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The Effect of Hospitalists on Average Length of Stay

Vinh, Khanhuyen P., DSc; Walston, Stephen L., PhD; Szychowski, Jeff, PhD; Hernandez, S. Robert, DPh

Journal of Healthcare Management: May-June 2019 - Volume 64 - Issue 3 - p 169–184
doi: 10.1097/JHM-D-18-00042
RESEARCH ARTICLES
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EXECUTIVE SUMMARY Hospitals experiencing financial pressures are seeking to gain efficiencies through innovation. One solution is to engage hospitalists to help reduce the average length of stay (ALOS). This study considers whether and to what extent hospitalists affect ALOS and whether an association exists between the number of hospitalists per occupied bed (density) and ALOS. We examined 2,858 hospitals nationwide, including 20,180 hospital-years of data from 2007 through 2015 derived from the American Hospital Association Annual Survey database. Key findings showed that hospitals using hospitalists reported a statistically significant shorter ALOS than hospitals without hospitalists. The results also indicated a statistically significant decrease in ALOS for an increase in hospitalist full-time equivalent per occupied bed. This study is important because of the generalizability of its results and suggests that hospitals may form partnerships with hospitalists to improve hospital efficiency.

principal, KV Healthcare Solutions, Houston, Texas

director, MHA Program, and professor, David Eccles School of Business, University of Utah, Salt Lake City

associate professor, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham

professor, Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham

For more information about the concepts in this article, contact Dr. Vinh at kvinh43@gmail.com.

Khanhuyen P. Vinh also works as an administrator at her spouse’s hospitalist practice in Houston, Texas. No individuals associated with this practice participated in or influenced this research.

The coauthors declare no conflicts of interest.

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INTRODUCTION

As hospitals grapple with financial pressures, they continually reevaluate their efficiency in care delivery. One innovative solution to mitigate financial pressures is to form partnerships with hospitalists, with the expectation that hospitalists will improve quality and reduce costs, resulting in a reduced average length of stay (ALOS) for patients (Rachoin et al., 2012; Wachter, 2008). The field of hospital medicine has grown dramatically since its emergence in the 1990s. Hospitalists dedicate virtually all of their time working in the hospital; therefore, they develop an expertise in managing and navigating inpatient care. In the 1990s, approximately 1,000 to 2,000 hospitalists practiced in the United States (Lurie, Miller, Lindenauer, Wachter, & Sox, 1999). By 2013, the number of practicing hospitalists increased to about 40,000 (Flansbaum, 2015), as the percentage of hospitals using hospitalists rose from 29.6% in 2003 to 59.8% in 2010 (Data Dig, 2012).

The actual effects of hospitalists, however, remain debatable, with conflicting results reported in the literature. As can be seen in Table 1, studies have shown both longer and shorter ALOS results with the use of hospitalists. This study explores the value of hospitalists in reducing ALOS and related costs. The advantage of this study is that it examines a national sample of 2,858 hospitals over a 9-year period and therefore yields more generalizable and definitive results. This study also investigates the effect of the density of hospitalists on ALOS, which past studies have not explored.

Table 1

Table 1

Reducing ALOS improves a hospital’s efficiency. ALOS reduction contributes to lowering the cost of patient care, which includes a reduction of expenses in person-hours, supplies, and procedures. Besides direct cost savings, indirect costs are also affected. Shorter ALOS reduces medical errors, hospital-acquired conditions, patient safety indicators, and readmissions, and increases bed capacity with more efficient patient throughput (Chartis Group, 2007; Fine et al., 2000; Gregory, Baigelman, & Wilson, 2003; Health Catalyst, 2016; Hostetter & Klein, 2013; Kahane, 2015; Sawyer, 2011).

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CONCEPTUAL FRAMEWORK

The resource-based view theory provides the framework for our study. Organizations respond to economic pressures by implementing strategies that seek competitive advantage. Hospitals react to financial pressures by finding a means to lower costs and improve quality of care. They view hospitalists as one solution to help overcome these pressures. Shorter length of stay should lead to improved efficiency by lowering direct and indirect patient care costs. This financial improvement, successfully maintained over time, will contribute to the hospital gaining sustainable competitive advantage.

