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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.
ProQuest document link
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]
Links: Linking Service
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 [email protected]
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.
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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/d
ocview/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=728
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