Ancillary Service Utilization in Academic Health
Center Hospitals: Use of Physical Therapy for the
Treatment of Stroke and Hip Arthroplasty
Janet K. Freburger, PhD, PT, and Robert E. Hurley, PhD
• Objective: To examine the relationship between aca-demic health center (AHC) hospital characteristics and use of physical therapy for the care of patients with stroke and hip arthroplasty and to examine the rela-tionship between use of physical therapy and the total cost of care.
• Design: Retrospective database study.
• Setting and participants: 6342 patients with stroke and 7495 patients with hip arthroplasty who were treated in 59 AHC hospitals during 1996. Only patients who survived their inpatient stay and received physical ther-apy were included in the 2 samples.
• Outcome measures: Physical therapy utilization was ex-pressed as a percentage of the total charges for each patient. Total cost of care was expressed as a percent-age of the expected total cost of care (based on patient characteristics and disease severity) for the patient. AHC hospital characteristics examined included health maintenance organization (HMO) penetration, medical school research intensity, medical school affiliation, number of beds, ownership, and percentage of private-ly insured patients. These characteristics were chosen as proxy measures of resource availability for the AHC hospitals.
• Results: A number of AHC hospital characteristics were associated with increased utilization of physical therapy for both patient groups. The most consistent findings were that medical school research intensity, public ownership, and mix of privately insured pa-tients were positively associated with increased use of physical therapy. Increased use of physical therapy was also associated with a total cost of care that was less than expected.
• Conclusion: The results suggest that AHC hospitals with scarce resources may increase their utilization of physical therapy, and that increased utilization of physical therapy is associated with a decrease in the total cost of care for patients with hip arthroplasty and stroke.
C
hanges in the financing and delivery of health care are exerting profound market pressures on academ-ic health center (AHC) hospitals. The health care market that once fostered the development of AHC hospitals has been changed dramatically in the past decade by the growth of managed care, leading to a reduction in revenues for AHC institutions and threatening their mission-related activities. AHC hospitals are responding to the changing health care environment with a variety of strategies aimed at reducing costs, developing new sources of revenue, and improving the provision of care [1,2].Although much has been written on the future of AHC hospitals and their strategies for survival, most of the litera-ture is speculative or descriptive in nalitera-ture. Furthermore, cur-rent empirical studies are limited because they focus on one or only a small number of hospitals [2–4]. Our understand-ing of AHC hospitals is also complicated because these insti-tutions, although broadly similar in regard to mission, are heterogeneous in regard to structure and operation [5]. How AHC hospitals are being affected by the changing health care market, how they are responding, and what strategies are most effective may vary depending on the characteristics of the institution and the environment in which they are located.
Increasing the utilization of ancillary services has been suggested as one strategy that AHC hospitals can adopt to improve the efficiency of their processes of care [6]. For example, it may be more cost-effective and efficient to trans-fer some patient care responsibilities from physicians to non-physicians. Also, increasing the utilization of ancillary ser-vices for diagnoses in specific need of these serser-vices may
Janet K. Freburger, PhD, PT, Assistant Professor, Division of Physical Therapy, AHRQ/NRSA Postdoctoral Fellow, Cecil G. Sheps Center for for Health Services Research, University of North Carolina, Chapel Hill, NC; Robert E. Hurley, PhD, Associate Professor, Department of Health Administration, Medical College of Virginia, Virginia Commonwealth University, Richmond, VA.
make the delivery of care more efficient. For example, in-creasing the utilization of physical therapy services for pa-tients in need of rehabilitation may prove cost-efficient by decreasing the time to discharge.
Little research has been conducted on factors associated with ancillary service utilization in AHC hospitals or on the relationship between ancillary service utilization and out-comes of care. Therefore, one purpose of our study was to examine the relationships between AHC hospital character-istics and the amount of physical therapy services used dur-ing the acute care of 2 contemporary cohorts of patients treated in U.S. AHC hospitals. We chose to examine patients with stroke and patients with hip arthroplasty because these conditions are common to the AHC setting, typically require physical therapy, and contribute significantly to health care costs. A second purpose of the study was to examine the relationship between the use of physical therapy services and total cost of care; that is, was the total cost of care more or less than expected after risk adjustment?
