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Fragmentation of practice: The adverse effect of surgeons moving around

  • J. Madison Hyer
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH

    Secondary Data Core, Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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  • Adrian Diaz
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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  • Aslam Ejaz
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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  • Diamantis I. Tsilimigras
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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  • Djhenne Dalmacy
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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  • Alessandro Paro
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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  • Timothy M. Pawlik
    Correspondence
    Reprint requests: Timothy M. Pawlik, MD, MPH, PhD, Professor and Chair, Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, The Ohio State University, Wexner Medical Center, 395 W. 12th Ave, Suite 670, Columbus, OH.
    Affiliations
    Department of Surgery, The Ohio State University Wexner Medical Center and James Cancer Hospital and Solove Research Institute, Columbus, OH
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Published:January 22, 2022DOI:https://doi.org/10.1016/j.surg.2021.12.010

      Abstract

      Background

      Whether surgical team familiarity is associated with improved postoperative outcomes remains unknown. We sought to characterize the impact of fragmented surgical practice on the likelihood that a patient would experience a textbook outcome, which is a validated patient-centric composite outcome representing an “ideal” postoperative outcome.

      Method

      Medicare beneficiaries aged 65 and older who underwent elective inpatient abdominal aortic aneurysm repair, coronary artery bypass graft, cholecystectomy, colectomy, or lung resection were identified. Rate of fragmented practice was calculated based on the total number of surgical procedures of interest performed over the study period (2013–2017) divided by the number of different hospitals in which the surgeon operated. Surgeons were categorized into “low,” “average,” “above average,” or “high” rate of fragmented practice categories using an unsupervised machine learning technique known k-medians cluster analysis.

      Results

      Among 546,422 Medicare beneficiaries who underwent an elective surgical procedure of interest (coronary artery bypass graft: n = 156,384, 28.6%; lung resection: n = 83,164, 15.2%; abdominal aortic aneurysm: n = 112,578, 20.6%; cholecystectomy: n = 42,955, 7.9%; colectomy: n = 151,341, 27.7%), median patient age was 74 years (interquartile range: 69–80), and most patients were male (n = 319,153, 58.4%). Machine learning identified 3 cutoffs to categorize rate of fragmented practice: 2.8%, 5.6%, and 10.6%. Overall, the majority of surgical procedures were performed by surgeons with a low rate of fragmented practice (n = 382,504, 70.0%); other surgical procedures were performed by surgeons with average (n = 109,141, 20.0%), above average (n = 44,249, 8.1%), or high (n = 10,528, 1.9%) rate of fragmented practice. On multivariable analyses, after controlling for patient demographics, individual surgeon volume, procedure type, and a random effect for hospital, patients who underwent a surgical procedure by a high versus low rate of fragmented practice surgeon had lower odds to achieve a postoperative textbook outcome (odds ratio 0.71, 95% confidence interval 0.77–0.84). Patients who underwent a procedure by a high rate of fragmented practice surgeon also had increased odds of a perioperative complication (odds ratio 1.30, 95% confidence interval: 1.23–1.37), extended length of stay (odds ratio 1.17, 95% confidence interval: 1.11–1.24), 90-day readmission (odds ratio 1.17, 95% confidence interval: 1.11–1.23), and 90-day mortality (odds ratio 1.29, 95% confidence interval: 1.17–1.42) (all P < .05).

      Conclusion

      Patients undergoing a surgical procedure by a surgeon with a high rate of fragmented practice had lower odds of achieving an optimal postoperative textbook outcome. Surgical team familiarity, measured by a surgeon rate of fragmented practice, may represent a modifiable mechanism to improve surgical outcomes.

      Introduction

      Surgery represents a complex, high-risk episode of care that can result in less than optimal outcomes ranging from postoperative complications, hospital readmission, and mortality.
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      In an attempt to mitigate the risk of adverse postoperative events, several studies have sought to understand how various hospital- and surgeon-level factors are associated with postoperative outcomes. At the hospital level, factors such as hospital size, nurse-to-bed ratio, and case-specific hospital volume have been demonstrated to impact postoperative outcomes.
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      At the surgeon level, factors such as surgeon case-specific volume have been strongly associated with postoperative outcomes, which has led to many hospitals instituting minimum volume requirements for surgeons performing various high-risk operations.
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      Although there is an extensive body of literature demonstrating a relationship between surgeon volume and outcomes, other surgeon-level factors have not been studied as extensively.
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      One such factor is represented by the familiarity of a surgeon with the operating room and perioperative hospital team.
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      This factor is intrinsically difficult to measure, however, and previous studies on this concept have been limited in scope. For instance, a single-institution study by Xiao et al primarily evaluated surgical team familiarity based on how often surgeons worked with the same nurses and surgical technologists.
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      The authors noted that higher surgical team familiarity was associated with decreased operative time, length of stay, and readmission rates among patients undergoing orthopedic procedures.
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      In the current study, we sought to evaluate the concept of surgical team familiarity using a surrogate measure defined as “rate of fragmented practice” (RFP). Specifically, RFP was defined as the number of surgeries a single surgeon performed relative to the number of different hospitals in which the surgeon operated. The RFP metric allowed for the evaluation of team familiarity across a wide range of surgical procedures and hospitals using a large national administrative database. In turn, the objective of the current study was to characterize the impact of RFP on the likelihood that a patient would experience a textbook outcome (TO), which is a validated patient-centric composite outcome representing an “ideal” postoperative outcome.
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      • Chen Q.
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      ,
      • Merath K.
      • Chen Q.
      • Bagante F.
      • et al.
      Textbook outcomes among medicare patients undergoing hepatopancreatic surgery.

