Patterns of Postoperative Care after Surgery in Freestanding Ambulatory Surgery Centers in South Carolina

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J Am Coll Surg

Scientific Forum Abstracts

readmission patterns after major cancer surgery at MSH within a large and racially diverse cohort. METHODS: Using the California Inpatient Database, we analyzed 110,857 patients who underwent 1 of 8 oncologic resections (lung, esophagus, stomach, pancreas, hepatobiliary, colon, rectum, and kidney) between 2004 and 2011 at 491 hospitals. Minority-serving hospitals were defined as hospitals that ranked in the top quartile for the proportion of black and Hispanic patients served. A multivariate logistic regression analysis was used to assess associations between major cancer surgery at MSH and their 30-day, 90-day, and repeated readmission rates. RESULTS: There were 122 MSH that served 18.3% of all patients. The 30-day, 90-day, and repeated readmissions rates were 12.2%, 19.3%, and 3.7%, respectively, among MSH; 10.9%, 16.9%, and 3.0% occurred at non-MSH, respectively. After controlling for covariates, patients at MSH experienced higher odds of 30-day, 90-day, and repeated readmission rates (Table). Table. Association Between Proportion of Hispanic/Black Patients and Rates of Readmission* Proportion of Hispanic/black patients (Ref ¼ lowest quartile) 2nd Quartile

30-d readmission, odds ratio (95% CI)

90-d readmission, odds ratio (95% CI)

1.05 (0.96, 1.14)

Repeated readmission, odds ratio (95% CI)

1.06 (0.98, 1.15) y

3rd Quartile

1.13 (1.04, 1.22)

4th Quartile (MSH)

1.16 (1.05, 1.29)y

1.14 (1.06, 1.22)

106 (0.92,1.23) z

1.18 (1.08, 1.29)y

120 (1.05,1.38)

y

128 (1.10,1.50)y

*Multivariable regression adjusted for age, sex, comorbidity, type of procedure, race, and year of admission. y p < 0.01 z p < 0.001

CONCLUSIONS: The MSH performed one-fifth of all major cancer operations, with a nearly 20% higher odds of readmission. This places these already financially vulnerable hospitals at additional risk for penalties. These findings continue to underscore some of the unintended consequences of the HRRP on MSH. NSQIP Surgical Risk Calculator and Frailty in Emergency General Surgery: A Measure of Surgical Resilience Moutamn Sadoum, MD, Bardiya Zangbar, MD, Peter M Rhee, MD, FACS, Narong Kulvatunyou, MD, FACS, Mazhar Khalil, MD, Terence O’Keeffe, MB, ChB, FACS, Andrew L Tang, MD, FACS, Rifat Latifi, MD, FACS, Randall S Friese, MD, FACS, Bellal Joseph, MD, FACS Univeristy of Arizona, Tucson, AZ INTRODUCTION: The aim of our study was to assess the association between American College of Surgeons NSQIP surgical risk calculator (SRC) and frailty index in emergency general surgery (EGS) patients. METHODS: Using the NSQIP-SRC, we calculated probability of complications for our prospectively maintained database of geriatric EGS patients. A frailty index (FI) was calculated using a

50-variable modified Rockwood FI for the same patients. Frail patients were defined as FI > 0.25. Regression analysis was used to establish a predictive model for all complications and serious complications based on FI and NSQIP-SRC. RESULTS: Overall, 194 patients (frail: 72 vs non-frail: 122) were included. Frail patients were more likely to have complications (43% vs 27%; p¼0.022) and serious complications (31% vs 18%; p¼0.04) compared with non-frail patients. Complication risk calculated using NSQIP-SRC was higher for all complications (22% vs 16%; p¼0.01) and serious complications (17% vs 11%; p<0.001) in frail patients compared with non-frail. In a regression model for all complications, FI was a significant predictor of complications (odds ratio 1.024; 95% CI, 1.001e1.047; p¼0.038); NSQIP-SRC estimated values were not. Although there was a weak correlation between NSQIP-SRC- and FI-predicted probability for serious complications (R2¼0.199; p<0.001), there was no correlation between the 2 for all complications (R2¼0.138; p¼0.05). CONCLUSIONS: In a small cohort of geriatric patients, FI predicted complications better than NSQIP surgical risk calculator. Although the validation of NSQIP surgical risk calculator is not questionable, the addition of frailty status to this calculator may increase the accuracy of the predicted complication risk in geriatric patients. Patterns of Postoperative Care after Surgery in Freestanding Ambulatory Surgery Centers in South Carolina George Molina, MD, Bridget A Neville, MPH, Stuart R Lipsitz, William Berry, MD, MPH, Atul A Gawande, MD, MPH, FACS, Alex B Haynes, MD, MPH Ariadne Labs at Brigham and Women’s Hospital and Harvard School of Public Health, Boston, MA; Massachusetts General Hospital, Boston, MA INTRODUCTION: Many surgical procedures formerly performed on an inpatient basis have been moved into the outpatient environment; this has led to the development of freestanding ambulatory surgery centers (ASCs). There is a paucity of outcomes data from freestanding ASCs, including rate of subsequent emergency department (ED) visits and hospital admissions. Previous studies have not distinguished between freestanding and hospital-affiliated ASCs. METHODS: This was a retrospective cohort study that analyzed the incidence of ED visits and hospital admissions at 1 and 7 days after operations in freestanding ASCs in South Carolina. Multivariate logistic regression clustering for freestanding ASCs was used to identify predictors of postoperative acute care. RESULTS: Between 2006 and 2013, 1,410,824 operations were performed in 85 freestanding ASCs in South Carolina. Among the 100 most common procedures (1,279,688; 90.7%), the rate of postoperative acute care per 1,000 procedures was 5.4 (95% CI, 5.3-5.5) and 16.7 (95% CI, 16.5-16.9) at 1 and 7 days. After adjusting for age, African Americans (compared to whites, odds ratio [OR] 1.18, 95% CI, 1.09e1.27) and patients with preoperative Charlson Comorbidity Index (CCI) scores >0 (1) OR 2.69, 95% CI, 2.55e2.85; (2) OR 3.31,

