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Figure.  Mean Length of Stay After Colectomy for the Enhanced Recovery in National Surgical Quality Improvement Program Pilot Compared With Controls
Mean Length of Stay After Colectomy for the Enhanced Recovery in National Surgical Quality Improvement Program Pilot Compared With Controls

The mean (SD) unadjusted length of stay in the pilot cohort was 6.9 (6.4) days before implementation and 5.2 (4.1) days after implementation, or a decrease of 1.7 days. The mean (SD) unadjusted length of stay in the control cohort was 6.4 (6.0) days before implementation and 6.0 (5.6) after implementation, or a decrease of 0.4 day. This amounts to a 1.3-day difference-in-differences length of stay for the association of pilot participation before vs after implementation. In a multivariable hierarchical generalized linear model, the association of pilot participation remained significant at a difference-in-differences (SE) of −1.1 (0.2) days (P < .001) after adjusting for patient risk factors, hospitals, and matched controls.

Table 1.  Patient Characteristics
Patient Characteristics
Table 2.  Hospital Characteristics in Pilot and Control Cohorts
Hospital Characteristics in Pilot and Control Cohorts
Table 3.  Postoperative Outcomes
Postoperative Outcomes
Table 4.  National Surgical Quality Improvement Program (ERIN) Pilot Tools for Enhanced Recovery Protocol (ERP) Implementation
National Surgical Quality Improvement Program (ERIN) Pilot Tools for Enhanced Recovery Protocol (ERP) Implementation
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Original Investigation
April 2018

Association of an Enhanced Recovery Pilot With Length of Stay in the National Surgical Quality Improvement Program

Author Affiliations
  • 1Department of Surgery, University of Chicago, Chicago, Illinois
  • 2Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, Illinois
  • 3Department of Surgery, Loyola University Medical Center, Maywood, Illinois
  • 4Department of Surgery, UCLA (University of California, Los Angeles)
  • 5Department of Surgery, Duke University, Durham, North Carolina
  • 6Department of Surgery, McGill University, Montreal, Quebec, Canada
JAMA Surg. 2018;153(4):358-365. doi:10.1001/jamasurg.2017.4906
Key Points

Question  How can the Enhanced Recovery in National Surgical Quality Improvement Program pilot help hospitals improve length of stay after colectomy?

Findings  In a cohort study of 4975 colectomies performed by 15 pilot hospitals and 9950 control colectomies, key lessons in implementing the pilot were guidance from experts, engaged multidisciplinary team leadership, continuous data collection and auditing, and collaboration across institutions through monthly conference calls. Fifteen hospitals in the pilot decreased length of stay by 1.7 days (compared with 0.4 day among propensity-matched controls); after risk adjustment for patient characteristics, hospitals, and matched pairs, the adjusted decrease was significant at −1.1 days.

Meaning  Lessons from the Enhanced Recovery in National Surgical Quality Improvement Program collaborative may benefit hospitals that want to implement enhanced recovery to improve length of stay.

Abstract

Importance  Enhanced recovery protocols (ERPs) are standardized care plans of best practices that can decrease morbidity and length of stay (LOS). However, many hospitals need help with implementation. The Enhanced Recovery in National Surgical Quality Improvement Program (ERIN) pilot was designed to support ERP implementation.

Objective  To evaluate the association of the ERIN pilot with LOS after colectomy.

Design, Setting, and Participants  Using a difference-in-differences design, pilot LOS before and after ERP implementation was compared with matched controls in a hierarchical model, adjusting for case mix and random effects of hospitals and matched pairs. The setting was 15 hospitals of varied size and academic status from the National Surgical Quality Improvement Program. Preimplementation and postimplementation colectomy cases (July 1, 2013, to December 31, 2015) were collected using novel ERIN variables. Emergency and septic cases were excluded. A propensity score match identified a 2:1 control cohort of patients undergoing colectomy at non-ERIN hospitals.

Interventions  Pilot hospitals developed and implemented ERPs that included expert guidance, multidisciplinary teams, data audits, and opportunities for collaboration.

Main Outcomes and Measures  The primary outcome was LOS, and the secondary outcome was serious morbidity or mortality composite.

