Hospital Topics, 92(4):105–111, 2014 C Taylor & Francis Group, LLC Copyright ISSN: 0018-5868 print / 1939-9278 online DOI: 10.1080/00185868.2014.968495
Managed Care and Organizational Influences on Hospitalist Program Adoption MATTHEW J. DEPUCCIO
while providing cost-effective patient care (Gregory, Baigelman, and Wilson 2003). One way hospitals have tried to meet these goals is by adopting hospitalist programs. Specifically, hospitalists are physicians who work strictly in the hospital and who oversee the care of complex patients so as to reduce the need of transferring patients from one physician to another (Wachter, Whitcomb, and Nelson 1999; Wachter and Goldman 2002). Thus, more coordinated time is devoted to inpatients, consequently leading to greater hospital efficiency. However, less is known about the role managed care and organizational characteristics play in the implementation of hospitalist programs. This research fills this gap by using logistic regression analysis to examine how case mix and managed care pressures influence the adoption of hospitalist programs in general hospitals. As more patients present to hospitals with multiple comorbidities, organizations may find it advantageous to employ hospitalists to mitigate the expenditures associated with complex patients. For instance, researchers have found evidence to suggest that a 10% increase in case mix—hospital’s cost per case relative to the national average measured by Medicare’s diagnosis-related group (DRG) index—is associated with about a 7% increase in hospital costs per discharge (Alexander and Morrisey 1988). If an organization has a high case mix it may be appropriate for that organization to
Abstract. Hospitalists help improve healthcare efficiency, but less is known about the factors that influence hospitals to utilize hospitalists. The purpose of this research was to investigate the influence of managed care and hospital case mix on hospitalist program adoption in general hospitals. Maximum likelihood estimation was used to estimate a nonlinear binary response model to predict hospitalist program adoption. Hospital case mix was positively and significantly associated with the adoption of a hospitalist program while health maintenance organization market share was negatively related to hospitalist program adoption. Managers may want to consider these factors when planning to adopt a hospitalist program. Keywords: hospitalists, health maintenance organizations, managed care, case mix, innovation
I
mproving the quality of patient care in hospitals and reducing costs have been major goals for the healthcare industry for well over a decade (Grol 2001). Concerns of poor patient outcomes associated with insufficient care coordination between providers (Peikes et al. 2009), and excessive costs as a result of rehospitalizations (Mor et al. 2010) are reasons why hospitals adopt innovations that make healthcare delivery more efficient. In a similar vein, there are increasing pressures from third-party payers and healthcare policy makers to reduce costs by constraining utilization of expensive or unnecessary resources and services. In response to environmental pressures such as reduced physician reimbursement, hospitals have had to find ways to increase patient throughput and decrease the patient’s length-of-stay in the hospital
Matthew J. DePuccio is a doctoral student in Health Services Organization and Research in the Department of Health Administration at Virginia Commonwealth University in Richmond, Virginia. 105
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consider a hospitalist program in order to improve cost-effectiveness and the quality of care provided to complex patients. In a similar vein, health maintenance organizations (HMOs) also have a stake in making sure hospitals are efficient with their services. These organizations contract with specific providers who demonstrate that they are willing to aggressively manage patient care in order to secure revenue (Roggenkamp, White, and Bazzoli 2005). Managed care organizations also incentivize enrollees to select a specific group of providers, which in turn has a direct impact on a physician’s income (Dranove, Simon, and White 1998). Overall, the greater the prevalence of managed care enrollees in the hospital’s service area, the less bargaining power the hospital has in negotiating reimbursement rates. Thus, a hospitalist program is one way for hospitals to reduce costs and save money in order to meet priorities set by third-party payers and healthcare policy makers. In the current research it was predicted that hospital case mix and HMO market share would be positively associated with hospitalist program adoption. METHODS Data Sources Data for this analysis come from merging four datasets including (1) the American Hospital Association (AHA) annual survey, (2) the Area Resource File (ARF), (3) the Centers for Medicare and Medicaid Services case mix file, and (4) Health Leaders/InterStudy managed care data. All information from these sources comes from 2003 and 2008 reporting years. Sample and Variables The sample for this analysis includes nonfederal, general medical-surgical hospitals that responded to the AHA survey and had not implemented a hospitalist program as of 2003. The response rate for general medical-surgical hospitals in this survey was high at about 83% (Horwitz and Nichols 2007). However, it is worth mentioning that the AHA survey only began asking whether the hospital had implemented a hospitalist program in 2003. Thus, to reduce selection bias, this analysis did not include the general hospitals that indicated they had adopted a hospitalist program in 2003 (n = 374). Due to the structure of the data it is not possible to understand to what extent a hospitalist program has
