‘Super’-ior Scheduling: A Novel Model for an Inpatient Internal Medicine Resident Service

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Kunnath, Maria Srour, Adam Fritz, Jason Lunt, Zarir Ahmed, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4355343/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Residency programs have adopted blocked scheduling to improve the learning climate yet more intensive rotations still impact resident wellness. The effect of changing the alignment of inpatient resident teams on patient care opportunities is not well known. We sought to evaluate the association of new daily schedule (dubbed ‘Mariokart’) compared to a traditional schedule on patient care opportunities, days off, and duty-hour violations. Methods A non-randomized pre - and post-intervention study examining the daily patient census for residents covering a general internal medicine inpatient service within an internal medicine residency program at a single academic medical center from July 2018 to June 2021. The main outcome was the median daily patient census per resident inpatient team before and after the implementation of the ‘Mariokart’ schedule. Secondary outcomes included days off in a 28-day cycle, patient-care opportunities, and resident-reported duty-hour violations. A two-sided t-test was used to determine differences between the groups. Results In comparing 20 months prior to implementation of the ‘Mariokart’ schedule to 15 months post-implementation, the median census of the resident service was 60 patients (IQR 53.0–67.0) for the traditional model and 54 (IQR 49.0–59.0) for the MarioKart model. The median census per team was 12 (IQR 10.0–15.0) for the traditional model and 13.5 (IQR 12.25–14.75) for the MarioKart model. Total patient days per team were 288 (IQR 254.4-321.6) for the traditional model and 303.8 (275.6-331.9) for the MarioKart model (p < 0.001). Under the MarioKart model, residents had an extra 1.5 days off per 28 days compared to the traditional model. Short break violations for the entire program and for the general inpatient service were significantly reduced. Conclusions In this nonrandomized study of an alternate day-to-day schedule that reduced days worked for residents in a general medicine inpatient service, there was no decline in patient-care opportunities. This alternate organization of residents suggests that residency programs can innovate at a systems level to adjust resident schedules to provide more time off without a detriment to patient care opportunities. Graduate Medical Education Duty-hours Resident Wellness Figures Figure 1 Figure 2 Background In the last decade, pediatric and internal medicine (IM) residency programs have adopted blocked scheduling, such as an “X + Y” system. 1, 2 Such systems addressed competing resident demands of a busy inpatient rotations and resident continuity clinic by separating the inpatient rotations as a block (X) alternating with clinic experiences (Y) in some ratio of weeks worked, such as 4 + 1 or 6 + 2. 1, 2 This has led to perceptions of improved teaching, inpatient workflow, and better continuity in clinic, 3 although others note a loss of patient continuity and of schedule flexibility. 1, 4 The intention of an X + Y model is to simplify the roles of an IM resident during a specific rotation and thus reduce stress and inefficiency of task switching between clinic and inpatient duties. 1, 3, 4 However, the problem of emotional exhaustion and rates of burnout persist among IM residents, 5, 6 only to be exacerbated by the COVID-19 pandemic in 2020. 7 What X + Y or traditional residency systems do not address is the structure of work within rotations, particularly within what many would deem the more ‘high stress’ rotations central to any IM residency program – including but not limited to general medical floors, night-float, and medical intensive-care units. 8 The work of medicine residents on general medicine wards is predominately spent on activities not at the bedside with frequent multi-tasking. 9 Residents in the role on general inpatient floors note higher amounts of moral distress 8 as compared to assignments in ambulatory or elective rotations. To alleviate the workload burden and attempt to improve the educational climate, some programs adopted additional roles such as a late-evening shift to cover patient care duties. 10 ( 10 ) Others have changed the ratio of resident learners to attendings, which improved the perceived educational experience without a change in patient outcomes. 11 During the onset of the COVID-19 pandemic, our department underwent a strategic restructuring to ensure availability of attendings to cover inpatient care, and our residency program was asked by departmental leadership to reduce the resident covered service from five one-resident, two-intern teams to four. At the same time, our program was under an Accreditation Council for Graduate Medical Education (ACGME) citation for work-hour violations due to frequent violations of the ACGME ‘short-break’, where residents would have less than ten hours absent of clinical duty between shifts. 12 Focused feedback sessions with residents to analyze root causes identified a high number of patients admitted during ‘call’ days. Additionally, senior residents noted that work-days when fellow residents and interns had scheduled days off resulted in residents staying much later than the expected sign out time. Rather than keep our traditional model, we developed and implemented an alternative schedule for our inpatient rotation. Herein we describe the effect of this schedule change on patient census, duty-hour violations, and patient care opportunities. Methods The Internal Medicine Residency Program at Saint Louis University has 83 residents, 76 of whom are categorical residents and the remainder preliminary interns. Traditionally, the program had five general floor teams, each staffed with one attending, one supervising senior resident and two interns for a 28-day rotation. Day call (where the team performed admissions and ran the code team) occurred every five days. Each resident had four days off during the block: the senior was off most pre-call days, and the two interns each had one day off between the call days. Thus, in a 28-day rotation, the entire team was together only for 16 of the days, and the senior had to serve in a ‘resitern’ capacity for eight days, in which they wrote notes for intern #1 on one day, and again for intern #2 the next day while supervising the remaining intern. There were also four days where the two interns were alone under the direct supervision of the attending. Due to the impetus to reduce the number of inpatient teams, we implemented an alternate daily resident schedule on our general inpatient service in April of 2020. The number of inpatient attending-supervised ward teams was reduced to four, yet our program preserved the five one-resident, two-intern teams who then rotated through those four ward teams in an alternate schedule dubbed the “MarioKart”. Each triad of resident and two interns was given a name based on a ‘MarioKart’ character to identify the triad as opposed to team name of the ward service. This also provided clarity in long-term assignment of residents throughout the year. During a given day, each resident/intern grouping is assigned to an inpatient team, with one entire resident/intern grouping having the day off. Figure 1 Part A shows how each group cycles through the care teams. For example, the four teams would be staffed by ‘Peach’’ (pink in Fig. 1 ) on inpatient team one, ‘Yoshi’ (yellow in Fig. 1 ) on inpatient team two, ‘Luigi’ (green in Fig. 1 ) on inpatient team three, and ‘Bowser’ (orange in Fig. 1 )’ on inpatient team four, with team ‘Mario’ (red in Fig. 1 )’ having two days off. After two days, team Yoshi will have two consecutive days off, having handed off team two to Mario, who will begin a run of eight days on inpatient service. When team Yoshi finishes their two days off, they take over team three for Luigi, who then receive two days off. Thus, on any given day, the MarioKart system has four resident teams working and one off for the duration of the 28-day rotation block. As a contrast, our ‘traditional’ way of scheduling is outlined in Fig. 1 Part B. As the new schedule was adopted our distribution of patients to resident teams changed. In creating a system where the team is fully complemented every day, patients were assigned based on team census numbers. For both models, the team ‘cap’ was 16 patients, thus the maximum patient census was 80 for the traditional model and 64 for the Mariokart model. To compare differences between the ‘traditional’ model and the MarioKart model we analyzed daily patient census numbers for the resident inpatient service and by assigned inpatient teams from July 1, 2018 to June 30, 2021. Data from the traditional model period (July 1, 2018 to March 31, 2020) were compared to data in the MarioKart period (April 1, 2020 to June 30, 2021). There are three reasons these dates and time intervals were chosen for analysis. First, collection of patient census data was part of a larger project within the Department; second, the Mariokart schedule was implemented near the beginning of the COVID-19 pandemic and the hospital census was low during the Spring of 2020; third, the three year period captured the experience of at least one full class in our program. Missing patient census data were imputed using predictive mean matching via the mice package in R. 13 Additionally, we also compared self-reported ACGME short-break violations via New-Innovations (Uniontown, OH), defined as time off of less than ten hours between work-related shifts and ACGME 80-hour violations, defined as a flag of worked hours more than 80 hours in a 7 day period. Numbers of both violations were collected for the overall program and for the general inpatient service where the MarioKart schedule was deployed during the study period. All violations were calculated as the mean number per month during the study period. Median and interquartile ranges (IQR) were calculated for patient census data. To assess the patient care opportunities for the residents during the rotation, we created a metric called total patient days per team. This metric is calculated as the multiple of the median, lower, and upper IQR bounds of patient census by the number of days worked per rotation (24 in the traditional model, 22.5 in the MarioKart model). This metric is used to provide an estimate for comparison of the number of patient encounters a resident team would have during the duration of the 28 day rotation. A two-sided t-test was used to compare differences between the traditional and MarioKart models. All statistical analyses were performed in R (R Studio, Boston, MA). The code for analysis is available on GitHub ( https://github.com/fbuckhold3/MarioKart/ ). This study was formally determined by our Institutional Review Board (IRB) to not constitute Human Subjects Research and did not require IRB approval. The study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) reporting guidelines. Results Data are presented in Table 1 . The median total census was 60 patients (IQR 53.0–67.0) for the traditional model and 54 (IQR 49.0–59.0) for the MarioKart model. Table 1 Metrics allowing for comparison of traditional model of resident scheduling to MarioKart model: Traditional (IQR) MarioKart (IQR) P - value Total Days in for each model 641 454 Total Median Resident Census 60 (53.0–67) 54 (49.0–59.0) < 0.001 Mean days worked per resident in 28 days 24 22.5 Days off in 28 days 4 5.5 Patient-days 288.0 (254.4-321.6) 303.8 (275.6–331.9) < 0.001 Median Duty Hour Violations (per Month): Short Break Entire Program 12.45 2.53 < 0.001 80 – Hour Entire Program 2.95 3.47 0.33 Short Break General Inpatient 2.95 0.6 < 0.001 80-Hour General Inpatient 0.9 1.13 0.009 Figure 2 demonstrates a box plot of differences in team census in the traditional versus MarioKart model. For each l team, the median census was 12 (IQR 10.0–15.0) for the traditional model and 13.5 (IQR 12.25–14.75) for the MarioKart model. Total patient days per team were 288 (IQR 254.4-321.6) for the traditional model and 303.8 (275.6-331.9) for the MarioKart model (p < 0.001). Residents worked a mean of 24 days and were off a mean 4 days in the traditional model. With MarioKart, residents worked a mean of 22.5 days and were off a mean of 5.5 days. Regarding ACGME duty hour violations, the numbers of short break violations were reduced in the MarioKart model across the entire program from 12.45 per month to 2.53 per month (p < 0.001) and within the general medicine service where the MarioKart schedule was implemented from 2.95 per month to 0.6 (p < 0.001). The number of 80-hour week violations was unchanged for the entire program, whereas the number increased slightly for the general medicine service from 0.9 to 1.13 (p = 0.009). However, a subsequent analysis of whether an average of 80 hours a week were worked over four weeks shows that there were five violations on the general medicine service in the traditional model, there were none in the MarioKart model. Discussion The inpatient schedule adjustment adopted by our program was a unique solution to feedback and organizational constraints that may be unique to our program. These data only describe the effect on patient census and work-duration for the trainees in the program. The effect of this schedule on patient care or educational outcomes was not analyzed in this study. Certainly, any consideration of a resident schedule change must appraise potential impacts on the clinical learning environment. While this new schedule is generally liked by our residents the lack of such data and the fact that this schedule is unique to one institution limits its generalizability to other programs. The MarkioKart model has drawbacks. The switching between inpatient teams by MarioKart teams creates additional handoffs which may lead to delays in care and increased length of stay. Faculty have anecdotally noted that interns who are promoted to supervisory roles have less experience working independently than in our traditional model, as they no longer have days were they work without a supervising resident as interns when the senior resident is off. This has led to more discomfort with our rising PGY2s in independently handling critical patient situations. Our solution has been to alter the team dynamic in the last quarter of the academic year, where interns assume more responsibilities including triaging of admissions and running critical patient care situations with direct observation by the much more experienced senior resident. An additional concern was the consideration of attendings on the service and medical students. Attendings in our program were typically assigned to one or two weeks and were used to working with the same residents for that time. Switching to the MarioKart model resulted in an attending working with multiple resident triads during their time attending, which can lead to less time observing and working with residents and students.. We decided to keep the third and fourth year clerkship medical students paired with the resident ‘MarioKart’ team, led to good continuity with the residents and additional days off during the rotation, but to more discontinuity between the students and the attendings. Last, the interpretation of duty hour violation data should be taken with caution, as the data is dependent on resident entry and recollection and may not fully reflect the reality of the trainee experience. The slight increase in 80 hour week violations with the MarioKart model might be a reflection that the schedule has the resident team working 8 days consecutively which might lead to more than eighty hours worked in a 7 day period. However, this period is sandwiched between two days off on each end, making the risk of a violation over a four week period low. In 2003, the ACGME limited duty-hours for residents to 80 hours a week and 10 hours off between shifts. 