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Research Reports |
JK Freburger, PT, PhD, is Assistant Professor, Division of Physical Therapy, University of North Carolina at Chapel Hill, CB 7135, Medical School Wing E, Chapel Hill, NC 27599-7135 (USA) (jfreburger{at}css.unc.edu)
Submitted August 16, 1999;
Accepted January 14, 2000
 | Abstract |
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Key Words: Health services research • Outcomes • Total hip arthroplasty
| Introduction |
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The rate of utilization of THA has increased in the past decade. In 1982, 75,000 THAs were performed in the United States as compared with 138,000 THAs in 1996.1,7,8 The number of THAs is also anticipated to continue to increase because of the aging of our population and because technological advances have decreased the risks of the procedure.1,9,10 Patients previously regarded as too old or too impaired to benefit sufficiently from a THA are now having the procedure done.1
Physical therapy is an important component of the acute care of patients after a THA. There is strong consensus in the physical therapy community that, following surgery, patients should be seen twice a day for exercise and patient education.11 The goals of physical therapy during the acute care period after a THA are usually to increase mobility and to educate the patient on exercises and precautions in preparation for discharge.5,11–13 Criteria for discharge home that are dependent on physical therapy frequently include the ability of the patient to physically demonstrate total hip precautions, verbally state total hip precautions, transfer independently, ambulate independently on level surfaces, and perform a home exercise program independently.11
Physical therapists play a major role in educating patients who have had a THA. Santavirta et al14 studied the effects of patient education on 60 patients who underwent a THA and found that over 60% of the patients named their physical therapist as the primary source of information on the procedure and description of its consequences as compared with physicians and nurses. There are also some data to suggest that patient education, both prior to and during the hospitalization, improves outcomes for patients following a THA.14–16
In addition to improving the functional outcomes of patients after a THA, the use of physical therapy may decrease the total cost of care for the acute care phase for these patients by accelerating the time to discharge (ie, decreasing the length of stay and thereby decreasing the total cost of care). Munin et al,17 for example, found that patients at high risk for requiring inpatient rehabilitation after elective hip and knee arthroplasty (ie, 70 years of age or older and living alone, 70 years of age or older with 2 or more comorbid conditions, or any age with 3 or more comorbid conditions) who began inpatient rehabilitation 3 days after surgery had shorter lengths of stay, lower total costs of care, and similar functional outcomes when compared with similar patients who began inpatient rehabilitation on postoperative day 7. The use of physical therapy during the acute care of patients after a THA may also influence discharge destination, which can have both patient and economic benefits. For example, the amount of physical therapy a patient receives after a THA may influence whether he or she will be discharged home or transferred to a rehabilitation or extended care facility.
With the aging of our population and technological advances in health care, patients receiving THAs will continue to be a major patient population in need of physical therapy. The proliferation of managed care and health care reform initiatives are also likely to continue, placing an emphasis on the delivery of cost-effective and efficient health care. Unfortunately, little research has been conducted on the outcomes of care, from both an economic and a functional perspective, for patients who have had THAs and who are receiving physical therapy in the acute care setting. The purpose of this study, therefore, was to examine the relationship between the amount of physical therapy services received by patients following a THA and the outcomes of care for these patients. Outcomes were examined in terms of the total cost of care for the patient and the patient's discharge status. Total cost of care was assessed by determining whether the total cost was less than expected (a better outcome) or more than expected (a worse outcome) based on data from participating medical centers. Discharge status was assessed by determining the probability of the patient being discharged home.
This study was conducted using secondary databases and a design that are described in a previous research report.18 The results of the previous study indicated that the use of physical therapy during the acute care of patients with stroke was directly related to a total cost of care that was less than expected and to an increased probability of discharge home. I hypothesized that my findings in this study would be similar to those of my previous study.18 That is, I hypothesized that (1) increased use of physical therapy would decrease the patient's length of stay and, therefore, would be directly related to a total cost of care that was less than expected, and (2) increased use of physical therapy would maximize the patient's functional abilities and, therefore, would be directly related to an increased probability of discharge home.