Proponents suggest that hospitalists’ contributions are manifold. Their accessibility and extensive experience in the hospital facilitate quick responses to changing health conditions experienced by patients and efficient management of the discharge process (Dynan et al., 2009; Hock Lee, Yang, Soong Yang, Chi Ong, & Seong Ng, 2011). In exclusively managing inpatients daily, hospitalists develop a clinical expertise. Their continuous presence, coupled with hospital familiarity, also promotes greater commitment to hospital improvement initiatives, as compared to nonemployed community primary care physicians. Their buy-in and participation on committees help drive hospital initiatives. Furthermore, outpatient-based primary care physicians increasingly look to hospitalists to care for their patients upon admission to the hospital. The Society of Hospital Medicine (SHM) notes that outpatient-based primary care physicians spend an average of only 12% of their time in hospitals (Izakovic, 2006). Hospitalists also positively contribute to hospitals’ operating margin (Epane et al., 2017).

Opponents reject the benefits of partnership with hospitalists for several reasons. They contend that patient handoffs among hospitalists promote discontinuity of care, leading to information loss among providers. They claim that patient dissatisfaction increases because patients must interact with another provider who is not their primary care physician and with whom they are not familiar. In addition, as hospitalists increasingly take over managing inpatient care, outpatient-based primary care physicians experience a loss of their acute care skills. Opponents also fear that hospitalists provide lower quality of care due to burnout from caring for a high volume of very sick patients (Jungerwirth, Wheeler, & Paul, 2014; Wachter, 1999).

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Hypotheses

We believe that hospitalists positively contribute to reducing ALOS. Hospitalists provide a specialized service in the management and coordination of patient care that affects throughput during an inpatient stay. We also believe that hospitalist density affects ALOS. At an appropriate hospitalist-to-patient ratio, patients will receive the specialized care they need for their changing conditions. Consequently, greater density of hospitalists will help reduce ALOS because more services that are appropriate will be available for patient care, thereby creating efficiencies during the patient stay. A lower hospitalist-to-patient ratio will undermine the capabilities of the hospitalist to provide specialized care and may introduce inefficiencies in the management and coordination of patient care.

Past empirical studies presented equivocal results for the impact of hospitalists on ALOS. For example, of the 16 studies shown in Table 1, 11 found that hospitalists reported shorter length of stay than nonhospitalists, four showed that hospitalists reported no favorable results, and one reported mixed results. A limitation of these studies is that all but five were conducted over a 1-year period (Elliott, Young, Brice, Aguiar, & Kolm, 2014; Everett, Uddin, & Rudloff, 2007; Kuo & Goodwin, 2010; Lindenauer et al., 2007; Meltzer et al., 2002), and all but two studies were conducted at single-site hospitals (Kuo & Goodwin, 2010; Lindenauer et al., 2007). None of these past studies examined the effect of the number of hospitalists per patient. Our study expands upon past studies to analyze hospitals nationwide and longitudinally to better determine whether hospitalists affect a hospital’s ALOS and, if so, to what extent. Our study also extends past research to examine the impact of the number of hospitalists per occupied bed, defined in our study as the density of hospitalists. Therefore, we suggest that:

Hypothesis 1 (H1): Hospitals with hospitalists will have a shorter ALOS than hospitals without hospitalists.

Hypothesis 2 (H2): There is a negative association between ALOS and the density of hospitalist full-time equivalents (FTEs) per occupied bed.

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METHODS

Data Source and Collection

Data sources for this study were the Centers for Medicare & Medicaid Services and the American Hospital Association (AHA) Annual Survey Database for years 2007 through 2015. Case mix index was obtained from the Centers for Medicare & Medicaid Services, and the following questions of primary interest were derived from the AHA survey:

  • Do hospitalists provide care for patients in your hospital? (Yes/No)
  • If yes, please report the total number of FTE hospitalists.
  • Do intensivists provide care for patients in your hospital? (Yes/No)
  • If yes, please report the total number of FTE intensivists (for intensive care specialties).

This study looked only at hospitals that responded to the AHA Annual Survey, provided general medical/surgical services to the majority of their patients, and were assigned to AHA regions 1 through 9. No data were imputed. Overall, this study examined 20,180 hospital-years of data (due to case mix availability) from 2007 through 2015, covering 2,858 unique hospitals identified by a distinct hospital identification number. Hospital-years describe each hospital’s annual data for each year available in the data set.

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Variables

ALOS represents the dependent variable in this study. ALOS is an important outcome measure that gauges the efficiency of patient throughput, thereby affecting the competitive position of the hospital.

The primary independent variables in this study were operationalized based on the reported use and the number of hospitalist FTEs at each institution. Use of hospitalists was first dichotomized, where use was defined as hospitals answering affirmatively to question 1 and identifying >0 hospitalist FTEs on question 2. Nonuse was defined as answering no to question 1 or specifying 0 FTEs on question 2. For the second hypothesis, hospitalist FTEs were subsequently scaled by the number of occupied beds to create a quantitative measure of hospitalist density.