Results  There were 4975 colectomies performed by 15 ERIN pilot hospitals (3437 before implementation and 1538 after implementation) compared with a control cohort of 9950 colectomies (4726 before implementation and 5224 after implementation). The mean LOS decreased by 1.7 days in the pilot (6.9 [interquartile range (IQR), 4-8] days before implementation vs 5.2 [IQR, 3-6] days after implementation, P < .001) compared with 0.4 day in controls (6.4 [IQR, 4-7] days before implementation vs 6.0 [IQR, 3-7] days after implementation, P < .001). Readmission did not differ pre-post for the pilot or controls. Serious morbidity or mortality decreased for pilot participants (485 [14.1%] before implementation vs 162 [10.5%] after implementation, P < .001), with no difference in controls, and remained significant after risk adjustment (adjusted odds ratio, 0.76; 95% CI, 0.60-0.96). After adjusting for differences in case mix and for clustering in hospitals and matched pairs, the adjusted difference-in-differences model demonstrated a decrease in LOS by 1.1 days in the pilot over controls (P < .001).

Conclusions and Relevance  Participating ERIN pilot hospitals achieved shorter LOS and decreased complications after elective colectomy, without increasing readmissions. The ability to implement ERPs across hospitals of varied size and resources is essential. Lessons from the ERIN pilot may inform efforts to scale this effective and evidence-based intervention.

Introduction

Enhanced recovery protocols (ERPs) are standardized perioperative care plans that incorporate evidence-based best practices to improve surgical outcomes.1,2 Interventions included in these multimodal pathways focus on minimizing physiologic stress, thereby promoting optimal and timely recovery. Quiz Ref IDProtocol elements before, during, and after surgery aim to control pain, reduce gut dysfunction, and promote nutrition and physical activity.3,4 There are many randomized trials, meta-analyses, and observational studies demonstrating the benefits of ERPs for reducing postoperative length of stay (LOS) and morbidity.5,6

Colectomy is one of the most common general surgery operations and accounts for a disproportionate share of postoperative morbidity and mortality.7 Postoperative complications contribute significantly to future negative outcomes (eg, end-organ dysfunction, 30-day mortality, reoperation, and readmission)8 and increased health care resource use.9-11 Even among patients without complications, LOS varies widely.12 Therefore, colectomy remains a priority for quality improvement activities. Implementation of ERPs can contribute to decreased postoperative complications, such as surgical site infection,13 and can improve metrics of health care resource use, such as LOS and readmission, contributing to potential cost savings.6,13-15 The results of recent studies16,17 support the cost-effectiveness of ERP implementation, particularly with regard to decreasing LOS after colorectal surgery.

Despite the observed benefits, implementation of ERPs remains slow and challenging. A survey of the Society of American Gastrointestinal and Endoscopic Surgeons found that 30% of member respondents were unfamiliar with ERPs.18 Hospitals and health care professionals often do not know where to begin in developing and implementing an ERP. Successful implementation often requires behavior change and coordination across multiple disciplines. Furthermore, health care in the United States is highly fragmented, and there is little opportunity for comparison across institutions. European collaborations have established large clinical data registries to support implementation of ERPs to monitor adherence with care processes and outcomes. However, no such registry was widely available in North America. To address this problem, the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) incorporated enhanced recovery process and outcome variables into the data platform and launched the Enhanced Recovery in National Surgical Quality Improvement Program (ERIN) pilot in 2014. The ERIN pilot was designed to facilitate implementation of ERPs by giving hospitals access to experts, resources, and data, while fostering cross-institution collaboration. The objective of this study was to evaluate the ERIN pilot for changes in LOS after colectomy compared with a cohort of control hospitals.

Methods
ERIN Pilot Project

In 2014, the ACS NSQIP launched the ERIN pilot to support implementation of ERPs for colorectal surgery in NSQIP hospitals. Fifteen hospitals that were high outliers on LOS participated in the pilot: each formed a steering committee (surgery, anesthesia, and nursing leaders), developed a tailored ERP, implemented the protocol, and collected ERIN-specific data during surgery. Pilot hospitals gained access to experts with implementation experience, example materials (patient education materials and order sets), and opportunities for multi-institutional collaboration through conference calls and annual in-person workshops. Monthly conference calls provided a regular, structured mechanism for information exchange and real-time trouble-shooting opportunities among participating hospitals.

This retrospective cohort study was deemed nonhuman research and exempt from review board oversight by the Chesapeake Institutional Review Board. Informed consent was not applicable.