      Methods

      Data source

      Data were obtained from 100% Medicare Standard Analytic Files (SAFs) between 2013 and 2017. The Centers for Medicare and Medicaid Services develop and maintain the SAFs. At the patient level, data were collected on age at the time of surgery, sex, and race/ethnicity. In-patient and outpatient encounter-level data such as diagnoses and procedures were also obtained based on International Classification of Disease, Ninth and Tenth Revision (ICD-9/10) codes; information on expenditures associated with a particular episode of care was also noted. Patient inclusion criteria were: (1) 65 years or older at the time of surgery; (2) enrolled in Medicare Parts A and B at the time of surgery; (3) not enrolled in a health maintenance organization; and (4) underwent either an elective inpatient abdominal aortic aneurysm (AAA), coronary artery bypass graft (CABG), cholecystectomy, colectomy, or lung resection between 2013 and 2017. To increase precision in estimating the primary independent variable of interest (ie, surgeon RFP), surgical procedures that were performed fewer than 20 times were not included in the analytic cohort. If a patient had undergone more than one procedure of interest, only the first surgery was analyzed.

      Study variables

      The primary independent variable was surgeon RFP; RFP was calculated based on the total number of surgical procedures of interest performed over the 5-year study period divided by the number of different hospitals in which the surgeon operated. RFP was assessed at the surgeon level and created to represent consistency of the hospital-level environment in which a surgeon operated. After calculation of RFP, surgeons were categorized into “low,” “average,” “above average,” or “high” RFP categories using an unsupervised machine learning technique known 2-stage, bisecting k-medians cluster analysis.
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      This process was completed in 2 stages in which the data were dichotomized according to natural breaking points via the k-medians clustering, which is similar to k-means clustering except the median is used for computing the cluster seeds, resulting in less influence from extreme values. The 2 resulting groups were then each dichotomized to create 4 categories, a decision that was made a priori. The resulting groups were categorized and interpreted similar to quartiles; however, the grouping was not made on the basis of arbitrary cutoffs, opting instead for a methodology that allowed for organic breaking points in the data to establish RFP groups. In addition, the use of machine learning facilitated categorizations that had minimum within group RFP variability and maximal RFP differences among the four groups. Surgeon procedure-specific volume was also included in all multivariable analyses to ensure assessment of RFP was not biased by surgeon volume. Charlson comorbidity index was calculated using a previously published and validated list of ICD-9/10 codes.
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      Hospitals were identified as teaching hospitals if the hospital was reimbursed for medical education during the index admission.
      The primary dependent variable was TO, which is a composite outcome metric that has been demonstrated to assess postoperative outcomes from a more global, patient-centric perspective.
      • Merath K.
      • Chen Q.
      • Bagante F.
      • et al.
      A Multi-institutional international analysis of textbook outcomes among patients undergoing curative-intent resection of intrahepatic cholangiocarcinoma.
      ,
      • Merath K.
      • Chen Q.
      • Bagante F.
      • et al.
      Textbook outcomes among medicare patients undergoing hepatopancreatic surgery.
      TO was defined as the absence of 90-day mortality, 90-day readmission, perioperative complication, and extended length of stay (LOS). Secondary outcomes were each of the 4 components of TO. LOS was defined as the number of days between surgery and discharge from the index hospitalization. Extended LOS was defined as an index hospitalization stay that was longer than the procedure-specific 75th percentile. Perioperative complications were identified using a previously published and validated list of complications and the respective ICD-9/10 codes.
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      Statistical analysis

      Descriptive statistics were presented as median (25th–75th percentiles [interquartile range: IQR]) and frequency (relative frequency [%]) for continuous and categorical variables, respectively. To assess the association between surgeon RFP with TO and each of the secondary outcomes, mixed-effects logistic regression with a random effect for hospital was used. Secondary analyses were performed stratified by surgeon volume relative to above and below average procedure-specific surgical volume. Statistical significance was assessed at α = .05. All analyses were performed using SAS v9.4. The study was deemed exempt by the Ohio State University Wexner Medical Center Institutional Review Board.

