Influences of hospital structure on medical malpractice claim costs.
Abstract: Malpractice is a significant concern in the provision of health care and can be an important performance measure for health care management. Utilizing the resource-based view of the firm, this study examines structural factors affecting the total amount of malpractice claims costs by hospitals in Florida in the year 2000. We found that hospitals employing a greater number of physicians had lower medical malpractice claims costs; however, hospitals employing a greater number of physician residents had higher medical malpractice claims costs. Interestingly, our study found that the number of employed nurses did not affect the medical malpractice claims costs of the hospital.
Article Type: Report
Subject: Medical care, Cost of (Analysis)
Nurses (Management)
Medical personnel (Malpractice)
Medical personnel (Analysis)
Authors: Young, Carlton C.
Williams, David R.
Pub Date: 01/01/2011
Publication: Name: Academy of Health Care Management Journal Publisher: The DreamCatchers Group, LLC Audience: Academic Format: Magazine/Journal Subject: Health care industry Copyright: COPYRIGHT 2011 The DreamCatchers Group, LLC ISSN: 1559-7628
Issue: Date: Jan, 2011 Source Volume: 7 Source Issue: 1
Topic: Event Code: 200 Management dynamics Computer Subject: Company business management
Product: Product Code: 8043100 Nurses NAICS Code: 621399 Offices of All Other Miscellaneous Health Practitioners
Geographic: Geographic Scope: United States Geographic Code: 1USA United States
Accession Number: 263157542

Over the past decade both public attention and scientific interest have been increasingly drawn to issues of quality in health care and, in particular, issues involving patient safety and errors as revealed in reports such as To Err is Human (Institute of Medicine, 1999). Judgments regarding the quality of health care services are often made on the basis of observations about the performance of health care providers on established quality indicators; numerous examples of this can be found through the National Quality Measures Clearinghouse (Berwick, Calkins, McCannon, & Hackbarth, 2006). These judgments are commonly made after observing compliance with clinical quality indicators, such as those used by the Centers for Medicare & Medicaid Services (CMS), i.e. giving aspirin to patients admitted to the emergency department for heart attacks. For those who study the performance of health care organizations there is as much to be learned by observing organizational level outcomes as there is for the clinician in observing patient outcomes. It has been noted that just as clinicians monitor the vital measures of patient health, we should develop and monitor the indicators that inform us of the progress that needs to be made in order to reach organizational goals (Dlugacz, 2006).

One of the most widely accepted models for assessing health care quality was proposed by Avedis Donabedian, and is based on three distinct domains: structure, process, and outcomes. Donabedian defined structure as the environment in which health care is provided; process as the method through which health care is delivered; and outcomes as the result of the health care delivered (Donabedian, 1966). Within these domains, research emphasis is expanding to supplement judgments on health care quality from those based exclusively upon subjective clinical evidence, to include a broader conceptual range of quality as well as judgments based upon objective quantitative indicators of health care services quality derived from these dimensions of quality of care (Van der Bij & Vissers, 1999).

As data becomes increasingly available, metrics drawn from various dimensions of quality of care are gaining in prominence as quality of care indicators (Romano, Geppert, & Davies, 2003). Additional dimensions of care which focus predominantly on quality are being aggressively developed; for example, the dimensions of effectiveness, safety, timeliness, and patient centeredness as reported in the second annual National Healthcare Quality Report (Agency for Healthcare Research and Quality, 2004). In selecting consequential dimensions of quality of care, it is essential to include aspects of care significant to patients, providers, and those responsible for the management of health care organizations. The examination of outcomes as they relate to health care quality meets this tripartite obligation. As health care organizations are held to higher standards of quality, while simultaneously facing restraints on the costs of providing health care, the examination of organizational outcomes that can inform managerial decisions relating to structure and/or process in health care organizations provides a significant perspective that serves to underscore the importance of looking at medical malpractice as a managerial concern (Young, 2005). Efforts to improve patient quality and safety require organizational learning and change, which is dependent on acquiring actionable data on performance from the domains of structure, process and outcomes (Rivard, Rosen, & Carroll, 2006).