Protocol development was tailored by hospital according to the individual workflow. The ACS NSQIP provided 13 ERP-specific process variables (eTable in the Supplement). Hospitals were encouraged to use available ERIN variables; however, these components were not mandatory in their respective protocols. In-person workshops were conducted in quarters 2 and 5 of the pilot to review protocol development, implementation, adherence, and plans for sustainability. Timing of initial protocol implementation was at the discretion of each hospital and proceeded in a staggered fashion. The individualized implementation date was set as time 0 for each of the 15 pilot hospitals. The pilot start date of July 1, 2014, was set as time 0 for control hospitals.

Data Source and Study Population

The ACS NSQIP is a clinical data registry collecting perioperative data for the purpose of quality improvement.19-21 Data are entered by trained surgical clinical reviewers and are audited for data accuracy.22,23 Briefly, the ACS NSQIP comprises more than 200 data points, including preoperative patient demographics, comorbidities, laboratory values, and postoperative surgical complications for 30 days after surgery.

To evaluate the ERIN pilot, we compared colectomy outcomes before and after ERP implementation. Cases performed at pilot hospitals from January 1, 2013, until the hospital-specific implementation date comprised the preimplementation pilot cohort. There were 4318 colectomy cases eligible for inclusion across the 15 pilot hospitals. Exclusion criteria were emergency cases (624 [14.5%]), American Society of Anesthesiologists (ASA) class 5 (0 cases), and preoperative systemic inflammatory response syndrome, sepsis, or septic shock (257 [6.0%]). The final preimplementation cohort for pilot hospitals was 3437. The postimplementation pilot cohort included colectomy cases performed at pilot hospitals from the hospital-specific implementation date until December 31, 2015, with complete ERIN data entered (regardless of compliance level). There were 1666 postimplementation ERIN colectomy cases across the 15 pilot hospitals. After excluding emergency cases (81 [4.9%]), ASA class 5 (1 [0.1%]), and preoperative systemic inflammatory response syndrome, sepsis, or septic shock (46 [2.8%]), the postimplementation cohort for pilot hospitals was 1538.

Quiz Ref IDTo evaluate the association of ERIN participation with LOS, while accounting for temporal trends, we identified colectomy cases derived from the ACS NSQIP data from January 1, 2013, to December 31, 2015, in non-ERIN hospitals, applying the same exclusion criteria. Cases from nonpilot hospitals using the ERIN variables outside of the pilot were not eligible as controls. There were 351 hospitals and 50 126 cases eligible for the propensity score match into the control cohort.

Data from the 2014 American Hospital Association Annual Survey were merged with the ACS NSQIP data to capture hospital-level characteristics (total number of licensed hospital beds and teaching affiliation). Teaching hospitals were those designated as “major” by the Council of Teaching Hospitals and Health Systems or as “minor,” approved to participate in training by the Accreditation Council for Graduate Medical Education or the American Osteopathic Association or those with medical school affiliation reported to the American Medical Association.

Statistical Analysis

Using a pre-post difference-in-differences design, we compared changes from baseline in postoperative LOS among ERIN colectomy cases with propensity-matched controls. A propensity score match was performed using a greedy 2:1 algorithm, with each colectomy case from pilot hospitals matched to 2 control cases from non-ERIN hospitals based on the year of the operation, hospital characteristics (total number of hospital beds and academic teaching status), and patient characteristics (age, sex, race, functional status before surgery, and American Society of Anesthesiologists [ASA] class). Propensity score match balance was assessed graphically comparing standardized differences in the means of matched variables before and after the match24 using a threshold of 0.1 to define match success.25,26 Standardized differences after the match appropriately fell within the threshold (eFigure 1 in the Supplement). There were parallel trends in preimplementation LOS: controls demonstrated a slope of −0.095 (95% CI, −0.193 to 0.003), while pilot sites demonstrated a slope of −0.039 (95% CI, −0.110 to 0.045).

Bivariate analyses compared preimplementation and postimplementation cohorts for pilot and control hospitals using t test, Wilcoxon rank sum test, Pearson product moment correlation χ2 test, and Fisher exact test for continuous and categorical variables as appropriate. Two-sided analyses used P < .05 for statistical significance. Patient characteristics available for risk adjustment were the following: sex, race, Hispanic ethnicity, body mass index, smoking status, functional status before surgery, diabetes, unintentional weight loss, chronic corticosteroid use, disseminated cancer, hypertension requiring medication, history of congestive heart failure, history of chronic obstructive pulmonary disease, bleeding disorder, ASA class, dyspnea, ascites, preoperative renal failure, and dialysis.

