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1. Thirty-Day Readmission Rates as a Measure of Quality: Causes of Readmission After Orthopedic Surgeries and Accuracy of Administrative Data/PRACTITIONER APPLICATIONDocument 1 of 1

Thirty-Day Readmission Rates as a Measure of Quality: Causes of Readmission After Orthopedic Surgeries and Accuracy of Administrative Data/PRACTITIONER APPLICATION

Author: McCormack, Richard; Michels, Ryan; Ramos, Nicholas; Hutzler, Lorraine; Slover, James D, MD; Bosco, Joseph A, MD; Khan, Arby

Publication info: Journal of Healthcare Management 58. 1 (Jan/Feb 2013): 64-76; discussion 76-7.

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Abstract: The rate of unplanned 30-day readmissions to the hospital after discharge is being used as a marker to compare the quality of care across hospitals and to set reimbursement levels for care. While the readmission rate can be reported using administrative data, the accuracy of these data is variable, and defining which readmissions are unplanned and preventable is often difficult. The purpose of this study was to review readmissions to a single orthopedic hospital to identify the causes for readmission and, in particular, which readmissions are planned versus unplanned. Using that hospital’s administrative database of patient records from 2007 to 2009, we identified all patients who were readmitted to the hospital within 30 days of a previous hospitalization for a procedure. Readmissions were broadly categorized as planned (a staged or rescheduled procedure or a direct transfer) or unplanned. Unplanned readmissions were defined as either surgical or nonsurgical complications (medical conditions not directly related to the procedure). Almost 30 percent of readmissions were planned. Of the unplanned readmissions, close to 60 percent were triggered by an infection or a concern for an infection. Nonsurgical complications accounted for 18.2 percent of unplanned readmissions. This study highlights the importance of careful data collection and abstraction when calculating early readmission rates. Preventing surgical site infection and better coordinating care between orthopedic surgeons and primary care and medical subspecialty physicians may significantly reduce readmission rates. [PUBLICATION ABSTRACT]