      Results

      Patient characteristics and text outcome

      Among the 546,422 Medicare beneficiaries who underwent an elective surgical procedure of interest (CABG: n = 156,384, 28.6%; lung resection: n = 83,164, 15.2%; AAA: n = 112,578, 20.6%; cholecystectomy: n = 42,955, 7.9%; colectomy: n = 151,341, 27.7%), median patient age was 74 years (IQR: 69–80) and most patients were male (n = 319,153, 58.4%) (Table I). The overwhelming majority of patients were White/non-Hispanic (n = 505,560, 92.5%) and had comorbidities (median Charlson comorbidity index: 2, IQR: 0–3). Following surgery, the overall incidence of TO was 56.9% (n = 310,903). In assessing the component elements of TO, prolonged LOS was observed most frequently (n = 119,121, 21.8%), whereas no 90-day mortality (n = 522,035, 95.5%) was the secondary outcome most commonly achieved (Table I). Roughly 1 in 5 patients experienced either a perioperative complication (n = 113,316, 20.7%) or readmission within 90 days (n = 106,560, 19.5%). TO varied by procedure type (CABG: n = 77,130, 49.3%; lung resection: n = 50,709, 61.0%; AAA: n = 66,811, 59.3%; cholecystectomy: n = 23,250, 54.1%; colon resection: n = 92,993, 61.4%). The most variability in TO relative to surgical procedure type was related to differences in the incidence of postoperative complications (CABG: n = 51,360, 32.8%; lung resection: n = 51,360, 32.8%; AAA: n = 17,193, 15.3%; cholecystectomy: n = 8,923, 20.8%; colon resection: n = 23,507, 15.5%). In contrast, there was considerably less variability relative to postoperative mortality (CABG: n = 7,109, 4.5%; lung resection: n = 3,494, 4.2%; AAA: n = 5,009, 4.4%; cholecystectomy: n = 2,391, 5.6%; colon resection: n = 6,384, 4.2%).
      Table IPatient, practice, and outcome characteristics stratified by procedure type
      TotalAAA

      N = 112,578
      CABG

      N = 156,384
      Chole.

      N = 42,955
      Colon

      N = 151,341
      Lung

      N = 83,164
      Rate of fragmented practice
       Low382,504 (70.0%)79,222 (70.4%)135,254 (86.5%)21,092 (49.1%)83,476 (55.2%)63,460 (76.3%)
       Average109,141 (20.0%)22,474 (20.0%)15,084 (9.6%)13,858 (32.3%)44,785 (29.6%)12,940 (15.6%)
       Above average44,249 (8.1%)8,841 (7.9%)4,764 (3.0%)6,406 (14.9%)18,741 (12.4%)5,497 (6.6%)
       High10,528 (1.9%)2,041 (1.8%)1,282 (0.8%)1,599 (3.7%)4,339 (2.9%)1,267 (1.5%)
      Age74 (69–80)79 (73–85)73 (69–77)74 (69–79)73 (69–79)72 (69–77)
      Charlson Comorbidity Index2 (0–3)2 (0–3)1 (0–2)2 (0–3)2 (0–3)3 (2–4)
      Los5 (3–7)2 (1–4)7 (5–9)4 (2–7)5 (3–7)4 (3–7)
      Female227,269 (41.6%)38,273 (34.0%)39,183 (25.1%)20,557 (47.9%)85,937 (56.8%)43,319 (52.1%)
      Race
       White/Non-Hispanic505,560 (92.5%)106,763 (94.8%)144,726 (92.5%)39,086 (91.0%)138,263 (91.4%)76,722 (92.3%)
       Black22,259 (4.1%)3,106 (2.8%)5,282 (3.4%)2,033 (4.7%)8,212 (5.4%)3,626 (4.4%)
       Hispanic1,696 (0.3%)297 (0.3%)493 (0.3%)226 (0.5%)505 (0.3%)175 (0.2%)
       Other16,907 (3.1%)2,412 (2.1%)5,883 (3.8%)1,610 (3.7%)4,361 (2.9%)2,641 (3.2%)
      Teaching hospital338,042 (61.9%)81,619 (72.5%)99,696 (63.8%)22,609 (52.6%)79,637 (52.6%)54,481 (65.5%)
      Outcomes
      Textbook outcome310,903 (56.9%)66,811 (59.3%)77,140 (49.3%)23,250 (54.1%)92,993 (61.4%)50,709 (61.0%)
       Extended LOS119,121 (21.8%)24,611 (21.9%)34,723 (22.2%)10,350 (24.1%)32,168 (21.3%)17,269 (20.8%)
       Complication113,316 (20.7%)17,193 (15.3%)51,360 (32.8%)8,923 (20.8%)23,507 (15.5%)12,333 (14.8%)
       90-day readmission106,560 (19.5%)23,298 (20.7%)31,114 (19.9%)9,332 (21.7%)27,622 (18.3%)15,194 (18.3%)
       90-day mortality24,387 (4.5%)5,009 (4.4%)7,109 (4.5%)2,391 (5.6%)6,384 (4.2%)3,494 (4.2%)
      For the total cohort and for each of the procedures of interest, descriptive statistics are presented as median (IQR) and frequency (%) for continuous and categorical variables, respectively.
      AAA, abdominal aortic aneurysm; CABG, coronary artery bypass graft; Chole., cholecystectomy; IQR, interquartile range; LOS, length of stay.