Medical malpractice has been an issue of concern in the provision of health care for thousands of years. First codified by the Babylonians circa 1800 B.C., this inclusion serves as an indication of the importance of this issue for most of recorded history (Smith, 1990). Common law doctrine in the U.S. provides that physicians and other medical providers owe a duty of care to their patients. The intent of the law is to serve as a deterrent to providers engaging in professional negligence, and failing that, to provide for compensation to the victim of the negligence. A breach of this duty that results in injury to the patient is commonly known as malpractice, and subjects the provider to tort liability if the provider is found to be legally culpable for the injuries (Congressional Budget Office, 2006).

Concerns over medical malpractice have been critically examined by numerous scholars over the past several decades (Danzon, 1994). Hyman and Silver state that "malpractice liability is the scourge of modern medicine" (Hyman & Silver, 2004, p. 2). The present study examines a dimension of health care organizational outcomes represented by the medical malpractice claims cost experience of Florida acute care hospitals. Given that all hospitals operate within finite resource limits (i.e. their structure), by logical extension medical malpractice costs are associated with reduced access due to increased costs in the provision of health care (Bodenheimer, 2005). Malpractice is a per se representative of diminished quality, as malpractice is by definition a failure to meet accepted standards of professional care (Young, 2005).


The release of the Institute of Medicine's (IOM) report To Err is Human: Building a Safer Health System, which estimated that deaths resulting from medical errors could be as high as 98,000 per year, provided the impetus for expanding emphasis within health care research to more closely examine issues related to quality of care (Institute of Medicine, 1999). The subsequent publication of Crossing the Quality Chasm: A New Health System for the 21st Century further increased the intensity of the study of health care quality and outcomes at all levels of health care delivery (Institute of Medicine, 2001). Yet, in July of 2004, Healthgrades released a study that reported that the state of health care quality and safety had not improved significantly since the release of the 1999 IOM's To Err is Human report and estimated that 195,000 patients die each year from preventable errors (HealthGrades, 2004).

In modern history, one of the goals of imposing malpractice liability has been to align the disincentives of the legal process with economic disincentives to encourage improvement in provider outcomes. In continuing the rationale of economic incentives from a financial perspective, reducing the negative economic consequences of malpractice claims can result in cost savings that may be viewed as positive economic incentives when applied toward meeting organizational goals related to improving access, quality and cost efficiency. Peteraf and Barney (2003) state that a firm has competitive advantage if it creates greater value than its competitor(s) who are producing at closer to breakeven margins. The reduction in costs by avoiding malpractice liability should directly decrease at least some economic costs of the firm, thereby allowing the firm to operate at greater margins with increased competitive advantage.

Medical malpractice claims are not only illustrative of diminished quality, but are associated with reduced access to care and increased costs in the provision of health care. As Donabedian observed: "Wasteful care is either directly harmful to health or is harmful by displacing more useful care" (Donabedian, 1988, p. 4). Hospitals are organizations whose primary purpose is to provide adequate structural and procedural components to deliver health care services that improve health. Medical malpractice is an outcome of this health care delivery process that exemplifies failure to adequately deliver health care. In most instances, this produces not the intended benefits, but a detriment to health. As a failure of the hospital health services delivery process to attain the desired outcome, malpractice does not contribute to organizational efforts to meet goals or achieve its mission. Hospital managers, in the vein of managers in other industries, have an intrinsic responsibility to manage the organization in a manner that is expected to achieve its goals (Davis, Schoorman, & Donaldson, 1997). Therefore as an outcome of an organization, malpractice is an indication of a degree of ineffectiveness in pursuit of the hospital's goals, and when viewed from this perspective, hospital malpractice claim costs are a metric that may be used to evaluate this type of organizational performance failure.