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Full text: Richard McCormack, MD, chief resident, New York (N.Y.) University Hospital for Joint Diseases; Ryan Michels, medical student, New York University School of Medicine; Nicholas Ramos, medical student, New York University School of Medicine; Lorraine Hutzler, quality project manager, New York University Hospital for Joint Diseases; James D. Slover, MD, assistant professor, New York University Hospital for Joint Diseases; and Joseph A. Bosco, MD, associate professor, New York University Hospital for Joint Diseases EXECUTIVE SUMMARY The rate of unplanned 30-day readmissions to the hospital after discharge is being used as a marker to compare the quality of care across hospitals and to set reimbursement levels for care. While the readmission rate can be reported using administrative data, the accuracy of these data is variable, and defining which readmissions are unplanned and preventable is often difficult. The purpose of this study was to review readmissions to a single orthopedic hospital to identify the causes for readmission and, in particular, which readmissions are planned versus unplanned. Using that hospital’s administrative database of patient records from 2007 to 2009, we identified all patients who were readmitted to the hospital within 30 days of a previous hospitalization for a procedure. Readmissions were broadly categorized as planned (a staged or rescheduled procedure or a direct transfer) or unplanned. Unplanned readmissions were defined as either surgical or nonsurgical complications (medical conditions not directly related to the procedure). Almost 30 percent of readmissions were planned. Of the unplanned readmissions, close to 60 percent were triggered by an infection or a concern for an infection. Nonsurgical complications accounted for 18.2 percent of unplanned readmissions. This study highlights the importance of careful data collection and abstraction when calculating early readmission rates. Preventing surgical site infection and better coordinating care between orthopedic surgeons and primary care and medical subspecialty physicians may significantly reduce readmission rates. For more information on the concepts in this article, please contact Dr. McCormack at richard.mccormack@gmail.com. INTRODUCTION Unplanned readmissions represent a large and increasing healthcare expense. In 2004, the cost to Medicare of unplanned rehospitalizations was estimated to be $17.4 billion (Anderson &Steinberg, 1984; Jencks, Williams, &Coleman, 2009). In an effort to decrease healthcare costs, the Centers for Medicare &Medicaid Services (CMS) is using the rate of 30-day readmissions to hospitals as an indicator of quality of care and hospital performance (Benbassat &Taragin, 2000; Halfen et al., 2006). The Medicare Payment Advisory Commission has recommended that Medicare publicly report 30-day readmission rates. The federal government has proposed reducing payments to hospitals that have relatively high readmission rates. Additionally, CMS is strongly considering bundling payments for an episode of care. An episode of care in this context is defined as all medical interventions for a 2-week period before surgery, the surgery and acute hospital care following the surgery, and any care rendered in the 30 days directly following discharge from the hospital. Readmissions within 30 days of discharge would be included in the episode of care and not be reimbursed separately (Epstein, 2009; Guterman, Davis, Schoenbaum, &Shih, 2009). Readmission rates have the potential to be an attractive measure of hospital performance. They can be calculated using administrative data already collected by hospitals, and they may serve as an indirect indicator of quality of care (Chambers &Clarke, 1990; Milne &Clarke, 1990). In a meta-analysis, Ashton et al. (1997) determined that the risk of 30-day readmissions is increased by 55 percent when care is of relatively low quality. The analysis also found that quality of care was associated with readmissions in those studies that included only unplanned readmissions, as opposed to all readmissions (Ashton, Del Junco, Souchek, Wray, &Mansyur, 1997). Jencks et al. (2009) performed regression analysis of Medicare data to indirectly determine that 10 percent of early readmissions in Medicare patients were “planned.” For early readmission rates to be a valid marker of quality of care, a significant proportion of the readmissions should also be avoidable, such that the readmission may have been prevented if a higher quality of hospital care had been provided. However, determining which readmissions are preventable is subjective and can vary by diagnosis and specialty (Clarke, 1990; Gautam, Macduff, Brown, &Squair, 1996; Oddone et al., 1996; Leng, Walsh, Fowkes, &Swainson, 1999). Husted, Otte, Kristensen, Orsnes, &Kehlet (2010) reported that the readmission rates within 90 days of surgery for total hip and total knee arthroplasty were 10.9 percent and 15.6 percent, respectively. Using readmission rates after orthopedic surgery as a marker of quality of care may be more useful than in other fields of medical practice, because unlike in medicine or general surgery, many orthopedic surgical procedures are elective. These patients tend to be relatively healthy prior to surgery, and 30-day readmissions theoretically should be a direct result of the adverse events following the performed procedure rather than progression of underlying disease. The purpose of this study was to review readmissions to a single orthopedic hospital and to determine their causes. The goal of this analysis was to understand all readmissions that occur during an episode of care around the time of an orthopedic procedure (including readmissions that require not only surgical revision but also optimization of underlying medical comorbidities that may be unrelated to the index operation). We endeavored to distinguish those readmissions that were unplanned, and thus potentially represent quality-of-care issues, from planned readmissions, which do not reflect adverse surgical outcomes. To understand the impact of each readmission, the length of stay for each was also calculated. METHODS Using the New York University Hospital for Joint Diseases (NYUHJD) billing department’s database of patient records from 2007 to 2009, we identified all patients who experienced a readmission to the hospital (the orthopedic specialty hospital) or an affiliated hospital (a large, multispecialty hospital) within 30 days of a previous orthopedic hospitalization. Of these patients, those who were readmitted within 30 days of a scheduled procedure were identified through a chart review. The chart review was performed by 2 fourth-year medical students under the direct supervision and guidance of an orthopedic surgery chief resident and a senior orthopedic surgeon. On the basis of the International Statistical Classification of Diseases and Related Health Problems (ICD) procedure codes listed on the patient’s chart and of the results from the chart review (including the operative report, daily progress notes, and imaging and laboratory results), readmissions were categorized as planned or unplanned. A planned readmission was defined as either a staged or a rescheduled procedure or a direct transfer. For inclusion in the staged procedure category, the patient was discharged with the expectation that he or she would be readmitted for the subsequent stage of a surgical procedure. A rescheduled procedure occurred when a patient was admitted on the day of surgery and either the procedure was canceled prior to surgery or the patient was discharged and the procedure was rescheduled to take place within a month. A direct transfer was defined as when a patient was discharged from the hospital and either admitted directly to an acute rehabilitation facility or transferred directly to another hospital for further care (for specialty care not offered at the primary hospital). Unplanned readmissions were characterized by either surgical or nonsurgical complications. Surgical complications were defined as surgical site infection or concern for infection, hardware complications requiring a revision procedure, prolonged incision drainage, uncontrolled pain, or other complications determined to be a direct result of the surgery. Surgical site infection included both superficial and deep infections. Patients readmitted with a suspicion of a superficial infection (erythema or prolonged wound drainage) who were started on antibiotics were included as readmissions due to a surgical site infection. Medical complications were defined as complications related to medical conditions not directly resulting from the surgery. Nonsurgical complications were broadly categorized as systemic, gastrointestinal, cardiac, respiratory, or neurological. Systemic nonsurgical complications included admissions for fever of unknown origin that resolved spontaneously without antibiotics or for admissions to the medical service for generalized weakness where a more specific diagnosis was not found. RESULTS Between 2007 and 2009, 490 patients were readmitted to NYUHJD within 30 days of discharge from the index admission for the procedure (see Table 1). Of those, 144 (29.4 percent) were planned, of which 64 were readmissions for a rescheduled procedure (13.1 percent of all readmissions), 67 were for a subsequent part of a staged procedure (13.7 percent of all readmissions), and 13 were for a transfer of service (2.7 percent of all readmissions). Of the total readmissions, 346 (70.6 percent) were unplanned. Of those, 283 (57.8 percent of all readmissions) were related to a surgical cause. The majority of the readmissions for surgical causes were related to infection (199 readmissions, representing 40.6 percent of all readmissions). Other causes of readmissions related to surgical problems included uncontrolled pain (19 readmissions; 3.9 percent of all readmissions), persistent wound drainage (17 readmissions; 3.5 percent of all readmissions), and hardware complications requiring surgical revision ( 1 6 réadmissions; 3.3 percent of all readmissions). Nonsurgical complications accounted for 63 readmissions (12.9 percent of all readmissions), with the majority of those from systemic (17), gastrointestinal (13), or cardiac (7) conditions. See Figures 1 and 2 for a breakdown of readmissions by planned and unplanned causes, respectively.