Hospitalists may practice in different hospital units and sometimes may be called intensivists. Some hospitals with low patient census in the intensive care units (ICUs) may depend on hospitalists to care for patients in the medical/surgical and cardiac ICUs in lieu of intensivists only. Intensivists technically refer to physicians who practice only in the general medical/surgical and cardiac ICUs. However, these hospitals may categorize general medical/surgical and cardiac intensivists as hospitalists. These intensivists were therefore included in this study to ensure that all physicians practicing medicine in the hospitalist role were identified. As a result, we replicated our analysis for secondary definitions of hospitalist use and density that included the use of intensivists. A dichotomous hospitalists/intensivists group was created, including hospitals responding affirmatively to the use of hospitalists (as defined earlier) and responding affirmatively to the use of intensivists (responding affirmatively to question 3 and reporting >0 FTEs on question 4). The hospitalist/intensivist density was subsequently scaled by the number of hospital beds.

We identified the following 10 control variables in the study: Medicare patient days, Medicaid patient days, bed size, case mix, location, system affiliation, teaching status, hospital type, region, and year. The subcategories for hospital location consisted of the metro area and the rural area, which comprised micropolitan (with populations of 10,000–50,000) and rural areas. System affiliation and teaching status were binary variables. Hospital type included nonprofit and investor-owned hospitals. Government hospitals were not included in this study because they operate on different inputs and structures that are different from nonprofit and investor-owned hospitals. Reference groups for each of the control variables were designated as follows: rural location, nonteaching status, no system affiliation, and investor-owned hospital type. Region 1 and year 2014 reflected the subset levels with the fewest number of hospitals and served as the reference group for their respective multilevel categorical control variables.

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Statistical Analyses

This retrospective cohort study examined multiple years of data on the same hospitals, where the hospital was the unit of analysis. The statistical analysis used multivariable generalized estimating equations (GEE) with robust standard errors to account for these repeated measures. All analyses were performed with Stata Release 13 (StataCorp, 2013), and statistical significance was evaluated at the 0.05 alpha level.

The primary independent variable for H1 was the use of hospitalists. The primary independent variable for H2 was the hospitalist density, as previously described. All multivariable models included terms for each of the specified control variables. Separate GEE models were generated for each study hypothesis, each including the primary independent variable corresponding to its associated hypothesis.

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RESULTS

Descriptive Statistics

The AHA Annual Survey Database from 2007 through 2015 consisted of 20,180 hospital-years of nonprofit and investor-owned hospitals that responded to the survey, offered primarily general medical/surgical services to the majority of their patients, and were assigned to AHA regions 1 through 9. Hospitalists and hospitalists/intensivists equate to a physician resource, which hospitals use to help manage ALOS. Hospitalist and hospitalist/intensivist FTEs refer to daily FTEs of the respective physician type per occupied bed.

Table 2 presents descriptive statistics for hospitals with and without hospitalists, along with descriptive statistics for hospitals with and without hospitalists/intensivists. Hospitals with hospitalists and hospitalists/intensivists, on average, cared for a higher acuity patient population and reported a higher number of Medicare and Medicaid patient days. These hospitals, on average, tended to be facilities with a larger bed size, located in the metro area, affiliated with a healthcare system, and operated primarily as nonprofit hospitals. They had an almost even distribution in teaching status designation. A greater concentration of hospitals with hospitalists and hospitalists/intensivists was located in the West, South, and Northeast regions.

Table 2

Table 2

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Multivariable Statistics

Hypothesis 1

Table 3 presents the results of the multivariable GEE model used to analyze the relationship between hospitalist and hospitalist/intensivist use and ALOS, adjusting for control variables.

Table 3

Table 3

Overall, our hypothesis was supported. The results of the multivariable GEE model supported H1 (Table 3). ALOS was 0.080 (p = .006) days fewer for hospitals with hospitalists as compared to those without hospitalists. A significant association also existed between ALOS and several control variables. Although minimal, a 1-day increase in Medicare patient day was associated with a < 0.001 (p = .005)-day increase in ALOS. Hospitals that have a teaching accreditation reported an increased ALOS of 0.070 (p = .007) days. Four of nine regions, when compared to the Northeast region, reported a negative association with ALOS. These were the West, Midwest, and South regions. Two of the 9 years also showed a negative association with ALOS when compared to 2014. In descending order of statistical significance, these years were 2012 and 2010. The remaining control variables have no association with ALOS: case mix, bed size, location, system affiliation, hospital type, and all four regions of the West, Midwest, South, and Northeast.