As derived from the management literature, we postulate that the resource-based view theory may offer insights into some degree of the variations in malpractice claims cost performance of the subject hospitals. The resource-based view of the firm (Barney, 1991; Penrsoe, 1959) is a widely acknowledged theory of how organizations obtain competitive advantage over competing firms (Fahy & Smithee, 1999). This theory suggests that organizations are dependent upon their resources and those in the external environment. Resource dependency is a perspective centered on organizational decisions and adaptation in reaction to environmental forces (Pfeffer & Salancik, 2003). The resource-based view proposes that managers make strategic decisions about the mix of resources acquired and maintained by the firm to generate a competitive advantage, and postulates that this competitive advantage may explain variations in performance between similarly situated firms (Barney, Wright, & Ketchen, 2001; Hoopes, Madsen, & Walker, 2003; Short, Palmer, & Ketchen, 2002). It is believed that these competitive advantages allow firms to realize superior performance (Peteraf & Bergen, 2003).

Previous studies have concluded that acquisition and deployment of resources is a factor in hospital performance (Hansen & Wernerfelt, 1989; Hitt, Bierman, Shimizu, & Kochhar, 2001). Within the domains of structure, process, and outcomes, both structure and process are highly dependent on the resources of the hospital providing health care services; in a less direct way, outcomes are dependent on resources as well. The cost of defending medical malpractice claims is a drain on health care financial resources estimated to be approximately $6.5 billion in 2001 (Anderson, Hussey, Frogner, & Waters, 2005). The Congressional Budget Office estimates that "the direct costs that providers will incur in 2009 for medical malpractice liability- which consist of malpractice insurance premiums together with settlements, awards, and administrative costs not covered by insurance-will total approximately $35 billion, or about two percent of total health care expenditures" (Congressional Budget Office, 2009). Prior research indicates that professional staffing levels, hospital size, hospital location, and the teaching status of hospitals are among some of the factors which are conceptually linked to the hospitals resource base, and may influence hospital performance (West, 2001).

The resource-based view of the firm substitutes two alternate assumptions in analyzing sources of competitive advantage. First, the resource-based view assumes that firms within an industry may be heterogeneous with respect to the strategic resources they control. Secondly, it assumes that these resources may not be perfectly mobile across firms, and thus heterogeneity can be long lasting. Of course, not all firm resources hold the potential of sustained competitive advantages. To have the potential of sustained competitive advantage, a firm's resource must have four attributes: (a) it must be valuable, in the sense that it can exploit opportunities and/or neutralize threats in a firm's environment, (b) it must be rare among a firm's current and potential competition, (c) it must be imperfectly imitable, and (d) there cannot be strategically equivalent substitutes for this resource that are valuable, but neither rare or imperfectly imitable (Barney, 1991). Professional employee assets can be a source of competitive advantage for the reason that their professional knowledge and inter-personal working relationships are difficult to imitate. Resources are valuable when they enable an organization to employ strategies that improve its efficiency or effectiveness (Mukamel, Zwanziger, & Bamezai, 2002). As in most markets there are shortages of key personnel (e.g. physicians and nurses) (American Hospital Association, 2006), and hospitals compete with each other for valuable resources in order to gain competitive advantage. An aspect of this competitive advantage is higher quality of care as indicated by lower malpractice costs. Thus, we hypothesize:

Hypothesis 1: Hospitals employing a greater number of registered nurses will experience lower malpractice claims cost than those hospitals employing fewer registered nurses.

Hypothesis 2: Hospitals employing a greater number of licensed practical nurses will experience lower malpractice claims cost than those hospitals employing fewer licensed practical nurses.

Hypothesis 3: Hospitals employing a greater number of physicians will experience lower malpractice claims cost than those hospitals employing fewer physicians.

Hypothesis 4: Hospitals employing a greater number of physician residents will experience lower malpractice claims cost than those hospitals employing fewer physician residents.