The average length of stay for all readmissions was 8.0 days. For planned readmissions, the average length of stay was 5.2 days, and for unplanned readmissions, it was 9.2 days (see Table 2). DISCUSSION Significance of Readmission Rates Thirty-day readmission rates are being used in a variety of capacities as a barometer of quality of care. For example, Kocher &Adashi (2011) reviewed readmission rates in terms of the Patient Protection and Affordable Care Act (ACA), which was enacted in part to motivate inpatient and outpatient settings to coordinate the care they provide. Strategies being implemented by organizations to comply with the ACA include a community-based care transition program to improve the quality and safety of transitions between the inpatient and outpatient setting; the Hospital Readmission Reduction Program (HRRP), which involves payment reform (designed to align payment with outcome), public reporting, and a patient safety-supported quality improvement program; and the National Pilot Program on Payment Bundling, which is designed to bundle Medicare payments into a single, comprehensive fee for an episode of care in an effort to improve coordination of care. Hospitals with high readmission rates will see Medicare reimbursement reduced up to 3 percent; up to half of all hospitals are estimated to be at risk of seeing some reduction in reimbursement as a result of the HRRP. Currently, all-cause readmissions within 30 days of an index hospitalization for acute myocardial infarction, heart failure, and pneumonia are considered in calculating early readmission rates under the HRRP. After fiscal year 2015, the HRRP is expected to expand to other discharge diagnoses, including elective procedures such as total knee and total hip arthroplasty (Kocher &Adashi, 2011).