      Surgeon RFP and outcomes

      Machine learning identified 3 cutoffs to categorize RFP: 2.8%, 5.6%, and 10.6%. Overall, the majority of surgical procedures were performed by surgeons with a low RFP (n = 382,504, 70.0%); other surgical procedures were performed by surgeons with average (n = 109,141, 20.0%), above average (n = 44,249, 8.1%), or high (n = 10,528, 1.9%) RFP (Fig). On multivariable analyses, after controlling for patient demographics, individual surgeon volume, and procedure type, as well as hospital-level factors and a random effect for hospital, surgeon RFP remained strongly associated with TO (Table II). Specifically, patients who underwent surgery by a high RFP surgeon had 19% lower odds (OR 0.81, 95% CI: 0.77–0.84) to achieve a TO versus patients who had an operation performed by a surgeon with a low RFP. Of note, the association of RFP relative to TO was incremental in nature (referent, low FRP, above average RFP: OR 0.92, 95% CI 0.80–0.94 vs average RFP: OR 0.96, 95% CI 0.94–0.98; both <.05).
      Figure thumbnail gr1
      Figure 1A scatterplot of surgeon volume and the rate of fragmented practice at the surgeon level, stratified by rate of fragmented practice group demonstrates the relationship between these 2 variables.
      Table IIOdds of achieving a Textbook outcome, as well as each component element, stratified by degree of fragmented practice
      OutcomeAverageAbove averageHigh
      OR95% CIOR95% CIOR95% CI
      Textbook outcome0.96
      P < .05.
      0.94–0.98
      P < .05.
      0.92
      P < .05.
      0.89–0.94
      P < .05.
      0.81
      P < .05.
      0.77–0.84
      P < .05.
       Extended LOS1.03
      P < .05.
      1.01–1.05
      P < .05.
      1.07
      P < .05.
      1.04–1.10
      P < .05.
      1.17
      P < .05.
      1.11–1.24
      P < .05.
       Complication1.07
      P < .05.
      1.05–1.09
      P < .05.
      1.13
      P < .05.
      1.10–1.17
      P < .05.
      1.30
      P < .05.
      1.23–1.37
      P < .05.
       90-day readmission1.04
      P < .05.
      1.02–1.07
      P < .05.
      1.09
      P < .05.
      1.06–1.13
      P < .05.
      1.17
      P < .05.
      1.11–1.23
      P < .05.
       90-day mortality1.05
      P < .05.
      1.01–1.10
      P < .05.
      1.11
      P < .05.
      1.05–1.18
      P < .05.
      1.29
      P < .05.
      1.17–1.42
      P < .05.
      All results are presented as odds ratio (95% confidence interval) and derived from a mixed-effects logistic regression with a random effect for hospitals unless otherwise noted. All models controlled for patient race/ethnicity, sex, Charlson Comorbidity Index, year of surgery and age as well as the procedure type, teaching hospital status, surgeon procedure-specific volume.
      CI, confidence interval; OR, odds ratio.
      P < .05.
      Surgeon RFP was also independently associated with the odds of achieving each individual component of TO. Specifically, patients who underwent a procedure by a high RFP surgeon had 30% increased odds of experiencing a perioperative complication (OR 1.30, 95% CI: 1.23–1.37) and 29% higher odds of 90-day mortality (OR 1.29, 95% CI: 1.17–1.42) compared with patients whose surgeon had a low RFP. In addition, the odds of extended LOS (OR 1.17, 95% CI: 1.11–1.24) and 90-day readmission (OR 1.17, 95% CI: 1.11–1.23) were also higher among patients who underwent a surgical procedure by a high versus low RFP surgeon (Table II).