Methods
Conceptual Model
The conceptual model for this study was derived from resource dependence theory [7] and Donabedian’s concep-tualization of medical care [8]. We hypothesized that AHC hospitals with scarcer resources would have greater pres-sures or incentives to increase the use of physical therapy services compared with AHC hospitals with more abundant resources. Therefore, we expected to find a positive relation-ship between AHC hospital characteristics indicative of re-source scarcity and the use of physical therapy services. We also hypothesized that increased use of physical therapy ser-vices would be associated with a total cost of care that was less than expected. Although increasing the use of physical therapy services adds to the cost of care, we expected the overall total cost of care to be less because additional reha-bilitation would likely reduce patients’ time to discharge and thus their length of stay. And finally, we hypothesized that our results would be more strongly supported for patients with hip arthroplasty than for patients with stroke. Because of the wide range of deficits that patients with stroke may have and the wide range of treatments available for their acute care, variation in the processes and outcomes of their care is likely to be greater than for patients with hip arthro-plasty. The treatment of stroke may also have a greater propensity for cost variation when compared to the treat-ment of conditions with more clearly delineated protocols such as hip arthroplasty.
Data Sources
The major source of data for the study was the University HealthSystem Consortium (UHC) clinical database [9],
which is a patient-level, risk-adjusted database compiled from participating hospitals’ discharge abstract summaries and UB-92 data. One portion of the risk adjustment process consists of calculating an expected total cost of care for each patient [9].First, UHC assigns a severity level to each patient in a given DRG. A normative population for each DRG grouping is then selected by removing outliers and bad data. The final step of the process is the development of a regres-sion model for each DRG grouping. The independent vari-ables in the model include patient severity, total number of comorbidities, age, sex, race, admit source, Medicaid status, and the 5 most commonly performed procedures. The geo-graphic location of the hospital is also taken into account. The regression model, which is based on the normative pop-ulation for each DRG, is then used to determine an expected total cost of care for each patient. The R2values for these models vary by DRG and range from 0.10 to 0.40 [9].
Other sources of data for the study were the Institutional Profile System of the American Association of Medical Colleges [10],the American Hospital Association annual sur-vey [11],and the InterStudy Competitive Edge database [12], which were used to obtain 1996 organizational-level infor-mation on the AHC hospitals.
Patients
Patient-level data for patients with hip arthroplasty in AHC hos-pitals in 1996 were identified using the following ICD-9-CM procedure codes: 81.51 (total hip replacement), 81.52 (partial hip replacement), and 81.53 (hip replacement revision). Records on patients with one of these codes who received physical therapy during their inpatient stay and who sur-vived their inpatient stay were extracted from the UHC data-base. The final hip arthroplasty sample consisted of 7495 patients from 59 AHC hospitals.
Patient-level data for patients with stroke were identified by the UHC-assigned DRG, which is based on the patient’s discharge diagnosis. Records on patients classified in DRG 14 (specific cerebrovascular disorders except transient ischemic attack) who received physical therapy during their inpatient stay and who survived their inpatient stay were extracted from the UHC database. The final stroke sample consisted of 6342 patients from 59 AHC hospitals.
Variables
External and internal AHC hospital characteristics were cho-sen as proxy measures of resource availability for the AHC hospitals. The external characteristics were health mainte-nance organization (HMO) penetration, medical school research intensity (total grant/contract dollars per number of faculty), and type of medical school affiliation (ie, com-mon ownership of AHC hospital and medical school or other relationship of hospital and school). The internal
characteristics were number of beds, type of ownership (public or private), and mix of privately insured patients seen annually. The mix of privately insured patients was obtained from the UHC database, which classifies the pri-mary payer for each patient in one of the following cate-gories: unknown; Medicaid; Medicare part A; Medicare part B; other government; worker’s compensation; private insur-ance; managed care; no charge; and other nongovernment. We considered the mix of privately insured patients to reflect the percentage of fee-for-service patients seen at the AHC hospital in 1996.