been or has not been implemented nor is it safe to assume all hospitalist programs are the same based on the organizational-level data provided. The final sample for this analysis included 2,605 hospitals. Previous research has found a relation between hospitalist programs and patient outcomes, teaching status, hospital size, Medicaid enrollment, and market characteristics. Teaching hospitals that already have residents rounding on patients may benefit less from hospitalist programs (Coffman and Rundall 2005; Lindenauer et al. 2007). Larger hospitals are more likely to have the infrastructure and resources to hire hospitalists and to support the organizational implementation of this particular innovation. Hospitals that see a high number of Medicaid enrollees (e.g., “safety net” hospitals) may benefit from the coordinated services provided by hospitalists. However, Medicaid reimbursement rates tend to be lower than that of private payers. Lower reimbursements associated with Medicaid care may be a significant barrier for some hospitals because it makes acquiring funds to support hospitalist program adoption and staffing needs more difficult (Conway et al. 2008). Researchers have also noted that organizations implementing the hospitalist model are more likely to be in communities with higher per capita income (Harrison and Curran 2009). Also, competitive markets could experience significant increases in hospitalist employment so to keep costs low and gain an advantage over neighboring service areas. Empirical Model The phenomenon that this research is attempting to explain is whether or not economic and organizational factors influence a hospital’s likelihood of adopting a hospitalist program. The dependent variable, hosp prog, takes a value of one if a hospital adopts a hospitalist program sometime between the year 2004 and 2008, and zero otherwise. This binary choice is modeled using a logistic specification with maximum likelihood methods. Robust standard errors that correct for possible heteroscedasticity are calculated. Specifically, P(hosp prog = 1|x) = Pr(β0 +β1 case mix) +(β2 HMO + Xβ +ε) > 0
Where, β 0 is a constant term and case mix is the hospital’s case mix index. This index represents a hospital’s average DRG relative weight. It is created
HOSPITAL TOPICS: Research and Perspectives on Healthcare
by summing DRG weights for all Medicare discharges and dividing by the total number of hospital discharges; HMO is the county-level HMO market share; X is a set of market- and hospital-level control variables measured in 2003; and ε is an error term. Marginal effects were calculated in order to communicate better the magnitude of the effect for this nonlinear model (Devlin and Parkin 2004). The marginal effect of each continuous explanatory variable was measured as the change in the probability that a hospital would adopt a hospitalist program associated with a change in the explanatory variable from the 25th to the 75th percentile of the sample distribution for that variable, leaving the other variables at their sample mean values. For dichotomous variables, the marginal effect was calculated as the change in the probability that a hospital would adopt a hospitalist program associated with a discrete change from 0 to 1 in the explanatory variable, holding all other variables at their mean values. RESULTS Hospital and Market Characteristics Table 1 summarizes hospital characteristics for adopters and nonadopters of hospitalist programs. More than half of all nonprofit hospitals in the sample adopted a hospitalist program between 2004 and 2008. The descriptive statistics suggest that there are significant differences between adopters and nonadopters of hospitalist programs. Namely, larger nonteaching hospitals that were non–churchaffiliated nonprofits, system members, with higher numbers of inpatient stays, admissions, and greater patient complexity were more likely to adopt a hospitalist program between the years 2004 and 2008. At a market level, there were statistically significant differences in hospitalist adopters and nonadopters, in that adopters were situated in environments with higher hospital competition, wealthier patients, and a higher proportion of Black and older adult patients. HMO market share was higher in adopting hospital regions than nonadopting regions. Logit Results Table 2 summarizes the analysis of the binomial logit specification. The premise that a hospital with a higher case mix would be more likely to adopt a hospitalist program, holding other factors constant, was supported at the 99% confidence interval (p < .01). The marginal effect of case mix says that as
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a hospital’s case mix increases from 1.08 to 1.30 the predicted probability of a hospital adopting a hospitalist program between the years 2004 and 2008 increases by 8.8 percentage points, holding all other explanatory variables at their mean values. Thus, hospitals with a high case mix in 2003 were more likely to adopt a hospitalist program between the years 2004 and 2008. On the other hand, HMO market share had a significantly negative effect (p < .10) on the adoption of a hospitalist program. More precisely, as the HMO market share increases from about 0.04 to 0.20 the predicted probability of a hospital adopting a hospitalist program decreases by 3.5 percentage points, holding all other explanatory variables at their mean values. In terms of other organization factors, being a non–church-affiliated nonprofit hospital was positively significantly associated with adopting a hospitalist program. The change in the predicted probability of adopting a hospitalist program increases 8.4 percentage points if the hospital is a non–churchaffiliated nonprofit hospital, holding all other explanatory variables at their means. Inpatient days, total admissions, and the percentage of days covered