Hospital readmission is a persistent and costly occurrence. A study of Medicare beneficiaries hospitalized in 2009 reported a 30-day readmission rate of 12.7% for surgical patients and 16.1% for medical patients1 and unplanned hospital readmissions were estimated to cost Medicare $17.4 billion in 2004.2 To encourage physicians and hospitals to reduce readmissions, the Centers for Medicare and Medicaid Services (CMS) began publicly reporting 30-day readmission rates for certain medical diagnoses in 2009.3 In addition, recent legislation provided for a hospital readmissions reduction program, in which hospital reimbursement is linked to performance.4 As a result, in October 2012, CMS began penalizing hospitals for excessive readmissions related to these medical diagnoses. The scope of the hospital readmission public reporting and pay-for-performance program is scheduled to expand in 2015 and it is speculated that elective surgical procedures will be among the additional diagnoses included.5 Finally, the Partnership for Patients, led by the Secretary of Health and Human Services and CMS, has set a goal of reducing all hospital readmissions by 20% by the end of 2013.6
There are a variety of reasons why a surgical patient may be readmitted. Some admissions may be planned (ie, chemotherapy or elective surgery) or unplanned but likely unrelated to the surgery performed (ie, trauma). However, the potentially preventable readmissions are those that are unplanned and likely related to or a direct consequence of events from the initial hospitalization. This category of readmissions includes those occurring for medical reasons such as an exacerbation of a preoperative comorbidity like congestive heart failure or diabetes, and readmissions resulting from postoperative complications.
In this study, we describe the population of patients being readmitted within 30 days of surgery and then examine the association between the occurrence of a postoperative complication and a readmission. Complications were identified from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and include surgical site infections as well as cardiac, pulmonary, neurologic, and renal complications. The hypothesis was that patients with an ACS-NSQIP postoperative complication would have a higher risk-adjusted probability of 30-day postoperative readmission compared with patients without an ACS-NSQIP postoperative complication. Finally, we estimated the effect that reducing ACS-NSQIP postoperative complication rates may have on reducing readmission rates and costs. Studies suggest that hospitals participating in ACS-NSQIP can successfully reduce rates of postoperative complications by 9% to 13%;7,8 however, the effect of these quality improvement efforts on hospital postoperative readmission rates has not been documented.
Data Sources and Study Sample
The study design and procedures were approved by the RAND Health institutional review board. The primary data sources for this study were Medicare inpatient claims and ACS-NSQIP, which have been previously described.9,10 Briefly, Medicare data were obtained from the 100% Medicare provider analysis and review file (MedPAR). Medicare is a health insurance program that enrolls people aged 65 years and older, some disabled people under the age of 65, and all people with end-stage renal disease receiving dialysis. Each record in MedPAR represents an inpatient hospital stay for a beneficiary and may include multiple claims. Diagnoses and procedures are recorded by International Classification of Diseases, Ninth Edition (ICD-9) code. Each Medicare beneficiary has a unique identification number allowing for linkage of subsequent hospitalizations without disclosing the patient's identity.
ACS-NSQIP is an institution-based, multispecialty, clinical surgical registry. Hospital participation in ACS-NSQIP is voluntary and requires a dedicated data abstractor who is trained to use strict variable definitions and collection methods. The sampling strategy includes collecting data for the first 40 cases performed within consecutive 8-day cycles, excluding trauma and transplantation. Hospitals are audited to ensure standardized data collection. Data collected include preoperative risk factors, procedures performed by Current Procedural Terminology code, and postoperative complications occurring within 30 days of the index operation.11,12
Eligible patient-level records from ACS-NSQIP, years 2005 to 2008, were linked to Medicare inpatient claim records in MedPAR using indirect patient identifiers and a deterministic linkage algorithm, as previously described.9 As previously reported, there was an excellent agreement between ACS-NSQIP and MedPAR records on death during the primary hospitalization, supporting the validity of the linkage procedure. Our study population was restricted to patients aged 65 years or older who underwent an inpatient surgical procedure during the years studied, were entered into the ACS-NSQIP database, and for whom we were able to successfully link the ACS-NSQIP record to Medicare claims data. We excluded patients for whom Medicare was not the primary payer and patients with procedures occurring in December 2008 because we lacked a full 30 days of follow-up in the Medicare data. We also excluded patients who did not survive to be discharged from the primary hospitalization (n = 3649) and patients who could not be readmitted within 30 days of the surgery date because they were still hospitalized (n = 2030). Our final sample consisted of 90,932 patients from 214 hospitals.
