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Original Investigation |

Underlying Reasons Associated With Hospital Readmission Following Surgery in the United States FREE

Ryan P. Merkow, MD, MS1,2,3,4; Mila H. Ju, MD, MS1,2,3; Jeanette W. Chung, PhD2,3; Bruce L. Hall, MD, PhD, MBA1,5,6,7,8; Mark E. Cohen, PhD1; Mark V. Williams, MD9; Thomas C. Tsai, MD, MPH10,11; Clifford Y. Ko, MD, MS, MSHS1,12,13; Karl Y. Bilimoria, MD, MS1,2,3
[+] Author Affiliations
1Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, Illinois
2Surgical Outcomes and Quality Improvement Center (SOQIC), Department of Surgery and Center for Healthcare Studies, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
3Northwestern Institute for Comparative Effectiveness Research in Oncology (NICER-Onc), Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois
4Division of Biological Sciences, Department of Surgery, University of Chicago, Chicago, Illinois
5School of Medicine, Department of Surgery, Washington University in St Louis, Barnes Jewish Hospital, St Louis, Missouri
6Center for Health Policy, Olin Business School, Washington University in St Louis, St Louis, Missouri
7Department of Surgery, John Cochran Veterans Affairs Medical Center, St Louis, Missouri
8BJC Healthcare, St Louis, Missouri
9Center for Health Services Research, Department of Internal Medicine, University of Kentucky, Lexington
10Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts
11Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
12Department of Surgery, University of California–Los Angeles
13VA Greater Los Angeles Healthcare System, Los Angeles, California
JAMA. 2015;313(5):483-495. doi:10.1001/jama.2014.18614.
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Published online

Importance  Financial penalties for readmission have been expanded beyond medical conditions to include surgical procedures. Hospitals are working to reduce readmissions; however, little is known about the reasons for surgical readmission.

Objective  To characterize the reasons, timing, and factors associated with unplanned postoperative readmissions.

Design, Setting, and Participants  Patients undergoing surgery at one of 346 continuously enrolled US hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) between January 1, 2012, and December 31, 2012, had clinically abstracted information examined. Readmission rates and reasons (ascertained by clinical data abstractors at each hospital) were assessed for all surgical procedures and for 6 representative operations: bariatric procedures, colectomy or proctectomy, hysterectomy, total hip or knee arthroplasty, ventral hernia repair, and lower extremity vascular bypass.

Main Outcomes and Measures  Unplanned 30-day readmission and reason for readmission.

Results  The unplanned readmission rate for the 498 875 operations was 5.7%. For the individual procedures, the readmission rate ranged from 3.8% for hysterectomy to 14.9% for lower extremity vascular bypass. The most common reason for unplanned readmission was surgical site infection (SSI) overall (19.5%) and also after colectomy or proctectomy (25.8%), ventral hernia repair (26.5%), hysterectomy (28.8%), arthroplasty (18.8%), and lower extremity vascular bypass (36.4%). Obstruction or ileus was the most common reason for readmission after bariatric surgery (24.5%) and the second most common reason overall (10.3%), after colectomy or proctectomy (18.1%), ventral hernia repair (16.7%), and hysterectomy (13.4%). Only 2.3% of patients were readmitted for the same complication they had experienced during their index hospitalization. Only 3.3% of patients readmitted for SSIs had experienced an SSI during their index hospitalization. There was no time pattern for readmission, and early (≤7 days postdischarge) and late (>7 days postdischarge) readmissions were associated with the same 3 most common reasons: SSI, ileus or obstruction, and bleeding. Patient comorbidities, index surgical admission complications, non-home discharge (hazard ratio [HR], 1.40 [95% CI, 1.35-1.46]), teaching hospital status (HR, 1.14 [95% CI 1.07-1.21]), and higher surgical volume (HR, 1.15 [95% CI, 1.07-1.25]) were associated with a higher risk of hospital readmission.

Conclusions and Relevance  Readmissions after surgery were associated with new postdischarge complications related to the procedure and not exacerbation of prior index hospitalization complications, suggesting that readmissions after surgery are a measure of postdischarge complications. These data should be considered when developing quality indicators and any policies penalizing hospitals for surgical readmission.

Readmission as a quality and cost-containment metric is now a major issue for hospitals, clinicians, and policy makers. Although the initial focus was on 3 medical conditions (myocardial infarction, heart failure, and pneumonia),13 the Centers for Medicare & Medicaid Services has since expanded its focus on readmissions to include 2 separate measures related to surgical patients: readmissions following total hip and knee arthroplasty as part of their Hospital Readmissions Reduction Program and hospital-wide readmissions (ie, includes all surgical patients), which are publicly reported.4 Future inclusion of additional individual operations is anticipated.5

Despite the emphasis on readmissions, studies have not comprehensively evaluated the underlying reasons and factors associated with readmissions after surgical hospitalizations using clinical data from a diverse, national sample of hospitals. Limitations of existing studies include the inability to identify a specific valid reason for readmission because of the use of data sources that lack clinical granularity.612 Another gap in the extant literature is insufficient understanding of the relationship between surgical complications occurring during the index surgical admission and the causes for readmission (ie, whether the readmission is related to a complication from the index hospitalization or rather is a new issue that developed after discharge). It is also unclear whether early and late postsurgical readmissions have similar underlying reasons. Identification of the reasons and factors associated with unplanned surgical readmissions can help direct future surgical quality improvement efforts and policy decisions designed to reduce surgical readmission rates.

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects clinical readmission information, including the primary reason for readmission, a data element that is not available in most other multi-institutional data sources.1214 Having prospectively collected information about surgical readmission, ACS NSQIP enables a more precise assessment of the causes for surgical readmission than have been previously reported. The objectives of this study were to characterize the reasons for and timing of readmissions and to examine factors associated with unplanned surgical readmissions.

Data Source and Study Population

The details of the ACS NSQIP, including sampling strategy, data abstraction procedures, variables collected, outcomes, and structure, are described elsewhere.1521 In 2012, ACS NSQIP included 374 adult hospitals, accounting for approximately 10% of all hospitals and 30% of operations performed in the United States. Of those hospitals, 346 were continuously enrolled for the entire study period. In brief, hospitals collect standardized and audited clinical data on patient demographics, preoperative risk factors, laboratory values, operative variables, and postoperative complications for a predefined sample of their patients.19,20 Trained clinical data abstractors use definitions standardized for all participating institutions. Onsite data audits are regularly performed. Patients are followed up for postoperative outcomes for 30 days after the index operation, irrespective of whether the patient is an inpatient, has been discharged to his or her home or another facility, or has been readmitted to another hospital. Patients are followed up by surgical clinical reviewers at each participating hospital who examine the medical record, query involved clinicians, and contact patients as needed to ascertain the required ACS NSQIP data elements.