by Medicaid were also significantly related to the probability of adopting a hospitalist program. For instance, as the number of hospital admissions changes from the 25th to 75th percentile of the sample distribution of admissions, the predicted probability of a hospital adopting a hospitalist program increases about 17 percentage points. Several market factors were significantly related to the probability that a hospital adopted a hospitalist program between 2004 and 2008. An increase in the Herfindahl index from the 25th to 75th percentile increased the probability that a hospital would adopt a hospitalist program by about 14.6 percentage points, holding all other explanatory variables at their means. This suggests it is more likely hospitalist programs are adopted as hospital market competition decreases. An increase from 12% to 17% of the population being 65 years or older decreases the probability the hospital adopted a hospitalist program by 7.9 percentage points. Likewise, as the percentage of office-based physicians working in primary care increases, the probability that a hospitalist program was adopted in subsequent years decreases by about 21 percentage points. DISCUSSION Since the implementation of hospitalist programs in the 1990s, empirical research suggests that
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TABLE 1. 2003 Hospital and Market Characteristics by Hospitalist Program Adoption Hospitalist program adopted: 2004–2008 Yes
No
All study hospitals
χ2
Organizational Variables Ownership status (%) Public Church-affiliated nonprofit For profit Non–church-affiliated nonprofit System affiliation (%) System member Not system member Teaching status (%) COTH member Non-COTH member Bed size Average length-of-stay Medicare case mix Expenses per adjusted admission (US$) Hospital inpatient days Hospital admissions % Medicaid inpatient days
18.75 13.18 11.48 56.59
33.39 11.36 12.87 42.38
28.45 11.98 12.40 47.18
53.30 46.70
46.55 53.45
48.83 51.17
10.61∗∗
6.48 1.16 93.52 98.84 181.89∗∗ 81.26 4.53 4.66 1.16 1.33∗∗ 6,424.75∗∗ 5,400.50 42,437.25∗∗ 16,275.92 8,660.90∗∗ 3,406.44 0.13 0.14∗ Market Variables 0.60 Herfindahl index 0.72∗∗ 0.13 HMO market share 0.17∗∗ % of population Black 0.11∗∗ 0.08 0.15 % of population 65+ 0.13∗∗ 0.47 % of physicians in primary care to total office-based physician 0.31∗∗ Area wage index 0.97∗∗ 0.93 Per capita income 28,765.31∗∗ 25,593.52 Community type/size (%) Rural 40.00 70.14 Urban, population 1 million 27.05 15.94 Sample size 880 1,725
2.96 97.04 115.26 4.62 1.22 5,746.50 25,113.53 5,181.46 0.14
57.45∗∗
0.68 0.14 0.09 0.14 0.42 0.95 26,665.00
— — — — — — —
59.96 7.83 12.51 19.70 2,605
71.39∗∗
— — — — — — —
229.32∗∗
Note. Chi-square or ANOVA tests conducted to assess whether differences between adopters and nonadopters were significantly different. COTH = Council of Teaching Hospitals; HMO = health maintenance organization. ∗ p < .05. ∗∗ p < .01
hospitalists reduce length-of-stay in teaching hospitals and have beneficial outcomes for patients (Auerbach et al. 2002; Diamond, Goldberg, and Janosky 1998). At the same time, such programs have had the ability to reduce average adjusted costs per patient by almost $800 (Meltzer et al. 2002). Though the implications of hospitalist programs are important to improving patient care and hospital efficiency, less is known about the factors that influence hospital policy decision making and the diffusion of hospitalist programs. Thus, this study contributes to hospitalist program research by analyzing the linkages between environmental and organizational
factors and the probability that a hospital will adopt this specific innovation. Overall, hospital case mix was positively associated with hospitalist program adoption and HMO market share was negatively associated with hospitalist program adoption. Contrary to what was expected, as HMO market share increased there was a lower probability of hospitalist program adoption. Even though hospitals and managed care organizations agree on the use of hospitalists (Wachter and Goldman 2002), the literature on hospitalist programs improving the quality of care is less conclusive. As HMOs and managed care plans rely
HOSPITAL TOPICS: Research and Perspectives on Healthcare
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TABLE 2. Binomial Logit Model of Hospitalist Program Adoption (n = 2,605)
Case mix HMO market share Average length of stay System member Public (reference) Church-affiliated nonprofit For profit Non–church-affiliated nonprofit Teaching hospital Bed size Expenses per adjusted admission Inpatient days Admissions % Medicaid days Herfindahl index % of population Black % of population >65 years old Primary care physicians Area wage index Per capita income Constant Wald χ 2 statistic (df = 19) Pseudo R 2
Coefficient
Robust SE
Marginal effecta
2.058206 −0.9819736 −0.0452436 0.1269118
0.4474547∗∗ 0.5028017† 0.0277391 0.1096839
0.0883 −0.0346 −0.0137 0.0263
−0.2279527 −0.3739718 0.4059703 −0.0169957 0.0024611 8.54e −06 −0.0000234 0.0001454 −1.11618 1.164457 0.1550633 −7.624368 −3.07295 0.6878228 0.000014 −3.004138 474.49∗∗ .2073
0.19357 0.1917212† 0.1278975∗∗ 0.3693496 0.0018652 0.0000228 9.40e −06∗ 0.0000408∗∗ 0.5720887† 0.2079023∗∗ 0.3849328 1.453337∗∗ 0.3457352∗∗ 0.5787621 8.93e −06 0.8397619∗∗
−0.0455 −0.0728 0.0844 −0.0035 0.0605 0.0047 −0.1489 0.1697 −0.0258 0.1455 0.0035 −0.0785 −0.2098 0.0208 0.0198
Note. Reference category is public, non–system-affiliated, nonteaching hospital. a Change in adoption probability with a discrete change (from 0 to 1) in each of the dichotomous explanatory variables, and a change in the continuous explanatory variables from the 25th to the 75th percentile of the sample distribution, with all other variables set to their mean values. HMO = health management organization. † p < .10. ∗ p < .05. ∗∗ p < .01.
