions year-over-year and within each class of trainees., Results: Year-over-year analysis revealed that post-point system implementation, call point distribution trended toward reduced variance in all 4 years, with significant reductions of 63% in 2016 (SD 4.9, P < .01), and 57% in 2017 (SD 5.8, P < .01). Analyzed by class, first-year trainees' SD decreased by 73% in 2016 (SD 2.5, P < .01), by 67% in 2017 (SD 3.1, P < .04), and 65% in 2018 (SD 3.3, P < .02) compared with the pre-point system year in 2014. The second year clinical anesthesia resident class SD decreased by 56% in 2015 (SD 5.9, P < .01), 41% in 2016 (SD 7.9, P < .02), and 49% in 2017 (SD 6.9, P < .01)., Conclusion: The computerized point system improved work distribution equity year-over-year and within trainee cohort groups., Competing Interests: Disclosures: The authors report no external funding for this study. The authors declare no conflicts of interest., (© 2021 Society for Education in Anesthesia.)