Background: The relationship between surgeon and hospital procedure volumes and clinical outcomes in total joint arthroplasty has long fueled a debate over regionalization of care. At the same time, numerous policy initiatives are focusing on improving quality by incentivizing surgeons to adhere to evidence-based processes of care. The purpose of this study was to evaluate the independent contributions of surgeon procedure volume, hospital procedure volume, and standardization of care on short-term postoperative outcomes and resource utilization in lower-extremity total joint arthroplasty.
Methods: An analysis of 182,146 consecutive patients who underwent primary total joint arthroplasty was performed with use of data entered into the Perspective database by 3421 physicians from 312 hospitals over a two-year period. Adherence to evidence-based processes of care was defined by administration of appropriate perioperative antibiotic prophylaxis, beta-blockade, and venous thromboembolism prophylaxis. Patient outcomes included mortality, length of hospital stay, discharge disposition, surgical complications, readmissions, and reoperations within the first thirty days after discharge. Hierarchical models were used to estimate the effects of hospital and surgeon procedure volume and process standardization on individual and combined surgical outcomes and length of stay.
Results: After adjustment in multivariate models, higher surgeon volume was associated with lower risk of complications, lower rates of readmission and reoperation, shorter length of hospital stay, and higher likelihood of being discharged home. Higher hospital volume was associated with lower risk of mortality, lower risk of readmission, and higher likelihood of being discharged home. The impact of process standardization was substantial; maximizing adherence to evidence-based processes of care resulted in improved clinical outcomes and shorter length of hospital stay, independent of hospital or surgeon procedure volume.
Conclusions: Although surgeon and hospital procedure volumes are unquestionably correlated with patient outcomes in total joint arthroplasty, process standardization is also strongly associated with improved quality and efficiency of care. The exact relationship between individual processes of care and patient outcomes has not been established; however, our findings suggest that process standardization could help providers optimize quality and efficiency in total joint arthroplasty, independent of hospital or surgeon volume.
Level of Evidence: Therapeutic Level III. See Instructions to Authors for a complete description of levels of evidence.
1Department of Orthopaedic Surgery, Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, 500 Parnassus Avenue, MUW320, San Francisco, CA 94143-0728. E-mail address: firstname.lastname@example.org
2Division of Hospital Medicine, Department of Medicine, University of California, San Francisco, 3333 California Street, Suite 430, Box 1211, San Francisco, CA 94118
3Center for Quality of Care Research, Baystate Medical Center, 759 Chestnut Street, Springfield, MA 01199
4Department of Orthopaedic Surgery, University of California, San Francisco, 500 Parnassus Avenue, MUW320, San Francisco, CA 94143-0728.
5Division of Hospital Medicine, University of California, San Francisco, 533 Parnassus Avenue, San Francisco, CA 94143-0131
Total hip arthroplasty and total knee arthroplasty have proven to be highly clinically effective interventions, with high rates of success reported at midterm and long-term follow-up in terms of reduced pain and improved quality of life and function in patients with disabling arthritis of the hip and knee1-7. However, the clinical efficacy and cost-effectiveness of total joint arthroplasty have been reported to vary widely on the basis of many factors, including patient age and activity level, surgeon and hospital procedure volumes, and choice of implant8-11. Previous investigators have reported improved patient outcomes and lower complication rates among surgeons and hospitals that perform higher procedure volumes12-16, which has led to a long-standing debate over the relative merits and drawbacks of regionalization of care in total joint arthroplasty17,18.
Recently, many national policy initiatives have focused on improving quality by incentivizing adherence to evidence-based process-of-care measures. However, controversy exists over whether the process measures used to define quality care are clinically relevant and adequate to discern differences in quality among providers19-22. Furthermore, many clinicians have questioned whether adherence to process-of-care measures equates to improved patient outcomes in total joint arthroplasty.
The purpose of this study was to evaluate the independent contributions of surgeon procedure volume, hospital procedure volume, and standardization of care on short-term postoperative outcomes and resource utilization in lower-extremity total joint arthroplasty.