14 Further restrictions by the ACGME in 2011 limited the maximum shift length for residents to 24 hours, one day off every seven days, and a now-removed 16-hour shift restriction. 15 The iCompare 16 and FIRST 17 trials compared the impact of the 2011 work hour restrictions to less-restrictive duty hour rules in internal medicine and surgical residencies, respectively, and found no significant difference in perception of time spent in direct patient care. Internal Medicine interns were less satisfied in the group with less-restrictive duty hour limits. 16 The advent of X + Y models in pediatric and internal medicine programs in the last decade arose out of the desire to simplify and separate the multitude of roles of a resident and adjust to mandated changes in resident work-hours. 1–3 Despite these changes, the prevalence of trainee burn-out remains high, the main driver of which are factors related to the learning environment. 18 Internal medicine residents on inpatient wards are at risk of moral distress, particularly in intensive care units and inpatient hospital rotations. 8 These results are comparable to the interns studied in the iCompare trial who had high scores on a burn-out inventory with known validity evidence regardless of study arm. 16 Medical residents on inpatient units also note high perceptions of perceived workload 19 and frequent interruptions impeding patient care and educational activities. 9 Additionally, a study of internal medicine residents who had more depressive symptoms, measured by the Patient Health Questionnaire-9 (PHQ-9), demonstrated that contributing factors to increased depressive symptoms were related to low-quality faculty feedback, poor learning experiences, and long work hours. 20 Managing learner workload and optimizing clinical experiences is essential to improve the clinical learning environment. 21 There have been attempts at reducing work-load by adding extra resident personnel, 10 but this places an added burden on programs in that more residents requiring assignment to inpatient duties rather than outpatient rotations or electives. Another Internal Medicine program prioritized a prolonged ‘ambulatory block’ to allow residents immersive time in the outpatient setting, with marked improvement in resident satisfaction. 22 More recent literature suggests that after a work-week longer than 48 hours, internal medicine residents are much likelier to be involved in medical adverse events, medical errors, motor vehicle collisions and near-miss crashes. 23 Additionally, a recent systemic review suggested that shorter resident duty hours was associated with less emotional exhaustion, improved sleep, and less dissatisfaction with overall well-being, without any substantial change to length of stay or adverse events for patients. 24 The MarioKart model represents a tradeoff of a systemic change to reduce duty-hour violations, provide a consistent structure and work allocation to resident teams, and provide residents more days off that outweighs the drawbacks for our program. It certainly suggests that there remains room for residency programs to innovate at a systems level to reduce trainee workload. While this specific model may or may not be applicable to other programs, our data suggest that there might be alternate ways of adjusting internal medicine resident schedules to provide more time off without a detriment to patient care opportunities. There are multiple features of the MarioKart that, while not directly studied, have potential benefit to training programs. First, there are more days off and assuming an average of 10 hour days, less duty hours within the MarioKart structure. Having two days off back-to-back consistently is an added bonus, residents have anecdotally noted that the first day allows them to decompress from the intensity of inpatient work and the second day is to enjoy. Second, every day has the full complement of learners, creating clear supervisory and team roles for the resident, interns, and students. This is in contrast to the traditional model of three residents which, accounting for the ACGME’s one day off in seven rule, has a team down one member in twelve days (43%) out of a twenty-eight day rotation. While data on perceived workload is unknown, having a full complement of team members daily likely improves the perception of workload. Last, our data suggest that reducing work hours or increasing the number of days off does not necessarily have to impact patient care opportunities or case volume for trainees. Conclusions The “Mario-Kart” resident scheduling model implemented by our program created an alternate daily schedule for Internal Medicine residents, creating a rotation of teams ensuring the daily presence of all team members. As a result, the inpatient census covered by the resident teams decreased slightly, but the median census of each individual team increased. The amount of patient encounters seen by residents over the rotation increased. On average, each resident received an additional 1.5 days (median 5.5 days) off in a 28-day rotational cycle and ACGME short break violations decreased within the program. Considering the relative success of this model, our program is discussing whether to expand this system to blend certain thematic rotations together - for instance blending an inpatient hospital rotation and rotating in our hospital discharge clinic. Alternately, we have considering creating longitudinal rotations where residents weave a five to eight day run of inpatient medicine and other rotations with lesser workload to provide balance in the schedule not unlike the schedule of a fully practicing internist. Such proposals incorporate concepts such as interleaving and spaced repetition that might enhance learning. 25 . Future areas of investigation include the impact of this scheduling system on patient-related outcomes such as hospital length of stay, readmissions, and cost-of-care. Finally, expanding this model beyond a single institution's experience would provide important comparative results to determine the reproducibility in these outcomes. Abbreviations ACGME: Accreditation Council of Graduate Medical Education COVID-19: Coronavirus Disease 2019 IM: Internal Medicine IQR: Intra-quartile Range STROBE: STrengthening the Reporting of OBservational studies in Epidemiology Declarations Ethics approval and consent to participate: This study was formally determined by our Institutional Review Board (IRB) to not constitute Human Subjects Research and did not require IRB approval. Consent for publication: Not Applicable Availability of data and materials: Data, and relevant coding using R, will be publicly available on GitHub repository, https://github.com/fbuckhold3/MarioKart/. Competing Interests: The authors declare that they have no competing interests Funding: No funding was utilized for this research Author Contribution: PK was involved in conception, design of work, interpretation of data, and has substantively revised the manuscript. MS made