Patients undergoing surgery at a continuously enrolled US ACS NSQIP hospital from January 1, 2012, to December 31, 2012, were included in this study. Six representative procedure groups based on Current Procedural Terminology (CPT) codes22 (eTable 1 in the Supplement) were also examined: bariatric surgery, colectomy or proctectomy, hysterectomy, total hip or knee arthroplasty, ventral hernia repair, and lower extremity vascular bypass. These procedures were selected based on their clinical and policy relevance, because they are frequently used in public reporting and pay-for-performance programs. The Northwestern University institutional review board deemed this study as exempt.

Readmission Variables

The ACS NSQIP collects data on whether a readmission occurred to the same or a different facility, whether the readmission was planned or unplanned at the time of index discharge, and the primary suspected reason for the readmission. The accuracy of these variables has been examined against physician chart review.13,14 The data abstractors can review inpatient and outpatient charts, contact other hospitals, and contact patients directly to ascertain whether a readmission occurred.

Consistent with other ACS NSQIP outcomes, readmission events were captured if they occurred within 30 days of the index procedure. A readmission event was defined as unplanned by the hospital’s data abstractor if it was not part of the treatment plan at the time of the index procedure discharge per the ACS NSQIP definition.13,14,23 The analyses focused on unplanned readmissions. The primary reason for readmission was identified as a standard ACS NSQIP postoperative complication (eg, surgical site infection [SSI], myocardial infarction) or, if not in the ACS NSQIP set of standard outcomes collected, a diagnosis classified by an International Classification of Diseases, Ninth Revision (ICD-9) code.23 The hospital’s clinical data abstractor assigned the reasons for the readmission after review of the entire medical record, discussions with treating physicians and outside hospitals, and contacting the patient directly as needed. Thus, although assigned an ICD-9 diagnostic code, this was still a clinically abstracted and not administratively coded reason for hospital readmission. Prior research has validated that this assigned reason is concordant with physician case reviews.13,14ICD-9 codes were grouped using the Agency for Healthcare Research and Quality Clinical Classification Software.24,25 The resulting categories were consolidated with the ACS NSQIP postoperative occurrence categories, resulting in a total of 18 potential reasons for readmission (eTable 2 in the Supplement).

The most common reasons for unplanned readmission within 30 days of the index procedure were assessed, as well as the proportion of patients readmitted for the same preexisting inpatient complication. In addition, the top 3 reasons for early (within 7 days of discharge) unplanned readmission were compared with those for late (more than 7 days after discharge) readmission.

Hospital Characteristics

Data from the 2010 American Hospital Association Annual Survey were used to evaluate whether certain hospital characteristics were associated with unplanned readmissions. Selected hospital characteristics used in previous studies of health care quality were selected a priori for inclusion in this study2632: hospital ownership, rural hospital designation, resident-to-bed ratio, teaching status designated by the Accreditation Council for Graduate Medical Education (ACGME), hospital volume, and hospital disproportionate share index. Hospitals with inpatient surgical volumes in the lowest quartile were designated as low-volume centers, and those with volumes in the highest quartile were designated as high-volume centers. To evaluate the relationship between a hospital’s care of vulnerable populations and readmissions, we used the Medicare disproportionate hospital share index.33 Using a previously described approach,34 hospitals with disproportionate hospital share index in the upper quartile were defined as safety net hospitals.

Statistical Analysis

Because ACS NSQIP captures readmission data within 30 days from the index procedure, we used time-to-event modeling using hierarchical Cox proportional hazards models with patients clustered within hospitals to characterize the time from discharge to readmission and to evaluate variables associated with readmissions. The time-to-event interval was measured from the date of surgery to the date of readmission. Patients were censored at 30 days from the index procedure or if they died prior to readmission. To focus on new postdischarge complications causing readmissions (ie, those not simply exacerbations of known complications), patients were excluded from the analyses of factors associated with readmission if they were readmitted for a complication that also occurred during their initial hospital stay. Candidate variables comprised clinical covariates, including the procedure (CPT codes), patient demographics, health summary status variables (eg, functional status, American Society of Anesthesiologists [ASA] class), specific comorbidities (eg, heart failure, diabetes), inpatient complications (that occurred during the index hospitalization), discharge destination, hospital characteristics, and hospital disproportionate share status. All variables were entered into the model. The proportionality assumption was confirmed graphically.

To determine the relative strength of association between each covariate and the risk of readmission, variables associated with unplanned readmission were selected into the models using a forward selection process (P < .05 as entry criterion). Although this approach offers some indication of the clinical relevance of the variables in predicting readmissions, the method is not specifically designed for this purpose. Variables selected earlier were the most significant of the remaining covariates after adjusting for any already selected covariates. The likelihood ratio test was used to compare all models with the null model. Only variables that had clinical relevance or that had been shown to have an association with readmissions in prior studies were included in the model. Index hospitalization complications, discharge destination, hospital characteristics, and hospital disproportionate share status were added into the models, even if not selected.

Using the individual variables examined in the models above, 5 categories of variables generally considered to be explanatory for readmission were assessed: patient factors, inpatient complications (except if the index hospitalization complication was the reason for the readmission), discharge destination, hospital characteristics, and hospital disproportionate share. The association between these factors (together and individually) and time to readmission was estimated in separate Cox models with robust clustered standard errors to account for hospital-level clustering. The proportion of variation in the outcome explained by these factors was estimated using the Royston R2 method.35

Statistical significance was set at P < .05, and all tests were 2-tailed. All analyses were performed using SAS version 9.3 (SAS Institute) and Stata version 13.1MP (StataCorp).

From 346 ACS NSQIP hospitals, 498 875 patients were identified, of which 18 143 underwent bariatric surgery, 35 112 colectomy or proctectomy, 33 895 ventral hernia repair, 25 119 hysterectomy, 38 671 hip or knee arthroplasty, and 6341 lower extremity vascular bypass (Table 1). The median length of stay ranged from 0 days for ventral hernia repair to 6 days for colectomy or proctectomy (Table 1). Specific patient characteristics by procedure type are detailed in eTable 3A in the Supplement. Among the 346 hospitals included in this study, 219 (63.3%) were teaching hospitals (eTable 3B in the Supplement).

Table Graphic Jump LocationTable 1.  Thirty-Day Readmission Rates, Length of Stay, and Days From Discharge to Readmission Following Surgery

Across procedure groups, planned readmissions were relatively infrequent, since the all-cause readmission rate of 6.1% closely approximated the unplanned readmission rate of 5.7%. The largest difference between rates of overall and unplanned readmission occurred in the patients undergoing lower extremity vascular bypass, for whom the planned readmission rate was 0.6%. The overall length of stay was 1 day (interquartile range, 0-4 days), and median time to readmission was 8 days (interquartile range, 3-14 days). For the individual procedures, the rate of unplanned readmission ranged from 3.8% after hysterectomy to 14.9% after lower extremity vascular bypass (Table 1).