This study uses cross-sectional data, with the individual hospital serving as the primary unit of analysis. Cross-sectional analysis depends entirely on variation across units of analysis for the demonstration of association (Kaluzny & Veney, 1980). The sample consists of general, non-federally owned, acute care hospitals in the state of Florida in the year 2000. Data come from three sources: (1) the Florida Department of Insurance's Medical Professional Liability Closed Claims (FMPLC) data set, which contains information on the malpractice claims experience of insured medical professionals and health care organizations, including hospitals, (2) the American Hospital Association's (AHA) Annual Survey of Hospitals data set which contains information on the characteristics of the hospitals in this study, and (3) the Centers for Medicare & Medicaid Services (CMS) which provides case mix information for each hospital.

As medical malpractice is an abstract construct that is difficult to measure directly, researchers typically measure it indirectly in the context of reported outcomes by examining administrative data for various proxy indicators believed to be logically correlated to the phenomenon (Thomas & Petersen, 2003). These proxy indicators include the frequency and magnitude of malpractice events, the frequency of claims made, the frequency of claims closed, the frequency of claims settled before trial, the frequency of claims dismissed by courts, the magnitude (i.e., dollar amount) of malpractice claims made, the magnitude of claims settled, or the magnitude of verdict awards. This study uses the outcome indicator of total malpractice claims costs by hospital, with such claims being considered "closed" as its proxy indicator or dependent variable for medical malpractice. The source of the dependent variable (e.g. hospital total claims cost) is from the FMPLC.

Data from a single state was selected as to eliminate state-to-state variations in tort laws that might influence the incidence of claims filed, or claims closed. Florida data was selected due to the state mandated reporting of claims, the standardization of reported data elements within the Florida dataset, and the public availability of the data. Other studies have examined single state administrative data to access the qualitative outcomes of hospitals. For example, a study of California hospitals on self-reported post-operative complications found that complication rates were under-reported (Romano, Chan, Schembri, & Rainwater, 2002). Furthermore, it is believed that the reporting of medical malpractice claims costs in the Florida data will likely approximate the true incidence of claims costs, as the reporting of claims are a legal requirement of operation, and failure to report claims and their costs is not "incentivised" as the reporting does not obviate the need to defend the claim or mitigate the potential costs of the claim to the hospital.

This study also uses existing descriptive data on the subject hospitals from the AHA's Annual Survey for the year 2000. In order to compare similar facilities, selections were made of hospitals by state (AHA variable coded mlocstcd = 39, Florida; with 245 records selected), type (general medical and surgical, AHA variable coded serv = 10; with 196 records selected), and whether they were short-term or long-term stay (AHA variable coded mlos = 1, short-term; with 194 records selected). Exclusion of hospitals not meeting the above criteria resulted in a reduction in the number of hospitals to be examined, leaving an intermediate sample of 194 hospitals (79 percent of the total Florida general medical and surgical hospitals in 2000 selected from the overall population of 245 Florida facilities reporting claims during the selected period).

The AHA Annual Survey dataset and the FMPLC dataset were merged using the unique federally issued Medicare identification number for each hospital as the matching variable. Only those hospitals that were present in both datasets were selected (i.e., only hospitals with closed claims). This resulted in an intermediate sample of 132 hospitals (53 percent of the total Florida general medical and surgical hospitals in 2000), with 870 medical professional liability claims.

As the sample included hospitals over a large geographic area and included all of the subject hospital's admissions (including DRGs), it was thought best to apply a risk adjustment factor to control for the variance in the severity of illness of patients admitted to the subject hospitals (Iezzoni, 1997). In order to risk adjust the hospital malpractice claims data for the differing severity of illness levels at each hospital, the Centers for Medicare & Medicaid Services' case-mix index data for the sample Florida hospitals were obtained and used as a control variable. We also controlled for tax status (e.g. for-profit/not-for-profit), size of the metropolitan statistical area (MSA) in which the hospital is located (a market characteristic variable), and size of the hospital in terms of the hospital's total expense. This data was derived from the AHA dataset.