Planned Versus Unplanned Réadmissions For readmission rates to be a useful measure in assessing quality of care, they must capture only those readmissions that could have been prevented. In a meta-analysis of studies looking at readmission rate and quality of care, Ashton et al. (1997) found that quality of care is associated with readmissions only when unplanned readmissions, as opposed to all readmissions, were evaluated. The results of the present study indicate that approximately one third of all readmissions (as strictly defined using administrative data) were planned, suggesting that primary administrative data do not distinguish well between planned and unplanned readmissions. Jencks et al. (2009) defined a planned readmission as one that occurred “by choice rather than clinical deterioration.” They identified three primary reasons for planned readmissions: an elective hospitalization that was unrelated to the initial hospitalization, a hospitalization to complete care (similar to what we defined as a staged procedure), and a hospitalization separated from the initial hospitalization to maximize revenue. With the hypothesis that rehospitalizations from clinical deterioration would decrease exponentially with time after discharge, Jencks et al. identified those diagnosis-related groups (DRGs) whose readmission rates did not decrease exponentially over time and, therefore, frequently experienced planned readmissions. These DRGs represented 9.4 percent of discharges in the top 100 DRGs, leading to the estimate that 9.4 percent of readmissions were planned (Jencks et al., 2009). The percentage of planned readmissions was higher in our study than that calculated by Jencks et al. A rescheduled procedure typically resulted from incomplete medical clearance or an acute change in the patient’s medical condition on the day of surgery. The number of rescheduled procedures may to some extent reflect the administrative efficiency of a hospital, but these are not readmissions and as such should not be included when calculating unplanned readmission rates. For a staged procedure, the orthopedic surgeon, at the time of the initial procedure, determines that the procedure is best performed in stages. Say a patient suffered a severe fracture of the bone in his lower leg that extends into his ankle joint (called a pilon fracture). Fixing these fractures at the time of the initial injury by anatomically reducing the fracture fragments and holding them in place with plates and screws has been shown to result in high rates of infections and postoperative complications. Instead, the recommended approach is to place an external fixator (which holds the bone out to length and provisionally reduces tfte fracture) initially and then perform the definitive surgery 1 to 2 weeks later, after the swelling has subsided. For this procedure, the orthopedic surgeon codes for the first procedure (placement of the external fixator) as usual. For the second procedure, the surgeon adds a modifier code (modifier 58) to the CPT (Current Procedural Terminology) code for internal fixation of the fracture so that both procedures will be fully reimbursed. Using ICD-9 diagnosis codes from administrative data, the second readmission may be improperly viewed as an unplanned readmission and would unfairly penalize surgeons who perform the recommended staged approach to care. Administrative data must capture these modifier codes, and physicians must use the codes appropriately for staged procedures to be documented and billed appropriately. At our institution, a direct transfer of service (from the orthopedic hospital to the affiliated rehabilitation center for acute rehabilitation or to the multispecialty hospital for more specialized care) requires the patient to be discharged from one hospital and admitted to the other. This transfer appears to be an early readmission, although it is a planned part of care in this system. To ensure accurate data, clinicians need to be involved in the data collection and analysis process and work closely with the administrative personnel who are abstracting the data so that the readmission rate accurately represents the rate of preventable readmissions. Calculating Readmission Rates Given the importance of eliminating planned readmissions, the question arises as to how best to calculate hospital readmission rates. At our institution, administrative data can be used to rapidly calculate the number of 30-day readmissions, which makes calculating a readmission rate relatively simple. However, no automated method of eliminating planned procedures currently exists. In our study, a retrospective chart review was performed to identify planned readmissions. This process can be time consuming. Assuming planned readmissions can be categorized as a rescheduled procedure, a staged procedure, or a transfer of service, these readmissions can be captured by adding a drop-down menu item in the electronic chart to allow die physician at the time of initial discharge to document if the patient is expected to be a planned readmission within 30 days. This solution would enable appropriate classification of these admissions by efficiently filtering out planned readmissions when calculating readmission rates by flagging them for removal in the reported rates. An alternative method to increase the validity of the 30-day readmission rate as a comparative measure of hospital quality of care is to standardize the surgeries that are evaluated to include those high-volume, primary surgeries that are comparable across hospitals. Complex, often staged, procedures involve more variables that affect outcomes – including patient comorbidities and increased severity of disease – than straightforward procedures do. This variability makes comparing readmissions across hospitals difficult. Readmissions following procedures to treat congestive heart failure have been extensively evaluated, and an algorithm for calculating readmission rates using Medicare claims data has been published (Keenan et al., 2008). For orthopedic specialty hospitals, standardized readmission rates may involve reporting outcomes after the most commonly performed orthopedic procedures, such as total knee replacement, total hip replacement, cervical spine fusion, and lumbar spine fusion. By focusing on only those patients who underwent these common primary procedures, much of the problem with rescheduled procedures would be eliminated because no admissions prior to the index surgery would be