In addition, I hypothesized that use of physical therapy services would explain a greater amount of the variation in the dependent variables (ie, total cost of care and probability of discharge home) for the THA data set as compared with the stroke data set. Although stroke and THA are conditions that often warrant the utilization of physical therapy during the acute care phase, these 2 conditions are very different. The patient with stroke may have a wide range of neurological impairments that require a wide range of individualized interventions.19 Total hip arthroplasty, however, is most often an elective procedure with few serious postoperative complications, a good prognosis for long-term improvement, and a fairly well-established protocol for postoperative treatment.1,20,21 The outcomes of care for patients with stroke, therefore, are likely to be less predictable than the outcomes for patients who have had a THA.
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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Data Sources
The major source of data for the study was the University HealthSystem Consortium (UHC) Clinical Data Base.25 This database was used primarily to obtain information on patients after a THA who were treated in AHC hospitals in 1996. One advantage of the UHC Clinical Data Base is that an effort is made to adjust for risk (ie, pertinent patient characteristics that may affect the outcomes of care are taken into account). Details of the risk-adjustment methods used by UHC are presented in one of their publications on the clinical database.26 One portion of the risk-adjustment process used by UHC consists of calculating an expected total cost of care for each patient in the database. This expected total cost of care takes into account factors such as the demographic characteristics of the patient, the patient's complications and comorbidities, and the types of procedures performed on the patient.26
The other sources of data for the study were the Institutional Profile System (IPS) of the American Association of Medical Colleges,27 the American Hospital Association (AHA) Annual Survey of Hospitals,28 and the InterStudy Competitive Edge Database.29 These 3 databases were used to obtain organizational-level information on the AHC hospitals. All of the databases used in this study are described in more detail in the previous research report.18
Sampling and Data Elements
Data from the UHC Clinical Data Base for the calendar year 1996 were examined in this study. Patient-level data for patients who had a THA were identified using the following International Classification of Disease, 9th revision, clinical modification procedure codes: 81.51—total hip replacement, 81.52—partial hip replacement, and 81.53—hip replacement revision.30 Patients who were classified according to 1 of these 3 procedure codes, who received physical therapy during their inpatient stay, and who survived their inpatient stay constituted the sample.
The following data were extracted from the UHC Clinical Data Base for each patient: sex, race, age, Medicaid status, rating of severity of the patient's condition, total physical therapy charges, total charges for care, discharge status (ie, whether patient went home or to another facility), actual total cost of care, and expected total cost of care.
Organizational-level data that were obtained from the UHC Clinical Data Base included number of licensed beds for each AHC hospital in 1996. Organizational-level data for 1996 were also obtained from the IPS, the AHA Annual Survey of Hospitals, and the InterStudy Competitive Edge Database. The IPS database was used to gather the following data on medical schools affiliated with the AHC hospitals: type of affiliation between the medical school and hospital, total dollars in research awards, and total number of faculty. The AHA Annual Survey of Hospitals was used to gather data on the ownership of the AHC hospitals (ie, public or private). The InterStudy Competitive Edge Database was used to gather data on health maintenance organization (HMO) market penetration in the metropolitan statistical areas occupied by each of the AHC hospitals.
The data elements extracted from the data sets were examined by doing frequency counts and performing univariate analyses. Any observations with incorrectly coded data (ie, values that were outside the possible range of values for the observation) or incomplete data were excluded from the study.
Measurement Variables
The dependent variable for the multiple linear regression analysis was the expected total cost of care/actual total cost of care for each patient. This ratio was multiplied by 100 for ease of interpretation of the statistical results. A better outcome, therefore, would be indicated by a number greater than 100, and a worse outcome would be indicated by a number less than 100. The dependent variable for the multiple logistic regression analysis was discharge status for each patient. This variable was dichotomized (ie, 0=discharge other, 1=discharge home).