Of the external AHC hospital characteristics, we consid-ered HMO penetration, medical school research intensity, and common ownership of the AHC hospital and medical school to be directly related to resource scarcity. The latter 2 characteristics may need some explanation. Although med-ical school researchers bring in money for the institution through external funding, they are often unable to fully re-cover the costs of research and therefore must rely on clinical revenues to supplement externally funded research and to cover uncompensated research costs [13]. Likewise, an AHC hospital under common ownership with their school of med-icine is likely to contribute more patient care revenues to cross subsidize the medical school than a free-standing AHC hos-pital. This is particularly relevant since the nation’s medical schools have become increasingly dependent on clinical ser-vice revenues to fund their operations [14].
Of the internal characteristics, we considered number of beds to be inversely related to resource scarcity due to the con-cept of economies of scale. We also considered the percentage of privately insured patients to be inversely related to resource scarcity. AHC hospitals with a lower percentage of privately insured patients have less patient care revenue compared with AHC hospitals with a higher percentage of privately insured patients. Finally, we considered public ownership of the AHC hospital to be directly related to resource scarcity. Although patient care revenues are diminishing for all AHC hospitals, these revenues are more scarce for public than for private AHC hospitals [15].
The process measure was utilization of physical therapy ser-vices, which was expressed as a percentage of total charges for each patient (physical therapy charges/total charges ×100). The outcome measure was total cost of care, which was expressed as the ratio of the expected total cost of care/actual cost of care
×100. A number greater than 100 would be indicative of a bet-ter or more cost-efficient outcome than a number less than 100.
The following patient characteristics were used as control variables in the analyses: age, sex, race, Medicaid status, patient severity, and length of stay. The type of hip arthro-plasty procedure (ie, revision or partial/total hip ty) was also used as a control variable for the hip arthroplas-ty data set.
Data Analysis
The data analyses were conducted at the patient level with the data on each patient coded with indicators representing the characteristics of the hospital where the patient received care. Descriptive statistics were calculated for each variable and a correlation matrix was generated to examine for mul-ticolinearity among the variables. Some exploratory regres-sion analyses and residual analyses were also conducted to determine if any of the variables needed transformation.
Ordinary least squares linear regression was used to ex-amine the relationship between AHC hospital characteristics and the use of physical therapy services while controlling for patient characteristics, and to examine the relationship be-tween use of physical therapy services and the total cost of care while controlling for patient and organizational charac-teristics. Separate analyses were conducted for the 2 data sets. The results of the analyses for the 2 data sets were then com-pared in a descriptive manner. Alpha was set at 0.01. All analyses were performed using SAS version 6.12.
Results
Descriptive statistics on the variables in the 2 data sets are presented in Table 1. The mean number of records from each hospital was 127 for the hip arthroplasty data set and 107 for the stroke data set. The correlation analyses indicated that the pair of variables HMO penetration and medical school research intensity were colinear for the hip arthroplasty data set. Because these variables were of interest in the first regression analysis (ie, the relationship between AHC hospi-tal characteristics and use of physical therapy services), sep-arate regression analyses were conducted with either one or the other colinear variable removed.
Preliminary residual analyses indicated that the variables use of physical therapy services and total cost of care were cur-vilinear. Use of physical therapy services was transformed by taking the square root of the value for both data sets [16]. The total cost of care variable was transformed by taking the square root of the value for the hip arthroplasty data set and by taking the natural logarithm of the value for the stroke data set [16].
The results of the regression analyses examining the rela-tionship between AHC hospital characteristics and use of physical therapy services are presented in Table 2. Public ownership of the AHC hospital, percentage of privately in-sured patients, and medical school research intensity were positively associated with increased use of physical therapy services for both data sets. HMO penetration was positively associated with increased use of physical therapy services for the hip arthroplasty data set, and common ownership of the AHC hospital and medical school was inversely related to use of physical therapy services for the stroke data set. Number of beds was positively associated with increased use of physical therapy services for the hip arthroplasty data set
and negatively associated with increased use of physical ther-apy services for the stroke data set.