counted. The disadvantage with this method is that the hospital’s performance in all procedures, including more complex cases, is not evaluated. This method relies on the assumption that hospitals that perform the most common procedures well also perform more complex procedures well. Likewise, readmission rates may not be an appropriate matrix for these more complex procedures, where multiple variables affect readmissions. Causes of Unplanned Readmissions: Targets for Quality Improvement The two major causes of unplanned readmissions after orthopedic procedures in our study were infection (57.5 percent of unplanned readmissions) and nonsurgical causes (18.2 percent of unplanned readmissions). The large proportion of readmissions related to infection is similar to other analyses of readmissions after orthopedic procedures. Husted et al. (2010) determined that almost half of readmissions after total hip and total knee arthroplasty were related to an infection or suspected infection. Adherence to sterile techniques and timely administration of preoperative antibiotics have been shown in previous studies to significantly reduce infection rates (Berenholtz et al., 2004; Rosenberg et al., 2008). Institutional prescreening for detection and treatment of methicillin-resistant Staphylococcus aureus has also recently shown the potential to reduce the risk of surgical site infection after orthopedic surgery (Kim et al., 2010; Parvizi, Matar, Saleh, Schmalzried, &Mihalko, 2010). Ensuring perioperative control of glucose levels, maximizing patient oxygenation, and maintaining normothermia are other measures shown to decrease die rate of surgical site infections (Fletcher, Sofianos, Berkes, &Obremskey, 2007). These interventions can be combined into a comprehensive surgical site infection prevention protocol to reduce infection rates. Unplanned readmission rates can be used as a matrix for measuring the clinical and economic efficacy of these prescreening and treatment programs. CMS has set a goal of a 20 percent reduction in hospital readmission rates by die end of 2013, estimating that this reduction would prevent 1.6 million hospitalizations and save an estimated $15 billion (Kocher &Adashi, 2011). This type of cost-savings analysis is especially applicable in orthopedic surgery. For example, the cost of a readmission for a surgical site infection can be calculated (including the cost of the hospital room, the operating room if an irrigation and debridement is required, and medications during the hospital stay and potentially the six weeks of intravenous antibiotic administration after surgery). The cost of implementing the prescreening program and infection control programs (including die staffing and resources required) can be weighed against the potential cost savings. A break-even point can be calculated to determine the percentage reduction in readmissions for infections required to make the project economically viable. Another future direction for study is to examine the patients readmitted with an infection after surgery to determine which patients and which surgeries present a high risk of infection. Subgroup analysis of those patients readmitted with an infection may help identify other areas for quality improvement and a further reduction in readmissions. Our data also revealed that approximately one fifth of unplanned readmissions were due to nonsurgical conditions. This rate is much lower than that reported by Jencks et al. (2009), who found that 70.5 percent of patients who were rehospitalized within 30 days after a surgical discharge were rehospitalized for a medical condition. Part of this difference may be that the majority of patients in our study underwent elective surgery and thus may be healthier than those surgical patients reviewed in previous studies. Implementing a coordinated discharge program while the patient is in the hospital and after discharge has been shown to reduce the rate of rehospitalization for patients hospitalized with medical conditions (lack et al., 2009). Coleman, Parry, Chalmers, &Min (2006) developed the Care Transitions Intervention, which focuses on educating patients at risk of readmission on how to better manage their illness with a home visit and telephone calls by trained “transitions coaches. ” The coaches review medications to be taken, educate patients on how to schedule follow-up visits, and help patients to recognize warning signs that should prompt them to call or immediately visit a care provider. This intervention demonstrated a 30 percent reduction in hospital readmissions in a randomized controlled trial and reduced readmissions in a cohort study from 20 percent to 12.8 percent (Coleman et al., 2006; Voss et al., 2011). Given the high rate of rehospitalization for medical conditions and the potential for early identification of surgical site infections, a similar discharge program addressing these areas may significantly reduce readmission rates. Limitations The lower proportion of readmissions for nonsurgical causes may also reflect the limitations inherent in calculating readmissions for a single subspecialty hospital that is a large referral center. While our study included readmissions to a specialty orthopedic care hospital and an affiliated multispecialty, tertiary care center, not all patients received their primary care within the institution, and as such, readmissions within 30 days of surgery may have occurred at another hospital system and were not captured with our administrative data. Using data abstracted from the Medicare Patient Safety Monitoring System, Vorhies, Wang, Herndon, Maloney, &Huddleston (2011, 2012) estimated that the 30-day readmission rates after total knee arthroplasty and total hip arthroplasty were 5.6 percent and 6.8 percent, respectively. In these studies, the most common causes of readmission were cardiac related. Infection was not discussed as a cause for readmission, potentially because it could not be captured by the ICD-9 codes used for analysis. In a separate analysis at our institution, we found our 30-day readmission rates after total knee and hip arthroplasty to be 3.4 percent and 3.1 percent, respectively, prior to implementation of a coordinated discharge and care transition program and 2.0 percent and 1.7 percent, respectively, after the program was instituted (Jordan et al., in press). The primary cause of readmission in this analysis was infection or concern for infection. The lower rate of readmission found in this study, compared