A third data source, the Nationwide Inpatient Sample (NIS), was used to determine the number of procedures performed in US hospitals on Medicare beneficiaries older than 65 years in 2009. The NIS contains discharge data from a systematic sample of hospitals nationwide.13 Procedures were identified by ICD-9 code, and national estimates were calculated using sample hospital weights.
Outcomes of Interest
Our primary outcomes of interest were 30-day postoperative readmission, defined as any admission to a short stay hospital within 30 days of the index operation, and the cost of the readmission. Both outcome variables were identified from MedPAR. We chose to focus on 30-day postoperative readmission rather than on 30-day postdischarge readmission, because we thought it would provide more relevant and useful information to surgeons and because it is the time frame traditionally used for monitoring postoperative outcomes. Admissions to long-stay hospitals or skilled nursing facilities were not considered readmissions, nor were admissions occurring on the same day as discharge, as these are likely transfers. In addition, admissions for maintenance chemotherapy or radiotherapy were considered to be planned and were not counted as readmissions. The perspective taken for the cost analysis was that of the Medicare program, so the cost of readmission was measured by the dollar amount paid to the hospital by Medicare.
Reason for readmission was identified from the first ICD-9 diagnosis code recorded in MedPAR. ICD-9 codes were grouped into clinically meaningful categories using Clinical Classification Software from the Agency for Healthcare Research and Quality.14
Other Variables Used for Analyses
Our explanatory variable of interest was a binary variable indicating whether or not a patient had a 30-day postoperative complication. This variable included any occurrence of 20 complications recorded in ACS-NSQIP: surgical site infection (superficial, deep, or organ-space), wound disruption, pneumonia, unplanned intubation, pulmonary embolism, on ventilator for more than 48 hours, progressive renal insufficiency or acute renal failure requiring dialysis, urinary tract infection, stroke, coma, cardiac arrest requiring cardiopulmonary resuscitation, myocardial infarction, bleeding requiring transfusion, deep venous thrombosis requiring therapy, sepsis or septic shock, or unplanned return to the operating room. Complications are recorded in ACS-NSQIP if they occur within 30 days of surgery, except bleeding requiring transfusion, which must occur within the first 72 hours after surgery.
Variables used for risk adjustment (Table 1) and procedures performed were identified from ACS-NSQIP. Procedures were identified by Current Procedural Terminology code and grouped into 73 broad categories, which were used to stratify the analyses.
Data preparation and analyses were performed using SAS (version 9.2, SAS Institute Inc, Cary, NC) and Stata (version 12, StataCorp LP, College Station, TX) software. We compared the rates of variables of interest between patients who were and were not readmitted. Missing values for body mass index and American Society of Anesthesiologists class were imputed using the hot deck method by procedure group. Chi-square and t tests were performed for categorical and quantitative variables, respectively. Postoperative readmissions were tabulated by procedure group. The top reasons for readmission recorded in Medicare inpatient claims were tabulated for patients with and without an ACS-NSQIP postoperative complication.
We developed a multivariate logistic regression model for each procedure group to determine the risk-adjusted association between occurrence of an ACS-NSQIP complication and 30-day postoperative readmission. These models included the covariates in Table 1. The C-statistics for procedure-specific models ranged from 0.68 to 0.85. These models were used to calculate the risk-adjusted mean-predicted probability of readmission for 3 groups: (1) patients who did not have an ACS-NSQIP complication; (2) patients who did have an ACS-NSQIP complication; and (3) the hypothetical case in which ACS-NSQIP complications could be prevented for patients in the second group. For this final group, we took the patients from the second group and recoded them as not having a complication, then recalculated the mean-predicted probability of readmission (ie, all other covariate values stay the same, but occurrence of a postoperative complication is set to zero) (see Supplemental Digital Content 1, available at http://links.lww.com/SLA/A376, flowchart of 30-day postoperative readmission for the earlier described 3 groups of patients). We expected that the predicted probability of readmission would be higher for this third group than for the first group as patients with a complication likely have other characteristics that make them more likely to be readmitted than patients without a complication, even if complications are prevented.