      Further analyses were then performed to examine the association of RFP with TO after stratifying by procedure-specific surgeon volume (Table III). Of note, the association of RFP and postoperative outcomes persisted independent of individual surgeon volume. For example, among surgeons who had below average overall surgical volume, the odds of achieving a TO was 16% (OR 0.84, 95% CI: 0.79–0.89) lower among patients who underwent a surgical procedure by a surgeon with a high versus low RFP surgeon. In addition, for each of the secondary outcomes, there was an elevated probability of experiencing an adverse outcome among high RFP surgeons compared with low RFP surgeons. Most notably, there was 35% higher odds of 90-day mortality (95% CI: 1.19–1.53) and 30% higher odds of experiencing a complication (95% CI: 1.21–1.40) after a procedure by a high versus low RFP surgeon. Interestingly, the impact of RFP was even more pronounced for surgeons with above average volume. Specifically, patients who underwent an operative procedure by an above average volume surgeon with a high RFP were more likely to experience a perioperative complication (OR 1.32, 95% CI: 1.12–1.55) and 90-day mortality (OR 1.41, 95% CI: 1.07–1.86) versus patients who had surgery performed by a low RFP with above average procedure-specific volumes.
      Table IIIOdds of achieving a Textbook outcome, as well as each component element, stratified by degree of fragmented practice taking into account surgeon procedure-specific volume
      OutcomeAverage RFPAbove average RFPHigh RFP
      OR95% CIOR95% CIOR95% CI
      Below average surgeon procedure-specific volume
      Textbook outcome0.93
      P < .05
      0.90–0.97
      P < .05
      0.92
      P < .05
      0.88–0.96
      P < .05
      0.84
      P < .05
      0.79–0.89
      P < .05
       Extended LOS1.040.99–1.090.990.94–1.041.09
      P < .05
      1.02–1.17
      P < .05
       Complication1.11
      P < .05
      1.06–1.16
      P < .05
      1.16
      P < .05
      1.10–1.22
      P < .05
      1.30
      P < .05
      1.21–1.40
      P < .05
       90-day readmission1.07
      P < .05
      1.03–1.12
      P < .05
      1.13
      P < .05
      1.08–1.19
      P < .05
      1.19
      P < .05
      1.12–1.28
      P < .05
       90-day mortality1.060.98–1.161.15
      P < .05
      1.05–1.27
      P < .05
      1.35
      P < .05
      1.19–1.53
      P < .05
      Above average surgeon procedure-specific volume
      Textbook outcome0.98
      P < .05
      0.96–0.99
      P < .05
      0.91
      P < .05
      0.88–0.95
      P < .05
      0.920.81–1.05
       Extended LOS0.990.96–1.141.09
      P < .05
      1.04–1.15
      P < .05
      0.80
      P < .05
      0.67–0.95
      P < .05
       Complication1.06
      P < .05
      1.03–1.09
      P < .05
      1.13
      P < .05
      1.07–1.19
      P < .05
      1.32
      P < .05
      1.12–1.55
      P < .05
       90-day readmission1.04
      P < .05
      1.02–1.07
      P < .05
      1.08
      P < .05
      1.03–1.13
      P < .05
      1.160.99–1.35
       90-day mortality1.09
      P < .05
      1.04–1.15
      P < .05
      1.13
      P < .05
      1.03–1.24
      P < .05
      1.41
      P < .05
      1.07–1.86
      P < .05
      Stratified by below/above average surgeon volume, all results are presented as odds ratio (95% confidence interval) and derived from a mixed-effects logistic regression with a random effect for hospitals unless otherwise noted. All models controlled for patient race/ethnicity, sex, Charlson Comorbidity Index, year of surgery and age as well as the procedure type, teaching hospital status, surgeon procedure-specific volume. Reference category is low RFP.
      CI, confidence interval; LOS, length of stay; OR, odds ratio; RFP, rate of fragmented practice.
      P < .05