Materials and Methods
Sites and Subjects
We collected and analyzed data on 182,146 patients who underwent primary hip or knee arthroplasty by 3421 physicians at 312 hospitals participating in Perspective (Premier, Charlotte, North Carolina) between October 1, 2003, and September 30, 2005. The Perspective database was developed to measure quality and health-care utilization and has been used by other investigators in previous health-care services research23-25. In addition to the standard hospital discharge file, Perspective contains a date-stamped log of all materials (e.g., serial compression devices used to prevent venous thromboembolism) and medications (e.g., beta-blockers, antibiotics, and low molecular weight heparin) that were charged for during hospitalization. Perspective sites are distributed throughout all regions of the United States and are representative of the U.S. hospital population26-28. Our institutional review board approved this study.
Patients were eligible for inclusion in the study if they were admitted between October 1, 2003, and September 30, 2005; were eighteen years of age or older; and underwent primary hip or knee arthroplasty as identified with use of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure codes 81.51 (primary total hip arthroplasty) or 81.54 (primary total knee arthroplasty).
In addition to patient age, sex, race or ethnicity, insurance information, and principal diagnosis, we classified medical comorbidities with use of the method of Elixhauser et al.29. Data regarding in-hospital deaths, discharge status (home or other), and readmission to the index hospital within thirty days after discharge were obtained from the Perspective discharge file. In addition, the database contained information about hospital size, teaching status, and geographic location.
Definition of Volume Measures
Because some hospitals in our cohort did not contribute data for the entire study period, we estimated the annual case volume by dividing each hospital’s or physician’s observed procedure volume by the total number of months that the hospital or physician contributed procedures to the dataset. These “annualized” volumes were then divided into quartiles, as has been done in previous studies30-33.
Definition of Missed Process-of-Care Measures (see Appendix)
Using hospital charge data, we translated the recommendations from the Surgical Care Improvement Project that were related to lower-extremity total joint arthroplasty into a series of dichotomous process-of-care measures. These measures were used to ascertain (1) whether appropriate antibiotics were used to prevent surgical site infection on the day of surgery, (2) whether the antibiotic was discontinued within twenty-four hours following surgery, (3) whether perioperative beta-blockers were given, within the first twenty-four hours following surgery, to patients who had documented risk factors for perioperative myocardial infarction27, and (4) whether appropriate chemoprophylaxis was given to prevent venous thromboembolism within the first twenty-four hours following surgery. It should be noted that, since we could not determine the specific time of antibiotic administration (only the date), we only evaluated the choice of antimicrobial medication and whether discontinuation of antibiotic prophylaxis occurred within twenty-four hours after surgery, and not the timing of preoperative antibiotic prophylaxis.
Because of the time frame of our study, inpatient diagnosis codes could not reliably be used to distinguish between complications and preexisting conditions. We therefore measured the proportion of ideal candidates for each care process who failed to receive that care, and we defined this as a “missed” process-of-care measure. For example, we considered the opportunity for beta-blocker use “missed” if a patient who had a diagnosis of coronary artery disease or angina did not receive the drug and did not have an ICD-9-CM-coded principal or secondary diagnosis of hypotension, heart block, or congestive heart failure (any of which would make the use of beta-blockers inappropriate) recorded in his or her hospital record. In order to provide a more sensitive measure of system-level ability to provide reliable care30-33, we also counted the total number of process-of-care measures that were missed during a given hospitalization.
The primary outcome measures, including length of hospital stay, discharge disposition (home discharge compared with discharge to another facility), readmission or reoperation within thirty days after discharge, other surgical complications, and mortality were obtained from Perspective discharge abstract data, as described. Length of stay (LOS) was log-transformed to account for the skewed nature of the distributions and to stabilize the variance of residuals in multivariate models. Beta estimates and 95% confidence intervals for log-transformed outcomes were converted to percent differences with use of the formula 100*(EXP [estimate]–1).
We first described study patients and hospitals by using basic descriptive statistics. Multivariate alternating logistic models34 (PROC GENMOD; SAS Institute, Cary, North Carolina) were used to account for clustering of patients within physicians and physicians within hospitals for dichotomous outcomes and to calculate adjusted odds ratios and adjusted estimates. Mixed effect models (PROC MIXED; SAS Institute) were used to account for clustering of patients within physicians and within hospitals for continuous variables. Covariates were selected for inclusion in models if they were associated with the outcome at a significance level of p < 0.05, because of observed confounding, or to maintain face validity of the model. Collinearity was assessed by testing for correlations between volume and process-of-care measures, and we found no evidence for correlation (p > 0.05).