To determine the cost of 30-day postoperative readmissions attributable to ACS-NSQIP complications, we developed multivariate linear regression models for the cost of readmission for each procedure group using the covariates listed in Table 1 and the binary complication variable. We used these models to calculate the risk-adjusted mean cost of 30-day postoperative readmission for the 3 groups of patients described earlier. The coefficient for the ACS-NSQIP complication variable was used to determine the risk-adjusted readmission cost attributable to occurrence of a complication.
Finally, we estimated the number of 30-day postoperative readmissions avoided and associated costs saved if ACS-NSQIP complications could be prevented or reduced by a relative 5%, 20%, or 100% (see Supplemental Digital Content 2, available at http://links.lww.com/SLA/A377, equations used for calculating estimates). National estimates for annual numbers of procedures performed on Medicare beneficiaries 65 years or older were calculated using figures from the NIS. Confidence intervals for point estimates for individual procedures were calculated using percentile-based bootstrapping with 2000 repetitions. For point estimate confidence intervals for the total of the 20 procedures, we assumed a binomial distribution with unequal variances.
The 30-day postoperative readmission rate was 12.8% (n = 11,639). There was no significant difference in the yearly readmission rate for 2005–2008 (P = 0.659). For the readmitted patient population, 75% were readmitted to the same hospital as the index hospitalization, whereas 25% were readmitted to a different hospital. Readmission rates varied by hospital geographic region: Northeast 13.2%, Midwest 12.9%, South 13.2%, and West 11% (P < 0.001). Table 1 compares rates of demographic and clinical characteristics for the readmitted vs nonreadmitted patient populations. The readmitted population had statistically significant differences in rates of all risk factors, except alcohol use. Although the magnitude of the difference was small, rates were consistently higher in the readmitted population. In aggregate, readmitted patients had an average of 2.8 comorbidities [standard deviation (SD) = 2.0), whereas nonreadmitted patients had 2.1 (SD = 1.7, P < 0.001).
Table 2 lists the top 20 procedures accounting for the greatest number of subsequent 30-day postoperative readmissions. Together, these 20 procedures account for 76% of readmissions, and the top 10 procedures account for 59%. Colectomy accounted for the greatest number of readmissions, followed by lower extremity bypass surgery and carotid endarterectomy. The number of readmissions associated with a procedure is a function of both the readmission rate and how frequently the procedure is performed. Vascular procedures had the highest rates of readmission: lower extremity amputation (23.2%); lower extremity embolectomy, thrombectomy, and vessel repair (19.5%); lower extremity thromboendarterectomy (19.1%); and lower extremity bypass (18.4%). Among the top 20 procedures, the 3 with the lowest readmission rates were mastectomy (6.1%), appendectomy (8.2%), and carotid endarterectomy (9.1%).
Among patients with an ACS-NSQIP postoperative complication, 40% had a 30-day postoperative readmission diagnosis recorded in Medicare inpatient claims specifically categorized as a complication of a surgical procedure, medical care, or a device, compared with 21% for patients without an ACS-NSQIP postoperative complication (Table 3). Fewer than 6% of all readmissions were for chronic medical conditions. Some readmission diagnosis categories are not specified as complications but are likely related to the index hospitalization and match with ACS-NSQIP complications (ie, septicemia, acute renal failure, pneumonia, urinary tract infection). Other readmission diagnosis categories do not match with an ACS-NSQIP complication but could still be considered as related to the index hospitalization (ie, fluid and electrolyte disorders, intestinal obstruction without hernia, gastrointestinal hemorrhage). The top 10 readmission diagnosis categories represent 61% of the readmissions for patients with a postoperative complication and 50% for patients without a complication, whereas the top 20 readmission diagnosis categories represent 75% and 64% of readmissions, respectively. The remaining 173 categories represented 1% or less of readmissions each.
The postoperative complication rate in our study population was 20%. Of the 11,639 patients who were readmitted, 6125 had at least 1 complication (53%) and of the 79,293 patients who were not readmitted, 12,406 had at least 1 complication (16%, P < 0.001). Individual complication rates were significantly higher among readmitted patients for each complication studied (see Supplemental Digital Content 3, available at http://links.lww.com/SLA/A377). Among readmitted and nonreadmitted patients, 31% and 10% had 1 complication, 14% and 3% had 2, 5% and 1% had 3, and 3% and 1% had more than 3 complications, respectively. Patients with an ACS-NSQIP postoperative complication were 4.3 times more likely to be readmitted than those without a complication (33% readmitted vs 8%, respectively, P < 0.001; relative risk of readmission = 4.3). The average costs of a 30-day postoperative readmission for patients with and without a complication were $13,532 and $8469, respectively.