to that reported by Vorhies et al. (2011, 2012), may reflect patients in our study who were readmitted to another facility and missed in our analysis. Further analysis, including patient interviews regarding readmissions to outside facilities during followup appointments at our institution, would help gauge the extent of this problem. In some cases we had difficulty identifying the specific reason for the readmission because patient charts were incomplete. As a result, these patients were excluded from analysis. In other cases, the final diagnosis for the readmission was unknown. This situation occurred primarily when a patient was readmitted for a nonsurgical condition where the final diagnosis was that of “fever of unknown origin” or when a patient was readmitted for worsening of a preexisting medical condition but no primary diagnosis was given. It is also difficult to control for bias when classifying procedures as staged retrospectively. To avoid this bias, procedures were classified as staged only when documentation at the time of the first procedure clearly indicated that a second stage was planned within 30 days of discharge. It is important to note that in our study, defining which readmissions were preventable focused on separating those readmissions that were planned from those that were unplanned. This is a necessary first step, but it is also important to understand that not all unplanned readmissions are avoidable or a reflection of low-quality care. Defining which readmissions are truly preventable is difficult and has been the subject of much debate (Axon &Williams, 2011). Risk stratification must be performed in comparing readmission rates across providers and institutions so that those providers who take care of very sick patients or patients with complex problems are not unfairly penalized. Likewise, documentation needs to reflect the severity of the problems treated so that proper coding can be performed. Further investigation is needed to determine how this risk stratification can be performed and what impact it has on the ability to compare readmission rates across institutions. Administrative data used for this study were based on ICD-9-CM (Clinical Modification) codes. ICD-10 consists of diagnostic and procedure codes similar to ICD-9 but with more specificity and a greater number of codes; ICD-9-CM has approximately 13,000 codes, whereas ICD-10-CM has approximately 68,000 codes. The number of procedure codes will increase from approximately 3,000 under ICD-9 to 87,000 under ICD-10. The deadline for implementation of ICD-10 is October 1, 2014. It is unclear exactly how this increased complexity will affect the accuracy of administrative data, but the hope is that it will help better identify the type and level of care delivered. However, as with all coding, success depends on having physicians actively involved in the coding process to ensure that the proper code is used for each patient and that documentation is specific and comprehensive to support higher levels of coding when appropriate (Law &Porucznik, 2009). CONCLUSION Planned readmissions must be eliminated when using administrative data to calculate preventable readmission rates following surgery. The majority of unplanned readmissions noted in this study were made to treat infection, a potential area for quality improvement. The high number of nonsurgical complications highlights the importance of ensuring coordinated follow-up care upon patient discharge. REFERENCES Anderson, G. R, and Steinberg, E. P. (1984). Hospital readmissions in the Medicare population. New England Journal of Medicine, 311 (21), 1349-1353. Ashton, C. M., Del Junco, D. J., Souchek, )., Wray, N. P., &Mansyur, C. L. (1997). The association between the quality of inpatient care and early readmission: A meta-analysis of the evidence. Medical Care, 35(10), 1044-1059. Axon, R. N., and Williams, M. V. (2011). Hospital readmission as an accountability measure. Journal of the American Medical Association, 305(5), 504-505. Benbassat, J., and Taragin, M. (2000). Hospital readmissions as a measure of quality of health care: Advantages and limitations. Archives of Internal Medicine, 160(8), 1074-1081. Berenholtz, S. M., Pronovost, P. I., Lipsett, P. A., Hobson, D., Earsing, K., Farley, 1. E., . . . Perl, T. M. (2004). Eliminating catheterrelated bloodstream infections in the intensive care unit. Critical Care Medicine, 32(10), 2014-2020. Chambers, M., and Clarke, A. (1990). Measuring readmission rates. BMI, 301(6761), 1134-1136. Clarke, A. (1990). Are readmissions avoidable? BMJ, 301(6761), 1136-1138. Coleman, E. A., Parry, C, Chalmers, S., &Min, S. J. (2006). The care transitions intervention: Results of a randomized controlled trial. Archives of Internal Medicine, 166(17), 1822-1828. Epstein, A. M. (2009). Revisiting readmissions – Changing the incentives for shared accountability. New England Journal of Medicine, 360(14), 1457-1459. Fletcher, N., Sorianos, D., Berkes, M. B., &Obremskey, W. T. (2007). Prevention of perioperative infection. Journal of Bone &Joint Surgery, 89(7), 1605-1618. Gautam, P., Macduff, C, Brown, I., &Squair, I. (1996). Unplanned readmissions of elderly patients. Health Bulletin (Edinburgh), 54(6), 449-457. Guterman, S., Davis, K., Schoenbaum, S., &Shih, A. (2009). Using Medicare payment policy to transform the health system: A framework for improving performance. Health Affairs (Millwood), 28(2), w238-w250. Halfon, P., Eggli, Y., Prêtre-Rohrbach, I., Meylan, D., Marazzi, A., &Burnand, B. (2006). Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Medical Care, 44(11), 972-981. Husted, H., Otte, K. S., Kristensen, B. B., Orsnes, T, &Kehlet, H. (2010). Readmissions after fast-track hip and knee arthroplasty. Archives of Orthopedic Trauma Surgery, 130(9), 1185-1191. Jack, B. W., Chetty, V. K., Anthony, D., Greenwald, J. L, Sanchez, G. M., Iohnson, A. E Culpepper, L. (2009). A reengineered hospital discharge program to decrease rehospital ization: A randomized trial. Annals of Internal Medicine, 150(3), 178-187. Jencks, S. F, Williams, M. V” &Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418-1428. Jordan, C, Ramos, N., et al. (in press). Reduction in readmissions in total joint procedures. Bulletin of the Hospital for Joint Diseases. Keenan, P. S., Normand, S. L., Lin, Z., Drye, E. E., Bhat, K. R., Ross, J. S., . . . Krumholz, H. M. (2008). An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circulation: Cardiovascular Quality and Outcomes, 1(1), 29-37. Kim, D. H., Spencer, M., Davidson, S. M., Li, L., Shaw, J. D., Gulczynski, D., . . . Richmond, J. C. (2010). Institutional prescreening for detection and eradication of methicillinresistant Staphylococcus aureus in patients undergoing elective orthopaedic surgery. Journal of Bone &joint Surgery, 92(9), 1820-1826. Kocher, R. P., &Adashi, E. Y. (2011). Hospital readmissions and the Affordable Care Act: Paying for coordinated quality care. journal of the American Medical Association, 306(16), 1794-1795. Law, L., &Porucznik, M. A. (2009, February). Switching to ICD-10: The impact on physicians. AAOS Now. Retrieved from http:// www.aaos.org/news/aaosnow/feb09 /reimbursement 1 .asp Leng, G. C, Walsh, D., Fowkes, F. G” &Swainson, C. P. (1999). Is the emergency readmission rate a valid outcome indicator? Quality in Health Care, S(4), 234-238. Milne, R., &Clarke, A. (1990). Can readmission rates be used as an outcome indicator? BMj 301 (6761), 1139-1140. Oddone, E. Z., Weinberger, M., Horner, M., Mengel, C, Goldstein, E, Ginier, P., . . . Feussner, J. R. (1996). Classifying general medicine readmissions. Are they preventable? Veterans Affairs Cooperative Studies in Health Services Group on Primary Care and Hospital Readmissions. Iournal of General Internal Medicine, 11 (10), 597-607. Parvizi, J., Matar, W. Y., Saleh, K. J” Schmalzried, T. P., &Mihalko, W. M. (2010). Decolonization of drug- resistant organisms before total joint arthroplasty, instructional Course Lectures, 59, 131-137. Rosenberg, A. D., Wambold, D., Kraemer, L., Begley-Keyes, M., Zuckerman, S. L, Singh, N Bennett, M. V. (2008). Ensuring appropriate timing of antimicrobial prophylaxis, journal of Bone &joint Surgery, 90(2), 226-232. Vorhies, J. S., Wang, Y, Herndon, ]., Maloney, W. I., &Huddleston, J. I. (2011). Readmission and length of stay after total hip arthroplasty in a national Medicare sample, journal of Arthroplasty, 26(6, Suppl.), 119-123. Vorhies, J. S., Wang, Y, Herndon, J., Maloney, W. )., &Huddleston, J. I. (2012). Decreased length of stay after TKA is not associated with increased readmission rates in a national Medicare sample. Clinical Orthopaedics and Related Research, 470(1), 166-171. Voss, R., Gardner, R., Baier, R., Butterfield, K., Lehrman, S., &Gravenstein, S. (2011). The care transitions intervention: Translating from efficacy to effectiveness. Archives of Internal Medicine, 171(14), 1232-1237. PRACTITIONER APPLICATION Arby Khan, MD, FACS, deputy national director of surgery, Veterans Health Administration, Washington, DC It is clear that healthcare delivery has to change fundamentally – and quickly – as sustaining the rate of increase in healthcare costs is impossible. As is widely reported, despite the large amount of money spent on healthcare in the United States – almost twice as much as any other country spends (OECD, 2012) – the qual ity of U.S. healthcare is rated as 37th in the world (LaPierre, 2012). This disconcerting discrepancy will increasingly compel payers and legislators to make adequate reimbursement conditional on delivery of good-quality healthcare, such as through the federal Hospital Readmission Reduction Program. However, most hospitals’ processes and operations are not designed to measure quality of care. The article by McCormack et al. demonstrates one of those deficiencies: the methodology used by the administrative database to capture and display readmission data. Many such processes exist within our healthcare system, and they need to change. However, diese processes will not change without an embedded culture of continuous attention to improving outcomes. Notable in this study is that the desire to decrease readmissions was stimulated by the threat of decreased reimbursements. This reaction implies, mostly correctly, that such interest in quality was not in place to begin with. Readmission rates constitute a small segment of the data used to measure outcomes and quality of care, as the authors point out. Healthcare leaders who look at readmission rates alone will miss errors and inefficiencies in many areas, such as in the preoperative process (How well was the patient prepared for surgery? Are those nonsurgical readmissions really nonsurgical?), the surgical consultative process (How long did it take for the patient to get an appointment with the surgeon?), the postoperative process (Was the patient’s physical rehabilitation optimal?), the complications that require admission to another hospital, the complications dealt with in the outpatient setting, and many more. Thus, it is important for hospitals to develop a prospective, comprehensive surgical-quality management program that measures patient flow processes, tracks outcomes (quality of care), and provides actionable data, which in turn allows improvement of the whole value chain in healthcare delivery. The Department of Veterans Affairs (VA), a leader in surgical quality, has in place such a program: the Veterans Affairs Surgical Quality Improvement Program (VASQIP). This program oversees quality of surgical care for 1 31 VA hospitals nationwide. VASQIP measures multiple parameters and processes and provides actionable data (e.g., operating room cancellation rates, risk-adjusted mortality and morbidity rates, degree of resident supervision) to the medical center directors. It is clear from experience with VASQIP over the past two decades that the quality of surgical healthcare suffers when hospital leaders do not create a culture of continuous improvement, do not have quality improvement processes in place that respond to the actionable data provided, or do not include all stakeholders in the quality improvement process (e.g., surgeons, anesthesiologists, nurses, room turnover teams, hospital leaders). These are the fundamentals of a comprehensive surgical-quality improvement program. The authors are to be commended for marching in the right direction. Identifying the inadequacy of the administrative database is a good start. However, the next step is critical – embracing a complete surgical-quality improvement program. All stakeholders will benefit from the long-term value it provides. REFERENCES LaPierre, T. A. (2012). Comparing the Canadian and US systems of health care in an era of health care reform, journal of Health Care Finance, 38(4), 18. Organisation for Economic Co-operation and Development (OECD). (2011). OECD health data 2011- Frequently requested data. Retrieved from http://www.oecd.org/document/ 16/0,3746 ,en_2649_34631_2085200_l_l_l_l,00.html