substantial contributions to the conception and design of the work. JL made substantial contributions to the conception and design of the work. AZ made substantial contributions to the conception and design of the work. PV made substantial contributions to the conception, design of the work and data acquisition. AF made substantial contributions to the conception and data acquisition . FB made substantial contributions to the conception, design of the work, acquisition, analysis, interpretation of data and drafted the work. All authors read and approved the final manuscript. Acknowledgements: The authors would like to thank Kristina Dzara, PhD, MMSc and the Center for Educator Development, Advancement, and Research (CEDAR) at Saint Louis University SOM for their support. References Shalaby M, Yaich S, Donnelly J, Chippendale R, DeOliveira MC, Noronha C. X + Y Scheduling Models for Internal Medicine Residency Programs-A Look Back and a Look Forward. J Grad Med Educ. 2014 Dec;6(4):639-42 Noronha C, Chaudhry S, Chacko K, et al. X + Y Scheduling Models in Internal Medicine Residency Programs: A National Survey of Program Directors' Perspectives. Am J Med. 2018 Jan;131(1):107-114. Myers RE, Thoreson L, Howell HB, Poitevien P, Wroblewski MB, Ponitz K, Lewis J. Three Years of X + Y Scheduling: Longitudinal Assessment of Resident and Faculty Perceptions. Acad Pediatr. 2022 Sep-Oct;22(7):1097-1104. DeWaters AL, Loria H, Mayo H, Chisty A, Nguyen OK. The Impact of Block Ambulatory Scheduling on Internal Medicine Residencies: a Systematic Review. 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Kunnath","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"V.","lastName":"Kunnath","suffix":""},{"id":300631568,"identity":"849cb27e-dc7d-4c27-95d7-2e977b86e67c","order_by":1,"name":"Maria Srour","email":"","orcid":"","institution":"Indiana University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Srour","suffix":""},{"id":300631571,"identity":"165d37c5-5c5a-4a45-a774-087337529e71","order_by":2,"name":"Adam Fritz","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Fritz","suffix":""},{"id":300631573,"identity":"8a078994-777f-42d3-9287-b734fe9b31ab","order_by":3,"name":"Jason Lunt","email":"","orcid":"","institution":"University of Washington Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Lunt","suffix":""},{"id":300631575,"identity":"3bdd088d-6f4c-4970-9a60-e790431e3edc","order_by":4,"name":"Zarir Ahmed","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Zarir","middleName":"","lastName":"Ahmed","suffix":""},{"id":300631577,"identity":"37104380-0b73-416d-bdc5-721ebfc7b1b4","order_by":5,"name":"Philip Vaidyan","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Vaidyan","suffix":""},{"id":300631578,"identity":"b0e69caf-5766-4550-8cd0-7f66141c67f1","order_by":6,"name":"Fred Buckhold","email":"data:image/png;base64,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","orcid":"","institution":"Saint Louis University","correspondingAuthor":true,"prefix":"","firstName":"Fred","middleName":"","lastName":"Buckhold","suffix":""}],"badges":[],"createdAt":"2024-05-01 17:08:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4355343/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4355343/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56543418,"identity":"f6d3d5a1-de19-4e82-a53b-ce23a8615782","added_by":"auto","created_at":"2024-05-15 14:37:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":605119,"visible":true,"origin":"","legend":"\u003cp\u003eA: sample of traditional scheduling for a single care team B: sample of MarioKart scheduling demonstrating how groups of residents cycle through each care team.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4355343/v1/992a380934213d9028e62b74.jpeg"},{"id":56543419,"identity":"7461c776-457a-46e2-ba5f-3bf1457480c4","added_by":"auto","created_at":"2024-05-15 14:37:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":256647,"visible":true,"origin":"","legend":"\u003cp\u003eBox-plot of median daily team census in traditional model and MarioKart model of inpatient resident scheduling.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4355343/v1/868ef3c0d584f3997d39ae89.jpeg"},{"id":58107569,"identity":"31811e16-15e5-4871-82ab-4de141de39dd","added_by":"auto","created_at":"2024-06-11 08:14:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4355343/v1/8f3ade68-3a0b-4da5-90f2-663e83395d8b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"‘Super’-ior Scheduling: A Novel Model for an Inpatient Internal Medicine Resident Service","fulltext":[{"header":"Background","content":"\u003cp\u003eIn the last decade, pediatric and internal medicine (IM) residency programs have adopted blocked scheduling, such as an \u0026ldquo;X\u0026thinsp;+\u0026thinsp;Y\u0026rdquo; system.\u003csup\u003e1, 2\u003c/sup\u003e Such systems addressed competing resident demands of a busy inpatient rotations and resident continuity clinic by separating the inpatient rotations as a block (X) alternating with clinic experiences (Y) in some ratio of weeks worked, such as 4\u0026thinsp;+\u0026thinsp;1 or 6\u0026thinsp;+\u0026thinsp;2.\u003csup\u003e1, 2\u003c/sup\u003e This has led to perceptions of improved teaching, inpatient workflow, and better continuity in clinic,\u003csup\u003e3\u003c/sup\u003e although others note a loss of patient continuity and of schedule flexibility.\u003csup\u003e1, 4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe intention of an X\u0026thinsp;+\u0026thinsp;Y model is to simplify the roles of an IM resident during a specific rotation and thus reduce stress and inefficiency of task switching between clinic and inpatient duties.\u003csup\u003e1, 3, 4\u003c/sup\u003e However, the problem of emotional exhaustion and rates of burnout persist among IM residents,\u003csup\u003e5, 6\u003c/sup\u003e only to be exacerbated by the COVID-19 pandemic in 2020.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhat X\u0026thinsp;+\u0026thinsp;Y or traditional residency systems do not address is the structure of work within rotations, particularly within what many would deem the more \u0026lsquo;high stress\u0026rsquo; rotations central to any IM residency program \u0026ndash; including but not limited to general medical floors, night-float, and medical intensive-care units.\u003csup\u003e8\u003c/sup\u003e The work of medicine residents on general medicine wards is predominately spent on activities not at the bedside with frequent multi-tasking.\u003csup\u003e9\u003c/sup\u003e Residents in the role on general inpatient floors note higher amounts of moral distress\u003csup\u003e8\u003c/sup\u003e as compared to assignments in ambulatory or elective rotations. To alleviate the workload burden and attempt to improve the educational climate, some programs adopted additional roles such as a late-evening shift to cover patient care duties.\u003csup\u003e10\u003c/sup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Others have changed the ratio of resident learners to attendings, which improved the perceived educational experience without a change in patient outcomes.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDuring the onset of the COVID-19 pandemic, our department underwent a strategic restructuring to ensure availability of attendings to cover inpatient care, and our residency program was asked by departmental leadership to reduce the resident covered service from five one-resident, two-intern teams to four. At the same time, our program was under an Accreditation Council for Graduate Medical Education (ACGME) citation for work-hour violations due to frequent violations of the ACGME \u0026lsquo;short-break\u0026rsquo;, where residents would have less than ten hours absent of clinical duty between shifts.