Risk adjustment factors associated with LOS were evaluated in a generalized linear model with stepwise forward selection (P < .05 for entry). To provide a risk-adjusted estimate of the association of ERP implementation in pilot hospitals with LOS, a hierarchical linear regression model using gaussian distribution for LOS was then constructed, controlling for the previously selected patient-level risk factors, adjusting for the hospital and matched pairs as random effects, and evaluating the interaction between pilot participation and the pre-post indicator. The subsequent coefficient for the interaction term represents the risk-adjusted difference-in-differences estimate of the decrease in LOS associated with ERIN implementation at pilot sites.

The secondary outcome, serious morbidity or mortality, was first evaluated using a multivariable logistic regression with forward selection (P < .05 for entry). Selected factors were entered into a hierarchical logistic regression model of the binary outcome of serious morbidity or mortality, adjusting for the hospital and matched pairs as random effects, and evaluating the interaction between pilot participation and the pre-post indicator. The interaction coefficient was exponentiated to identify the odds ratio and 95% CI. All analyses were conducted using SAS statistical software (version 9.4; SAS Institute Inc).

Results

There were 3437 colectomies at the 15 ERIN pilot hospitals before ERP implementation and 1538 after implementation (Table 1). Compared with before implementation, there were fewer black or African American patients after implementation (378 [11.0%] vs 103 [6.7%], P < .001), fewer patients with partial or total functional dependence (97 [2.8%] vs 27 [1.8%], P = .03), and fewer patients with disseminated cancer (276 [8.0%] vs 88 [5.7%], P = .004). The control cohort also differed between the preimplementation and postimplementation periods in black or African American race (517 [10.9%] vs 462 [8.8%], P < .001) and in partially or totally dependent functional status before surgery (161 [3.4%] vs 130 [2.5%], P = .007).

Of the 15 pilot hospitals, 10 (66.7%) were major teaching hospitals, treating 3877 patients (77.9%) (Table 2). The matched patients came from 189 hospitals, of which 90 (47.6%) were major teaching hospitals, treating 7936 patients (79.8%). Pilot and control hospitals varied in size. Two hospitals (13.3%) with less than 200 beds accounted for 216 patients (4.3%), and 4 hospitals (26.7%) with at least 800 beds accounted for 2006 patients (40.3%). There were 26 hospitals (13.8%) with less than 200 beds, accounting for 290 patients (2.9%), and 17 hospitals (9.0%) with at least 800 beds, accounting for 3596 patients (36.1%).

Setting the implementation date as time 0 for each ERIN hospital, adherence to process elements was tracked over time (eFigure 2 in the Supplement). Adherence to preoperative counseling improved from 51.7% (208 of 402) in quarter 1 after implementation to 82.7% (81 of 98) in quarter 6, and shortened fluid fast improved from 41.8% (168 of 402) to 63.3% (62 of 98). Adherence to maintenance of normothermia, multimodal management of pain, and antiemetic prophylaxis was consistently above 90% throughout the pilot period. Postoperative mobilization increased modestly but then declined (eg, mobilization on postoperative day [POD] 0 began at 66.7% [268 of 402] in quarter 1, rose to 85.9% [226 of 263] in quarter 3, and then fell to 61.2% [60 of 98] in quarter 6). Clear liquid diet on POD 0 improved from 73.4% (295 of 402) in quarter 1 to 91.8% (90 of 98) in quarter 6, while solid diet on POD 1 improved from 44.8% (180 of 402) to 65.3% (64 of 98). Foley catheter discontinuation on POD 1 was consistently greater than 80%, while discontinuation of intravenous fluid on POD 1 was consistently less than 50% throughout the pilot.

Before ERIN, the mean LOS for the pilot cohort was 6.9 (median, 5.0; interquartile range [IQR], 4-8) days (Table 3). Quiz Ref IDAfter implementation, the mean LOS was 5.2 (median, 4.0; IQR, 3-6) days (P < .001). Quiz Ref IDAmong controls, the mean LOS was 6.4 (median, 5.0; IQR, 4-7) days before implementation and 6.0 (median, 5.0; IQR, 3-7) days after implementation (P < .001). The decrease in LOS between the preimplementation and postimplementation periods was greater in the pilot cohort than controls (Figure). The unadjusted difference-in-differences in LOS was −1.3 days. In a hierarchical linear model adjusted for patient risk factors (sex, functional status before surgery, unintentional weight loss, chronic corticosteroid use, disseminated cancer, history of congestive heart failure, history of chronic obstructive pulmonary disease, bleeding disorder, ASA class, ascites, and renal failure) and clustering within hospitals and matched pairs, the pilot remained significant at an adjusted difference-in-differences (SE) of −1.1 (0.2) days (P < .001).