The independent variable for the analysis was physical therapy utilization. Physical therapy utilization for each patient was represented by physical therapy charges/total charges. This ratio was multiplied by 100 for ease of interpretation of the statistical results. Physical therapy charges, therefore, were expressed as a percentage of total charges for the care of the patient. Representing physical therapy charges in this manner was done to take into account variation in charges across AHC hospitals.
The patient-level control variables for the study were as follows: sex (0=female, 1=male), race (0=Caucasian, other; 1=African American), Medicaid status (0=receiving Medicaid, 1=not receiving Medicaid), rating of severity of patient's condition (0=no substantial complications or comorbidities, 1=moderate complications or comorbidities, 2=major complications or comorbidities, 3=catastrophic complications or comorbidities), and type of procedure (0=partial hip arthroplasty or THA, 1=revision). Medicaid status was used as a proxy for socioeconomic status. A review of the literature indicated that these patient characteristics contribute to variation in the outcomes of care for patients after a THA.12,16,20,22,23
The data on each patient were also coded with organizational indicators to allow me to control, in the regression analyses, for some of the differences among the AHC hospitals. The organizational characteristics that were controlled for in this study were chosen for a combination of reasons that included: the results of a literature review on AHC hospitals31,32 and on the acute care of patients following a THA,22–24 the type of data that were available, and the concepts of an organizational theory.33,34 The organizational-level variables that were controlled for in the study were as follows: hospital ownership (0=public, 1=private), medical school affiliation (0=common affiliation of hospital and medical school, 1=other), medical school research intensity (total research grants and contracts dollars/number of medical school faculty), number of beds, and HMO penetration (percentage of HMO penetration in the metropolitan statistical area of the AHC hospital). These variables are indirect measures of resource availability at the AHC hospital and were hypothesized to be related to outcomes of care. The AHC hospitals with scarcer resources were hypothesized to have more incentive or need to contain costs and improve outcomes than AHC hospitals with more abundant resources.
Data Analysis
All data were managed and analyzed using SAS Version 6.12* statistical software on an IBM
SP 590 mainframe computer running AIX. A univariate analysis of all of the study variables was conducted to examine the distribution of the data, to verify that each variable had sufficient variance, and to detect outliers. Some exploratory regression analyses and residual analyses were also conducted, and a correlation matrix was generated to examine the data for multicollinearity.35 After the preliminary analyses, a multiple linear regression analysis was conducted to examine the relationship between physical therapy utilization (ie, physical therapy charges/total charges) and the expected total cost of care/actual total cost of care, while controlling for patient and organizational characteristics. A multiple logistic regression analysis was also conducted to examine the relationship between physical therapy utilization and the probability of discharge home, while controlling for patient and organizational characteristics. The explanatory power of the logistic regression equation was assessed with the use of the Huberty test statistic.36 The percentage of patients who were correctly classified (ie, either as discharged home or discharged elsewhere) using the logistic regression equation was compared with the percentage of patients who would be correctly classified by chance alone.
| Results |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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Descriptive statistics on patients in the final data set (n=7,495) are presented in Table 2. The patient characteristics of the final data set are consistent with previously reported data on the demographics of patients who have had a THA.10,16 Sharkness et al10 hypothesized that the lower prevalence of hip implants among African Americans may be due to a decreased incidence of hip problems in African Americans as compared with Caucasian individuals and to differences in the patterns of use of health services among racial subgroups. The variables of the eliminated records (n=152) were also examined with the use of descriptive statistics and were found to be similar to the final data set, indicating that the eliminated records differed from the final data set only in regard to total cost values.
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Multiple Linear Regression Analysis—Examination of the Relationship Between Utilization of Physical Therapy and the Total Cost of Care
The dependent variable for this analysis was: (expected total cost of care/actual total cost of care) x 100. The independent variable for this analysis was: (physical therapy charges/total charges) x 100. Patient-level control variables for the analysis were age, sex, race, rating of severity of the patient's condition, Medicaid status, and type of procedure (Tab. 2). Organizational-level control variables for the analysis were medical school affiliation, number of beds, HMO penetration, ownership, and medical school research intensity (Tab. 2).