The variation in the number of days from discharge to unplanned readmission was estimated across procedures. As a reflection of the variability in when readmissions occurred, the interquartile range of unplanned readmissions was 13 days after bariatric surgery, 11 days after ventral hernia repair, 11 days after lower extremity bypass, 13 days after hip or knee arthroplasty, 10 days after hysterectomy, and 9 days after colectomy or proctectomy. There was no particular peak in when readmissions occurred: The readmissions occurred relatively linearly over the 30-day follow-up period overall and for each of the individual operations examined (eFigure in the Supplement).

Reasons for Unplanned Readmissions

The reasons for unplanned readmissions are shown in Table 2. The most common reason for readmission was SSI (19.5%), ranging from 11.4% after bariatric surgery to 36.4% after lower extremity vascular bypass. The most common reason for readmission after bariatric surgery was ileus or obstruction (24.5%), and ileus or obstruction was the second most common reason for readmission overall (10.3%) and for colectomy or proctectomy, ventral hernia repair, and hysterectomy. Other common causes included dehydration or nutritional deficiency, bleeding or anemia, venous thromboembolism, and prosthesis or graft issues (after arthroplasty and lower extremity vascular bypass procedures).

Table Graphic Jump LocationTable 2.  Ten Most Frequent Reasons for Unplanned Readmissions After Surgery (Overall and for 6 Selected Procedures)a

When examining early (within 7 days of discharge) and late (more than 7 days after discharge) unplanned readmissions separately, the top 3 reasons for readmission were similar overall (SSI, ileus or obstruction, and bleeding) and when examining each of the 6 procedure groups individually (eTable 4 in the Supplement).

When examining the percentage of patients readmitted for a complication that also occurred during their index hospitalization (ie, exacerbation of a known issue), the most common reason was bleeding (21.0% of patients readmitted for bleeding also experienced a bleeding-related event during index surgical admission), followed by pulmonary complications (6.2%) and sepsis (5.5%) (Table 3). Overall, however, only 2.3% of patients were readmitted for a preexisting complication (ie, reason for readmission also occurred during the index hospitalization). Among patients readmitted for SSIs (the most common reason for readmission overall), only 3.3% of these patients had experienced an SSI during their index hospitalization.

Table Graphic Jump LocationTable 3.  Percentage of Inpatients Readmitted for Preexisting Complications, by Specific Complicationa
Factors Associated With Unplanned Readmission

The association of patient factors, inpatient complications, discharge destination, and hospital characteristics with unplanned readmissions was examined (Table 4). Because we found that patients were rarely readmitted for the same type of complication that they had also experienced during the index surgical admission (2.3%)( Table 3) and most readmissions were attributable to new complications that occurred after discharge, patients readmitted for the same complication they had experienced as an inpatient were excluded from this analysis to focus on postdischarge complications and not simply exacerbations of known issues. Patient factors associated with readmission were higher ASA class, presence of ascites, disseminated cancer, bleeding disorder, renal failure, steroid use, and weight loss. Experiencing an inpatient complication (particularly bleeding, cardiac complication, sepsis, urinary tract infection, and venous thromboembolism) was associated with unplanned readmission (for a different reason). Patients discharged to a location other than home (eg, nursing facility) were also more likely to be readmitted (odds ratio [HR], 1.40 [95% CI, 1.35-1.46]). Although hospital control or ownership was not associated with unplanned readmission, teaching hospitals had a higher likelihood of unplanned readmission (HR, 1.14 [95% CI, 1.07-1.21]). Highest-volume centers also had a higher likelihood of unplanned readmission when compared with low-volume centers (HR, 1.15 [95% CI, 1.07-1.25]). Hospital disproportionate share was not significantly associated with unplanned postsurgical readmission.

Table Graphic Jump LocationTable 4.  Factors Associated With Unplanned Readmission for All Inpatient Surgical Cases

Next, the order of selection of variables into the models was assessed using forward selection as a general indicator of the strength of the association between individual variables and readmission. Factors selected earlier were the most significant when adjusting for any already selected covariates. In the overall group of all operations, the factors associated with readmissions selected earliest into the model (same model as in Table 4) included ASA class, index hospitalization complications, and surgical specialty (Table 5, eTable 5 in the Supplement). Across the individual procedures, either the specific procedure performed (based on CPT codes) or ASA class were selected earliest for the variables associated with unplanned readmission for 5 of the 6 procedures groups. For lower extremity vascular bypass procedures, discharge destination after the index hospitalization was associated with unplanned readmissions and was the first variable selected into the model. Complications during the index hospitalization were frequently selected early in the overall (second) and the individual procedures models examined (ranging from second for bariatric to tenth for lower extremity vascular bypass).

Table Graphic Jump LocationTable 5.  Variables Included in the Risk-Adjustment Models in Their Order of Selection Into the Modelsa

Last, the relative amount of variation in risk of readmission for categories of variables was examined. Patient factors alone accounted for most of the variation in patient-level outcomes (Royston R2 = 0.244) (Table 6). Including inpatient complications, discharge destination, hospital characteristics, and hospital disproportionate share in the model did not substantially increase the amount of variation explained (full-model Royston R2 = 0.270). Individual models relying on inpatient complications, discharge destination, hospital characteristics, or hospital disproportionate share explained less than 10% of variation in outcomes.

Table Graphic Jump LocationTable 6.  Contribution of Individual Categories in Explaining the Risk of Readmission

Using clinical data prospectively collected for readmission information from 346 hospitals, we found that readmissions were associated with new postoperative complications that surfaced after discharge in the majority of cases, and 2 complications, SSI (19.5%) and obstruction or ileus (10.3%), were the most frequent reasons for both early and late readmissions.

There are at least 2 main policy implications. First, because most readmissions were attributable to well-described postoperative complications, readmissions after surgery are mostly a proxy measure for postdischarge complications and in effect penalize hospitals twice for postoperative complications (ie, other pay-for-performance programs include postoperative complications such as SSI). Second, the majority of hospital readmissions were related to SSI and ileus. Identifying clinical interventions to reduce the occurrence of these complications to below current levels has been challenging. Thus, implementation of a policy penalizing hospitals for readmitting patients with these complications may be ineffective and even potentially counterproductive, because performance targets without accepted courses of intervention might be more prone to unintended or ineffective behaviors and consequences. Nonetheless, hospitals can use our findings when identifying targets for readmissions reduction efforts.

Prior studies examining surgical readmissions have not detailed the underlying reasons for the readmissions across a broad array of procedures using clinically abstracted data for which the data regarding the readmission reason was ascertained in a standardized fashion from the medical record, from discussions with involved clinicians, and by contacting the patient directly when needed (eTable 6 in the Supplement). The most common reasons for surgical readmissions were SSIs and obstruction or ileus complications, and the remaining reasons for readmission varied based on the individual procedure. Because surgical readmissions are predominantly related to postoperative complications, this information regarding the underlying reason for the readmission can be used by hospitals as they work to develop efforts to reduce readmissions.