There was no significant difference in unadjusted rates of readmission across pre-post periods for either the pilot cohort or controls. Unadjusted rates of serious morbidity or mortality decreased for the pilot cohort (485 [14.1%] before implementation vs 162 [10.5%] after implementation, P < .001) (Table 3). There was no difference in serious morbidity or mortality in controls. Quiz Ref IDIn a hierarchical model adjusted for patient characteristics (age, sex, Hispanic ethnicity, smoking status, functional status before surgery, unintentional weight loss, chronic corticosteroid use, disseminated cancer, hypertension requiring medication, history of congestive heart failure, history of chronic obstructive pulmonary disease, bleeding disorder, and ASA class) and controlling for clustering within hospitals and matched pairs, cases from pilot sites after implementation were significantly less likely to have serious morbidity or mortality (adjusted odds ratio, 0.76; 95% CI, 0.60-0.96).

Discussion

The ERIN pilot harnessed expert guidance, provided a basic structure for team leadership, facilitated data collection with 13 specific enhanced recovery processes, and encouraged collaboration through regularly scheduled conference calls. Through the ERIN pilot, 15 hospitals successfully decreased LOS by 1.7 days among patients undergoing colectomy, which was significant compared with the 0.4-day decrease observed in a propensity-matched cohort of patients undergoing colectomy at control hospitals. After risk adjustment, accounting for patient risk factors, hospitals, and matched pairs, the ERIN pilot experience was significant, with average LOS decreased by an additional 1.1 days beyond temporal trends. Given the range of small to large pilot hospitals with varied resources, the pilot experience may inform enhanced recovery at other hospitals. Future collaboratives may consider drawing on lessons of the ERIN pilot—external expertise, team leadership, data audits, and collaboration—to facilitate enhanced recovery implementation.

Given variable implementation and adherence,27 there is a need to better understand barriers and facilitators for ERPs. Effective implementation may require care reorganization, including building interdisciplinary teams, counseling patients on anticipated recovery, and ensuring coordination across siloed disciplines.28 Stakeholder interviews in an Australian hospital found barriers related to patients, staff, resources, and the overall practice workflow.29 Semistructured interviews across 7 University of Toronto–affiliated Canadian hospitals identified lack of support staff, poor communication, and a need to appropriately set patient expectations as barriers to ERP implementation.30 Furthermore, many physicians cited their own resistance to change (or resistance among others) as a major barrier.30

Enhanced recovery implementation poses challenges associated with culture change, staff limitations, and financial resources. Because many ERP studies are conducted in the setting of large tertiary academic hospitals, smaller hospitals may be concerned about feasibility. However, key enablers for success do not depend on hospital size or teaching status: an engaged champion who believes in the value of the ERP may succeed by establishing a good fit between the champion and the team, engaging stakeholders at all levels, and normalizing the ERP as part of expected routine.31 Regardless of resource limitations, small hospitals may have the advantage of decreased bureaucracy and improved communication and collaboration across disciplines. The ERIN pilot study included hospitals of various sizes, indicating that both small and large hospitals can successfully decrease LOS with implementation of an ERP.

The ERIN pilot sought to ease the learning curve by providing hospitals access to experts in the field, a structure for team leadership, a mechanism to audit adherence with care processes and outcomes, and a platform for collaboration across institutions (Table 4). A systematic review of regional collaboration identified key factors for quality improvement success, including the ability to establish trust and share best practices among a network of peers, availability of accurate and relevant data, strong institutional support and clinical leadership, and resources and infrastructure for quality improvement initiatives.32 With regard to enhanced recovery, collaboratives should allow the flexibility for participating hospitals to develop a tailored ERP paired with an implementation strategy, engage local multidisciplinary champions to facilitate collaboration and communication, provide patient educational materials, and establish an audit and feedback mechanisms.33

