International Journal of Operations & Production ManagementService specialization and operational performance in hospitalsVedran Capkun Martin Messner Clemens Rissbacher

Article information:To cite this document:Vedran Capkun Martin Messner Clemens Rissbacher, (2012),"Service specialization and operationalperformance in hospitals", International Journal of Operations & Production Management, Vol. 32 Iss 4 pp.468 - 495Permanent link to this document:http://dx.doi.org/10.1108/01443571211223103

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Service specializationand operational performance

in hospitalsVedran Capkun and Martin Messner

Department of Accounting & Management Control, HEC Paris,Paris, France, and

Clemens RissbacherDepartment of Health, Infrastructure & Science,

Government of the Province of Tirol, Innsbruck, Austria

Abstract

Purpose The purpose of this paper is to examine the link between service specialization andoperational performance in hospitals. Existing literature has mostly been concerned with theperformance effects of operational focus, which can be seen as an extreme form of specialization. It isnot clear, however, whether an effect similar to the focus effect can be observed also in cases wherespecialization takes on less extreme forms. The authors analyze this effect up to and above the effectsof volume, learning and patient selection.

Design/methodology/approach Ordinary least squares (OLS) and two-stage regression modelswere used to analyze patient data from 142 Austrian hospitals over the 2002-2006 period. The samplecontains 322,193 patient groups (841,687 patient group-year observations).

Findings The authors find that increased specialization in a service leads to a more efficientprovision of this service in terms of shorter length of stay. The analysis shows that this effect holdseven after controlling for volume, learning, and patient selection effects. The authors suggest that thepure specialization effect is due to the increased administrative and medical attention that is given to aservice when the relative importance of that service increases.

Practical implications The papers results indicate hospital managers should pay attention to theimpact of specialization when making service-mix decisions. If two services have the same or a similarlevel of operational performance, then this does not mean that hospital managers should be indifferentas to the relative volume of these services.

Originality/value The paper provides additional insights into the impact of service-levelspecialization not examined in prior literature.

Keywords Austria, Hospitals, Customer service management, Operations management,Operational performance, Service specialization

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0144-3577.htm

The authors would like to thank Erkko Autio, Ari-Pekka Hameri, Gilles Hilary, Thomas Jeanjean,Tobias Johansson, Matthias Mahlendorf, Svenja Sommer, Herve Stolowy and participants at theresearch seminar at HEC Paris and University of Lausanne-HEC Lausanne for helpful commentson earlier drafts of this paper. Vedran Capkun and Martin Messner acknowledge the financialsupport of the HEC Foundation. Vedran Capkun is a member of GREGHEC, CNRS unit, UMR2959.

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Received 18 September 2010Revised 7 January 2011,14 March 2011Accepted 15 March 2011

International Journal of Operations& Production ManagementVol. 32 No. 4, 2012pp. 468-495q Emerald Group Publishing Limited0144-3577DOI 10.1108/01443571211223103

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1. IntroductionIn the last few decades, hospitals across the world have faced increased economicpressure. An important part of this pressure stems from the introduction ofperformance-based reimbursement systems that many countries have adopted fromthe 1970s on. These systems are based on taxonomies of services or treatments (e.g. in theform of diagnosis-related groups) and they imply that hospitals are reimbursed for theseservices not on the basis of their actual costs, but on the basis of standard or allowablecosts. Arguably, this has spurred hospitals concern with efficiency, motivating them tolook more closely at their operations and cost drivers. This is well documented in researchthat has sought to understand the performance implications of managerial choicesconcerning operational issues such as location, size, or technology (Butler et al., 1996;Goldstein et al., 2002; Li et al., 2002; Li and Benton, 2003; McDermott and Stock, 2007).

At the same time, hospitals are now increasingly competing for patients.Competition means that clinical outcomes and patient satisfaction become importantpoints of attention for hospital managers, if market share and revenues are to bemaintained or improved. Also, hospitals now increasingly seek to change their serviceoffering in such a way that they can attract patient-cases that are particularlyattractive to them. For example, Krishnan et al. (2004) find that hospitals merge withother hospitals in order to get a higher share of those customers who require servicesthat are more profitable. Several studies have looked into the performance effects ofsuch service-mix related strategies (Duchessi, 1987; Douglas and Ryman, 2003;Krishnan et al., 2004).