Preliminary residual analyses indicated that both the dependent and independent variables were curvilinear, increasing in an exponential manner. The variables, therefore, were transformed to linearize the data.37 The dependent measure of expected total cost of care/actual total cost of care was transformed by taking the square root of the value. Physical therapy utilization was also transformed by taking the square root of the value (ie, square root of physical therapy charges/total charges). Residual analyses also indicated that the assumptions of the multiple regression analysis were generally not violated.37
The results of the multiple linear regression analysis are presented in Table 3. Physical therapy utilization (ie, [square root of physical therapy charges/total charges] x 100) was directly associated with a total cost of care that was less than expected. That is, increased use of physical therapy services was associated with a better outcome in terms of total cost of care. Other patient-level control variables that were inversely related to a total cost of care that was less than expected were rating of severity of the patient's condition, race, and procedure type. Patients with more complications and comorbidities, patients who were African American, and patients who underwent a revision procedure had worse outcomes in terms of total cost of care. Organizational-level control variables that were related to the dependent variable were number of beds, medical school affiliation, and ownership. Number of beds and common affiliation of the AHC hospital and medical school were inversely related to a total cost of care that was less than expected. Private ownership of the AHC hospital was directly related to a total cost of care that was less than expected.
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Multiple Logistic Regression Analysis—Examination of the Relationship Between Utilization of Physical Therapy and Discharge Status
The dependent variable for the analysis was discharge status (ie, discharge to home or elsewhere). The independent variable for the analysis was: (physical therapy charges/total charges) x 100. The patient-level control variables for the analysis were age, sex, race, rating of severity of the patient's condition, Medicaid status, and procedure type. The organizational-level control variables for the analysis were medical school affiliation, number of beds, HMO penetration, ownership, and medical school research intensity (Tab. 2).
The multiple logistic regression equation was modeled after the probability of discharge home. The results of the analysis are presented in Table 4. Physical therapy utilization (ie, physical therapy charges/total charges) was positively associated with an increased probability of discharge home. Patient-level control variables (P<.05) that were related to the probability of discharge home were age, rating of severity of the patient's condition, sex, and procedure type. Age and severity of the patient's condition were inversely related to an increased probability of discharge home. Male patients and patients with revision surgery were more likely to be discharged home than female patients and patients with a partial or total hip arthroplasty. Organizational-level control variables (P<.05) that were related to the probability of discharge home were medical school affiliation, number of beds, medical school research intensity, and ownership. Number of beds, common affiliation of the AHC hospital and medical school, and private ownership were negatively associated with an increased probability of discharge home. Medical school research intensity was positively associated with an increased probability of discharge home.
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| Discussion |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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Relationship Between Physical Therapy Utilization and Total Cost of Care
Although the explanatory power of the multiple linear regression model in this study was weak, the statistically significant and positive association between physical therapy use and a total cost of care that was less than expected is noteworthy, considering the size of the sample and considering that the variable representing physical therapy use explained most of the variation in the total cost of care measure (Tab. 3). Based on the results of the multiple linear regression equation, a 1% increase in the ratio of physical therapy charges to total charges was associated with a 0.6% increase in the ratio of expected total cost of care to actual total cost of care, if all other variables were constant. One explanation for this finding is that increasing the use of physical therapy services decreases the total cost of care by accelerating the time to discharge. This concept can be further illustrated by using mean values from the data set. The mean expected total cost of care for the data set was $11,531, and the mean actual total cost of care was $12,383 (ie, 93.1%). A 0.6% increase to 93.7% could be gained by a $76 decrease in the actual total cost of care. A 1% increase in physical therapy charges to total charges, therefore, would be associated with a $76 decrease in the total cost of care. Although these cost savings may seem small, when applied to the thousands of patients who receive THAs each year, the cost savings are substantial.