      Discussion

      Individual surgeon volume has been well-established as an important factor that drives high-quality postoperative outcomes.
      • Schmidt C.M.
      • Turrini O.
      • Parikh P.
      • et al.
      Effect of hospital volume, surgeon experience, and surgeon volume on patient outcomes after pancreaticoduodenectomy: a single-institution experience.

      Gaylis FD, Sass M. Re: Annual surgical caseload and open radical prostatectomy outcomes: improving temporal trends. L. Budaus, F. Abdollah, M. Sun, et al. J Urol. 2010;184:2285–2290. J Urol. 2011;186:757–758; author reply 8–9.

      • Wen H.C.
      • Tang C.H.
      • Lin H.C.
      • Tsai C.S.
      • Chen C.S.
      • Li C.Y.
      Association between surgeon and hospital volume in coronary artery bypass graft surgery outcomes: a population-based study.
      • Maruthappu M.
      • Gilbert B.J.
      • El-Harasis M.A.
      • et al.
      The influence of volume and experience on individual surgical performance: a systematic review.
      In turn, minimum volume standards have been implemented for several high-risk procedures in an attempt to improve the quality and safety of surgical care.
      • Morche J.
      • Renner D.
      • Pietsch B.
      • et al.
      International comparison of minimum volume standards for hospitals.
      One possible mechanism for the observed volume–outcome relationship may relate to surgeon familiarity with the surgical team and the hospital environment. To this point, team familiarity has been demonstrated to improve performance in several sectors from business to sports, and even health care.
      • Huckman R.S.
      • Staats B.R.
      Fluid tasks and fluid teams: the impact of diversity in experience and team familiarity on team performance.
      • Kurmann A.
      • Keller S.
      • Tschan-Semmer F.
      • et al.
      Impact of team familiarity in the operating room on surgical complications.
      • Sieweke J.
      • Zhao B.
      The impact of team familiarity and team leader experience on team coordination errors: a panel analysis of professional basketball teams.
      • Ching K.
      • Forti E.
      • Rawley E.
      Extemporaneous coordination in specialist teams: the familiarity complementarity.
      The study of team/environment consistency/familiarity has not been well studied within the field of surgery. The current study was, therefore, important because we specifically used a measure of surgeon familiarity with the surgical team and the hospital environment—rate of fragmented practice (RFP). RFP was based on the number of surgeries performed by a single surgeon relative to the number of different hospitals in which the surgeon operated. As such, RFP represented a single composite metric of the degree to which a given surgeon’s practice was “fragmented” across different hospitals. Of note, higher levels of RFP were strongly associated with an increased risk of adverse postoperative outcomes. In particular, patients who underwent a surgical procedure by a high RFP surgeon had 30% increased odds of experiencing a postoperative complication, 29% increased odds of 90-day mortality, and 17% higher odds of having a prolonged LOS or a readmission within 90 days of surgery versus patients who had been operated on by a low RFP surgeon (Table II). Even after stratifying by individual surgeon procedure-specific case volume—a widely used surgeon level metric—complications, prolonged LOS, and 90-day mortality were more common among patients who had an operation by a high versus low RFP surgeon (Table III).
      The association of surgeon volume and postoperative outcomes has been well documented for a broad range of procedures.
      • Birkmeyer J.D.
      • Siewers A.E.
      • Finlayson E.V.
      • et al.
      Hospital volume and surgical mortality in the United States.
      ,
      • Birkmeyer J.D.
      • Stukel T.A.
      • Siewers A.E.
      • Goodney P.P.
      • Wennberg D.E.
      • Lucas F.L.
      Surgeon volume and operative mortality in the United States.
      For example, Modrall et al noted a negative association between surgeon volume and in-hospital mortality among patients undergoing AAA.
      • Modrall J.G.
      • Minter R.M.
      • Minhajuddin A.
      • et al.
      The surgeon volume-outcome relationship: not yet ready for policy.
      Individual surgeon volume has been demonstrated to be an important driver of patient outcomes even after controlling for certain hospital-level factors. For example, Paredes et al reported that patients who underwent surgery by a low-volume surgeon had increased odds of experiencing a complication after pancreaticoduodenectomy independent of total hospital surgical volume and nurse-to-patient ratio.
      • Paredes A.Z.
      • Hyer J.M.
      • Tsilimigras D.I.
      • Sahara K.
      • White S.
      • Pawlik T.M.
      Interaction of surgeon volume and nurse-to-patient ratio on post-operative outcomes of Medicare beneficiaries following pancreaticoduodenectomy.
      Given the well-accepted relationship between surgeon volume and postoperative outcomes, we examined the impact of RFP relative to individual surgeon volume using multivariable analyses that adjusted for surgeon volume as well as hospital center using a random effect. In addition, we performed additional stratification according to individual surgical volume. Both multivariable and stratified analyses demonstrated surgeon RFP remained strongly associated with postoperative outcomes independent of procedural volume. In particular, a higher surgeon RFP markedly decreased the odds of achieving a postoperative TO. These data strongly suggested that surgeons who had less fragmented care (ie, operated at fewer hospitals) were more likely to achieve optimal outcomes for their patients. In turn, these results suggest that, although surgeon volume was strongly associated with postoperative outcomes, it was not the only surgeon-specific driving factor.
      Surgeon RFP was calculated based on a surgeon case volume and the number of inpatient facilities at which the surgeon had performed these cases. In turn, RFP functioned as a surrogate metric to assess consistency of the surgical environment. Previous studies suggested an association of surgical team consistency and cohesiveness with better perioperative outcomes.
      • Xu R.