In this paper, we consider an organizational phenomenon that has the potential toinfluence both the operational efficiency of a hospital and the attractiveness of itsservice offering. We examine specialization in particular services and how it influencesoperational performance. Specialization refers to the relative emphasis that a hospital(or hospital department) puts on certain types of services and it is opposed to the ideathat a hospital (department) should pay equal attention to all services. For example, if ahospitals department of surgery is specialized in diseases of the digestive system, thenit would have a high share of patients with such diseases compared to patients withother treatment needs. Put another way, the higher the relative importance of a service,the higher is the degree of specialization in this service.

Most of the existing studies on specialization in hospitals consider settings in whichhospitals or hospital units are dedicated to a very limited number of services, such as inthe case of cardiac specialty hospitals (Cram et al., 2005; Barro et al., 2006). In theoperations management literature, such an extreme form of specialization is usuallyreferred to as a focus strategy. According to Skinner (1974, p. 114), focusing on a limited,concise, manageable set of products, technologies, volumes, and markets allows afocused factory to achieve a cost and quality advantage over an unfocused one. Theapplicability of the focus concept in health care settings is not uncontested. Pieters et al.(2010) have recently illustrated that there may be practical difficulties when trying toorganize health care units as focused factories. In particular, they emphasize thathospitals may not always be able to pre-assign a patient to the right focus unit, because apatients treatment needs may not be fully known ex ante. Despite this caveat, manyhealth care units are organized as focused units, and several studies have providedevidence for a positive performance effect of such focus (Cram et al., 2005; Huckman andZinner, 2008; Hyer et al., 2009). Some of these studies have pointed out that the observed

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performance effect may be due to different reasons. For example, Cram et al. (2005)compare patient outcomes for coronary surgeries between general hospitals andspeciality cardiac hospitals. While they find evidence for lower mortality rates inspeciality hospitals as compared to general hospitals, this difference disappears whencontrolling for patient volume and characteristics of admitted patients. In other words,the authors find no evidence for a focus effect above and beyond the effect of volume andpatient selection that goes along with focus. Huckman and Zinner (2008) providecontrasting findings. They examine whether investigative sites that are focused on drugtrials outperform those that mix trials with other clinical services. The results ofHuckman and Zinner (2008) show that focused sites indeed outperform unfocused onesin terms of the number of enrolled patients. This effect holds even after controlling forthe effects of volume and patient selection. In other words, Huckman and Zinner (2008)find evidence for a pure focus effect, which they associate with the narrowness of theservice portfolio. This narrowness allows for a reduction of complexity throughmechanisms such as standardized processes, simpler scheduling, and fewer productchangeovers.

Specialization may not always take the extreme form of focus, however. It may wellbe that organizations are relatively more specialized in a certain type of service than inothers, while at the same time offering a broad portfolio of services (Capon et al., 1988;Farley and Hogan, 1990; Brush and Karnani, 1996). Public hospitals are a case in point.An exclusive focus on a limited number of services will often not be feasible in publichospitals, given their duty to treat all incoming patients whose treatment needs they canfulfill. In such a situation, the service offering will automatically be rather broad and, inthis sense, unfocused. However, a hospital will still have possibilities to emphasizecertain services in which it has a particularly strong expertise or interest or in whichdemand is particularly strong. It can do so by increasing the share of these serviceswithin its service-mix and, in this sense, become more specialized in these services.

In such a setting, the focus effect may not materialize. At the same time, a positiveoperational performance effect resulting from economies of scale, learning, or patientselection may well exist. But will there be some additional (positive or negative) effectof specialization that goes beyond these other effects? Something similar to the focuseffect, but not triggered by the narrowness of the service offering? In our paper, wedemonstrate the existence of such a positive effect of specialization that is independentfrom volume, learning, and patient selection. We suggest that this specialization effectrelates to the increased medical and administrative attention that is given to a servicewhen the relative importance of that service increases.