Data auditing is consistently identified as a key facilitator for both implementation and sustainability.33,34 Protocol adherence may fluctuate over time,35,36 and lax compliance can threaten early gains. Compliance with postoperative protocol elements is consistently lower than preoperative or intraoperative elements27,37 and may decline with time.35 In the context of symptoms or complications, nonadherence may be unavoidable (eg, nausea and vomiting requiring cessation of oral intake). However, one study38 found that 20% or more of protocol deviations may have no medically justified rationale. Compliance within the ERIN pilot was lowest for minimizing intravenous fluids, consistent with previously observed variability in fluid management strategies in which high volume was associated with LOS, cost, and ileus.39

Strengths and Limitations

A strength of this study is the inclusion of small and large institutions. Drawing on the ACS NSQIP platform, the ERIN pilot was able to accrue sufficient numbers for a meaningful analysis. Pilot clinical reviewers were specifically trained on the definitions of adherence with the ERIN variables. Furthermore, using a difference-in-differences analysis with a cohort of patients undergoing colectomy as a comparator group, rather than relying on historical controls or patients undergoing a different operation, takes into account secular trends in outcomes.

However, this study is not without limitations. First, the ACS NSQIP includes hospitals actively engaged in quality efforts, and pilot participation was voluntary, limiting generalizability. Second, there are no historical data on ERIN process elements; therefore, changes in adherence cannot be tracked from before to after implementation. Third, pilot hospitals controlled the development and implementation of the ERP in accordance with local workflow. None of the protocol elements were required, and there is likely substantial variation in the full protocols implemented across pilot hospitals. Fourth, preimplementation and postimplementation patient cohorts differed, possibly due to selection of lower-risk patients for participation in ERPs. We have attempted to adjust for this difference with propensity matching and multivariable risk adjustment. Fifth, to provide flexible protocol elements, the ERIN variables may lack granularity. Hospitals examining ERP components in detail (eg, distance or duration of mobilization) may gain insight to improve implementation or sustainability of the ERPs.

Motivated hospitals may achieve success independently; however, it remains unclear who will lead implementation and dissemination of enhanced recovery in the future. The ACS, the Johns Hopkins Medicine Armstrong Institute for Patient Safety and Quality, and the Agency for Healthcare Research and Quality have recently launched the “Improving Surgical Care and Recovery” program to provide more than 750 hospitals with tools, experts, and other resources for implementation of ERPs. The program is one opportunity for hospitals seeking implementation guidance. Whichever implementation strategy is selected, we strongly believe that surgeon engagement and leadership in such initiatives are critical to sustained success.

Conclusions

The ERIN pilot successfully decreased LOS compared with a control cohort of patients undergoing colectomy. Key lessons in implementing the ERIN pilot were external expertise, team leadership, data audits, and cross-institutional collaboration. The pilot may serve to inform future implementation efforts across hospitals varied in size, location, and resource availability.

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Article Information

Accepted for Publication: August 25, 2017.

Corresponding Author: Julia R. Berian, MD, MS, Department of Surgery, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 (julia.berian@uchospitals.edu).

Published Online: December 20, 2017. doi:10.1001/jamasurg.2017.4906

Author Contributions: Dr Berian had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Berian, Ban, Liu, Ko, Thacker, Feldman.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Berian, Thacker.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Berian, Liu, Sullivan.

Administrative, technical, or material support: Berian.

Study supervision: Ko, Thacker, Feldman.

Conflict of Interest Disclosures: Dr Berian reported receiving salary support from The John A. Hartford Foundation. Dr Ban reported receiving salary support from the Agency for Healthcare Research and Quality. Dr Ko reported being coprincipal investigator for the grants from The John A. Hartford Foundation and the Agency for Healthcare Research and Quality. Dr Thacker reported having financial relationships with the following entities: Merck, Edwards Lifesciences, Cheetah Medical, Covidien-Medtronic, Premier, and Abbott Nutritional. Dr Feldman reported receiving an investigator-initiated research grant from Merck. No other disclosures were reported.

Meeting Presentation: This work was presented in part at the American Society of Colon and Rectal Surgeons 2017 Annual Scientific and Tripartite Meeting; June 11, 2017; Seattle, Washington.

Additional Contributions: Jennifer L. Paruch, MD, MS (North Shore Medical Group, Evanston, Illinois) and Sanjay Mohanty, MD, MS (Henry Ford Health System, Detroit, Michigan) assisted in organizing the pilot and drafting the variables. No compensation was received. We acknowledge the Enhanced Recovery in National Surgical Quality Improvement Program (ERIN) pilot hospitals, particularly their respective data abstractors and clinical leaders, for their work in this collaborative.

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