The risk-adjusted association between occurrence of a postoperative complication and postoperative readmission for the top 20 procedures accounting for the greatest number of readmissions is reported in Table 4. Using colectomy as an example, preventing complications reduces the average predicted probability of 30-day postoperative readmission by 71% (from 28.4% to 8.1%) and reduces the risk-adjusted average predicted cost of a readmission by 41% (from $13,419 to $7954). The cost of readmission attributable to postoperative complications for colectomy was estimated to be $5465.
Table 5 lists estimated savings in terms of 30-day postoperative readmissions and costs associated with reducing the postoperative complication rate by a relative 5%, 20%, and 100%. In 2009, an estimated 120,675 Medicare beneficiaries aged 65 or older underwent a colectomy nationwide. In our database, colectomy patients have an observed ACS-NSQIP postoperative complication rate of 27.0%. Reducing the complication rate by a relative 5% (to 25.6%) could result in a relative 2.0% [95% confidence interval (CI) = 1.9–2.2] reduction in the readmission rate. Nationwide, this could mean the annual prevention of 330 (95% CI = 303–357) readmissions and an associated annual savings to Medicare of $5.2 million (95% CI = 4.5–5.8). These annual savings increase to 1320 (95% CI = 1212–1430) readmissions prevented and $20.7 million (95% CI = 18.1–23.3) saved if postoperative complication rates are reduced by 20%, and to 6601 (95% CI = 6059–7148) readmissions prevented and $103.0 million (95% CI = 90.7–116.0) saved if all ACS-NSQIP colectomy postoperative complications could be prevented.
Summed together, the top 20 procedures accounting for the greatest number of 30-day postoperative readmissions were performed on an estimated 821,795 patients in 2009. Reducing the ACS-NSQIP complication rate for each procedure by a relative 5% could result in the prevention of 2092 (95% CI = 1984–2201) readmissions per year and a savings to Medicare of $31.0 million (95% CI = 28.9–33.2). This cost savings is due to both the decreased number of readmissions occurring and the decreased cost of readmissions that occur despite the absence of a postoperative complication. A 20% relative reduction in the complication rates could result in the prevention of 8369 (95% CI = 7939–8800) readmissions and savings of $124.2 million (95% CI = 115.4–133.0). Finally, preventing all ACS-NSQIP complications for these procedures could result in the prevention of 41,846 (95% CI = 39,689–44,004) readmissions and a savings to Medicare of $620.3 million (577.4–663.2).
The first step toward preventing hospital readmissions is understanding why they occur. Although preoperative risk factors for postoperative readmission have been identified in previous studies, overall hospital readmission has proven difficult to predict through modeling.15–18 In this study, we found statistically significant but clinically relatively small differences in the preoperative profile of readmitted and nonreadmitted patients. However, readmitted patients were 3.4 times more likely to have an ACS-NSQIP postoperative complication than nonreadmitted patients (53% with a complication vs 16%, respectively; relative risk of complication occurrence = 3.4) and patients with an ACS-NSQIP complication had higher risk-adjusted predicted probabilities of 30-day postoperative readmission than those without a complication for the procedures studied. This demonstrates that reducing complication rates could result in a substantial reduction in readmissions and cost savings, even after adjusting for patient comorbidities and clinical severity.
Previous studies focused on postoperative readmission have largely focused on a single surgical procedure using retrospective case review data from a single institution19–21 or multi-institutional data from administrative claims.22–26 Although these studies have identified factors significantly associated with readmission, including preoperative comorbidities, length of hospital stay, and postoperative complications, the magnitude of the effect is generally modest. Our multi-institutional study builds on this previous work by incorporating high quality, reliable data from a clinical registry: ACS-NSQIP. Although ACS-NSQIP complications were not responsible for all readmissions, they do represent a clearly defined and potentially modifiable target for quality improvement efforts aimed at preventing postoperative readmissions.