The AHA/FMPLC sample dataset was merged with the CMS case-mix index data using the unique federally issued Medicare identification number for each hospital as the matching variable. Only those hospitals that were present in all three datasets were selected. In addition, only FMPLC claims closed in the year 2000 were selected. This resulted in a sample of 119 hospitals (49 percent of the population of general medical and surgical hospitals, and 90 percent of the hospitals with closed claims in 2000).

Our independent variables are number of licensed practical nurses (LPNs), number of registered nurses (RNs), number of physician residents, and number of employed physicians. This information was found in the AHA dataset. The values for total claims costs, metropolitan statistical area, hospital total expense, number of licensed practical nurses, number of registered nurses, number of physician residents, and number of employed physicians had non-normal distributions that were positively skewed. These variables were log transformed, with positive values added prior to logarithmic transformation where appropriate. Logarithmic transformation is an accepted method of correcting for positive skewness (Newton & Rudestam, 1999). After log transformation, an extreme value was identified and eliminated from the sample, leaving a sample of 118 hospitals (48 percent of the population of general medical and surgical hospitals, and 89 percent of the hospitals with closed claims in 2000). The sample size exceeds the recommended seven observations to one variable ratio suggested by Hair (Hair, Anderson, Tatham, & Black, 1998). We performed multiple regression analysis using the SPSS (version 14.0) statistical software package.


Table 1 lists the means, standard deviations and correlations for all 118 hospitals. There are significant positive correlations between the total claims costs and the metropolitan statistical area, case mix index, hospital total expense, number of licensed practical nurses, number of registered nurses, and number of physician residents.

Table 2 presents the coefficients for the multiple regression analysis (N=118). To test for multicollinearity, we assessed the variance inflation factor (VIF). The highest VIF for the variables is less than 6.8, which is below the rule that VIF should not exceed a value of 10 (Hair, et al., 1998). The linear combination of hospital and market characteristics were significantly related to the hospital total claims cost, F (8, 116) = 5.068, p = .000. The sample multiple correlation coefficient was .522, indicating that approximately 27.3 percent of the variance of the criterion variable hospital total claims costs in the sample can be accounted for by the linear combination of hospital and market characteristics. The results from the multiple regression analysis found two variables (e.g. number of physician residents and number of employed physicians) that were statistically significant (p = < .05) within the overall model. There were no statistically significant relationships between our control variables (e.g. MSA size, for-profit status, hospital total expenses, case mix) and the hospital total claims costs.

The results from the analysis verify hypothesis 3 that there is a negative relationship between hospital total claims costs and the number of employed physicians on a hospital's medical staff. Despite the statistically significant findings related to number of physician residents and hospital total claims costs, the results from the analysis do not, however, verify hypothesis 4 in that the direction of the relationship is counter to that proposed. We had hypothesized that there was a negative relationship between number of physician residents and hospital total claims costs. Our findings indicate a positive relationship exists within this model. Neither of the two variables related to hypothesis 1 (e.g. registered nurses) nor hypothesis 2 (e.g. licensed practical nurses) were found to be statistically significant. Thus, we were able to verify only one of the four hypotheses.


There are several limitations to our study related to generalizability. We recognize that the sample size is modest. This is due to the fact that we chose one state and one year. We have previously noted the reasons for limiting our sample to this state and year. We also acknowledge that all the firms selected had "closed" malpractice claims. Thus, we do not know if our results would apply to hospitals with "open" claims or hospitals with no claims whatsoever. Further research is needed using other states, time periods, and comparisons between hospitals with claims and those without claims. A longitudinal study using Florida data may shed light on this issue as well. It would also be beneficial to verify our hypothesis using other proxy indicators (e.g. the frequency and magnitude of malpractice events, the frequency of claims made, the frequency of claims settled before trial, the frequency of claims dismissed by courts) besides the proxy of hospital total claims costs to verify or challenge our results.