Vol. 221, No. 4S1, October 2015

Scientific Forum Abstracts

95% CI, 3.11e3.53; (3+) OR 4.99, 95% CI, 4.64e5.37 had higher odds of accessing acute care after surgery. CONCLUSIONS: Significant predictors of acute care at 7 days after operation in freestanding ASCs in South Carolina were African-American race and CCI scores >0. As the operative volume in freestanding ASCs rises, further research is needed on the safety and efficiency of surgery in this environment. Postoperative Readmission Time Interval and Model Performance: Implications for Hospital Profiling Andrew A Gonzalez, MD, JD, MPH, Aslam Ejaz, MD, MPH, Nicholas H Osborne, MD, Amir A Ghaferi, MD, FACS University of Michigan, Ann Arbor, MI; University of Illinois at Chicago, Chicago, IL INTRODUCTION: Despite growing popularity in pay-for-performance programs, 30-day readmissions have been widely criticized for being unable to distinguish high- vs low-quality hospitals. This may, in part, stem from the arbitrary selection of a 30-day time interval. It remains unknown if the use of other intervals would improve the ability to profile hospitals on postoperative readmissions. METHODS: This retrospective study of national Medicare data (2005 to 2009) included 2,540,694 patients undergoing 1 of 10 high-risk surgical procedures. We used logistic regression to model the probability of being readmitted at the following intervals from discharge: 1-30 days, 1-5 days, 1-10 days, 1-15 days, 6-10 days, 11-15 days, and 16-30 days. We evaluated model performance using pseudo-R2 (a measure of explained variability), c-statistics (the ability of the model to predict the outcome), and year-to-year reliability (a measure random variation unassociated with quality). Our models adjusted for traditional patient-level variables available in administrative data: demographics, comorbidities, complications, and operation performed.

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CONCLUSIONS: Profiling hospital readmissions using a 30-day interval may be suboptimal due to either factors unmeasured in administrative datasets or random variation. Model performance might be improved by shortening the interval to 5 or 10 days. Redesigned Electronic Medical Notes Allow Automated Clinical Data Extraction and Decrease Provider Documentation Time Jose G Christiano, MD University of Rochester Medical Center, Rochester, NY INTRODUCTION: Over the last few years, widespread implementation of electronic medical records (EMR) has increased the burden of documentation on providers. Nevertheless, retrieval of meaningful data from EMR for research or quality control purposes continues to be mostly renegaded to individual chart review and/or manual entry in databanks. We hypothesized that standard EMR notes could be redesigned to provide customary documentation and allow automated clinical data retrieval by commercially available text data extraction software (TDES), with minimal disruption to provider workflow (documentation time). METHODS: Twenty fictitious patients undergoing reduction mammoplasty were created. Fictitious encounters included initial consultation, preoperative visit, operation, and postoperative visits at 1, 8, and 25 weeks. Each encounter was documented with our previous standard note (SN) and a redesigned “data-friendly” note (DFN). Documentation time was measured and compared between the 2 note groups. All DFNs were then exported in PDF format and fed into a TDES for data accrual. Seventy-six variables were assigned for monitoring and retrieval, spanning from patient demographics, to elements of the history and physical, to operative details, to clinical outcomes.

RESULTS: Model performance decreased with lengthening readmission intervals (Table). For example, 5-day readmissions performed better than 30-day readmissions on all statistical tests: 0.080 vs 0.052 (pseudoR2), 0.71 vs 0.66 (c-statistic), 0.54 vs 0.47 (next-year reliability).

RESULTS: The TDES successfully analyzed all 120 DFNs (300 pages) in less than 10 seconds, generating a database containing 4,850 clinical data points. Total documentation time per patient was found to be less in the DFN group (20.51.5 min) than in the SN group (21.3 1.6 min), reaching statistical significance (p<0.01).

Table. Model Performance and Reliability of Individual Readmission Measures

CONCLUSIONS: Redesigned “data-friendly” EMR notes allow automated clinical data retrieval by commercially available text data extraction software and decrease provider documentation time.

Logistic regression model performance

Year-to-year reliability* Readmissions % of 2005-07 2005-07 measure patients Pseudo-R2 c-statistic vs 2008 vs 2009

30-d 1-5 d 1-10 d 1-15 d 6-10 d 11-15 d 16-30 d

17.8 8.7 11.5 13.6 2.9 2.1 4.1

0.052 0.080 0.060 0.055 0.016 0.014 0.018

0.66 0.71 0.68 0.67 0.62 0.61 0.61

0.47 0.54 0.51 0.50 0.03 0.01 0.02

0.41 0.47 0.44 0.42 0.03 0.00 0.03

*The square of the coefficient of correlation between readmission rates assessed in each timeframe.

Safety-Net Burden Affects Cost and Outcomes at Academic Hospitals Richard S Hoehn, MD, Koffi Wima, Matthew A Vestal, MHA, Drew J Weilage, MHA, Daniel E Abbott, MD, FACS, Vishad Y Nabili, MD University of Cincinnati, Cincinnati, OH; Sg2 Health Care and Hospital System Consultancy, Chicago, IL; Centura Health, Denver, CO INTRODUCTION: Hospital safety-net burden is known to correlate with inferior patient outcomes. The aim of this study was to assess the influence of patient and hospital factors on these outcomes.