on clinical performance measures to identify where health delivery needs to be improved, hospitals in environments with high HMO market share may be pressured to not adopt a hospitalist program because there is no clear evidence that hospitalist’s improve patient-specific outcomes. For instance, a recent literature review found that hospitalists provide care that is comparable to other providers, but empirical research has had difficulty discerning whether there are significant differences between the quality of care provided by hospitalists and other practitioners (White and Glazier 2011). Similarly, medical errors are common in hospitals when hospitalists and other physicians or nurses do not communicate frequently or in a timely fashion (Gittell et al. 2008). Researchers (Dressler et al. 2006; Harrison and Curran 2009) have noted that there is often a break in the communication between primary care physicians and hospitalists that could reduce quality of care and potentially result in a hospital readmission. Therefore, with no substantial evidence suggesting hospitalist programs improve
clinical quality, HMOs may be less likely to negotiate subsequent contracts with individual hospitalists or hospitalists groups. It would be advantageous for future researchers to closely examine this relationship since the data for this analysis does not support clinical quality indicators. Wachter and Goldman (2002) noted that managed care organizations do not mandate that hospitals implement hospitalist care, but it would be interesting to further investigate whether early adopters of hospitalist programs are motivated more by economic forces compared to later adopters, who may be more prone to institutional or mimetic forces to adopt the hospitalist program. This research fills a few gaps in the hospitalist research. Previous work by Harrison and Curran (2009) on hospitalist program adoption did not include case mix as a control variable or as a risk-adjuster in their logistic model, thus raising concerns about omitted variable and selection bias. Harrison and Curran (2009) used data on a single reporting year to analyze the relationship between
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organizational and market factors on hospitalist adoption, therefore, could only interpret a correlational relationship between independent and dependent variables. To some extent, this project enhances our ability to suggest a pathway between environmental and organizational characteristics and the implementation of hospitalist programs in general hospitals. CONCLUSIONS Based on the current study’s findings there are practical implications related to hospitalist programs at the organizational and policy levels. These consequences are also important for future health services researchers to evaluate considering they were not of primary significance in this study. First, practitioners that derive their compensation from rounding on patients in the hospital may not like the idea of having a hospitalist program implemented if they believe that the hospitalist will be a major driver for managing throughput, thus altering other staff members’ income (Gregory, Baigelman, and Wilson 2003). These perceptions are not accounted for in the data. Second, health economists have also provided evidence that medical technology and expensive equipment plays a complementary role in the provision of inpatient care and hospitalist utilization. There is a strong positive association between a hospital’s access to advanced technology that treats cancer and Parkinson’s disease and the utilization of hospitalists (David, Helmchen, and Henderson 2009). It is possible that technological regulations at the state-level (e.g., certificate of need regulations) may also affect what type of sophisticated medical technologies are used in the hospital, thus allowing the organization to have bargaining power when looking to incentivize hospitalists to work at their facility. Third, some facilities may do a better job at adapting and responding to current and future customer needs, an aspect of corporate culture (Hurley and Hult 1998). If an organization can align their norms and values (i.e., culture) with that of external stakeholders the hospital may develop the capacity and willingness to consider hospitalist program adoption. However, it was not possible to gauge an organization’s culture or market orientation with the data that were available for this research. Fourth, due to data limitations, it is not possible to draw conclusions about how organizational and market variables influenced the adoption of the hospitalist program before 2003.
In the future it would be useful to see whether institutional factors play a more prominent role in innovation adoption decision making. For example, hospitals that are considered late adopters might have been more likely to implement a hospitalist program if a neighboring hospital had also adopted the program. This type of mimetic behavior is common in markets where competition for patients is high and the organization wants to demonstrate that they are committed to improving quality and reducing costs (Scott and Davis 2007). Finally, little is known about the relationship between hospitalist program designs and incentives for physicians who refer to hospitals that use a hospitalist program. Wachter, Whitcomb, and Nelson (1999) suggested that hospitals that are reimbursed based on DRGs are more likely to support a hospitalist program in order to better manage length-of-stay, but are less likely to influence a primary care physician’s decision to admit the patient to the hospital. Finally, managers and researchers would benefit from research that examines the underlying mechanisms that are important for successful hospitalist program innovation. Similarly, as more hospitals link care delivery innovations to pay-forperformance programs it will become increasingly important to examine how hospitalist care can meet both hospital quality and cost-reduction goals as pressure from hospital stakeholders increases. ACKNOWLEDGMENTS
The author acknowledges Dr. Leslie Stratton, the editors, and the anonymous reviewers for their valuable comments and suggestions on previous versions of this manuscript. REFERENCES Alexander, J. A., and M. A. Morrisey. 1988. Hospital-physician integration and hospital costs. Inquiry 25:388–401. Auerbach, A. D., R. M. Wachter, P. Katz, J. Showstack, R. B. Baron, and L. Goldman. 2002. Implementation of a voluntary hospitalist service at a community teaching hospital: Improved clinical efficiency and patient outcomes. Annals of Internal Medicine 137:859–65. Coffman, J., and T. G. Rundall. 2005. The impact of hospitalists on the cost and quality of inpatient care in the United States: A research synthesis. Medical Care Research and Review 62:379–406. Conway, P. H., R. Tamara Konetzka, J. Zhu, K. G. Volpp, and J. Sochalski. 2008. Nurse staffing ratios: Trends and policy implications for hospitalists and the safety net. Journal of Hospital Medicine 3:193–99. David, G., L. A. Helmchen, and R. A. Henderson. 2009. Does advanced medical technology encourage hospitalist use and their direct employment by hospitals? Health Economics 18:237–47.
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Devlin, N., and D. Parkin. 2004. Does NICE have a costeffectiveness threshold and what other factors influence its decisions? A binary choice analysis. Health Economics 13:437–52. Diamond, H. S., E. Goldberg, and J. E. Janosky. 1998. The effect of full-time faculty hospitalists on the efficiency of care at a community teaching hospital. Annals of Internal Medicine 129:197–203. Dranove, D., C. J. Simon, and W. D. White. 1998. Determinants of managed care penetration. Journal of Health Economics 17:729–45. Dressler, D. D., M. J. Pistoria, T. L. Budnitz, S. C. W. McKean, and A. N. Amin. 2006. Core competencies in hospital medicine: Development and methodology. Journal of Hospital Medicine 1 (S1):48–56. Gittell, J. H., D. B. Weinberg, A. L. Bennett, and J. A. Miller. 2008. Is the doctor in? A relational approach to job design and the coordination of work. Human Resource Management 47:729–55. Gregory, D., W. Baigelman, and I. B. Wilson. 2003. Hospital economics of the hospitalist. Health Services Research 38:905–18. Grol, R. 2001. Improving the quality of medical care. The Journal of the American Medical Association 286:2578–85. Harrison, J. P., and L. Curran. 2009. The hospitalist model: Does it enhance health care quality? Journal of Health Care Finance 35 (3):22–34. Horwitz, J. R., and A. Nichols. 2007. What do nonprofits maximize? Nonprofit hospital service provision and market ownership mix. Cambridge, MA: National Bureau of Economic Research. Hurley, R. F., and G. T. M. Hult. 1998. Innovation, market orientation, and organizational learning: An integration and empirical examination. The Journal of Marketing 62 (3):42–54.