The results of the analyses examining the relationship between use of physical therapy services and the total cost of care variable are presented in Table 3. Use of physical
thera-py services was directly related to a total cost of care that was less than expected for both data sets.
In both regression analyses, the measures of model fit were higher for the hip arthroplasty data set than for the stroke data set. For each of the regression analyses, there
Table 1.Study Variable Characteristics
Arthroplasty Stroke
Variable (n= 7495) (n= 6342) Data Source
External hospital characteristics
Mean ± SD percentage of HMO penetration 28 ± 15 26 ± 16 ISCE Medical school affiliation, n (%)
Common ownership 4983 (66) 2533 (40) IPS
Other 2512 (34) 3089 (60) IPS
Mean ± SD medical school research 1.13 ± 0.64 1.03 ± 0.56 AHA intensity, $100,000s*
Internal hospital characteristics
Mean ± SD number of beds 582 ± 203 611 ± 225
Ownership, n (%)
Public 3038 (41) 2710 (43)
Private 4457 (59) 3632 (57)
Mean ± SD percentage of privately insured 22 ± 14 20 ± 12 patients
Process measure
Mean ± SD physical therapy use 2.64 ± 1.48 3.49 ± 2.88 Outcome measure
Mean ± SD total cost of care 106 ± 35 131 ± 85
Control Mean ± SD age, yr 64 ± 17 67 ± 16 Sex, n (%) Female 4440 (59) 3345 (53) UHC Male 3055 (41) 2997 (47) Race, n (%) Caucasian/other 6492 (87) 4423 (70) African American 1003 (13) 1919 (28) Medicaid coverage, n (%) Not on Medicaid 6992 (93) 5792 (91) On Medicaid 5031(7) 5501(9) Patient severity, n (%) No substantial CCs 2799 (37) 1835 (29) Moderate CCs 2002 (27) 3505 (55) Major CCs 2285 (31) 1002 (16) Catastrophic CCs 4091(5) NA
Mean ± SD length of stay, days 6.41 ± 4.06 8.64 ± 7.59 Procedure type, n (%)
Partial/total 5827 (78) NA
Revision 1668 (22) NA
AHA = American Hospital Association; CCs = complications and comorbidities; HMO = health maintenance organization; IPS = Institutional Profile System; ISCE = InterStudy Competitive Edge; NA = not applicable; UHC = University HealthSystem Consortium.
were similarities and differences among the coefficients gen-erated from the 2 data sets. In most cases, the regression coef-ficients were similar in sign and significance for both data sets, or a regression coefficient that was significant for one was nonsignificant for the other. Only in the instance of the relationship between number of beds and use of physical therapy services were the regression coefficients from each data set statistically significant and opposite in sign (Table 2).
Discussion
As we hypothesized, several AHC hospital characteristics indicative of resource scarcity were associated with increased use of physical therapy services for the treatment of stroke and hip arthroplasty. The most consistent findings were the positive relationships between both medical school research intensity and public ownership of the AHC hospital and the use of physical therapy services as treatment for both condi-tions. One explanation for these findings may be related to
the scarcity of patient care revenues at AHC hospitals. Because federal funding for research at AHCs is increasing at a slower rate than in previous decades [14] and because the proliferation of managed care is threatening the ability of medical schools to obtain external funding [17], medical schools may be relying more heavily on clinical revenues to supplement externally funded research. Patient care rev-enues are also diminishing at a greater rate for public AHC hospitals than for private ones [15]. AHC hospitals with less patient care revenues, such as AHC hospitals affiliated with research-intensive medical schools and public AHC hospi-tals, may have more incentive or need to contain costs than AHC hospitals with more abundant patient care revenues. As mentioned, increasing the use of ancillary services has been suggested as one way to improve the efficiency of health care delivery. As we hypothesized and our results suggest, in-creased use of physical therapy services was associated with a more cost-efficient outcome for both patient groups.