The results of the second multivariable GEE model also supported H1 (Table 3). Hospitals with hospitalists/intensivists reported a shorter ALOS of 0.076 (p = .014) days than hospitals without this physician resource. Otherwise, the same control variables were statistically significant in this model as in the prior GEE model, with only slight differences in beta coefficient values for Region 8 (β = –0.77, p < .0001), Region 4 (β = –0.48, p = .012), and year 2010 (β = –0.066, p = .007).

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Hypothesis 2

The results also supported H2 (Table 4). ALOS was an average of 0.40 (p = .008) fewer days for every hospitalist FTE per occupied bed. ALOS was minimally greater by < 0.001 (p = .006) for every additional Medicare patient day and greater by 0.070 (p = .007) days for teaching hospitals. An association also existed between ALOS and hospitals in the West, Midwest, and South regions, as well as years 2010, 2012, and 2013.

Table 4

Table 4

Likewise, the results of the second multivariate GEE model also supported H2 (Table 4). ALOS decreased by 0.30 (p = .004) days for each additional hospitalist/intensivist FTE per occupied bed. An association existed between ALOS and the same control variables as in the model for hospitalists only, with approximately the same beta coefficients.

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DISCUSSION

The results of this study suggest that a negative association exists between ALOS and hospitalists and hospitalists/intensivists. The presence and density of hospitalists and hospitalists/intensivists are associated with shorter ALOS of a hospital. These results support the rationale behind the beneficial partnership between hospitals and hospitalists. Many hospitals have entered into a partnership with hospitalists to manage their cost of patient stays with improved efficiency, which our research suggests to have occurred with shorter ALOS.

Our results are strengthened by the fact that hospitals with hospitalists or hospitalists/intensivists reported a higher case mix and patient acuity levels when compared to hospitals without this resource. Although we control for case mix in our analyses, shorter ALOS results when hospitalists are used. Furthermore, hospitalists predominantly operate in nonprofit hospitals that do not answer to shareholders. About half of hospitals with hospitalists or hospitalists/intensivists were in teaching hospitals and urban settings, where competition may have required hospitals to seek greater economies of scale and efficiencies.

Other factors that might have influenced the level of competition include the location in regions with high health maintenance organization penetration in some states in the West, South, and Northeast. The need to gain or maintain competitive advantage further forces hospitals to deliver patient care efficiently by using hospitalists. Congruent with the literature, this study found that more hospitals continue to recognize the value of hospitalists, reflected by the growth in the number of hospitals with hospitalists or hospitalists/intensivists across the years of our study.

The results of H1 support the assertion that hospitals using hospitalists or hospitalists/intensivists have a statistically significant shorter ALOS of 0.08 and 0.076 days (Table 3), respectively, than hospitals without hospitalists or hospitalists/intensivists. The decrease in ALOS is important, given that hospitals with hospitalists and hospitalists/intensivists are taking care of sicker (higher case mix) patients. The results still showed a statistically significant negative association between ALOS and the hospitalist partnership. These results highlight the importance of hospitalists in reducing ALOS—they can efficiently navigate processes and systems in hospitals to benefit their patients and hospitals by decreasing ALOS. Because they dedicate their time solely to the hospital setting, hospitalists are available to respond quickly to the changing conditions experienced by patients.

Reduction in ALOS may also lead to cost savings. An ALOS reduction of 0.08 days equates to 1.92 hr saved (0.08 days × 24 hr). The adjusted cost of an inpatient day in the United States was $2,271 in 2015 (Kaiser Family Foundation’s State Health Facts, 2015a). The reduction of ALOS by 0.08 days would lower costs by about $182 per patient per day ($2,271 per day/24 hr × 1.92 hr). A hospital with an average daily census (ADC) of 200 patients would realize about $36,400 in cost savings per day, which translates to approximately $13.3 million per year (200 patients × $182/patient/day × 365 days). In 2015, the adjusted cost per inpatient day was $2,413 at nonprofit hospitals and $1,831 at for-profit hospitals (Kaiser Family Foundation’s State Health Facts, 2015b). Therefore, the potential cost savings at nonprofit hospitals may be higher.

Additionally, shorter ALOS reduces medical errors, hospital-acquired conditions, patient safety indicators, and readmissions. Other benefits include increased bed capacity and patient throughput (Chartis Group, 2007; Fine et al., 2000; Gregory et al., 2003; Health Catalyst, 2016; Hostetter & Klein, 2013; Kahane, 2015; Sawyer, 2011).