Although the example may better illustrate the results of this study, the interpretation of the results requires some caution. Although physical therapy use explains a small portion of the variation in the expected total cost of care/actual total cost of care measure, much of the variation in this total cost of care measure is still unexplained. The value of the regression coefficient representing the relationship between physical therapy use and the expected total cost of care/actual total cost of care measure would likely change, at least slightly, with the inclusion of other statistically significant independent or control variables.
In my previous study of people who had strokes,18 the multiple linear regression equation describing the relationship between physical therapy utilization and the total cost of care explained 5% of the variation in the dependent variable. In this study, the explanatory power of the multiple linear regression equation was greater (R2=.10). Furthermore, the standardized regression coefficient for physical therapy utilization was higher in this study (.188 versus .117). These results indicate that utilization of physical therapy services explains more of the variation in the total cost of care measure for patients after a THA than for patients with stroke. These results are not surprising when considering the differences between these 2 conditions. Because THA is often an elective procedure, with few serious postoperative complications and a good prognosis, the impact of physical therapy on outcomes of care is, in my opinion, likely to be more predictable for this patient group than for patients with stroke.
Relationship Between Physical Therapy Utilization and the Probability of Discharge Home
The explanatory power of the multiple logistic regression model in this study, in my view, was fair,36 with 71% of the patients correctly classified as either discharged home or discharged elsewhere (Tab. 4). Utilization of physical therapy services was directly related to an increased probability of discharge home and had the third highest standardized regression coefficient in the model (ie, .179). Increased use of physical therapy services, therefore, was one of the variables that contributed the most to explaining the variation in the probability of discharge home. The other 2 variables that contributed the most to explaining the variation in the probability of discharge home were age (standardized regression coefficient=.4944) and ownership (standardized regression coefficient=.2113).
As with the results of the multiple linear regression analysis, the results of the multiple logistic regression analysis can be used to illustrate how physical therapy use affects the probability of discharge home. Using values to represent an "average" patient (eg, 64 years of age, female, Caucasian, severity of the patient's condition=0) treated in an "average" AHC hospital (eg, number of beds=582, public ownership), the probability of discharge home is P=.38, with the percentage of physical therapy charges to total charges equal to 1. The probability of discharge home increases to P=.44 if physical therapy utilization is increased to 2% of the total charges, and it increases to P=.49 if physical therapy utilization is increased to 3% of the total charges. One plausible explanation for this finding is that an increase in the use of physical therapy services increases the probability of discharge home by maximizing the patient's function. The logistic regression equation generated in this analysis, however, did not explain all of the variation in the probability of discharge home. Furthermore, variables associated with the patient and with the characteristics of the hospital were used in the examples I have given. The probability of discharge home would change if different values were used. For example, if 50 years was used instead of 64 years or male sex was used instead of female sex, the probabilities of discharge home in each of the examples given would be higher.
In my previous study,18 the multiple logistic regression equation describing the relationship between physical therapy utilization and probability of discharge home correctly classified 64% of the patients, which was higher than the chance prediction rate of 52%. In the study reported here, the multiple logistic regression equation correctly classified 71% of the patients, which was higher than the chance prediction rate of 51%. These findings suggest that the explanatory power of the multiple logistic regression equation generated from the THA data is greater than that for the stroke data. A comparison of the standardized regression coefficients from the 2 analyses also indicates that utilization of physical therapy explained more of the variation in the probability of discharge home for patients after a THA (coefficient=.179) than for patients with stroke (coefficient=.049). These differences are likely due to the differences in presentation and prognosis for the 2 conditions.
Control Variables
As might be expected, the rating of severity of the patient's condition was inversely related to a better outcome in terms of total cost of care and to an increased probability of discharge home. That is, as the rating of severity of the patient's condition increased, the ratio of expected total cost of care/actual total cost of care decreased and the probability of discharge home decreased. The type of procedure performed on the patient also had an effect on outcomes. Patients undergoing revision surgery had worse outcomes in terms of total cost of care (ie, total cost of care was more than expected) than patients undergoing a partial hip arthroplasty or THA. Patients undergoing revision surgery, however, were more likely to be discharged home.