Table 2.Association between AHC Hospital Characteristics and Use of Physical Therapy Services Beta Coefficients
Arthroplasty Stroke
Variable Specification in Model (n= 7495) PValue (n = 6342) PValue
External hospital characteristics
HMO penetration Continuous variable 0.0046 0.0001 –0.0002 0.6527 Research intensity Continuous variable 0.0353* 0.0002 0.0673 0.001 Medical school affiliation 0–other; 1–common –0.0090 0.4461 –0.1201 0.0001 Internal hospital characteristics
Number of beds Continuous variable 0.0004 0.0001 –0.0002 0.0001
Ownership 0–public; 1–private –0.0735 0.0001 –0.1574 0.0001
Percentage of privately Continuous variable 0.0029 0.0001 0.0029 0.0001 insured patients
Control variables
Age Continuous variable 0.0009 0.003 0.0060 0.0001
Sex 0–female; 1–male –0.0356 0.0003 –0.0122 0.4591
Race 0–Caucasian/other; –0.0439 0.0024 –0.0040 0.8340
1–African American
Patient severity Continuous variable –0.0461 0.0001 –0.1467 0.0001
Medicaid status 0–on Medicaid; –0.0077 0.7010 –0.1196 0.4591
1–not on Medicaid
Length of stay Continuous variable 0.0136 0.0001 0.0091 0.0001
Procedure type 0–partial/total; – 0.1193 0.0001 NA NA
1–revision
Constant 1.1397 0.0001 1.668 0.0001
Model R2 0.10 0.05
NOTE: Dependent variable is use of physical therapy services (square root of [physical therapy charges/total charges] x 100). Significant variables (P < 0.005) are in boldface type. AHC = academic health center; HMO = health maintenance organization; NA = not applicable.
As expected, HMO penetration was positively associated with increased use of physical therapy services for patients with hip arthroplasty but not for patients with stroke. An explanation for these findings may be differences in the care that these patient groups receive. Patients with stroke may be treated using a wide range of interventions based on the pa-tient’s individual deficits and care needs. In contrast, acute care for hip arthroplasty patients is often based on well estab-lished protocols whose adoption might be promoted by man-aged care organizations. Our results suggest that AHC hospi-tals interested in improving the efficiency of care delivery are likely to be most successful with diagnoses that have a rela-tively uncomplicated and predictable course of treatment, such as hip arthroplasty. The regression models generated from the hip arthroplasty data explained more of the variation in the use of physical therapy services and the total cost of care measures than the models generated from the stroke data.
The positive relationship between the mix of privately insured patients treated at an AHC hospital and the use of physical therapy services was consistent between the data sets but contrary to our hypothesis. We used the mix of
pri-vately insured patients treated at the AHC hospital as a proxy for the annual percentage of patients covered by traditional indemnity plans. The mix of privately insured patients, how-ever, probably represented more than just patients in fee-for-service plans, and possibly included preferred provider orga-nization patients who pay discounted fees for service. The questionable validity of this variable as a measure of resource availability may be one explanation for the lack of support for our hypothesis. Why the mix of privately insured patients was positively associated with increased use of physical ther-apy services for both data sets is unclear.
The findings regarding number of beds and medical school affiliation were conflicting, nonsignificant, or contrary to our hypotheses. Number of beds may not be an adequate measure of resource availability at AHC hospitals. Likewise, although the literature suggests that AHC hospitals under common ownership with their medical schools have less flexibility in managed care contracting and contribute more clinical revenues to their affiliated medical schools [1], com-mon ownership may not be an adequate measure of re-source availability at the AHC hospital.