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Lindenauer, P. K., M. B. Rothberg, P. S. Pekow, C. Kenwood, E. M. Benjamin, and A. D. Auerbach. 2007. Outcomes of care by hospitalists, general internists, and family physicians. New England Journal of Medicine 357:2589–600. Meltzer, D., W. G. Manning, J. Morrison, M. N. Shah, L. Jin, T. Guth, and W. Levinson. 2002. Effects of physician experience on costs and outcomes on an academic general medicine service: Results of a trial of hospitalists. Annals of Internal Medicine 137:866–74. Mor, V., O. Intrator, Z. Feng, and D. C. Grabowski. 2010. The revolving door of rehospitalization from skilled nursing facilities. Health Affairs 29:57–64. Peikes, D., A. Chen, J. Schore, and R. Brown. 2009. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries. The Journal of the American Medical Association 301: 603–18. Roggenkamp, S. D., K. R. White, and G. J. Bazzoli. 2005. Adoption of hospital case management: economic and institutional influences. Social Science & Medicine 60:2489–500. Scott, W. R., and G. F. Davis. 2007. Organization of the environment. In Organizations and organizing: Rational, natural, and open systems perspectives, 258–77. Upper Saddle River, NJ: Pearson Prentice Hall. Wachter, R. M., and L. Goldman. 2002. The hospitalist movement 5 years later. The Journal of the American Medical Association 287:487–94. Wachter, R. M., W. F. Whitcomb, and J. R. Nelson. 1999. Financial implications of implementing a hospitalist program. Healthcare Financial Management: Journal of the Healthcare Financial Management Association 53 (3):48–51. White, H. L., and R. H. Glazier. 2011. Do hospitalist physicians improve the quality of inpatient care delivery? A systematic review of process, efficiency and outcome measures. BMC Medicine 9 (58):1–22.
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Managed Care and Organizational Influences on Hospitalist Program Adoption MATTHEW J. DEPUCCIO
while providing cost-effective patient care (Gregory, Baigelman, and Wilson 2003). One way hospitals have tried to meet these goals is by adopting hospitalist programs. Specifically, hospitalists are physicians who work strictly in the hospital and who oversee the care of complex patients so as to reduce the need of transferring patients from one physician to another (Wachter, Whitcomb, and Nelson 1999; Wachter and Goldman 2002). Thus, more coordinated time is devoted to inpatients, consequently leading to greater hospital efficiency. However, less is known about the role managed care and organizational characteristics play in the implementation of hospitalist programs. This research fills this gap by using logistic regression analysis to examine how case mix and managed care pressures influence the adoption of hospitalist programs in general hospitals. As more patients present to hospitals with multiple comorbidities, organizations may find it advantageous to employ hospitalists to mitigate the expenditures associated with complex patients. For instance, researchers have found evidence to suggest that a 10% increase in case mix—hospital’s cost per case relative to the national average measured by Medicare’s diagnosis-related group (DRG) index—is associated with about a 7% increase in hospital costs per discharge (Alexander and Morrisey 1988). If an organization has a high case mix it may be appropriate for that organization to
Abstract. Hospitalists help improve healthcare efficiency, but less is known about the factors that influence hospitals to utilize hospitalists. The purpose of this research was to investigate the influence of managed care and hospital case mix on hospitalist program adoption in general hospitals. Maximum likelihood estimation was used to estimate a nonlinear binary response model to predict hospitalist program adoption. Hospital case mix was positively and significantly associated with the adoption of a hospitalist program while health maintenance organization market share was negatively related to hospitalist program adoption. Managers may want to consider these factors when planning to adopt a hospitalist program. Keywords: hospitalists, health maintenance organizations, managed care, case mix, innovation
I
mproving the quality of patient care in hospitals and reducing costs have been major goals for the healthcare industry for well over a decade (Grol 2001). Concerns of poor patient outcomes associated with insufficient care coordination between providers (Peikes et al. 2009), and excessive costs as a result of rehospitalizations (Mor et al. 2010) are reasons why hospitals adopt innovations that make healthcare delivery more efficient. In a similar vein, there are increasing pressures from third-party payers and healthcare policy makers to reduce costs by constraining utilization of expensive or unnecessary resources and services. In response to environmental pressures such as reduced physician reimbursement, hospitals have had to find ways to increase patient throughput and decrease the patient’s length-of-stay in the hospital
Matthew J. DePuccio is a doctoral student in Health Services Organization and Research in the Department of Health Administration at Virginia Commonwealth University in Richmond, Virginia. 105
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consider a hospitalist program in order to improve cost-effectiveness and the quality of care provided to complex patients. In a similar vein, health maintenance organizations (HMOs) also have a stake in making sure hospitals are efficient with their services. These organizations contract with specific providers who demonstrate that they are willing to aggressively manage patient care in order to secure revenue (Roggenkamp, White, and Bazzoli 2005). Managed care organizations also incentivize enrollees to select a specific group of providers, which in turn has a direct impact on a physician’s income (Dranove, Simon, and White 1998). Overall, the greater the prevalence of managed care enrollees in the hospital’s service area, the less bargaining power the hospital has in negotiating reimbursement rates. Thus, a hospitalist program is one way for hospitals to reduce costs and save money in order to meet priorities set by third-party payers and healthcare policy makers. In the current research it was predicted that hospital case mix and HMO market share would be positively associated with hospitalist