\u003csup\u003e12\u003c/sup\u003eFocused feedback sessions with residents to analyze root causes identified a high number of patients admitted during \u0026lsquo;call\u0026rsquo; days. Additionally, senior residents noted that work-days when fellow residents and interns had scheduled days off resulted in residents staying much later than the expected sign out time. Rather than keep our traditional model, we developed and implemented an alternative schedule for our inpatient rotation. Herein we describe the effect of this schedule change on patient census, duty-hour violations, and patient care opportunities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe Internal Medicine Residency Program at Saint Louis University has 83 residents, 76 of whom are categorical residents and the remainder preliminary interns. Traditionally, the program had five general floor teams, each staffed with one attending, one supervising senior resident and two interns for a 28-day rotation. Day call (where the team performed admissions and ran the code team) occurred every five days. Each resident had four days off during the block: the senior was off most pre-call days, and the two interns each had one day off between the call days. Thus, in a 28-day rotation, the entire team was together only for 16 of the days, and the senior had to serve in a \u0026lsquo;resitern\u0026rsquo; capacity for eight days, in which they wrote notes for intern #1 on one day, and again for intern #2 the next day while supervising the remaining intern. There were also four days where the two interns were alone under the direct supervision of the attending.\u003c/p\u003e \u003cp\u003eDue to the impetus to reduce the number of inpatient teams, we implemented an alternate daily resident schedule on our general inpatient service in April of 2020. The number of inpatient attending-supervised ward teams was reduced to four, yet our program preserved the five one-resident, two-intern teams who then rotated through those four ward teams in an alternate schedule dubbed the \u0026ldquo;MarioKart\u0026rdquo;.\u003c/p\u003e \u003cp\u003eEach triad of resident and two interns was given a name based on a \u0026lsquo;MarioKart\u0026rsquo; character to identify the triad as opposed to team name of the ward service. This also provided clarity in long-term assignment of residents throughout the year. During a given day, each resident/intern grouping is assigned to an inpatient team, with one entire resident/intern grouping having the day off. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Part A shows how each group cycles through the care teams. For example, the four teams would be staffed by \u0026lsquo;Peach\u0026rsquo;\u0026rsquo; (pink in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) on inpatient team one, \u0026lsquo;Yoshi\u0026rsquo; (yellow in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) on inpatient team two, \u0026lsquo;Luigi\u0026rsquo; (green in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) on inpatient team three, and \u0026lsquo;Bowser\u0026rsquo; (orange in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u0026rsquo; on inpatient team four, with team \u0026lsquo;Mario\u0026rsquo; (red in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u0026rsquo; having two days off. After two days, team Yoshi will have two consecutive days off, having handed off team two to Mario, who will begin a run of eight days on inpatient service. When team Yoshi finishes their two days off, they take over team three for Luigi, who then receive two days off. Thus, on any given day, the MarioKart system has four resident teams working and one off for the duration of the 28-day rotation block. As a contrast, our \u0026lsquo;traditional\u0026rsquo; way of scheduling is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Part B.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs the new schedule was adopted our distribution of patients to resident teams changed. In creating a system where the team is fully complemented every day, patients were assigned based on team census numbers. For both models, the team \u0026lsquo;cap\u0026rsquo; was 16 patients, thus the maximum patient census was 80 for the traditional model and 64 for the Mariokart model.\u003c/p\u003e \u003cp\u003eTo compare differences between the \u0026lsquo;traditional\u0026rsquo; model and the MarioKart model we analyzed daily patient census numbers for the resident inpatient service and by assigned inpatient teams from July 1, 2018 to June 30, 2021. Data from the traditional model period (July 1, 2018 to March 31, 2020) were compared to data in the MarioKart period (April 1, 2020 to June 30, 2021). There are three reasons these dates and time intervals were chosen for analysis. First, collection of patient census data was part of a larger project within the Department; second, the Mariokart schedule was implemented near the beginning of the COVID-19 pandemic and the hospital census was low during the Spring of 2020; third, the three year period captured the experience of at least one full class in our program.\u003c/p\u003e \u003cp\u003eMissing patient census data were imputed using predictive mean matching via the mice package in R.\u003csup\u003e13\u003c/sup\u003e Additionally, we also compared self-reported ACGME short-break violations via New-Innovations (Uniontown, OH), defined as time off of less than ten hours between work-related shifts and ACGME 80-hour violations, defined as a flag of worked hours more than 80 hours in a 7 day period. Numbers of both violations were collected for the overall program and for the general inpatient service where the MarioKart schedule was deployed during the study period. All violations were calculated as the mean number per month during the study period.\u003c/p\u003e \u003cp\u003eMedian and interquartile ranges (IQR) were calculated for patient census data. To assess the patient care opportunities for the residents during the rotation, we created a metric called total patient days per team. This metric is calculated as the multiple of the median, lower, and upper IQR bounds of patient census by the number of days worked per rotation (24 in the traditional model, 22.5 in the MarioKart model). This metric is used to provide an estimate for comparison of the number of patient encounters a resident team would have during the duration of the 28 day rotation. A two-sided t-test was used to compare differences between the traditional and MarioKart models.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in R (R Studio, Boston, MA). The code for analysis is available on GitHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/fbuckhold3/MarioKart/\u003c/span\u003e\u003cspan address=\"https://github.com/fbuckhold3/MarioKart/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This study was formally determined by our Institutional Review Board (IRB) to not constitute Human Subjects Research and did not require IRB approval. The study followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) reporting guidelines.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eData are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median total census was 60 patients (IQR 53.0\u0026ndash;67.0) for the traditional model and 54 (IQR 49.0\u0026ndash;59.0) for the MarioKart model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetrics allowing for comparison of traditional model of resident scheduling to MarioKart model:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraditional (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarioKart (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP - value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Days in for each model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Median Resident Census\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (53.0\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (49.0\u0026ndash;59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean days worked per resident in 28 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays off in 28 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient-days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288.0 (254.4-321.