      • Carty M.J.
      • Orgill D.P.
      • Lipsitz S.R.
      • Duclos A.
      The teaming curve: a longitudinal study of the influence of surgical team familiarity on operative time.
      • Maruthappu M.
      • Duclos A.
      • Zhou C.D.
      • Lipsitz S.R.
      • Wright J.
      • Orgill D.
      • et al.
      The impact of team familiarity and surgical experience on operative efficiency: a retrospective analysis.
      • Xiao Y.
      • Jones A.
      • Zhang B.B.
      • et al.
      Team consistency and occurrences of prolonged operative time, prolonged hospital stay, and hospital readmission: a retrospective analysis.
      • Elbardissi A.W.
      • Duclos A.
      • Rawn J.D.
      • Orgill D.P.
      • Carty M.J.
      Cumulative team experience matters more than individual surgeon experience in cardiac surgery.
      ,
      • Stucky C.H.
      • De Jong M.J.
      Surgical team familiarity: an integrative review.
      For example, in an analysis of patients who underwent knee arthroplasty or lumbar laminectomy, Parker et al reported that surgical team familiarity was correlated with duration of surgery.
      • Parker S.H.
      • Lei X.
      • Fitzgibbons S.
      • Metzger T.
      • Safford S.
      • Kaplan S.
      The impact of surgical team familiarity on length of procedure and length of stay: inconsistent relationships across procedures, team members, and sites.
      In a different study, El Bardissi et al noted that operative time among patients undergoing CABG was associated primarily with the familiarity of the operative team rather than the experience of the operating surgeon.
      • Elbardissi A.W.
      • Duclos A.
      • Rawn J.D.
      • Orgill D.P.
      • Carty M.J.
      Cumulative team experience matters more than individual surgeon experience in cardiac surgery.
      Surgical team familiarity has also been associated with more effective communication, fewer workflow disruptions, and higher clinician safety.
      • Stucky C.H.
      • De Jong M.J.
      • Kabo F.W.
      Military surgical team communication: implications for safety.
      • Gillespie B.M.
      • Chaboyer W.
      • Fairweather N.
      Interruptions and miscommunications in surgery: an observational study.
      • Henaux P.L.
      • Michinov E.
      • Rochat J.
      • Hemon B.
      • Jannin P.
      • Riffaud L.
      Relationships between expertise, crew familiarity and surgical workflow disruptions: an observational study.
      • Sexton K.
      • Johnson A.
      • Gotsch A.
      • Hussein A.A.
      • Cavuoto L.
      • Guru K.A.
      Anticipation, teamwork and cognitive load: chasing efficiency during robot-assisted surgery.
      • Myers D.J.
      • Lipscomb H.J.
      • Epling C.
      • et al.
      Surgical team stability and risk of sharps-related blood and body fluid exposures during surgical procedures.
      Additionally, surgical team familiarity has been associated with risk of complications as well as length of stay and readmission among patients after knee and hip replacement.
      • Xiao Y.
      • Jones A.
      • Zhang B.B.
      • et al.
      Team consistency and occurrences of prolonged operative time, prolonged hospital stay, and hospital readmission: a retrospective analysis.
      These studies were, however, based on single-center data and focused only on a single procedure, which limited their generalizability.
      • Xu R.
      • Carty M.J.
      • Orgill D.P.
      • Lipsitz S.R.
      • Duclos A.
      The teaming curve: a longitudinal study of the influence of surgical team familiarity on operative time.
      • Maruthappu M.
      • Duclos A.
      • Zhou C.D.
      • Lipsitz S.R.
      • Wright J.
      • Orgill D.
      • et al.
      The impact of team familiarity and surgical experience on operative efficiency: a retrospective analysis.
      • Xiao Y.
      • Jones A.
      • Zhang B.B.
      • et al.
      Team consistency and occurrences of prolonged operative time, prolonged hospital stay, and hospital readmission: a retrospective analysis.
      • Elbardissi A.W.
      • Duclos A.
      • Rawn J.D.
      • Orgill D.P.
      • Carty M.J.
      Cumulative team experience matters more than individual surgeon experience in cardiac surgery.
      ,
      • Stucky C.H.
      • De Jong M.J.
      Surgical team familiarity: an integrative review.
      The current study was novel in that we used RFP, which accounted for the proportion of cases performed at different hospitals, to examine the impact of surgical team familiarity on a wide range of surgical procedures. Notably, a higher RFP for a given surgeon was associated with a lower likelihood of achieving overall as well as each component element of TO (ie, complications, length of stay, readmission) among patients undergoing 5 commonly performed surgical procedures.
      The reason for the association of RFP with postoperative outcomes was likely multifactorial. Surgeons with high RFP more often performed operations at multiple different hospitals with different surgical teams. In turn, these surgeons and operative cases were likely more prone to variable perioperative teams and inconsistent hospital environments. In turn, the better outcomes among patients operated on by low FRP surgeons may have been related to increased familiarity and standardization of operative teams. Effective standardization can streamline tasks and processes to decrease ambiguity, improve quality, and drive operational performance. To this point, Ghafari et al reported that much of the variation in mortality observed among hospitals was not necessarily related to differences in postoperative complications, but rather with failure-to-rescue.
      • Ghaferi A.A.
      • Birkmeyer J.D.
      • Dimick J.B.
      Variation in hospital mortality associated with inpatient surgery.
      In effect, hospital- and system-level experience and consistency can drive improved perioperative outcomes. Similarly, operative teams that frequently work with each other may be more likely to identify common challenges and tasks and have processes in place to identify and intervene quickly when complications occur. In addition, consistency of care likely continued beyond the operating room among low RFP surgeons driving center-level effects that are known to be associated with better patient outcomes.