program adoption. METHODS Data Sources Data for this analysis come from merging four datasets including (1) the American Hospital Association (AHA) annual survey, (2) the Area Resource File (ARF), (3) the Centers for Medicare and Medicaid Services case mix file, and (4) Health Leaders/InterStudy managed care data. All information from these sources comes from 2003 and 2008 reporting years. Sample and Variables The sample for this analysis includes nonfederal, general medical-surgical hospitals that responded to the AHA survey and had not implemented a hospitalist program as of 2003. The response rate for general medical-surgical hospitals in this survey was high at about 83% (Horwitz and Nichols 2007). However, it is worth mentioning that the AHA survey only began asking whether the hospital had implemented a hospitalist program in 2003. Thus, to reduce selection bias, this analysis did not include the general hospitals that indicated they had adopted a hospitalist program in 2003 (n = 374). Due to the structure of the data it is not possible to understand to what extent a hospitalist program has
been or has not been implemented nor is it safe to assume all hospitalist programs are the same based on the organizational-level data provided. The final sample for this analysis included 2,605 hospitals. Previous research has found a relation between hospitalist programs and patient outcomes, teaching status, hospital size, Medicaid enrollment, and market characteristics. Teaching hospitals that already have residents rounding on patients may benefit less from hospitalist programs (Coffman and Rundall 2005; Lindenauer et al. 2007). Larger hospitals are more likely to have the infrastructure and resources to hire hospitalists and to support the organizational implementation of this particular innovation. Hospitals that see a high number of Medicaid enrollees (e.g., “safety net” hospitals) may benefit from the coordinated services provided by hospitalists. However, Medicaid reimbursement rates tend to be lower than that of private payers. Lower reimbursements associated with Medicaid care may be a significant barrier for some hospitals because it makes acquiring funds to support hospitalist program adoption and staffing needs more difficult (Conway et al. 2008). Researchers have also noted that organizations implementing the hospitalist model are more likely to be in communities with higher per capita income (Harrison and Curran 2009). Also, competitive markets could experience significant increases in hospitalist employment so to keep costs low and gain an advantage over neighboring service areas. Empirical Model The phenomenon that this research is attempting to explain is whether or not economic and organizational factors influence a hospital’s likelihood of adopting a hospitalist program. The dependent variable, hosp prog, takes a value of one if a hospital adopts a hospitalist program sometime between the year 2004 and 2008, and zero otherwise. This binary choice is modeled using a logistic specification with maximum likelihood methods. Robust standard errors that correct for possible heteroscedasticity are calculated. Specifically, P(hosp prog = 1|x) = Pr(β0 +β1 case mix) +(β2 HMO + Xβ +ε) > 0
Where, β 0 is a constant term and case mix is the hospital’s case mix index. This index represents a hospital’s average DRG relative weight. It is created
HOSPITAL TOPICS: Research and Perspectives on Healthcare
by summing DRG weights for all Medicare discharges and dividing by the total number of hospital discharges; HMO is the county-level HMO market share; X is a set of market- and hospital-level control variables measured in 2003; and ε is an error term. Marginal effects were calculated in order to communicate better the magnitude of the effect for this nonlinear model (Devlin and Parkin 2004). The marginal effect of each continuous explanatory variable was measured as the change in the probability that a hospital would adopt a hospitalist program associated with a change in the explanatory variable from the 25th to the 75th percentile of the sample distribution for that variable, leaving the other variables at their sample mean values. For dichotomous variables, the marginal effect was calculated as the change in the probability that a hospital would adopt a hospitalist program associated with a discrete change from 0 to 1 in the explanatory variable, holding all other variables at their mean values. RESULTS Hospital and Market Characteristics Table 1 summarizes hospital characteristics for adopters and nonadopters of hospitalist programs. More than half of all nonprofit hospitals in the sample adopted a hospitalist program between 2004 and 2008. The descriptive statistics suggest that there are significant differences between adopters and nonadopters of hospitalist programs. Namely, larger nonteaching hospitals that were non–churchaffiliated nonprofits, system members, with higher numbers of inpatient stays, admissions, and greater patient complexity were more likely to adopt a hospitalist program between the years 2004 and 2008. At a market level, there were statistically significant differences in hospitalist adopters and nonadopters, in that adopters were situated in environments with higher hospital competition, wealthier patients, and a higher proportion of Black and older adult patients. HMO market share was higher in adopting hospital regions than nonadopting regions. Logit Results Table 2 summarizes the analysis of the binomial logit specification. The premise that a hospital with a higher case mix would be more likely to adopt a hospitalist program, holding other factors constant, was supported at the 99% confidence interval (p < .01). The marginal effect of case mix says that as