Reducing ACS-NSQIP postoperative complications can itself be a formidable challenge for hospitals and it is unclear what level of reduction should be set as a realistic and achievable goal. Hospitals participating in national quality improvement programs and local collaboratives have demonstrated substantial improvements in postoperative complication rates. In the 1990s, the Veterans Health Administration (VA) developed the first NSQIP (VA-NSQIP), founded on the principles of collection of robust clinical data from hospitals and feedback of risk-adjusted complication and mortality rates. In the first few years of the program, the VA-NSQIP demonstrated marked improvement in surgical quality, with postoperative complication rates declining by 30% between 1994 and 1997.27 This program was subsequently implemented in the private sector as the ACS-NSQIP where improvements have also been demonstrated. One study found that 82% of ACS-NSQIP hospitals improved their risk-adjusted complication rates between 2006 and 2007.8 ACS-NSQIP hospitals collect data in a uniform manner, whereas many regions have formed local collaboratives that meet regularly to discuss surgical quality and share best practices. One such group, the Michigan Surgical Quality Collaborative, reported a 9% reduction in postoperative complications.7 The combination of standardized data collection and benchmarking on a national level with collaborative quality improvement efforts on a local level appears to be an effective paradigm for reducing postoperative complications.
On the basis of the findings from these studies, we chose to examine 5% and 20% as potentially achievable relative reductions in complication rates. A 5% relative reduction represents a fairly modest decrease (eg, 27.0%–25.7% for colectomy or 28.3%–26.9% for lower extremity bypass procedures), whereas 20% represents a greater decrease (eg, 27.0%–21.6% for colectomy or 28.3%–22.6% for lower extremity bypass procedures). For the procedures studied, the percent reduction in readmissions was about one fifth to one half of the reduction in complications. For example, a 5% relative reduction in complications is associated with 1.1% reduction in readmissions for exploratory laparotomy and 2.5% reduction for mastectomy. For the 20 procedures together, preventing all ACS-NSQIP complications could result in 39% reduction in readmissions.
Analysis of the reasons for 30-day postoperative readmission recorded in the Medicare inpatient claims supports a causal relationship between ACS-NSQIP postoperative complications and readmission and demonstrates that our estimates of savings are likely conservative. The postoperative complications routinely collected by ACS-NSQIP represent complications that commonly occur after a range of surgical procedures. Complications related to specific procedures are not currently captured (ie, ileus or intestinal obstruction after abdominal procedures), nor are complications that are potentially difficult to clearly define for systematic data collection (ie, persistent postoperative pain, nausea, or vomiting). Thus, the ACS-NSQIP postoperative complications represent a starting point for quality improvement efforts in surgery and should likely be followed by procedure-specific efforts to realize even greater savings.
Our finding that less than 6% of readmissions were for chronic medical conditions demonstrates that surgical patients may benefit somewhat from current efforts to reduce medical readmissions for diagnoses such as congestive heart failure. Efforts to reduce readmission for medical patients have focused on interventions that improve care coordination during transitions of care and increase access to outpatient care. Key features of many of these interventions are a comprehensive and reliable discharge plan combined with coaching for the patient.28,29 Such interventions may be effective for surgical patients as well.
The results of our study should be interpreted in light of the following limitations. First, we linked records between ACS-NSQIP and Medicare inpatient claims using indirect identifiers, and thus there is less certainty regarding the accuracy of the matches. Second, ACS-NSQIP hospitals in this data set are predominantly larger medical centers, which may limit the generalizability of our findings, though the observed readmission rate for ACS-NSQIP hospitals was similar to national estimates. Third, ACS-NSQIP utilizes a systematic sampling methodology rather than a 100% population-based method. Fourth, our study population was limited to the general Medicare population eligible by age (>65 years), and our results may not be generalizable beyond this population. Fifth, our database includes only inpatient surgical procedures and it is possible that the addition of outpatient procedures would change our results. Sixth, the complication variable included 20 different postoperative complications, which may individually have different associations with readmission. Finally, we did not account for possible marginal effects of changes in readmission rates and costs and the possibility of a nonlinear relationship with postoperative complications.
Hospital readmissions that are unplanned and related to a recent hospital stay are certain to be viewed as a poor outcome by patients and are known to be a source of excess cost for payers. Policymakers have taken note of this and are increasingly focused on hospital readmission as a quality metric for public reporting and pay-for-performance. This study provides substantial evidence that efforts to reduce hospital readmissions for surgical patients should begin by focusing on postoperative complications that can be reliably and validly measured. Such an approach will not eliminate all postoperative readmissions but will likely have a major effect on readmission rates.