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e303.8 (275.6\u0026ndash;331.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Duty Hour Violations (per Month):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Break Entire Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 \u0026ndash; Hour Entire Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Break General Inpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80-Hour General Inpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates a box plot of differences in team census in the traditional versus MarioKart model.\u003c/p\u003e \u003cp\u003eFor each l team, the median census was 12 (IQR 10.0\u0026ndash;15.0) for the traditional model and 13.5 (IQR 12.25\u0026ndash;14.75) for the MarioKart model. Total patient days per team were 288 (IQR 254.4-321.6) for the traditional model and 303.8 (275.6-331.9) for the MarioKart model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Residents worked a mean of 24 days and were off a mean 4 days in the traditional model. With MarioKart, residents worked a mean of 22.5 days and were off a mean of 5.5 days.\u003c/p\u003e \u003cp\u003eRegarding ACGME duty hour violations, the numbers of short break violations were reduced in the MarioKart model across the entire program from 12.45 per month to 2.53 per month (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and within the general medicine service where the MarioKart schedule was implemented from 2.95 per month to 0.6 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The number of 80-hour week violations was unchanged for the entire program, whereas the number increased slightly for the general medicine service from 0.9 to 1.13 (p\u0026thinsp;=\u0026thinsp;0.009). However, a subsequent analysis of whether an average of 80 hours a week were worked over four weeks shows that there were five violations on the general medicine service in the traditional model, there were none in the MarioKart model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe inpatient schedule adjustment adopted by our program was a unique solution to feedback and organizational constraints that may be unique to our program. These data only describe the effect on patient census and work-duration for the trainees in the program. The effect of this schedule on patient care or educational outcomes was not analyzed in this study. Certainly, any consideration of a resident schedule change must appraise potential impacts on the clinical learning environment. While this new schedule is generally liked by our residents the lack of such data and the fact that this schedule is unique to one institution limits its generalizability to other programs.\u003c/p\u003e \u003cp\u003eThe MarkioKart model has drawbacks. The switching between inpatient teams by MarioKart teams creates additional handoffs which may lead to delays in care and increased length of stay. Faculty have anecdotally noted that interns who are promoted to supervisory roles have less experience working independently than in our traditional model, as they no longer have days were they work without a supervising resident as interns when the senior resident is off. This has led to more discomfort with our rising PGY2s in independently handling critical patient situations. Our solution has been to alter the team dynamic in the last quarter of the academic year, where interns assume more responsibilities including triaging of admissions and running critical patient care situations with direct observation by the much more experienced senior resident. An additional concern was the consideration of attendings on the service and medical students. Attendings in our program were typically assigned to one or two weeks and were used to working with the same residents for that time. Switching to the MarioKart model resulted in an attending working with multiple resident triads during their time attending, which can lead to less time observing and working with residents and students.. We decided to keep the third and fourth year clerkship medical students paired with the resident \u0026lsquo;MarioKart\u0026rsquo; team, led to good continuity with the residents and additional days off during the rotation, but to more discontinuity between the students and the attendings. Last, the interpretation of duty hour violation data should be taken with caution, as the data is dependent on resident entry and recollection and may not fully reflect the reality of the trainee experience. The slight increase in 80 hour week violations with the MarioKart model might be a reflection that the schedule has the resident team working 8 days consecutively which might lead to more than eighty hours worked in a 7 day period. However, this period is sandwiched between two days off on each end, making the risk of a violation over a four week period low.\u003c/p\u003e \u003cp\u003eIn 2003, the ACGME limited duty-hours for residents to 80 hours a week and 10 hours off between shifts.\u003csup\u003e14\u003c/sup\u003e Further restrictions by the ACGME in 2011 limited the maximum shift length for residents to 24 hours, one day off every seven days, and a now-removed 16-hour shift restriction.\u003csup\u003e15\u003c/sup\u003e The iCompare\u003csup\u003e16\u003c/sup\u003eand FIRST\u003csup\u003e17\u003c/sup\u003e trials compared the impact of the 2011 work hour restrictions to less-restrictive duty hour rules in internal medicine and surgical residencies, respectively, and found no significant difference in perception of time spent in direct patient care. Internal Medicine interns were less satisfied in the group with less-restrictive duty hour limits.\u003csup\u003e16\u003c/sup\u003e The advent of X\u0026thinsp;+\u0026thinsp;Y models in pediatric and internal medicine programs in the last decade arose out of the desire to simplify and separate the multitude of roles of a resident and adjust to mandated changes in resident work-hours.\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite these changes, the prevalence of trainee burn-out remains high, the main driver of which are factors related to the learning environment.\u003csup\u003e18\u003c/sup\u003e Internal medicine residents on inpatient wards are at risk of moral distress, particularly in intensive care units and inpatient hospital rotations.\u003csup\u003e8\u003c/sup\u003e These results are comparable to the interns studied in the iCompare trial who had high scores on a burn-out inventory with known validity evidence regardless of study arm.\u003csup\u003e16\u003c/sup\u003e Medical residents on inpatient units also note high perceptions of perceived workload\u003csup\u003e19\u003c/sup\u003e and frequent interruptions impeding patient care and educational activities.\u003csup\u003e9\u003c/sup\u003e Additionally, a study of internal medicine residents who had more depressive symptoms, measured by the Patient Health Questionnaire-9 (PHQ-9), demonstrated that contributing factors to increased depressive symptoms were related to low-quality faculty feedback, poor learning experiences, and long work hours.