Surgical site infections are the leading reason for surgical readmissions. However, it has been challenging to identify solutions resulting in reduced SSI rates. Most hospitals in the United States have high adherence rates for the Surgical Care Improvement Project (SCIP) SSI-prevention process measures; however, compliance with these process measures has not been shown to be strongly associated with reduced SSI rates.3638 Moreover, few other SSI best practices have been translated into valid process measures, and SSI reduction projects have shown modest, if any, improvements.39 Thus, hospitals with high SSI rates or high readmission rates attributable to SSI may find improvement challenging. The existing high rates of SCIP compliance, coupled with our finding that SSI is the leading cause for readmission, indicates that SSI research should be a major priority for the surgical community if postsurgical readmission rates are to be reduced. Implementation of policies requiring reductions in readmissions without understanding how to effect improvement may be counterproductive.

However, there may be several other opportunities to reduce readmissions based on the underlying reasons for readmissions identified in the study. First, many readmissions are attributable to expected complications (eg, dehydration from a stoma after colorectal surgery); thus, better coordination of care with the outpatient care team (eg, close monitoring of stoma output by clinic nurses) could reduce hospital readmissions. Second, minimizing fragmentation in postdischarge care may reduce readmissions: when a patient is initially evaluated at a hospital other than that at which the surgery occurred, ensuring that the physicians from the outside hospital are in communication with the clinicians who treated the patient at the index admission may be beneficial in reducing readmissions.40 Third, there are widespread concerns regarding the quality of education and discharge instructions provided to patients, as evidenced by several existing quality indicators that measure the quality of discharge instructions and a patient experience measure focused on this topic as well.4144 Effective patient education to set the postoperative expectations and warn about potential complications may help reduce readmission rates, but more work is needed to improve the effectiveness of the process. This education process should start preoperatively. Ensuring a postdischarge plan with clear discharge instructions and clear follow-up details may be an opportunity to reduce readmissions and improve patient experience.45

Last, although little supporting evidence exists, conceivably, some complications resulting in readmissions could be treated in the outpatient setting rather than necessitating a readmission. For example, SSIs could be treated in an advanced outpatient clinic where wounds could be opened and debrided and peripherally inserted central catheters could even be placed to facilitate intravenous antibiotic administration. This approach would be patient-centered, because avoiding the inconvenience and nosocomial risks of a readmission are important to patients and caregivers. An advanced clinic may also be cost-effective compared with a multiday inpatient readmission, but this should be formally tested. Focusing readmission efforts on reducing postoperative complications and optimizing management of those complications in the outpatient setting offers an opportunity to reduce hospital readmissions, even if the means of actually preventing or reducing the overall rate of some these complications (eg, SSIs) remains uncertain.

Dehydration and fluid or electrolyte abnormalities were an important readmission reason for several operations, particularly bariatric and colorectal surgery. For bariatric surgery, patients may be unable to tolerate oral intake until swelling at their anastomosis subsides, so the readmission occurs for intravenous hydration and electrolyte monitoring. For colorectal surgery, patients often experience dehydration attributable to high ostomy outputs or poor oral intake attributable to prolonged ileus. There are opportunities in which the dehydration could be monitored closely by an outpatient care team or these complications could be treated in the outpatient setting, similar to SSI, if an advanced outpatient clinic were available to deliver fluids and monitor laboratory values.

Prior evaluations of chronic medical conditions have suggested that most readmissions are unrelated to the reason for the index admission.3 However, surgical patients undergo a discrete invasive event (ie, the operation), which results in complications clearly related to the surgery (eg, SSI), exacerbation of existing comorbidities (eg, fluid overload causing a myocardial infarction in a patient with coronary artery disease), or new organ system complications (eg, renal failure). A prior analysis of Medicare claims data suggested that approximately 70% of readmissions after hospitalizations for surgical procedures were attributable to “medical conditions.”46 However, we found that readmissions were more frequently attributable to complications directly related to the surgery. Moreover, the “medical conditions” in the study by Jencks et al were more likely complications related to the surgery (eg, myocardial infarction due to postoperative fluid overload). Thus, these conditions should not be interpreted as being unrelated to the surgery, and they do reflect potential targets for hospitals working to reduce postsurgical readmissions. Although the study by Jencks et al did attempt to understand why readmissions occurred, Jencks et al relied on administrative diagnosis related group (DRG) codes, which provide incomplete outpatient data for readmissions, to determine why readmissions occurred. In contrast, we were better able to identify why patients were readmitted because this information was specifically collected prospectively by a trained clinical data abstractor at each hospital.

It is also important to note that our readmission rates are lower than those from the study by Jencks et al, and this is partly attributable to differences in the follow-up time frame for readmissions between the 2 studies (ie, 30 days from discharge for Jencks et al vs 30 days from surgery for ACS NSQIP). The difference in rates may also be attributable to differences in the types of hospitals included in our study compared with those included by Jencks et al.

Nearly all readmissions were related to complications that occurred once the patient was discharged, rather than to a failure to treat a complication identified at the index admission or a failure to appropriately coordinate postdischarge care, as evidenced by the variable timing after discharge at which readmissions occurred (ie, no distinct peak readmission day). The reason for readmission was only an exacerbation of a previously identified complication in 2.3% of patients. This differs from the underlying basis for readmissions for medical conditions, such as CHF; readmissions for CHF exacerbations are more common (35%), and the quality of postdischarge coordination of care may be a driving issue.3 Unlike readmissions occurring after admissions for medical conditions that are often related to coordination of care transitions and social issues,1,47 our results demonstrate that surgical readmissions are related to well-described complications of surgery.

The timing of unplanned readmissions is also an important consideration when focusing on how to reduce readmissions. For example, if readmissions generally occur within a certain number of days after discharge, then an intervention such as an outpatient clinic visit or house call could be strategically timed to avert potential readmissions, as has been shown effective for patients with CHF.3,48 Jencks et al postulated that readmissions after surgery could be reduced by providing earlier medical follow-up to mitigate comorbidities and coordinate postdischarge care. However, our findings contradict this suggestion. A peak day or certain time interval by which most readmissions occurred was not evident in our analysis. Rather, readmissions appeared relatively dispersed from discharge to 30 days after surgery. Although early follow-up after hospital discharge has been associated with reduced readmission rates among patients initially hospitalized with heart failure or chronic obstructive pulmonary disease,49 it is unlikely that early follow-up after surgical discharge on a particular day will reduce readmissions. For example, wound infections can be clinically silent on postoperative day 8 and evident on day 9, so early follow-up on day 7 would not necessarily be helpful in averting or mitigating the complications or the resulting readmission.

Although some may argue that keeping patients in the hospital longer at the index admission may alleviate the potential subsequent readmissions a few days later, our data argue against this hypothesis, because readmissions occurred relatively uniformly over the postoperative period, there was no particular peak postdischarge day on which readmissions occurred, and early and late readmissions had similar underlying reasons. Thus, keeping patients in the hospital a few more days would not eliminate most readmissions after surgery.