The other patient-level control variable that was associated with the total cost of care measure was race. African-American patients had poorer outcomes in terms of total cost of care than non–African-American patients. Although some researchers9,38 have reported that the incidence of THA is lower in African Americans than in Caucasian populations, few data are available on factors that influence outcomes of care for African-American patients undergoing THA.
The other patient-level control variables that were related to the probability of discharge home were age and sex. As might be expected, an increase in age decreased the probability of discharge home. Furthermore, the standardized regression coefficient for age (Tab. 4) indicates that this variable explained most of the variation in the probability of discharge home. Women were also less likely than men to be discharged home. This finding may be related to living status39 (ie, more of the female patients may have lived alone).
The organizational-level control variables that were significant varied somewhat for each of the regression models (Tabs. 3 and 4). Of particular note are the variables representing medical school affiliation and number of beds. In both regression analyses, these variables were inversely related to the dependent measures. The somewhat inconsistent findings with the other organizational-level control variables used in the analysis were expected. Although research indicates there is variation among hospitals in the outcomes of care for the treatment of patients who have had a THA, even after controlling for patient differences,22–24 the sources of this variation are less clear. The results of this study, in combination with the results of my previous study,18 however, indicate that type of affiliation between the medical school and AHC hospital, type of ownership of the AHC hospital, number of beds, and medical school research intensity are the organizational characteristics that had the most consistent influence on outcomes of care.
Strengths and Limitations of the Study
Because I used secondary databases to examine the relationship between utilization of physical therapy services and outcomes of care, I was able to analyze thousands of records. A weakness of using secondary databases, however, is that it is difficult to completely verify the accuracy of the data. Although the UHC has a number of processes in place designed to increase the accuracy of their data,18 there is still the possibility that some of the data in the sample were coded inaccurately. The fact that the characteristics of the subjects in the sample were similar to previously reported data on the demographics of patients who have had a THA offers some evidence to support the accuracy of the data entry. Furthermore, because of the large sample size, I believe the results of the study are unlikely to be greatly influenced by a few errors.
Although the regression equations in this study explained more of the variation in the data for the dependent measures than the regression equations generated from the stroke data,18 a large percentage of the variation in the data for the dependent variables was still unaccounted for. Inclusion of more patient-level variables (eg, types of physical therapy interventions, functional status of the patient prior to admission) would likely explain more of the variation in the data obtained for the dependent variables. Including the utilization of other ancillary services, such as occupational therapy and social work, and other organizational-level control variables, such as number of orthopedic surgeons at the AHC hospital, may also explain more of the variation in the data for the dependent measures.
Other limitations of the study are related to the cross-sectional design, the limited sample, and the questionable validity of the data for some of the variables.18 Despite the limitations, the results of this study along with those of the previous analysis18 provide suggestive evidence and perhaps even more compelling evidence of the benefits of physical therapy for the treatment of patients in the acute care setting.
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| Footnotes |
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* SAS Institute Inc, PO Box 8000, Cary, NC 27511. 
International Business Machines Corp, New Orchard Rd, Armonk, NY 10504.
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This article has been cited by other articles:
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  L. Resnik, D. Liu, D. L Hart, and V. Mor Benchmarking Physical Therapy Clinic Performance: Statistical Methods to Enhance Internal Validity When Using Observational Data Physical Therapy, September 1, 2008; 88(9): 1078 - 1087. [Abstract] [Full Text] [PDF]
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J. K Freburger and G. M Holmes Physical Therapy Use by Community-Based Older People Physical Therapy, January 1, 2005; 85(1): 19 - 33. [Abstract] [Full Text] [PDF]
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 M. Perron, F. Malouin, and H. Moffet Assessing advanced locomotor recovery after total hip arthroplasty with the timed stair test Clinical Rehabilitation, July 1, 2003; 17(7): 780 - 786. [Abstract] [PDF]
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