Subject: Studies; Orthopedics; Data collection; Hospital costs; Patient admissions

MeSH: Databases, Factual, Hospitals, University — standards, Humans, New York — epidemiology, Postoperative Complications — epidemiology, Time Factors, Medical Records — standards (major), Orthopedics; (major), Patient Readmission (major), Patient Readmission (major) — statistics & numerical data, Quality Indicators, Health Care (major), Quality Indicators, Health Care (major) — statistics; & numerical data

Classification: 9130: Experiment/theoretical treatment; 8320: Health care industry

Publication title: Journal of Healthcare Management

Volume: 58

Issue: 1

Pages: 64-76; discussion 76-7

Number of pages: 14

Publication year: 2013

Publication date: Jan/Feb 2013

Year: 2013

Publisher: Health Administration Press

Place of publication: Chicago

Country of publication: United States

Publication subject: Public Health And Safety, Environmental Studies, Health Facilities And Administration, Physical Fitness And Hygiene

ISSN: 10969012

CODEN: JHMAFB

Source type: Scholarly Journals

Language of publication: English

Document type: Feature, Journal Article

Document feature: Tables; Graphs; References

Accession number: 23424819

ProQuest document ID: 1287987928

Document URL:

http://libproxy.csun.edu/login?url=http://search.proquest.com/docview/1287987928?accountid=7285

Copyright: Copyright Health Administration Press Jan/Feb 2013

Last updated: 2013-03-15

Database: ABI/INFORM Complete

BibliographyBibliography

Citation style: APA 6th – American Psychological Association, 6th Edition

McCormack, R., Michels, R., Ramos, N., Hutzler, L., Slover, J. D., M.D., Bosco, J. A., M.D., & Khan, A. (2013). Thirty-day readmission rates as a measure of quality: Causes of readmission after orthopedic surgeries and accuracy of administrative Data/PRACTITIONER APPLICATION. Journal of Healthcare Management, 58(1), 64-76; discussion 76-7. Retrieved from http://search.proquest.com/docview/1287987928?accountid=7285

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