This study has certain limitations that should be considered. First, the reason for readmission can be difficult to ascertain; however, our study is the first national comprehensive evaluation, to our knowledge, that offers examination of a clinically abstracted reason for the readmission. The clinical data abstractors at the hospital identified the reasons for readmission. The reason may be challenging to determine and may be multifactorial, but the reason coded by the abstractors has been validated against physician panel chart reviews.13,14 Second, although we examined all operations together, we examined only 6 operations separately, and the reasons for readmissions for these operations may not be representative of all operations. Third, this study included only ACS NSQIP participating hospitals and therefore may not be generalizable to all hospitals in the United States. Although ACS NSQIP includes a number of smaller, community hospitals, ACS NSQIP hospitals are not representative of all hospitals in the United States because of the disproportionately higher number of larger, academic centers.

It is important to note that many readmissions may be unavoidable and are actually the correct course of action for surgical patients. Many complications should be treated in the inpatient setting, and surgeons should not be deterred from readmitting patients because of concerns about quality measure performance and resulting penalties.

Readmissions after surgery were mostly associated with postdischarge complications related to the procedure and not with exacerbation of prior index admission complications. Early and late readmissions occurred for similar reasons, and readmissions occurred throughout the postoperative period. Understanding the underlying reasons for readmission, the timing, and the associated factors should help hospitals to undertake targeted quality improvement initiatives to reduce readmissions. However, surgical readmissions mostly reflect postdischarge complications, and readmission rates may be difficult to reduce until effective strategies are put forth to reduce common complications such as SSI. Efforts should focus on reducing complication rates overall than simply those that occur after discharge, and this will subsequently reduce readmission rates as well. Readmissions after surgery may not be an appropriate measure for pay-for-performance programs but rather better suited as measure for hospitals to track internally.

Corresponding Author: Karl Y. Bilimoria, MD, MS, Surgical Outcomes and Quality Improvement Center (SOQIC), Department of Surgery and Center for Healthcare Studies, Feinberg School of Medicine, Northwestern University, 633 N St Clair St, 20th Floor, Chicago, IL 60611 (k-bilimoria@northwestern.edu).

Author Contributions: Drs Merkow and Ju had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Merkow and Ju contributed equally to this work.

Study concept and design: Merkow, Ju, Ko, Bilimoria.

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

Drafting of the manuscript: Merkow, Ju, Hall, Bilimoria.

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

Statistical analysis: Merkow, Ju, Chung, Cohen, Bilimoria.

Obtained funding: Ko, Bilimoria.

Administrative, technical, or material support: All authors.

Study supervision: Hall, Cohen, Ko, Bilimoria.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: Dr Merkow is supported by the American College of Surgeons Clinical Scholars in Residence Program and the Northwestern Institute for Comparative Effectiveness Research in Oncology (NICER-Onc). Dr Ju is supported by the American College of Surgeons Clinical Scholars in Residence Program and National Institutes of Health (NIH) grant 5T32HL094293. Dr Hall is a consultant to the American College of Surgeons. Dr Bilimoria reported support from the NIH, Agency for Healthcare Research and Quality, American Cancer Society, National Comprehensive Cancer Network, American College of Surgeons, American Board of Surgery, Accreditation Council for Graduate Medical Education, Health Care Services Corporation, and the Robert H. Lurie Cancer Center of Northwestern University.

Role of the Funders/Sponsors: The study funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article do not necessarily represent the views of the US government or the American College of Surgeons.

Additional Contributions: We would like to thank Allison Dahlke, MPH (Surgical Outcomes and Quality Improvement Center, Northwestern University), for her assistance with manuscript revisions and Emily Pavey, MS (Surgical Outcomes and Quality Improvement Center, Northwestern University), for statistical consultation. We also thank Cindy Barnard, MBA, and Terri Halverson, both of Quality Strategies, Northwestern Memorial Hospital, for their assistance. None of these persons were compensated for their contributions.