Hierarchical multivariate models were constructed to test the independent effects of patient factors, patient factors plus volume, and patient factors plus volume plus adherence to processes of care. Models first assessed the separate effects of volume categories and individual dichotomous process-of-care measures on inpatient mortality and readmission, while adjusting for patient and hospital characteristics; these results are reported as odds ratios and 95% confidence intervals. We then estimated the relative odds of these outcomes as a function of volume measures and the total number of process-of-care measures missed.
Source of Funding
This study was funded by the Orthopaedic Research and Education Foundation and the California HealthCare Foundation. The funding sources did not play a role in this investigation.
Patient Characteristics (Table I)
The average age (and standard deviation) of the total joint arthroplasty patients in our study was 66.4 ± 11.4 years, and 62% were female; 67% of the patients underwent primary total knee arthroplasty, and 33% underwent primary total hip arthroplasty. The majority of patients were white (74%) and were insured by Medicare (59%). Of all patients, 35% had an All-Patient Refined Diagnosis-Related Groups Severity of Illness (APR-DRG SOI) score of 1, and 45% had an APR-DRG SOI score of 235. The APR-DRG SOI is a weighted index that is intended to reflect a patient’s baseline medical health and ranges from 1 (lowest severity of illness) to 4 (highest severity of illness).
Hospital Characteristics (Table II)
The vast majority (89%) of hospitals included in the analysis were urban hospitals. Hospital bed size ranged from <100 (3%) to ≥500 (34%). Twenty-six percent were teaching hospitals, and they were distributed throughout the four geographic census regions of the United States.
Process Standardization and Patient Outcomes (Table III)
None of the four process-of-care measures were missed in 58% of the patients, whereas one measure was missed in 33%, two measures were missed in 8%, and three or more measures were missed in only 0.6% of the patients. The average length of hospital stay was 3.9 days, and 53% of patients were discharged directly home from the hospital. Four percent of patients were readmitted within thirty days of discharge, 3.1% of patients underwent a reoperation during the index hospitalization or at the time of readmission, and 3.0% experienced a surgical complication during the index hospitalization or at the time of readmission. The mortality rate was 0.1% at thirty days, and 7% of patients experienced one or more of these unfavorable outcomes (death, readmission, reoperation, or surgical complication).
Hospital and Surgeon Volume and Process Standardization (Table IV)
The median hospital annual procedure volumes ranged from 181 for the hospitals in the lowest quartile to 1007 for the hospitals in the highest quartile. The median surgeon annual procedure volumes ranged from twenty-four for the surgeons in the lowest quartile to 278 for the surgeons in the highest quartile. Although the absolute differences were small, there was a weak negative correlation between surgeon and hospital procedure volume and the number of missed process-of-care measures (e.g., lower-volume hospitals and surgeons had a greater number of missed process measures than higher-volume hospitals and surgeons had). The correlation coefficients between hospital and surgeon volume and the number of process measures missed were –0.08 and –0.05, respectively.
Effects of Volume and Process Standardization on Patient Outcomes (Table V and Figures 1-A and 1-B)
After adjustment for patient and hospital characteristics and for clustering at the hospital and surgeon level, higher surgeon procedure volume was associated with a lower risk of complications, a lower rate of readmission or reoperation, a shorter length of hospital stay, and a higher likelihood of being discharged directly to home. Higher hospital procedure volume was associated with a lower risk of mortality, a lower risk of readmission, and a higher likelihood of being discharged directly to home. Certain missed individual process-of-care measures were associated with a higher risk of an adverse clinical outcome or an increased length of stay. In particular, missed adherence to the antibiotic-related process measures (appropriate choice of antimicrobial and discontinuation of antibiotics within twenty-four hours after surgery) was associated with a higher likelihood of having an adverse clinical outcome and an increased length of hospital stay. However, failure to administer beta-blockers in patients with documented cardiac risk factors was not associated with adverse patient outcomes or increased length of hospital stay.