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a hospital’s case mix increases from 1.08 to 1.30 the predicted probability of a hospital adopting a hospitalist program between the years 2004 and 2008 increases by 8.8 percentage points, holding all other explanatory variables at their mean values. Thus, hospitals with a high case mix in 2003 were more likely to adopt a hospitalist program between the years 2004 and 2008. On the other hand, HMO market share had a significantly negative effect (p < .10) on the adoption of a hospitalist program. More precisely, as the HMO market share increases from about 0.04 to 0.20 the predicted probability of a hospital adopting a hospitalist program decreases by 3.5 percentage points, holding all other explanatory variables at their mean values. In terms of other organization factors, being a non–church-affiliated nonprofit hospital was positively significantly associated with adopting a hospitalist program. The change in the predicted probability of adopting a hospitalist program increases 8.4 percentage points if the hospital is a non–churchaffiliated nonprofit hospital, holding all other explanatory variables at their means. Inpatient days, total admissions, and the percentage of days covered by Medicaid were also significantly related to the probability of adopting a hospitalist program. For instance, as the number of hospital admissions changes from the 25th to 75th percentile of the sample distribution of admissions, the predicted probability of a hospital adopting a hospitalist program increases about 17 percentage points. Several market factors were significantly related to the probability that a hospital adopted a hospitalist program between 2004 and 2008. An increase in the Herfindahl index from the 25th to 75th percentile increased the probability that a hospital would adopt a hospitalist program by about 14.6 percentage points, holding all other explanatory variables at their means. This suggests it is more likely hospitalist programs are adopted as hospital market competition decreases. An increase from 12% to 17% of the population being 65 years or older decreases the probability the hospital adopted a hospitalist program by 7.9 percentage points. Likewise, as the percentage of office-based physicians working in primary care increases, the probability that a hospitalist program was adopted in subsequent years decreases by about 21 percentage points. DISCUSSION Since the implementation of hospitalist programs in the 1990s, empirical research suggests that
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TABLE 1. 2003 Hospital and Market Characteristics by Hospitalist Program Adoption Hospitalist program adopted: 2004–2008 Yes
No
All study hospitals
χ2
Organizational Variables Ownership status (%) Public Church-affiliated nonprofit For profit Non–church-affiliated nonprofit System affiliation (%) System member Not system member Teaching status (%) COTH member Non-COTH member Bed size Average length-of-stay Medicare case mix Expenses per adjusted admission (US$) Hospital inpatient days Hospital admissions % Medicaid inpatient days
18.75 13.18 11.48 56.59
33.39 11.36 12.87 42.38
28.45 11.98 12.40 47.18
53.30 46.70
46.55 53.45
48.83 51.17
10.61∗∗
6.48 1.16 93.52 98.84 181.89∗∗ 81.26 4.53 4.66 1.16 1.33∗∗ 6,424.75∗∗ 5,400.50 42,437.25∗∗ 16,275.92 8,660.90∗∗ 3,406.44 0.13 0.14∗ Market Variables 0.60 Herfindahl index 0.72∗∗ 0.13 HMO market share 0.17∗∗ % of population Black 0.11∗∗ 0.08 0.15 % of population 65+ 0.13∗∗ 0.47 % of physicians in primary care to total office-based physician 0.31∗∗ Area wage index 0.97∗∗ 0.93 Per capita income 28,765.31∗∗ 25,593.52 Community type/size (%) Rural 40.00 70.14 Urban, population 1 million 27.05 15.94 Sample size 880 1,725
2.96 97.04 115.26 4.62 1.22 5,746.50 25,113.53 5,181.46 0.14
57.45∗∗
0.68 0.14 0.09 0.14 0.42 0.95 26,665.00
— — — — — — —
59.96 7.83 12.51 19.70 2,605
71.39∗∗
— — — — — — —
229.32∗∗
Note. Chi-square or ANOVA tests conducted to assess whether differences between adopters and nonadopters were significantly different. COTH = Council of Teaching Hospitals; HMO = health maintenance organization. ∗ p < .05. ∗∗ p < .01
hospitalists reduce length-of-stay in teaching hospitals and have beneficial outcomes for patients (Auerbach et al. 2002; Diamond, Goldberg, and Janosky 1998). At the same time, such programs have had the ability to reduce average adjusted costs per patient by almost $800 (Meltzer et al. 2002). Though the implications of hospitalist programs are important to improving patient care and hospital efficiency, less is known about the factors that influence hospital policy decision making and the diffusion of hospitalist programs. Thus, this study contributes to hospitalist program research by analyzing the linkages between environmental and organizational
factors and the probability that a hospital will adopt this specific innovation. Overall, hospital case mix was positively associated with hospitalist program adoption and HMO market share was negatively associated with hospitalist program adoption. Contrary to what was expected, as HMO market share increased there was a lower probability of hospitalist program adoption. Even though hospitals and managed care organizations agree on the use of hospitalists (Wachter and Goldman 2002), the literature on hospitalist programs improving the quality of care is less conclusive. As HMOs and managed care plans rely
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TABLE 2. Binomial Logit Model of Hospitalist Program Adoption (n = 2,605)
Case mix HMO market share Average length of stay System member Public (reference) Church-affiliated nonprofit For profit Non–church-affiliated nonprofit Teaching hospital Bed size Expenses per adjusted admission Inpatient days Admissions % Medicaid days Herfindahl index % of population Black % of population >65 years old Primary care physicians Area wage index Per capita income Constant Wald χ 2 statistic (df = 19) Pseudo R 2
Coefficient
Robust SE
Marginal effecta
2.058206 −0.9819736 −0.0452436 0.1269118
0.4474547∗∗ 0.5028017† 0.0277391 0.1096839
0.0883 −0.0346 −0.0137 0.0263
−0.2279527 −0.3739718 0.4059703 −0.0169957 0.0024611 8.54e −06 −0.0000234 0.0001454 −1.11618 1.164457 0.1550633 −7.624368 −3.07295 0.6878228 0.000014 −3.004138 474.49∗∗ .2073
0.19357 0.1917212† 0.1278975∗∗ 0.3693496 0.0018652 0.0000228 9.40e −06∗ 0.0000408∗∗ 0.5720887† 0.2079023∗∗ 0.3849328 1.453337∗∗ 0.3457352∗∗ 0.5787621 8.93e −06 0.8397619∗∗
−0.0455 −0.0728 0.0844 −0.0035 0.0605 0.0047 −0.1489 0.1697 −0.0258 0.1455 0.0035 −0.0785 −0.2098 0.0208 0.0198
Note. Reference category is public, non–system-affiliated, nonteaching hospital. a Change in adoption probability with a discrete change (from 0 to 1) in each of the dichotomous explanatory variables, and a change in the continuous explanatory variables from the 25th to the 75th percentile of the sample distribution, with all other variables set to their mean values. HMO = health management organization. † p < .10. ∗ p < .05. ∗∗ p < .01.