\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eManaging learner workload and optimizing clinical experiences is essential to improve the clinical learning environment.\u003csup\u003e21\u003c/sup\u003e There have been attempts at reducing work-load by adding extra resident personnel,\u003csup\u003e10\u003c/sup\u003e but this places an added burden on programs in that more residents requiring assignment to inpatient duties rather than outpatient rotations or electives. Another Internal Medicine program prioritized a prolonged \u0026lsquo;ambulatory block\u0026rsquo; to allow residents immersive time in the outpatient setting, with marked improvement in resident satisfaction.\u003csup\u003e22\u003c/sup\u003e More recent literature suggests that after a work-week longer than 48 hours, internal medicine residents are much likelier to be involved in medical adverse events, medical errors, motor vehicle collisions and near-miss crashes.\u003csup\u003e23\u003c/sup\u003e Additionally, a recent systemic review suggested that shorter resident duty hours was associated with less emotional exhaustion, improved sleep, and less dissatisfaction with overall well-being, without any substantial change to length of stay or adverse events for patients.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe MarioKart model represents a tradeoff of a systemic change to reduce duty-hour violations, provide a consistent structure and work allocation to resident teams, and provide residents more days off that outweighs the drawbacks for our program. It certainly suggests that there remains room for residency programs to innovate at a systems level to reduce trainee workload. While this specific model may or may not be applicable to other programs, our data suggest that there might be alternate ways of adjusting internal medicine resident schedules to provide more time off without a detriment to patient care opportunities.\u003c/p\u003e \u003cp\u003eThere are multiple features of the MarioKart that, while not directly studied, have potential benefit to training programs. First, there are more days off and assuming an average of 10 hour days, less duty hours within the MarioKart structure. Having two days off back-to-back consistently is an added bonus, residents have anecdotally noted that the first day allows them to decompress from the intensity of inpatient work and the second day is to enjoy. Second, every day has the full complement of learners, creating clear supervisory and team roles for the resident, interns, and students. This is in contrast to the traditional model of three residents which, accounting for the ACGME\u0026rsquo;s one day off in seven rule, has a team down one member in twelve days (43%) out of a twenty-eight day rotation. While data on perceived workload is unknown, having a full complement of team members daily likely improves the perception of workload. Last, our data suggest that reducing work hours or increasing the number of days off does not necessarily have to impact patient care opportunities or case volume for trainees.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe \u0026ldquo;Mario-Kart\u0026rdquo; resident scheduling model implemented by our program created an alternate daily schedule for Internal Medicine residents, creating a rotation of teams ensuring the daily presence of all team members. As a result, the inpatient census covered by the resident teams decreased slightly, but the median census of each individual team increased. The amount of patient encounters seen by residents over the rotation increased. On average, each resident received an additional 1.5 days (median 5.5 days) off in a 28-day rotational cycle and ACGME short break violations decreased within the program.\u003c/p\u003e \u003cp\u003eConsidering the relative success of this model, our program is discussing whether to expand this system to blend certain thematic rotations together - for instance blending an inpatient hospital rotation and rotating in our hospital discharge clinic. Alternately, we have considering creating longitudinal rotations where residents weave a five to eight day run of inpatient medicine and other rotations with lesser workload to provide balance in the schedule not unlike the schedule of a fully practicing internist. Such proposals incorporate concepts such as interleaving and spaced repetition that might enhance learning.\u003csup\u003e25\u003c/sup\u003e. Future areas of investigation include the impact of this scheduling system on patient-related outcomes such as hospital length of stay, readmissions, and cost-of-care. Finally, expanding this model beyond a single institution's experience would provide important comparative results to determine the reproducibility in these outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACGME: Accreditation Council of Graduate Medical Education\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eIM: Internal Medicine\u003c/p\u003e\n\u003cp\u003eIQR: Intra-quartile Range\u003c/p\u003e\n\u003cp\u003eSTROBE: STrengthening the Reporting of OBservational studies in Epidemiology\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This study was formally determined by our Institutional Review Board (IRB) to not constitute Human Subjects Research and did not require IRB approval.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not Applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: Data, and relevant coding using R, will be publicly available on GitHub repository, https://github.com/fbuckhold3/MarioKart/.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003eFunding: No funding was utilized for this research\u003c/p\u003e\n\u003cp\u003eAuthor Contribution: PK was involved in conception, design of work, interpretation of data, and has substantively revised the manuscript. MS made substantial contributions to the conception and design of the work. JL made substantial contributions to the conception and design of the work. AZ made substantial contributions to the conception and design of the work. PV made substantial contributions to the conception, design of the work and data acquisition. AF made substantial contributions to the conception and data acquisition . FB made substantial contributions to the conception, design of the work, acquisition, analysis, interpretation of data and drafted the work.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: The authors would like to thank Kristina Dzara, PhD, MMSc and the Center for Educator Development, Advancement, and Research (CEDAR) at Saint Louis University SOM for their support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShalaby M, Yaich S, Donnelly J, Chippendale R, DeOliveira MC, Noronha C. X + Y Scheduling Models for Internal Medicine Residency Programs-A Look Back and a Look Forward. J Grad Med Educ. 2014 Dec;6(4):639-42\u003c/li\u003e\n\u003cli\u003eNoronha C, Chaudhry S, Chacko K, et al. X + Y Scheduling Models in Internal Medicine Residency Programs: A National Survey of Program Directors\u0026apos; Perspectives. Am J Med. 2018 Jan;131(1):107-114.\u003c/li\u003e\n\u003cli\u003eMyers RE, Thoreson L, Howell HB, Poitevien P, Wroblewski MB, Ponitz K, Lewis J. Three Years of X + Y Scheduling: Longitudinal Assessment of Resident and Faculty Perceptions. Acad Pediatr. 2022 Sep-Oct;22(7):1097-1104.\u003c/li\u003e\n\u003cli\u003eDeWaters AL, Loria H, Mayo H, Chisty A, Nguyen OK. The Impact of Block Ambulatory Scheduling on Internal Medicine Residencies: a Systematic Review. J Gen Intern Med. 2019 May;34(5):731-739.\u003c/li\u003e\n\u003cli\u003eFang Y, Bohnert ASB, Pereira-Lima K, et al. Trends in Depressive Symptoms and Associated Factors During Residency, 2007 to 2019 : A Repeated Annual Cohort Study. Ann Intern Med. 2022 Jan;175(1):56-64.