We then calculated adjusted rates of our combined negative outcome (defined as death, readmission, reoperation, or surgical complication) according to the total number of missed process-of-care measures and hospital or surgeon volume (Table V and Figs. 1-A and 1-B). Although higher-volume hospitals and surgeons had consistently lower adjusted rates of adverse outcomes, a higher number of missed process-of-care measures was associated with worse combined clinical outcomes irrespective of hospital or surgeon procedure volume. For instance, the absolute differences in adverse outcome rates from lowest to highest surgeon volume ranged between 1% and 1.5%. In contrast, missing just one process-of-care measure produced 1% to 1.2% excess in combined negative outcomes, and two or more missed measures produced 1.8% to 2.0% excess in adverse outcomes.
As a test for whether any of our key variables (volume or process standardization) modified the effects of another, we created a series of interaction terms and entered them into multivariate models. For our combined surgical outcome, there were significant interactions between hospital and surgeon volume (p < 0.0001), but not between hospital or surgeon procedure volume and process standardization (p > 0.05 for both comparisons). For length of stay, all three interactions (hospital × surgeon volume, hospital or surgeon volume × process standardization) were significant (p < 0.001).
In this observational cohort of patients undergoing elective primary hip or knee arthroplasty surgery, we observed an association between greater hospital and surgeon procedure volume and shorter length of hospital stay, a higher likelihood of being discharged directly home, fewer surgical complications, fewer readmissions and reoperations within thirty days after discharge, and lower mortality. We also observed a strong correlation between overall process-of-care standardization and patient outcomes, despite an inconsistent correlation between adherence to individual processes of care and patient outcomes.
A large body of literature describes the relationship between higher surgeon and hospital procedure volume and better outcomes after hip and knee arthroplasty12-16,36. This observation has led to the endorsement of case volume as a way for purchasers to identify preferred sites and improve patient outcomes33—an approach that is aptly termed “follow the crowd.”37 However, regionalization of services poses practical problems for both patients and providers38, and the evidence for the ability of volume benchmarks to accurately identify “best” sites has limitations39-43. The measurement and feedback of hospital (or surgeon) performance on specific processes of care is an alternative approach, with the Surgical Care Improvement Program (SCIP)44 providing a notable—and publicly reported—example of such an effort. A key principle of performance measurement and feedback, whether as part of guidelines from professional societies or national reporting bodies45, is that it focuses on care practices that should be followed regardless of procedure volume. Because these practices occur commonly, process measurement also overcomes the statistical shortcomings of rare events such as mortality, potentially providing an ability to better identify sites with poorer performance46.
The health-care performance measurement movement in the United States has evolved over the past decade. Various measurement subdomains have been proposed, including structural measures, experience measures, efficiency measures, outcome measures, and process-of-care measures47,48. Structural measures, such as nursing ratios and use of health information technology, are easy to measure but rarely actionable. Experience measures provide useful insight into patient satisfaction but are dependent on the preoperative expectations of the patient, which may vary considerably among patients. Efficiency measures, although easy to define and measure, are poorly correlated with quality and have been criticized by providers as being akin to “economic profiling.” Outcome measures arguably have the most clinical relevance, but they are the most difficult to measure and they require sophisticated risk-adjustment tools. Process-of-care measures, such as the appropriate selection and administration of antibiotics and venous thromboembolism prophylaxis, are relatively easy to measure and can be used to provide actionable feedback to clinicians. However, clinicians and health-services research investigators have highlighted the limited ability of current process measures to account for the variation observed in outcomes, as well as their relevance to patients and payers19. Publicly reported process-of-care measures for medical conditions such as acute myocardial infarction, pneumonia, or congestive heart failure have been shown to explain only a small amount of the mortality variation seen across care delivery sites49. Our data suggest that although adherence to individual process-of-care measures is inconsistently correlated with quality and efficiency in total joint arthroplasty, maximizing adherence to all process-of-care measures is strongly correlated with improved clinical outcomes and more efficient use of resources, independent of hospital or procedure volumes.