Berenson  RA, Paulus  RA, Kalman  NS.  Medicare’s readmissions-reduction program—a positive alternative. N Engl J Med. 2012;366(15):1364-1366.
PubMed   |  Link to Article
Joynt  KE, Orav  EJ, Jha  AK.  Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA. 2011;305(7):675-681.
PubMed   |  Link to Article
Dharmarajan  K, Hsieh  AF, Lin  Z,  et al.  Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355-363.
PubMed   |  Link to Article
QualityNet. Overview: Readmission Measures: Publicly reported risk-standardized, 30-day readmission measures for AMI, HF, PN, HWR, and THA/TKA. QualityNet website. http://www.qualitynet.org/dcs/ContentServer?pagename=QnetPublic%2FPage%2FQnetTier3&cid=1219069855273. Accessed June 3, 2013.
Fontanarosa  PB, McNutt  RA.  Revisiting hospital readmissions. JAMA. 2013;309(4):398-400.
PubMed   |  Link to Article
Curran  T, Zhang  JQ, Lo  RC,  et al.  Risk factors and indications for readmission after lower extremity amputation in the American College of Surgeons National Surgical Quality Improvement Program. J Vasc Surg. 2014;60(5):1315-1324.
PubMed   |  Link to Article
Zhang  JQ, Curran  T, McCallum  JC,  et al.  Risk factors for readmission after lower extremity bypass in the American College of Surgeons National Surgery Quality Improvement Program. J Vasc Surg. 2014;59(5):1331-1339.
PubMed   |  Link to Article
Tsai  TC, Joynt  KE, Orav  EJ, Gawande  AA, Jha  AK.  Variation in surgical-readmission rates and quality of hospital care. N Engl J Med. 2013;369(12):1134-1142.
PubMed   |  Link to Article
Tsai  TC, Orav  EJ, Joynt  KE.  Disparities in surgical 30-day readmission rates for Medicare beneficiaries by race and site of care. Ann Surg. 2014;259(6):1086-1090.
PubMed   |  Link to Article
Girotti  ME, Shih  T, Revels  S, Dimick  JB.  Racial disparities in readmissions and site of care for major surgery. J Am Coll Surg. 2014;218(3):423-430.
PubMed   |  Link to Article
Fox  JP, Suter  LG, Wang  K, Wang  Y, Krumholz  HM, Ross  JS.  Hospital-based, acute care use among patients within 30 days of discharge after coronary artery bypass surgery. Ann Thorac Surg. 2013;96(1):96-104.
PubMed   |  Link to Article
Sacks  GD, Dawes  AJ, Russell  MM,  et al.  Evaluation of hospital readmissions in surgical patients: do administrative data tell the real story? JAMA Surg. 2014;149(8):759-764.
PubMed   |  Link to Article
Sellers  MM, Merkow  RP, Halverson  A,  et al.  Validation of new readmission data in the American College of Surgeons National Surgical Quality Improvement Program. J Am Coll Surg. 2013;216(3):420-427.
PubMed   |  Link to Article
Wick  EC, Shore  AD, Hirose  K,  et al.  Readmission rates and cost following colorectal surgery. Dis Colon Rectum. 2011;54(12):1475-1479.
PubMed   |  Link to Article
Khuri  SF, Daley  J, Henderson  W,  et al; National VA Surgical Quality Improvement Program.  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. Ann Surg. 1998;228(4):491-507.
PubMed   |  Link to Article
Khuri  SF, Henderson  WG, Daley  J,  et al; Principal Investigators of the Patient Safety in Surgery Study.  Successful implementation of the Department of Veterans Affairs’ National Surgical Quality Improvement Program in the private sector: the Patient Safety in Surgery study. Ann Surg. 2008;248(2):329-336.
PubMed   |  Link to Article
Khuri  SF, Henderson  WG, Daley  J,  et al; Principal Site Investigators of the Patient Safety in Surgery Study.  The Patient Safety in Surgery study: background, study design, and patient populations. J Am Coll Surg. 2007;204(6):1089-1102.
PubMed   |  Link to Article
American College of Surgeons. National Surgical Quality Improvement Program: Semiannual Report, July 2012. Chicago, IL: American College of Surgeons; 2012.
American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). ACS NSQIP: How It Works. ACS NSQIP website. http://site.acsnsqip.org/wp-content/uploads/2012/02/TechnicalPaper1.pdf. Accessed December 3, 2014.
Cohen  ME, Ko  CY, Bilimoria  KY,  et al.  Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus. J Am Coll Surg. 2013;217(2):336-46.
PubMed   |  Link to Article
Ingraham  AM, Richards  KE, Hall  BL, Ko  CY.  Quality improvement in surgery: the American College of Surgeons National Surgical Quality Improvement Program approach. Adv Surg. 2010;44:251-267.
PubMed   |  Link to Article
American Medical Association. CPT 2013: Current Procedural Terminology. Chicago, IL: American Medical Association; 2013.
American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Chapter 4: Variable definitions. In: ACS NSQIP Operations Manual. Chicago, IL: American College of Surgeons; 2013.
Duffy  SQ, Elixhauser  A, Sommers  JP. Diagnosis and Procedure Combinations in Hospital Inpatient Data: Healthcare Cost and Utilization Project (HCUP 3) Research Note 5. Rockville, MD: Agency for Healthcare Policy and Research; 1996. AHCPR Publication 96-0047.
Cowen  ME, Dusseau  DJ, Toth  BG, Guisinger  C, Zodet  MW, Shyr  Y.  Casemix adjustment of managed care claims data using the clinical classification for health policy research method. Med Care. 1998;36(7):1108-1113.
PubMed   |  Link to Article
Bilimoria  KY, Bentrem  DJ, Stewart  AK, Winchester  DP, Ko  CY.  Comparison of commission on cancer-approved and -nonapproved hospitals in the United States: implications for studies that use the National Cancer Data Base. J Clin Oncol. 2009;27(25):4177-4181.