on clinical performance measures to identify where health delivery needs to be improved, hospitals in environments with high HMO market share may be pressured to not adopt a hospitalist program because there is no clear evidence that hospitalist’s improve patient-specific outcomes. For instance, a recent literature review found that hospitalists provide care that is comparable to other providers, but empirical research has had difficulty discerning whether there are significant differences between the quality of care provided by hospitalists and other practitioners (White and Glazier 2011). Similarly, medical errors are common in hospitals when hospitalists and other physicians or nurses do not communicate frequently or in a timely fashion (Gittell et al. 2008). Researchers (Dressler et al. 2006; Harrison and Curran 2009) have noted that there is often a break in the communication between primary care physicians and hospitalists that could reduce quality of care and potentially result in a hospital readmission. Therefore, with no substantial evidence suggesting hospitalist programs improve
clinical quality, HMOs may be less likely to negotiate subsequent contracts with individual hospitalists or hospitalists groups. It would be advantageous for future researchers to closely examine this relationship since the data for this analysis does not support clinical quality indicators. Wachter and Goldman (2002) noted that managed care organizations do not mandate that hospitals implement hospitalist care, but it would be interesting to further investigate whether early adopters of hospitalist programs are motivated more by economic forces compared to later adopters, who may be more prone to institutional or mimetic forces to adopt the hospitalist program. This research fills a few gaps in the hospitalist research. Previous work by Harrison and Curran (2009) on hospitalist program adoption did not include case mix as a control variable or as a risk-adjuster in their logistic model, thus raising concerns about omitted variable and selection bias. Harrison and Curran (2009) used data on a single reporting year to analyze the relationship between
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organizational and market factors on hospitalist adoption, therefore, could only interpret a correlational relationship between independent and dependent variables. To some extent, this project enhances our ability to suggest a pathway between environmental and organizational characteristics and the implementation of hospitalist programs in general hospitals. CONCLUSIONS Based on the current study’s findings there are practical implications related to hospitalist programs at the organizational and policy levels. These consequences are also important for future health services researchers to evaluate considering they were not of primary significance in this study. First, practitioners that derive their compensation from rounding on patients in the hospital may not like the idea of having a hospitalist program implemented if they believe that the hospitalist will be a major driver for managing throughput, thus altering other staff members’ income (Gregory, Baigelman, and Wilson 2003). These perceptions are not accounted for in the data. Second, health economists have also provided evidence that medical technology and expensive equipment plays a complementary role in the provision of inpatient care and hospitalist utilization. There is a strong positive association between a hospital’s access to advanced technology that treats cancer and Parkinson’s disease and the utilization of hospitalists (David, Helmchen, and Henderson 2009). It is possible that technological regulations at the state-level (e.g., certificate of need regulations) may also affect what type of sophisticated medical technologies are used in the hospital, thus allowing the organization to have bargaining power when looking to incentivize hospitalists to work at their facility. Third, some facilities may do a better job at adapting and responding to current and future customer needs, an aspect of corporate culture (Hurley and Hult 1998). If an organization can align their norms and values (i.e., culture) with that of external stakeholders the hospital may develop the capacity and willingness to consider hospitalist program adoption. However, it was not possible to gauge an organization’s culture or market orientation with the data that were available for this research. Fourth, due to data limitations, it is not possible to draw conclusions about how organizational and market variables influenced the adoption of the hospitalist program before 2003.
In the future it would be useful to see whether institutional factors play a more prominent role in innovation adoption decision making. For example, hospitals that are considered late adopters might have been more likely to implement a hospitalist program if a neighboring hospital had also adopted the program. This type of mimetic behavior is common in markets where competition for patients is high and the organization wants to demonstrate that they are committed to improving quality and reducing costs (Scott and Davis 2007). Finally, little is known about the relationship between hospitalist program designs and incentives for physicians who refer to hospitals that use a hospitalist program. Wachter, Whitcomb, and Nelson (1999) suggested that hospitals that are reimbursed based on DRGs are more likely to support a hospitalist program in order to better manage length-of-stay, but are less likely to influence a primary care physician’s decision to admit the patient to the hospital. Finally, managers and researchers would benefit from research that examines the underlying mechanisms that are important for successful hospitalist program innovation. Similarly, as more hospitals link care delivery innovations to pay-forperformance programs it will become increasingly important to examine how hospitalist care can meet both hospital quality and cost-reduction goals as pressure from hospital stakeholders increases. ACKNOWLEDGMENTS
The author acknowledges Dr. Leslie Stratton, the editors, and the anonymous reviewers for their valuable comments and suggestions on previous versions of this manuscript. REFERENCES Alexander, J. A., and M. A. Morrisey. 1988. Hospital-physician integration and hospital costs. Inquiry 25:388–401. Auerbach, A. D., R. M. Wachter, P. Katz, J. Showstack, R. B. Baron, and L. Goldman. 2002. Implementation of a voluntary hospitalist service at a community teaching hospital: Improved clinical efficiency and patient outcomes. Annals of Internal Medicine 137:859–65. Coffman, J., and T. G. Rundall. 2005. The impact of hospitalists on the cost and quality of inpatient care in the United States: A research synthesis. Medical Care Research and Review 62:379–406. Conway, P. H., R. Tamara Konetzka, J. Zhu, K. G. Volpp, and J. Sochalski. 2008. Nurse staffing ratios: Trends and policy implications for hospitalists and the safety net. Journal of Hospital Medicine 3:193–99. David, G., L. A. Helmchen, and R. A. Henderson. 2009. Does advanced medical technology encourage hospitalist use and their direct employment by hospitals? Health Economics 18:237–47.
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