\u003c/li\u003e\n\u003cli\u003eLu DW, Germann CA, Nelson SW, Jauregui J, Strout TD. \u0026quot;Necessary Compromises\u0026quot;: A Qualitative Exploration of the Influence of Burnout on Resident Education. AEM Educ Train. 2020 Aug 5;5(2):e10500.\u003c/li\u003e\n\u003cli\u003eVijay A, Yancy CW. Resident Physician Wellness Postpandemic: How Does Healing Occur? JAMA. 2022 Jun 7;327(21):2077-2078.\u003c/li\u003e\n\u003cli\u003eSajjadi S, Norena M, Wong H, Dodek P. Moral distress and burnout in internal medicine residents. Can Med Educ J. 2017 Feb 24;8(1):e36-e43.\u003c/li\u003e\n\u003cli\u003eChaiyachati KH, Shea JA, Asch DA, et al. Assessment of Inpatient Time Allocation Among First-Year Internal Medicine Residents Using Time-Motion Observations. JAMA Intern Med. 2019 Jun 1;179(6):760-767.\u003c/li\u003e\n\u003cli\u003eAl-Kofahi M, Mohyuddin GR, Taylor ME, Eck LM. Reducing Resident Physician Workload to Improve Well Being. Cureus. 2019 Jun 29;11(6):e5039.\u003c/li\u003e\n\u003cli\u003eSpellberg B, Lewis RJ, Sue D, et al. A controlled investigation of optimal internal medicine ward team structure at a teaching hospital. PLoS One. 2012;7(4):e35576.\u003c/li\u003e\n\u003cli\u003eAccreditation Council for Graduate Medical Education. Intent, ACGME Common Program Requirements Section VI with Background and. www..acgme.org. [Online] 2017. [Cited: March 13, 2024.] https://www.acgme.org/globalassets/PFAssets/ProgramRequirements/CPRs_Section-VI_with-Background-and-Intent_2017-01.pdf.\u003c/li\u003e\n\u003cli\u003evan Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011: 45: 1-67.\u003c/li\u003e\n\u003cli\u003eAccreditation Council for Graduate Medical Education. Report of the Work Group on Resident Duty Hours and the Learning Environment. 2002.\u003c/li\u003e\n\u003cli\u003eNasca TJ, Day SH, Amis ES Jr; ACGME Duty Hour Task Force. The new recommendations on duty hours from the ACGME Task Force. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2010;363(2):e3. \u003c/li\u003e\n\u003cli\u003eDesai SV, Asch DA, Bellini LM, et al. Education Outcomes in a Duty-Hour Flexibility Trial in Internal Medicine. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2018;378(16):1494-1508. \u003c/li\u003e\n\u003cli\u003eBilimoria KY, Chung JW, Hedges LV, et al. National Cluster-Randomized Trial of Duty-Hour Flexibility in Surgical Training. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2016;374(8):713-727. \u003c/li\u003e\n\u003cli\u003eDyrbye L, Shanafelt T. A narrative review on burnout experienced by medical students and residents. \u003cem\u003eMed Educ\u003c/em\u003e. 2016;50(1):132-149. \u003c/li\u003e\n\u003cli\u003eFletcher KE, Visotcky AM, Slagle JM, et al. Self-reported subjective workload of on-call interns. \u003cem\u003eJ Grad Med Educ\u003c/em\u003e. 2013;5(3):427-432. \u003c/li\u003e\n\u003cli\u003ePereira-Lima K, Gupta RR, Guille C, Sen S. Residency Program Factors Associated With Depressive Symptoms in Internal Medicine Interns: A Prospective Cohort Study. \u003cem\u003eAcad Med\u003c/em\u003e. 2019;94(6):869-875. \u003c/li\u003e\n\u003cli\u003eDyrbye LN, Lipscomb W, Thibault G. Redesigning the Learning Environment to Promote Learner Well-Being and Professional Development. \u003cem\u003eAcad Med\u003c/em\u003e. 2020;95(5):674-678. \u003c/li\u003e\n\u003cli\u003eWarm EJ, Schauer DP, Diers T, et al. The ambulatory long-block: an accreditation council for graduate medical education (ACGME) educational innovations project (EIP). \u003cem\u003eJ Gen Intern Med\u003c/em\u003e. 2008;23(7):921-926. \u003c/li\u003e\n\u003cli\u003eBarger LK, Weaver MD, Sullivan JP, Qadri S, Landrigan CP, Czeisler CA. Impact of work schedules of senior resident physicians on patient and resident physician safety: nationwide, prospective cohort study. \u003cem\u003eBMJ Med\u003c/em\u003e. 2023;2(1):e000320. Published 2023 Mar 30.\u003c/li\u003e\n\u003cli\u003eSephien A, Reljic T, Jordan J, Prida X, Kumar A. Resident duty hours and resident and patient outcomes: Systematic review and meta-analysis. \u003cem\u003eMed Educ\u003c/em\u003e. 2023;57(3):221-232.\u003c/li\u003e\n\u003cli\u003eGooding HC, Mann K, Armstrong E. Twelve tips for applying the science of learning to health professions education. \u003cem\u003eMed Teach\u003c/em\u003e. 2017;39(1):26-31. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Graduate Medical Education, Duty-hours, Resident Wellness","lastPublishedDoi":"10.21203/rs.3.rs-4355343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4355343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eResidency programs have adopted blocked scheduling to improve the learning climate yet more intensive rotations still impact resident wellness. The effect of changing the alignment of inpatient resident teams on patient care opportunities is not well known. We sought to evaluate the association of new daily schedule (dubbed \u0026lsquo;Mariokart\u0026rsquo;) compared to a traditional schedule on patient care opportunities, days off, and duty-hour violations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA non-randomized pre - and post-intervention study examining the daily patient census for residents covering a general internal medicine inpatient service within an internal medicine residency program at a single academic medical center from July 2018 to June 2021. The main outcome was the median daily patient census per resident inpatient team before and after the implementation of the \u0026lsquo;Mariokart\u0026rsquo; schedule. Secondary outcomes included days off in a 28-day cycle, patient-care opportunities, and resident-reported duty-hour violations. A two-sided t-test was used to determine differences between the groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn comparing 20 months prior to implementation of the \u0026lsquo;Mariokart\u0026rsquo; schedule to 15 months post-implementation, the median census of the resident service was 60 patients (IQR 53.0\u0026ndash;67.0) for the traditional model and 54 (IQR 49.0\u0026ndash;59.0) for the MarioKart model. The median census per team was 12 (IQR 10.0\u0026ndash;15.0) for the traditional model and 13.5 (IQR 12.25\u0026ndash;14.75) for the MarioKart model. Total patient days per team were 288 (IQR 254.4-321.6) for the traditional model and 303.8 (275.6-331.9) for the MarioKart model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Under the MarioKart model, residents had an extra 1.5 days off per 28 days compared to the traditional model. Short break violations for the entire program and for the general inpatient service were significantly reduced.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this nonrandomized study of an alternate day-to-day schedule that reduced days worked for residents in a general medicine inpatient service, there was no decline in patient-care opportunities. This alternate organization of residents suggests that residency programs can innovate at a systems level to adjust resident schedules to provide more time off without a detriment to patient care opportunities.\u003c/p\u003e","manuscriptTitle":"‘Super’-ior Scheduling: A Novel Model for an Inpatient Internal Medicine Resident Service","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 14:37:02","doi":"10.21203/rs.3.rs-4355343/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e777df25-1d5c-40c2-a1e8-3b97914e6c83","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-11T08:06:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 14:37:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4355343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4355343","identity":"rs-4355343","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}