A highly coordinated care delivery system—which in our study is indicated by a hospital or surgeon not missing any process-of-care measures—is thought to be a measure of a health-care system’s ability to deliver reliable, high-quality, efficient care25,50-53. This is in contrast to measuring individual care delivery processes, the responsibility for which may be divided across members of a care team and which have inconsistent associations with patient outcomes34. Our findings of a strong association between no process-of-care measures missed and improved clinical outcomes and decreased length of hospital stay support the value of process standardization in total joint arthroplasty49,54. Over the past thirty years, standardized clinical-care pathways for patients undergoing total joint arthroplasty have been developed and refined as tools to improve the quality and efficiency of perioperative care55. One of the key elements of clinical care pathways with regard to total joint arthroplasty is adherence to processes of care derived from evidence-based clinical-practice guidelines, such as the process measures we analyzed in our study. In a recent meta-analysis, Barbieri and colleagues56 demonstrated a strong correlation between implementation of total joint arthroplasty clinical-care pathways and improved quality of care. Other investigators have reported lower rates of complications57 and shorter lengths of hospital stay58,59 following the implementation of standardized clinical-care pathways for patients undergoing total joint arthroplasty. Furthermore, care pathways for patients undergoing total joint arthroplasty have been shown to be associated with decreased length of hospital stay and more efficient use of resources in both academic60,61 and community62 hospitals. Our results suggest that standardization of care via clinical-care pathways is most effective when adherence to the pathway is greatest, that care pathways can improve patient outcomes independent of surgeon and hospital volumes, and that process standardization may even magnify benefits related to hospital or surgeon procedure volumes.
Despite its novel findings, our study also has several limitations. Because we relied on administrative claims data rather than chart abstraction, it is possible that we missed certain clinically relevant complications or that the administratively coded processes of care and outcomes do not accurately reflect the clinical record. Also, the process-of-care measures that we used parallel but do not entirely replicate chart-abstracted process measures. However, it is important to note that identification of the key medications that were used in our study was based on the charges that were collected automatically by hospitals as part of their business activities, and previous investigators have reported strong associations between medication use detected by charges and patient outcomes, with use of a similar approach with the Perspective dataset26-28,63,64. Moreover, the rates of venous thromboembolism prophylaxis, antibiotic prophylaxis, and beta-blocker use are consistent with those reported in other studies on the basis of clinical data, therein arguing against substantial omissions or data inaccuracy within the Perspective dataset. Another limitation of our study is that we were unable to include all of the process-of-care measures used in the Surgical Care Improvement Project in our analysis, since certain processes of care, such as hair removal, are not well documented in the administrative record. Also, it is possible that some surgeons who were included in the analyses may have performed procedures in non-Premier hospitals, and therefore we may have underestimated the procedure volumes for those surgeons. We would note, however, that evidence from the cardiac surgery literature65 suggests that surgeon volume effects may not be transferable across hospitals (e.g., a surgeon’s performance at a given hospital may improve significantly with increases in his or her procedure volume at that hospital, but does not significantly improve with increases in his or her volume at other hospitals). Other limitations were that we were only able to evaluate short-term (thirty days) postoperative outcomes and we were unable to measure patient-reported pain and functional outcomes. Finally, since discharge disposition (e.g., discharge to a skilled nursing and/or acute rehabilitation facility versus discharge to home) may influence length of stay and since affiliation with a post-acute care facility may facilitate earlier transfer of patients from the acute care setting to a post-acute care facility, length of hospital stay may be an imperfect measure of the efficiency of a total joint arthroplasty program.
In summary, our data suggest that although higher surgeon and hospital procedure volumes are unquestionably correlated with improved patient outcomes in total joint arthroplasty, maximizing adherence to process-of-care measures is also strongly associated with better patient outcomes and more efficient resource utilization. Therefore, we believe that process standardization could help optimize quality and efficiency in total joint arthroplasty, independent of hospital or surgeon procedure volume.
A Technical Appendix with the definition of selected quality measures and of covariates included in multivariate models (in addition to quality and volume predictor variables) is available with the electronic version of this article on our web site at jbjs.org (go to the article citation and click on “Supporting Data”).
NOTE: The authors acknowledge Vanessa Chiu, MPH, for her help in preparing this manuscript and the Orthopaedic Research and Education Foundation and the California HealthCare Foundation for their financial support of this work.
Investigation performed at the University of California, San Francisco, San Francisco, California
Disclosure: In support of their research for or preparation of this work, one or more of the authors received, in any one year, outside funding or grants in excess of $10,000 from the Orthopaedic Research and Education Foundation (OREF) and the California HealthCare Foundation. In addition, one or more of the authors or a member of his or her immediate family received, in any one year, payments or other benefits in excess of $10,000 or a commitment or agreement to provide such benefits from commercial entities (United Healthcare, Pacific Business Group on Health, Integrated Healthcare Association, and DePuy).
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