In addition, all of the data used consisted primarily of administrative data. The omission of clinical data is acknowledged to be a potential concern which may reduce the predictive potential of the model. Studies which lack clinical data have been criticized for not making adequate adjustments to account for the variation in the underlying patient factors or the hospital case mix. Our study controlled for case mix; however, further research is needed utilizing both finer-grain clinical data and multiple clinical variables. Malpractice claims cost analysis is also subject to hindsight bias (Thomas & Petersen, 2003).

Another limitation is that we only measure the number of employed physicians, physician residents, and nurses. We do not make any distinctions between types (e.g. specialties), training, or credentials of physicians. It may be informative to know if hospitals with physicians with more training (i.e., with more specialists) had higher total medical malpractice costs or not. Also, we do not know what effect other non-employed physicians would have on our model.


This study seeks to understand the relationship between the resources available to a hospital and its experience with medical malpractice costs. This is important to researchers and practitioners alike as malpractice claims costs are associated with reduced access, diminished quality, and increased cost in health care. Borrowing from the resource-based view of the firm, we hypothesized that those firms with greater resources (as measured by our proxy variables consisting of LPNs, RNs, physician residents, and employed physicians) would have fewer hospital total claims costs.

Our study found limited support for our hypotheses, as it found that hospitals with a greater number of employed physicians on its staff had fewer hospital total claims costs. Interestingly and counter to our hypothesis, hospitals with a greater number of physician residents also experienced greater hospital total claims costs. There was no statistically significant relationship between hospital total claims costs and the number of licensed practical nurses or registered nurses. Neither was there a statistically significant relationship between hospital total claims costs and the hospital and market characteristic control variables. For hospitals (and within the limitations as specified), this may suggest that adding employed physicians to their medical staff might reduce medical malpractice costs; whereas, adding physician residents may increase medical malpractices costs, while adding nurses (either LPNs or RNs) would have little effect either way on medical malpractice costs.

Our findings suggest that irrespective of the size of the metropolitan statistical area in which the hospital was located, tax status (control type) of the hospital, size of the hospital as measured by total expense, or severity of illness of patients as measured by their case mix index, hospitals with more employed physicians on their medical staff incurred less total claims costs. There may be several reasons for this. Although the focus of this paper is not on vertical integration, we may be seeing one aspect of the much touted positive effects of vertical integration between hospitals and physicians. Proponents of vertical integration have argued that employing physicians should lead to better coordination of care and reduced costs (Harris, Ozgen and Ozcan, 2000; Wan, Blossom and Allen, 2002). Reduced hospital total claims costs may be one aspect of this.

It also may be that hospitals with more employed physicians were able to attract other physicians in the first place due to its reputation as a "quality" facility. Hospitals with more employed physicians also may have more or better technology compared to hospitals with fewer physicians--it should be noted that physicians (not nurses) drive technology acquisition and use--with access to technology potentially alleviating claims costs--we do not know. Hospitals with more employed physicians may also have more physicians with more specialized training leading to more accurate diagnosis and treatment. Associated with this, hospitals with more employed physicians may have more stringent credentialing criteria for their physicians. Thus, the medical staffs of hospitals with more physicians may be able to restrict physicians with less training from caring for patients that they otherwise would not had there been more (qualified) physicians available. We do not know if this is true, but as mentioned in the above limitations section, more research is needed in this area. Our findings, however, suggest that there is a relationship between larger employed physician bodies and fewer hospital total claims costs.

Our findings also suggest that hospitals with more physician residents had higher hospital total claims costs. We did not make a distinction between hospitals that were primarily academic teaching hospitals and hospitals that were not primarily academic teaching facilities but had physician residents on their medical staff. This is an additional limitation to the study. We viewed physician residents as an additional resource for the hospital, supplementing existing resources and producing a higher quality of outcomes (Allison, et al., 2000). One explanation of our finding may be that physician residents are not a supplement but rather a substitute for physicians. From this perspective, our findings may suggest that hospitals need to be careful of adding physician residents without also adding additional physicians to monitor their activities.