In order to examine the impact of specialization on operational performance, we usepatient, service, department and hospital level data from a sample of 142 Austrianhospitals over the 2002-2006 period. The results of our empirical analysis show thatincreased specialization in a service leads to a more efficient provision of that service interms of shorter length of stay, even after controlling for volume, learning, and patientselection effects. Our results are both statistically and economically significant and assuch have important implications for hospital management. Given the choice ofincreasing or decreasing the relative importance of a service in a department(specialization), hospital managers have to take into account the consequences of such adecision in terms of an increase (decrease) in operational performance in the servicewhose importance increases (decreases). Taking the impact of specialization into account

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allows hospital managers to evaluate whether changing the relative weight of services isa good decision that increases operational performance at the department and/or at thehospital level.

Our paper is structured in the following way. In the next section we review therelevant literature and develop our hypothesis. In Section 3 we present our dataanalysis design. Section 4 describes the sample that we use for our empirical analysisand the findings of our study. The final section provides a discussion and conclusion.

2. Background literature and hypothesis developmentFocus and performanceThe question of whether or not to focus an organizations activities on a limited set ofproducts, markets and processes has been widely discussed in the managementliterature. There are two levels at which advantages and disadvantages of focus may beobserved. At the operational level, focus relates to the production of a narrow range ofproducts or services. This is the idea behind Skinners (1974) notion of a focusedfactory, which is a factory dedicated to a limited, concise, manageable set of products,technologies, volumes, and markets (Skinner, 1974, p. 114). Such focus, argues Skinner(1974), will allow a plant to achieve a cost and quality advantage over more conventional,unfocused factories:

The focused factory does a better job because repetition and concentration in one area allow itswork force and managers to become effective and experienced in the task required for success.

While operational focus is usually expected to bring improvements in operationalperformance, such improvements account only for part of the potential performanceeffects on the firm level. On the one hand, operational focus should translate into positiveeffects also on the demand side, given that operational improvements in cost or qualitycan help a firm achieve a competitive advantage in the market (Porter, 1985), thusincreasing its sales and profitability. On the other hand, offering only a limited range ofproducts prevents a firm from benefiting from possible demand externalities(Siggelkow, 2003) and may be a more risky strategy when demand for a particularproduct strongly fluctuates (Ketokivi and Jokinen, 2006). In such cases, diversification ofthe product portfolio may well be a better strategy than focus. In a study of the USmutual fund industry, Siggelkow (2003) finds that mutual funds belonging to focusedfund providers outperform competing funds offered by more diversified firms,suggesting a positive focus effect on the operational level, whereas diversified fundproviders are found to be more profitable overall than non-diversified providers,suggesting a negative focus effect on the firm level.

In line with most operations management research, our interest in this paper is withoperational performance only. While the results of existing studies in this area aresomewhat mixed, the majority of them provide evidence for a positive effect of focus onoperational performance (Anderson, 1995; MacDuffie et al., 1996; Bozarth andEdwards, 1997; Tskikriktsis, 2007; Huckman and Zinner, 2008). Some of this literatureuses examples from hospital or medical settings and is thus of particular interest to ourpaper. Hyer et al. (2009), for example, analyze the performance impact of reorganizinghospital services into a focused hospital unit. They conduct a longitudinal case studyin the trauma unit of one US hospital and find evidence for performance improvementsafter the trauma centre was reorganized into a focused unit. More specifically, they find

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that patient length of stay was significantly reduced due to higher standardization andimproved efficiency in work processes. They also find that financial margins increasedconsiderably after the establishment of the trauma unit, mainly due to increasesin revenues.

Barro et al. (2006) examine the performance effects of specialty hospitals, i.e. hospitalsfocused on a particular type of treatments. They distinguish between direct effects(within the specialty hospitals) and spillover effects to non-specialized hospitals. Theirresults suggest that markets experiencing the entry of specialized hospitals exhibitlower expenditure growth after that entry, while patient outcomes remain stable.However, specialty hospitals are found to attract healthier patients and to provide higherlevels of intensive procedures of questionable cost-effectiveness. Thus, while the focusstrategy of specialty hospitals tends to pay off for these hospitals, the effect on the healthcare system overall is more ambiguous (Casalino et al., 2003).