      • Katz J.N.
      • Losina E.
      • Barrett J.
      • et al.
      Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States Medicare population.
      ,
      • Sternberg S.
      Hospitals move to limit low-volume surgeries.
      ,
      • Birkmeyer J.D.
      • Siewers A.E.
      • Finlayson E.V.
      • et al.
      Hospital volume and surgical mortality in the United States.
      ,
      • Birkmeyer J.D.
      • Skinner J.S.
      • Wennberg D.E.
      Will volume-based referral strategies reduce costs or just save lives?.
      RFP may therefore be a means to measure the level of surgeon-team familiarity broadly construed, which can in turn be linked to quality metrics.
      Fragmented care can occur when different health care providers and/or health care organizations do not work together routinely. The association of high RFP and risk of worse postoperative outcomes highlights how lack of collaboration/shared responsibility/teamwork can isolate health care providers into different silos. In turn, this fragmentation can lead to wide variations in perioperative practices, intraoperative “flow,” and postoperative management. Although the solution to the problem of fragmentation is challenging and undoubtedly multifaceted, several initiatives may help mitigate the consequences of care fragmentation. If possible, fragmentation of surgical practices should be minimized by having a given surgeon work more routinely with the same core group of team members/same location. Familiarity among the surgical team has been previously noted to decrease both operative time and risk of adverse events.
      • Obermair A.
      • Simunovic M.
      • Janda M.
      The impact of team familiarity on surgical outcomes in gynaecological surgery.
      ,
      • Xu R.
      • Carty M.
      • Orgill D.
      • Lipsitz S.
      • Duclos A.
      The teaming curve: a longitudinal study of the influence of surgical team familiarity on operative time.
      In the airline industry, research has linked effective training in teamwork to flight safety. Within many other industries, the science of human factors has sought to improve performance and reduce harm through supporting the cognitive and physical work of health care professionals.
      • Mazzocco K.
      • Petitti D.B.
      • Fong K.T.
      • et al.
      Surgical team behaviors and patient outcomes.
      For example, crew resource management (CRM) trainings have been adapted from aviation for health care teams as an instrument to address human factors, especially in complex fragmented care settings with multiple team members. In particular, CRM training may help to improve safety as well as to standardize processes and procedures among health care team members who may not work together frequently.
      • Gross B.
      • Rusin L.
      • Kiesewetter J.
      • et al.
      Crew resource management training in healthcare: a systematic review of intervention design, training conditions and evaluation.
      The current study had several limitations that should be considered when interpreting the results. The SAFs are an administrative billing database that provided a complete and comprehensive list of all health care encounters, as well as all diagnoses and procedures at each encounter; however, the database did not contain procedure-level clinical data (ie, operating time, labs, etc) that may have impacted results. In addition, only Medicare beneficiaries were included, which limited the generalizability of the results to patients 65 years or older. The calculation of RFP was also based only on Medicare beneficiaries. In turn, although total surgeon volume may have been underestimated, RFP was a proportion, and therefore the relative comparison of the different RFO cutoffs should not have necessarily been affected. In addition, other surgeon-specific factors such as age, years of experience, level of technical expertise, and caseload may also be associated with postoperative outcomes and possibly RFP but were not known, and thus not included in this study.
      In conclusion, patients who underwent 1 of 5 commonly performed procedures by a high RFP surgeon were more likely to develop postoperative complications, have an extended LOS, and experience 90-day readmission or 90-day mortality versus individuals who had an operation by a low RFP surgeon. In turn, there was a strong association between higher RFP surgical care and lower odds of achieving a composite TO after surgery. Surgeon RFP had an impact on postoperative outcomes regardless of an individual surgeons surgical volume. The present study should prompt researchers to further investigate surgeon RFP as a metric to assess heterogeneity in postoperative outcomes. Reduction of fragmentation of surgical practices across multiple hospitals may lead to increased team cohesiveness as well as improved patient outcomes after complex surgical procedures. Future studies should investigate whether a surgeon RFP that changes over time may be associated with changing rates of TO as well as investigate the subset of surgeons with high RFP and good outcomes to ascertain what possible geographical, surgeon-, or hospital-level characteristics mitigate the adverse effect of RFP noted in the current study.

      Funding/Support

      None declared.

      Conflicts of interest/Disclosure

      None to report.

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      Linked Article

      • Invited Commentary on “Fragmentation of Practice: The Adverse Effect of Surgeons Moving Around”
        SurgeryVol. 172Issue 2
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          Innumerable factors contribute to postoperative outcome. Various patient, physician, health system, and infrastructure-level variables have been shown to influence these outcomes and impact health care cost and delivery.1 However, the level to which surgeon-driven factors, such as procedure-specific case volume across different hospitals, and familiarity with a specific operating room team/hospital system affect outcomes is relatively unknown. While it has been previously shown that outcomes improve with increased case volumes, the potential patient, surgeon, and system-level impact of surgeons operating at multiple hospitals has yet to be studied.
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