For hospitals, our findings related to nurses suggest that adding nurses have little effect on hospital total claims costs. An interesting aspect of our findings is that the direction of the relationship between types of nurses (e.g. RN, LPN) and hospital total claims costs is different. As hypothesized, there is a negative relationship between hospital total claims costs and registered nurses, though not statistically significant. However, like physician residents, there is a positive relationship between hospital total claims costs and licensed practical nurses. Though not tested, this finding does suggest that hospitals that substitute LPNs for RNs may risk increasing their hospital total claims costs.

When taken together, our results indicate to hospitals that increasing the number of employed physicians may reduce their total claims costs, while adding physician residents may increase their total claims costs. The number of nurses employed by a hospital does not appear to have an effect on hospital total claims costs, however, the mix of nurses (e.g. RNs, LPNs) may be worthy of consideration by hospitals as they think about their effect on hospital total claims costs, and more importantly, their quality of care for which hospital total claims costs could be but one proxy.


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Carlton C. Young, Mississippi State University

David R. Williams, Appalachian State University
Table 1: Means, Standard Deviations, and Correlations for the
Variables Used in the Study

                     Mean      S.D.       1          2           3

1 Hospital Total    13.402    1.588       --
Claims Cost (LN)

2 MSA (LN)           2.607     .131    .270 **

3 For Profit         .500      .502      .021      -.007

4 Case Mix Index     1.450     .236    .311 **    .283 **      -.169

5 Hospital Total    18.291     .890    .443 **    .382 **    -.373 **
Expense (LN)

6 LPNs (LN))         3.785     .602    .366 **    .254 **      -.153

7 RNs (LN)           5.500     .907    .421 **    .364 **    -.284 **

8 Residents (LN)     2.640     .674    .270 **    .241 **    -.331 **

9 Physicians         2.809     .642      .118      .210 *    -.458 **

                       4          5          6          7

1 Hospital Total
Claims Cost (LN)

2 MSA (LN)

3 For Profit

4 Case Mix Index

5 Hospital Total    .681 **
Expense (LN)

6 LPNs (LN))        .433 **    .704 **

7 RNs (LN)          .621 **    .902 **     771 **

8 Residents (LN)    .305 **     497 **    .402 **    .466 **

9 Physicians        .364 **     557 **    .553 **    .535 **


1 Hospital Total
Claims Cost (LN)

2 MSA (LN)

3 For Profit

4 Case Mix Index

5 Hospital Total
Expense (LN)

6 LPNs (LN))

7 RNs (LN)

8 Residents (LN)

9 Physicians        .733 **

* p < .05 ** p < .001 N = 118

Table 2: Multiple Regression Analysis Results

                              Unstandardized Coefficients

                                B      Std. Error

MSA (LN)                       .961       1.084
For Profit                     .319        .310
Case Mix Index                 .168        .752
Hospital Total Expense (LN)    .621        .390
LPNs (LN)                      .519        .362
RNs (LN)                      -.025        .366
Residents (LN)                 .742        .287
Physicians                    -.969        .346

                              Standardized Coefficients       t


MSA (LN)                                 .079                .887
For Profit                               .103               1.032
Case Mix Index                           .025                .223
Hospital Total Expense (LN)              .339               1.593
LPNs (LN)                                .199               1.433
RNs (LN)                                -.014               -.067
Residents (LN)                           .321               2.586
Physicians                              -.399              -2.803


MSA (LN)                      .377
For Profit                    .304
Case Mix Index                .824
Hospital Total Expense (LN)   .114
LPNs (LN)                     .155
RNs (LN)                      .946
Residents (LN)                .011
Physicians                    .006
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