PubMed   |  Link to Article
Bilimoria  KY, Chung  J, Ju  MH,  et al.  Evaluation of surveillance bias and the validity of the venous thromboembolism quality measure. JAMA. 2013;310(14):1482-1489.
PubMed   |  Link to Article
Werner  RM, Bradlow  ET.  Relationship between Medicare’s hospital compare performance measures and mortality rates. JAMA. 2006;296(22):2694-2702.
PubMed   |  Link to Article
Lehrman  WG, Elliott  MN, Goldstein  E, Beckett  MK, Klein  DJ, Giordano  LA.  Characteristics of hospitals demonstrating superior performance in patient experience and clinical process measures of care. Med Care Res Rev. 2010;67(1):38-55.
PubMed   |  Link to Article
Schmaltz  SP, Williams  SC, Chassin  MR, Loeb  JM, Wachter  RM.  Hospital performance trends on national quality measures and the association with Joint Commission accreditation. J Hosp Med. 2011;6(8):454-461.
PubMed   |  Link to Article
Brand  CA, Barker  AL, Morello  RT,  et al.  A review of hospital characteristics associated with improved performance. Int J Qual Health Care. 2012;24(5):483-494.
PubMed   |  Link to Article
Friese  CR, Earle  CC, Silber  JH, Aiken  LH.  Hospital characteristics, clinical severity, and outcomes for surgical oncology patients. Surgery. 2010;147(5):602-609.
PubMed   |  Link to Article
Centers for Medicare & Medicaid Services (CMS). Disproportionate Share Hospital (DSH): The Medicare DSH Adjustment (42 CFR 412.106). CMS website. http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/dsh.html. 2012. Accessed April 2, 2014.
Chatterjee  P, Joynt  KE, Orav  EJ, Jha  AK.  Patient experience in safety-net hospitals: implications for improving care and value-based purchasing. Arch Intern Med. 2012;172(16):1204-1210.
PubMed   |  Link to Article
Royston  P.  Explained variation for survival models. Stata J. 2006;6(1):83-96.
Ingraham  AM, Cohen  ME, Bilimoria  KY,  et al.  Association of Surgical Care Improvement Project infection-related process measure compliance with risk-adjusted outcomes: implications for quality measurement. J Am Coll Surg. 2010;211(6):705-714.
PubMed   |  Link to Article
Stulberg  JJ, Delaney  CP, Neuhauser  DV, Aron  DC, Fu  P, Koroukian  SM.  Adherence to Surgical Care Improvement Project measures and the association with postoperative infections. JAMA. 2010;303(24):2479-2485.
PubMed   |  Link to Article
Hawn  MT, Vick  CC, Richman  J,  et al.  Surgical site infection prevention: time to move beyond the surgical care improvement program. Ann Surg. 2011;254(3):494-499.
PubMed   |  Link to Article
Tillman  M, Wehbe-Janek  H, Hodges  B, Smythe  WR, Papaconstantinou  HT.  Surgical care improvement project and surgical site infections: can integration in the surgical safety checklist improve quality performance and clinical outcomes? J Surg Res. 2013;184(1):150-156.
PubMed   |  Link to Article
Tsai  TC, Orav  EJ, Jha  AK.  Care fragmentation in the postdischarge period: surgical readmissions, distance of travel, and postoperative mortality [published online December 3, 2014]. JAMA Surg. doi:10.1001/jamasurg.2014.2071.
National Quality Foundation (NQF). Venous Thromboemolism Warfarin Discharge Instructions (VTE-5 NwnoLaD). NQF website. http://www.qualityforum.org/Home.aspx. 2014. Accessed January 13, 2015.
National Quality Foundation (NQF). Heart Failure: Detailed Discharge Instructions (HF-1 NwnoLaD). NQF website. http://www.qualityforum.org/Home.aspx. 2014.
Alper  J, Hernandez  L. Facilitating Patient Understanding of Discharge Instructions: Workshop Summary. Washington, DC: Institute of Medicine; 2014.
Hospital Compare. Survey of Patients’ Experiences. Medicare.gov website. http://www.medicare.gov/hospitalcompare/Data/Overview.html?AspxAutoDetectCookieSupport=1. Accessed December 14, 2014.
Detsky  AS, Krumholz  HM.  Reducing the trauma of hospitalization. JAMA. 2014;311(21):2169-2170.
PubMed   |  Link to Article
Jencks  SF, Williams  MV, Coleman  EA.  Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428.
PubMed   |  Link to Article
McCarthy  D, Johnson  MB, Audet  AM.  Recasting readmissions by placing the hospital role in community context. JAMA. 2013;309(4):351-352.
PubMed   |  Link to Article
Hernandez  AF, Greiner  MA, Fonarow  GC,  et al.  Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716-1722.
PubMed   |  Link to Article
Sharma  G, Kuo  YF, Freeman  JL, Zhang  DD, Goodwin  JS.  Outpatient follow-up visit and 30-day emergency department visit and readmission in patients hospitalized for chronic obstructive pulmonary disease. Arch Intern Med. 2010;170(18):1664-1670.
PubMed   |  Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1.  Thirty-Day Readmission Rates, Length of Stay, and Days From Discharge to Readmission Following Surgery
Table Graphic Jump LocationTable 2.  Ten Most Frequent Reasons for Unplanned Readmissions After Surgery (Overall and for 6 Selected Procedures)a
Table Graphic Jump LocationTable 3.  Percentage of Inpatients Readmitted for Preexisting Complications, by Specific Complicationa
Table Graphic Jump LocationTable 4.  Factors Associated With Unplanned Readmission for All Inpatient Surgical Cases
Table Graphic Jump LocationTable 5.  Variables Included in the Risk-Adjustment Models in Their Order of Selection Into the Modelsa
Table Graphic Jump LocationTable 6.  Contribution of Individual Categories in Explaining the Risk of Readmission