The results of Barro et al. (2006) further suggest that focused hospitals or hospitalunits may outperform unfocused ones for a number of reasons, such as economies ofscale, learning curve effects, or favorable patient selection. The existence of these effectsposes a problem for an understanding of the true benefits of focus, since all these effectsmay also exist in the absence of a focus strategy. Indeed, there is a large body of literaturethat provides empirical evidence on each of these effects individually. In a recent paper,Huckman and Zinner (2008) set out to distinguish the pure focus effect from these othereffects. Their research looks at the pharmaceutical industry, where firms often outsourcethe conduct of drug trials to teams of researchers or so-called investigative sites.Huckman and Zinner (2008) examine whether sites that are focused on drug trialsoutperform those that mix trials with other clinical services. Their results show thatfocused sites indeed outperform unfocused ones in terms of output and productivity.Importantly, they show that this effect holds even after accounting for differences inscale, learning and favorable risk selection.

Hypotheses developmentThe above reviewed literature addresses the performance impact of operational focus.Our interest in this paper is on forms of specialization that are less extreme than the focusstrategy. Generally speaking, being specialized in one service does not necessarily meanthat other services are not offered at all, as the notion of focus would suggest. It maysimply mean that other services have, in relative terms, a lower importance in theservice-mix. We adopt this understanding of specialization as a continuous variablebecause many organizations may not be able to dedicate themselves to a small numberof products or services (focus). Nevertheless, they may have some discretion with respectto the relative emphasis they can put on certain products or services (specialization).This is the case for general hospitals that are the empirical focus of this paper.

While focus constitutes an extreme form of specialization, the theoretical argumentsregarding the benefits of a focus strategy may not apply in the same way to weakerforms of specialization. The focus literature argues that the positive performance effectof focus stems from the small number of products and processes and the correspondingreduction of complexity. Such a reduction of complexity does not necessarily happen,however, if an organization simply increases its degree of specialization in one serviceat the expense of other services, while maintaining a broad portfolio of services. In thiscase, there is no focus, but there is specialization.

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One can imagine that the administrative and medical attention given to a particularservice increases as its relative importance in the service-mix grows. Heightenedawareness will mean that more effort is dedicated to the service in question, simplybecause of the perceived importance of that service for the hospital or department. Insuch a case, an improvement in operational performance can take place. We thushypothesize that specialization in a service will have a positive effect on operationalperformance of that service. We further hypothesize that this effect should existindependently of the effects of volume, learning and patient selection that arecorrelated with, but not the same as, the specialization effect.

Past research on specialization mostly focuses on firm- or industry-wide performanceand its link to service-mix (product-mix) specialization. For example, Brush and Karnani(1996) analyze the impact of specialization on the productivity of US manufacturingplants. Using industry level data on specialization and plant performance, they find onlylimited evidence in support of the link between specialization and performance. In thehospital setting, Farley and Hogan (1990) find that hospital specialization increasedbetween 1980 and 1985; that it lowered costs; and that increases in hospitalspecialization are most visible in those hospitals that have the greatest incentive toreduce costs. Eastaugh (2001) finds specialization in hospitals to be associated with areduction in unit cost per patient admission and the increase in quality of care.

While the results of the above-cited studies indicate that there may be benefits fromspecialization at the hospital level, they do not reveal if there is an underlying pure effectof specialization on the performance of the service in question. The results of the studiesmight be driven by the fact that hospitals select services in which performance effectsare the largest (Eldenburg and Kallapur, 1997) or admit patients with the best outcomes(Kc and Terwiesch, 2009). Or they may be a consequence of an increased volume of aservice or of learning effects over time (Pisano et al., 2001; Huckman and Zinner, 2008). Inthis study, we examine the impact of pure specialization on operational performance,after controlling for the potential other factors that influence performance.

According to our above elaboration, we formulate the following hypothesesregarding the performance impact of service-related variables. The first threehypotheses relate to effects that may go along with specialization. The fourthhypothesis concerns the pure specialization effect that may exist above and beyondthese other effects. This is the effect we are primarily interested in:

H1. An increase in the volume of a service leads to lower length of stay for thatservice.