The density of hospitalists appears to matter, too. A statistically significant negative association exists between ALOS and hospitalist FTE per occupied bed (–0.40 days) and hospitalist/intensivist FTE per occupied bed (–0.30 days). These results underscore the importance of not only a hospitalist’s presence inside the hospital but also the number of hospitalists per patient. More hospitalists per patient appear to help reduce ALOS. They provide specialized care in the management and coordination of patient care, thereby improving efficiencies.

Hospital managers may consider the net gain of engaging a hospitalist FTE in relation to cost savings. The Medical Group Management Association reported that the median salary of an adult hospitalist was $278,746 in 2015 (Quinn, 2016). As our data showed that ALOS decreases by 0.40 days for every hospitalist FTE per occupied bed, this reduction in ALOS results in approximately $182,000 in savings per day at a hospital reporting ADC of 200 ([0.40 days × 24 hr] × [$2,271/day/24 hr] × 200 ADC). Annually, the cost savings would approximate $66 million. Based on this information, the cost savings from ALOS reduction appear to far outweigh the cost of engaging additional hospitalist FTEs. A detailed cost–benefit analysis of adding more hospitalists would require additional cost estimates associated with hospitalists.

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Study Limitations

Several limitations exist in this research. First, the multivariable GEE models explain correlation and statistical significance but cannot concretely determine causality between hospitals using hospitalists and ALOS. For example, this study does not measure any specific characteristics or variations among hospitalists (such as number of tests ordered, familiarity with hospital processes, the number of years in practice, or whether hospitalists are board certified in their field of practice) that may make a greater impact on ALOS. Second, the level of efficiency and coordination for specific structures and processes of each hospital is unknown, along with whether hospitals invested significant resources to alter their structures and processes before or after their partnership with hospitalists. Third, this study does not investigate the type and contractual type of the hospitalist model in practice at hospitals. Organizations that employ hospitalists or contract with independent hospitalist groups may experience different results based on their hospital relationship. For example, employed hospitalists may schedule more frequent patient handoffs because of their fixed work hours. Independent hospitalist groups may also elect to work more hours; therefore, they may provide longer continuity of care throughout a patient hospital stay. These variations may affect the number of patient handoffs, which may lead to additional unnecessary tests ordered and an impact on ALOS.

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Future Research

Future research may apply the results of this study to better comprehend the contributions of hospital medicine physicians. Studies may explore the impact of hospitalists on quality indicators, medical errors, and readmission rates. Also, further investigation is needed to gauge if and to what extent discontinuity of care by hospitalists affects the patient experience. The possible transfer of patients between multiple hospitalists during their hospital stay may affect the hospitalist–patient relationship. This relationship may be strained further because the hospitalist is not the outpatient-based primary care physician with whom the patient has established a relationship. Additionally, investigation into the optimal number of hospitalists to provide the greatest reduction in ALOS would help hospitals plan their resources to obtain maximum return on investment. An analysis of the density of hospitalists in relation to ALOS may yield a curvilinear relationship that identifies the optimum benefit.

Other areas of interest are the type of hospital–hospitalist relationship and conducting a detailed cost–benefit assessment of engaging additional hospitalist FTEs. Future research could examine which specific relationship model, such as the employed or contractual, best reduces ALOS. A detailed cost–benefit analysis would investigate whether reduction in ALOS justifies the cost of additional hospitalists. It is also of interest to assess whether hospitalists contribute to shortening ALOS in rural hospitals, along with other issues related to engaging hospitalists in this setting.

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CONCLUSION

The purposes of this study were to examine the extent that hospitals with hospitalists and hospitalists/intensivists influenced a hospital’s ALOS and identify the association between ALOS and density of hospitalist FTEs per occupied bed. Findings supported its hypotheses and found that hospitalists contributed to shorter ALOS. Hospitals that used hospitalists to care for patients at their hospitals reported a decrease in ALOS, as compared to hospitals that did not engage hospitalists. Furthermore, ALOS decreased as the number of hospitalist FTEs per occupied bed increased.

These findings highlight the importance of hospitals forming partnerships with hospitalists to become more efficient, while signaling that those hospitals without this partnership should consider making the investment and engaging hospitalists. As healthcare providers continue to face challenges, hospitals must continue to explore new partnerships to improve their efficiency. We suggest that hospitalists may be successful partners.

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ACKNOWLEDGMENT

The authors would like to thank Nancy Borkowski, DBA, CPA, FACHE, FHFMA, for her contributions.

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