1. Goodman DC, Fisher ES, Chang C. After Hospitalization: A Dartmouth Atlas Report on Post-Acute Care for Medicare Beneficiaries. Hanover, NH: Dartmouth College; 2011.
2. Centers for Medicare & Medicaid Services. Medicare & Medicaid Statistical Supplement. Baltimore, MD: Centers for Medicare & Medicaid Services; 2007.
5. Kocher RP, Adashi EY. Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA. 2011;306:1794–1795.
7. Campbell DA Jr, Englesbe MJ, Kubus JJ, et al. Accelerating the pace of surgical quality improvement
: the power of hospital collaboration. Arch Surg. 2010;145:985–991.
8. Hall BL, Hamilton BH, Richards K, et al. Does surgical quality improve in the American College of Surgeons National Surgical Quality Improvement
Program: an evaluation of all participating hospitals. Ann Surg. 2009;250:363–376.
9. Lawson EH, Ko CY, Louie R, et al. Linkage of a clinical surgical registry with Medicare inpatient claims data using indirect identifiers. Surgery. 2013;153:423–430.
10. Lawson EH, Louie R, Zingmond DS, et al. A comparison of clinical registry versus administrative claims data for reporting of 30-day surgical complications
. Ann Surg. 2012;256:973–981.
11. Ingraham AM, Richards KE, Hall BL, et al. Quality improvement
in surgery: the American College of Surgeons National Surgical Quality Improvement
Program approach. Adv Surg. 2010;44:251–267.
12. Khuri SF. The NSQIP: a new frontier in surgery. Surgery. 2005;138:837–843.
13. HCUP Nationwide Inpatient Sample (NIS). Healthcare Cost and Utilization Project (HCUP). 2009. Rockville, MD: Agency for Healthcare Research and Quality. www.hcup-us.ahrq.gov/nisoverview.jsp
. Accessed October 12, 2011.
15. Azimuddin K, Rosen L, Reed JF III, et al. Readmissions after colorectal surgery cannot be predicted. Dis Colon Rectum. 2001;44:942–946.
16. Kiran RP, Delaney CP, Senagore AJ, et al. Outcomes and prediction of hospital readmission
after intestinal surgery. J Am Coll Surg. 2004;198:877–883.
17. Keenan PS, Normand SL, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission
rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1:29–37.
18. Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission
rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4:243–252.
19. Kent TS, Sachs TE, Callery MP, et al. Readmission
after major pancreatic resection: a necessary evil? J Am Coll Surg. 2011;213:515–523.
20. Kassin MT, Owen RM, Perez SD, et al. Risk factors for 30-day hospital readmission
among general surgery patients. J Am Coll Surg. 2012;215:322–330.
21. Guinier D, Mantion GA, Alves A, et al. Risk factors of unplanned readmission
after colorectal surgery: a prospective, multicenter study. Dis Colon Rectum. 2007;50:1316–1323.
22. Wick EC, Shore AD, Hirose K, et al. Readmission
rates and cost following colorectal surgery. Dis Colon Rectum. 2011;54:1475–1479.
23. Schneider EB, Hyder O, Brooke BS, et al. Patient readmission
and mortality after colorectal surgery for colon cancer: impact of length of stay relative to other clinical factors. J Am Coll Surg. 2012;214:390–398; discussion 398–399.
24. Greenblatt DY, Greenberg CC, Kind AJ, et al. Causes and implications of readmission
after abdominal aortic aneurysm repair. Ann Surg. 2012;256:595–605.
25. Hendren S, Morris AM, Zhang W, et al. Early discharge and hospital readmission
after colectomy for cancer. Dis Colon Rectum. 2011;54:1362–1367.
26. Yermilov I, Bentrem D, Sekeris E, et al. Readmissions following pancreaticoduodenectomy for pancreas cancer: a population-based appraisal. Ann Surg Oncol. 2009;16:554–561.
27. Khuri SF, Daley J, Henderson W, et al. The Department of Veterans Affairs’ NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement
Program. Ann Surg. 1998;228:491–507.
28. Phillips CO, Wright SM, Kern DE, et al. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. 2004;291:1358–1367.
29. Hansen LO, Young RS, Hinami K, et al. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155:520–528.