References

Berenson  RA, Paulus  RA, Kalman  NS.  Medicare’s readmissions-reduction program—a positive alternative. N Engl J Med. 2012;366(15):1364-1366.
PubMed   |  Link to Article
Joynt  KE, Orav  EJ, Jha  AK.  Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA. 2011;305(7):675-681.
PubMed   |  Link to Article
Dharmarajan  K, Hsieh  AF, Lin  Z,  et al.  Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA. 2013;309(4):355-363.
PubMed   |  Link to Article
QualityNet. Overview: Readmission Measures: Publicly reported risk-standardized, 30-day readmission measures for AMI, HF, PN, HWR, and THA/TKA. QualityNet website. http://www.qualitynet.org/dcs/ContentServer?pagename=QnetPublic%2FPage%2FQnetTier3&cid=1219069855273. Accessed June 3, 2013.
Fontanarosa  PB, McNutt  RA.  Revisiting hospital readmissions. JAMA. 2013;309(4):398-400.
PubMed   |  Link to Article
Curran  T, Zhang  JQ, Lo  RC,  et al.  Risk factors and indications for readmission after lower extremity amputation in the American College of Surgeons National Surgical Quality Improvement Program. J Vasc Surg. 2014;60(5):1315-1324.
PubMed   |  Link to Article
Zhang  JQ, Curran  T, McCallum  JC,  et al.  Risk factors for readmission after lower extremity bypass in the American College of Surgeons National Surgery Quality Improvement Program. J Vasc Surg. 2014;59(5):1331-1339.
PubMed   |  Link to Article
Tsai  TC, Joynt  KE, Orav  EJ, Gawande  AA, Jha  AK.  Variation in surgical-readmission rates and quality of hospital care. N Engl J Med. 2013;369(12):1134-1142.
PubMed   |  Link to Article
Tsai  TC, Orav  EJ, Joynt  KE.  Disparities in surgical 30-day readmission rates for Medicare beneficiaries by race and site of care. Ann Surg. 2014;259(6):1086-1090.
PubMed   |  Link to Article
Girotti  ME, Shih  T, Revels  S, Dimick  JB.  Racial disparities in readmissions and site of care for major surgery. J Am Coll Surg. 2014;218(3):423-430.
PubMed   |  Link to Article
Fox  JP, Suter  LG, Wang  K, Wang  Y, Krumholz  HM, Ross  JS.  Hospital-based, acute care use among patients within 30 days of discharge after coronary artery bypass surgery. Ann Thorac Surg. 2013;96(1):96-104.
PubMed   |  Link to Article
Sacks  GD, Dawes  AJ, Russell  MM,  et al.  Evaluation of hospital readmissions in surgical patients: do administrative data tell the real story? JAMA Surg. 2014;149(8):759-764.
PubMed   |  Link to Article
Sellers  MM, Merkow  RP, Halverson  A,  et al.  Validation of new readmission data in the American College of Surgeons National Surgical Quality Improvement Program. J Am Coll Surg. 2013;216(3):420-427.
PubMed   |  Link to Article
Wick  EC, Shore  AD, Hirose  K,  et al.  Readmission rates and cost following colorectal surgery. Dis Colon Rectum. 2011;54(12):1475-1479.
PubMed   |  Link to Article
Khuri  SF, Daley  J, Henderson  W,  et al; National VA Surgical Quality Improvement Program.  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. Ann Surg. 1998;228(4):491-507.
PubMed   |  Link to Article
Khuri  SF, Henderson  WG, Daley  J,  et al; Principal Investigators of the Patient Safety in Surgery Study.  Successful implementation of the Department of Veterans Affairs’ National Surgical Quality Improvement Program in the private sector: the Patient Safety in Surgery study. Ann Surg. 2008;248(2):329-336.
PubMed   |  Link to Article
Khuri  SF, Henderson  WG, Daley  J,  et al; Principal Site Investigators of the Patient Safety in Surgery Study.  The Patient Safety in Surgery study: background, study design, and patient populations. J Am Coll Surg. 2007;204(6):1089-1102.
PubMed   |  Link to Article
American College of Surgeons. National Surgical Quality Improvement Program: Semiannual Report, July 2012. Chicago, IL: American College of Surgeons; 2012.
American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). ACS NSQIP: How It Works. ACS NSQIP website. http://site.acsnsqip.org/wp-content/uploads/2012/02/TechnicalPaper1.pdf. Accessed December 3, 2014.
Cohen  ME, Ko  CY, Bilimoria  KY,  et al.  Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus. J Am Coll Surg. 2013;217(2):336-46.
PubMed   |  Link to Article
Ingraham  AM, Richards  KE, Hall  BL, Ko  CY.  Quality improvement in surgery: the American College of Surgeons National Surgical Quality Improvement Program approach. Adv Surg. 2010;44:251-267.
PubMed   |  Link to Article
American Medical Association. CPT 2013: Current Procedural Terminology. Chicago, IL: American Medical Association; 2013.
American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Chapter 4: Variable definitions. In: ACS NSQIP Operations Manual. Chicago, IL: American College of Surgeons; 2013.
Duffy  SQ, Elixhauser  A, Sommers  JP. Diagnosis and Procedure Combinations in Hospital Inpatient Data: Healthcare Cost and Utilization Project (HCUP 3) Research Note 5. Rockville, MD: Agency for Healthcare Policy and Research; 1996. AHCPR Publication 96-0047.
Cowen  ME, Dusseau  DJ, Toth  BG, Guisinger  C, Zodet  MW, Shyr  Y.  Casemix adjustment of managed care claims data using the clinical classification for health policy research method. Med Care. 1998;36(7):1108-1113.
PubMed   |  Link to Article
Bilimoria  KY, Bentrem  DJ, Stewart  AK, Winchester  DP, Ko  CY.  Comparison of commission on cancer-approved and -nonapproved hospitals in the United States: implications for studies that use the National Cancer Data Base. J Clin Oncol. 2009;27(25):4177-4181.
PubMed   |  Link to Article
Bilimoria  KY, Chung  J, Ju  MH,  et al.  Evaluation of surveillance bias and the validity of the venous thromboembolism quality measure. JAMA. 2013;310(14):1482-1489.
PubMed   |  Link to Article
Werner  RM, Bradlow  ET.  Relationship between Medicare’s hospital compare performance measures and mortality rates. JAMA. 2006;296(22):2694-2702.
PubMed   |  Link to Article
Lehrman  WG, Elliott  MN, Goldstein  E, Beckett  MK, Klein  DJ, Giordano  LA.  Characteristics of hospitals demonstrating superior performance in patient experience and clinical process measures of care. Med Care Res Rev. 2010;67(1):38-55.
PubMed   |  Link to Article
Schmaltz  SP, Williams  SC, Chassin  MR, Loeb  JM, Wachter  RM.  Hospital performance trends on national quality measures and the association with Joint Commission accreditation. J Hosp Med. 2011;6(8):454-461.
PubMed   |  Link to Article
Brand  CA, Barker  AL, Morello  RT,  et al.  A review of hospital characteristics associated with improved performance. Int J Qual Health Care. 2012;24(5):483-494.
PubMed   |  Link to Article
Friese  CR, Earle  CC, Silber  JH, Aiken  LH.  Hospital characteristics, clinical severity, and outcomes for surgical oncology patients. Surgery. 2010;147(5):602-609.
PubMed   |  Link to Article
Centers for Medicare & Medicaid Services (CMS). Disproportionate Share Hospital (DSH): The Medicare DSH Adjustment (42 CFR 412.106). CMS website. http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/dsh.html. 2012. Accessed April 2, 2014.
Chatterjee  P, Joynt  KE, Orav  EJ, Jha  AK.  Patient experience in safety-net hospitals: implications for improving care and value-based purchasing. Arch Intern Med. 2012;172(16):1204-1210.
PubMed   |  Link to Article
Royston  P.  Explained variation for survival models. Stata J. 2006;6(1):83-96.
Ingraham  AM, Cohen  ME, Bilimoria  KY,  et al.  Association of Surgical Care Improvement Project infection-related process measure compliance with risk-adjusted outcomes: implications for quality measurement. J Am Coll Surg. 2010;211(6):705-714.
PubMed   |  Link to Article
Stulberg  JJ, Delaney  CP, Neuhauser  DV, Aron  DC, Fu  P, Koroukian  SM.  Adherence to Surgical Care Improvement Project measures and the association with postoperative infections. JAMA. 2010;303(24):2479-2485.
PubMed   |  Link to Article
Hawn  MT, Vick  CC, Richman  J,  et al.  Surgical site infection prevention: time to move beyond the surgical care improvement program. Ann Surg. 2011;254(3):494-499.
PubMed   |  Link to Article
Tillman  M, Wehbe-Janek  H, Hodges  B, Smythe  WR, Papaconstantinou  HT.  Surgical care improvement project and surgical site infections: can integration in the surgical safety checklist improve quality performance and clinical outcomes? J Surg Res. 2013;184(1):150-156.
PubMed   |  Link to Article
Tsai  TC, Orav  EJ, Jha  AK.  Care fragmentation in the postdischarge period: surgical readmissions, distance of travel, and postoperative mortality [published online December 3, 2014]. JAMA Surg. doi:10.1001/jamasurg.2014.2071.
National Quality Foundation (NQF). Venous Thromboemolism Warfarin Discharge Instructions (VTE-5 NwnoLaD). NQF website. http://www.qualityforum.org/Home.aspx. 2014. Accessed January 13, 2015.
National Quality Foundation (NQF). Heart Failure: Detailed Discharge Instructions (HF-1 NwnoLaD). NQF website. http://www.qualityforum.org/Home.aspx. 2014.
Alper  J, Hernandez  L. Facilitating Patient Understanding of Discharge Instructions: Workshop Summary. Washington, DC: Institute of Medicine; 2014.
Hospital Compare. Survey of Patients’ Experiences. Medicare.gov website. http://www.medicare.gov/hospitalcompare/Data/Overview.html?AspxAutoDetectCookieSupport=1. Accessed December 14, 2014.
Detsky  AS, Krumholz  HM.  Reducing the trauma of hospitalization. JAMA. 2014;311(21):2169-2170.
PubMed   |  Link to Article
Jencks  SF, Williams  MV, Coleman  EA.  Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428.
PubMed   |  Link to Article
McCarthy  D, Johnson  MB, Audet  AM.  Recasting readmissions by placing the hospital role in community context. JAMA. 2013;309(4):351-352.
PubMed   |  Link to Article
Hernandez  AF, Greiner  MA, Fonarow  GC,  et al.  Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716-1722.
PubMed   |  Link to Article
Sharma  G, Kuo  YF, Freeman  JL, Zhang  DD, Goodwin  JS.  Outpatient follow-up visit and 30-day emergency department visit and readmission in patients hospitalized for chronic obstructive pulmonary disease. Arch Intern Med. 2010;170(18):1664-1670.
PubMed   |  Link to Article
CME


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Multimedia

Supplement.

eTable 1. Current Procedural Terminology (CPT) Codes

eTable 2. Categories of Reasons for 30-Day Readmission With Representative Examples

eTable 3A. Patient Characteristics by Procedure Groups

eTable 3B. Hospital Characteristics by Procedure Groups

eTable 4. Top Three Reasons for Early (Within 7 Days of Discharge) Vs Late (Beyond 7 Days From Discharge) Unplanned Readmissions by Procedure Groups

eTable 5. Variables in the Order of Selection Into the Models Predicting Readmission

eTable 6. Selected Recent Studies Evaluating Surgical Readmissions

eFigure. Time (Days) To Readmission Based on Kaplan-Meier Method

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