Table 3.Relationship between Use of Physical Therapy Services and Total Cost of Care
Beta Coefficients
Arthroplasty Stroke
Variable Specification in Model (n= 7495) PValue (n = 6342) PValue
Use of physical therapy services* Continuous variable 0.7322 0.0001 –0.1108 0.0001 Control variables
HMO penetration Continuous variable –0.0016 0.2885 –0.0026 0.0001 Medical school affiliation 0–other; 1–common –0.3024 0.0001 0.0077 0.7864 Research intensity Continuous variable 0.0802 0.0494 0.0804 0.0001 Number of beds Continuous variable –0.0010 0.0001 –0.0004 0.0001
Ownership 0–public; 1–private 0.3076 0.0001 0.0365 0.0704
Percentage of privately Continuous variable 0.0092 0.0001 –0.0026 0.0001 insured patients
Age Continuous variable 0.0005 0.7072 0.0021 0.0001
Sex 0–female; 1–male –0.0014 0.9723 0.0069 0.6560
Race 0–Caucasian/other; –0.3397 0.0001 –0.0304 0.0839
1–African American
Patient severity Continuous variable –0.2441 0.0001 0.0651 0.0001
Medicaid status 0–on Medicaid; 0.1770 0.0269 0.0077 0.7864
1–not on Medicaid
Procedure type 0–partial/total; 1–revision – 0.4201 0.0001 NA NA
Constant 9.9353 0.0001 4.5407 0.0001
Model R2 0.10 0.05
NOTE: Dependent variable is total cost of care (expected total cost of care/actual total cost of care x 100) transformed to square root of its value for arthroplasty data and to natural logarithm of its value for stroke data. Significant variables (P < 0.001) are in boldface type. HMO = health maintenance organization; NA = not applicable.
Physical therapy services represent only a small portion of the total charge of care. However, our findings indicate that there is variation in the use of physical therapy services for stroke and hip arthroplasty, even after risk adjustment, and that some of this variation can be explained by AHC hospital characteristics. Our results also suggest that increas-ing the use of physical therapy services durincreas-ing the acute care of patients with stroke and hip arthroplasty may be a cost-efficient strategy, possibly by reducing length of stay. Al-though these findings may suggest that patients receiving more physical therapy services are merely being discharged to subacute facilities more quickly, additional analyses with these data indicated that increased use of physical therapy services for these 2 conditions was also associated with an increased probability of discharge to home [18,19].
Our study was exploratory in nature and used a relative-ly simple model and research design to investigate a topic that has received little attention in the health care literature. Unfortunately, the cross-sectional nature of our study pre-cludes any assessment of causality among the relationships we examined. Furthermore, from a model specification standpoint, the low R2values in our analyses indicate that much of the variation in the dependent variables was unex-plained. Additional organizational-level variables (eg, phys-ical therapy staffing levels) and patient-level variables (eg, preadmission status) would probably explain more of the variation in the dependent variables and should be incorpo-rated in subsequent studies.
Despite the limitations, our findings provide evidence regarding factors that are associated with ancillary service utilization in AHC hospitals and how ancillary service uti-lization may impact the total cost of care. Our findings may provide guidance to physicians and hospital administrators interested in improving the efficiency of care delivery in AHC hospitals. They also may serve as a point of departure for fur-ther research in the area of ancillary service utilization and on the relationship between utilization and outcomes of care. For example, future studies might consider whether other AHC hospital characteristics influence the utilization of phys-ical therapy or other ancillary services. In the case of physphys-ical therapy services, factors such as physicians’ attitudes and therapist-to-patient ratio may have an impact on utilization. Furthermore, it would be useful to know why these charac-teristics impact ancillary service utilization, especially if out-comes of care are affected by ancillary service utilization. Finally, it would be useful to know how ancillary service uti-lization impacts other outcomes of patient care, such as func-tional status, health status, and health-related quality of life.
Author addresses: Dr. Freburger: 725 Airport Road, CB# 7590, University of North Carolina, Chapel Hill, NC 27599-7590, jfreburger@css.unc.edu. Dr. Hurley: PO Box 980203, Richmond, Virginia 23298-0203, rhurley@hsc.vcu.edu.
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