H2. An increase in cumulative volume (learning) leads to lower length of stay forthat service.

H3. Length of stay will be lower in the case of favorable patient selection.

H4. Specialization in a service reduces length of stay for that service.

3. Research designIn order to test our hypotheses, we need detailed data on hospital operations that allowus to observe how an increase or decrease in a service relates to changes in theperformance of that service. To this end, we use a hospital and patient database providedto us by the Austrian Ministry of Health. Similar to other countries, publicly financed

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hospitals in Austria are reimbursed for the costs of ongoing operations on the basis of aDRG system (BMGFJ, 2008). The Austrian DRG system relies on a detaileddocumentation[1] of patient-cases according to requirements defined by the Ministryof Health[2]. The DRG system only applies to inpatients, while ambulant care is financedon a lump sum basis. The total number of DRG points per hospital and year is translatedinto a money value once the whole number of DRG points of all hospitals within therespective province is known. This money value constitutes the revenues of thehospital and these revenues are supposed to cover the costs for inpatient care[3].

Our database contains information on inpatients in 150 Austrian hospitals[4] for thefive-year period between 2002 and 2006. Data on the treatment of these patients areprovided in aggregated form. For each hospital, patients are classified into patientgroups by age and geographical origin. In total, there are 21 age groups (eachrepresenting a five year age interval) and ten patient origin groups (nine Austrianprovinces and Foreign patients), thus equaling 210 different patient groups. A patientgroup thus contains all patients within a certain age group (e.g. 0-5 years) and within acertain region (e.g. Lower Austria). Information on the treatment of these patients isprovided in the form of 21 disease types as represented by the InternationalClassification of Diseases and Related Health Problems (ICD codes). These ICD codesconstitute broad service groups and may relate to rather different functionalspecializations within a hospital. In order to better capture these specializations, wecombine ICD codes with information on hospital departments, which is also availablefor each patient group. Hospitals contain up to 27 departments each of which may treatup to 21 disease types. This gives us 567 different department-ICD combinations(27 department types x 21 disease types) for every hospital. We refer to thesecombinations as services throughout our paper.

Hospital level data include information on hospital location by Austrian province andownership information. Department level data include information on the type ofdepartment, number of beds, number of intensive care beds, use of capacity, and numberof employees by employee category. For each patient group and service combination, thedatabase contains data on the number of patients and the total length of stay.

We proceed by defining the variables used in our analysis. These variables representthe constructs we referred to in our literature review section, namely, operationalperformance, specialization, volume, learning, patient characteristics, departmentcharacteristics, and finally hospital characteristics. Following our discussion andhypothesis development, we expect specialization to have an impact on operationalperformance above and beyond the impact of volume, learning, patient characteristics,department characteristics, and hospital characteristics.

Operational performanceHow well an organization performs may be measured in a variety of ways, depending onthe dimension of performance one is interested in (Venkatraman and Ramanujam, 1986).In public and non-profit hospitals, financial or economic viability is usually regarded asonly one of several important dimensions of performance. Stakeholders, such as owners,patients, or the general public, usually associate hospital performance to an importantextent with additional criteria such as quality of care or patient satisfaction. Therefore,research on hospital performance should ideally consider both measures of economicperformance and quality measures (Li and Benton, 1996; Berk and Moinzadeh, 1998).

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Li and Benton (1996) classify performance measures into internal quality measures,external financial status measures, external quality measures, and internal costmeasures. Measures relating to the external financial status may concern, for example,the overall financial status of the hospital (e.g. profits) or its market position (e.g. marketshare). Internal quality may be measured at different stages in hospital operations, suchas for clinical outcomes, customer service, or internal processes (Kc and Terwiesch,2009). External quality may be measured through perceived quality and patientsatisfaction. Finally, Li and Benton (1996) divide internal cost measures into measures ofproduction efficiency (length of stay and case mix) and measures of utilization(occupancy rate). While including measures of quality (internal and external) wouldprovide for a more comprehensive picture of performance, most existing studies havefocused on internal cost measures, such as average length of stay (Kim et al., 2000;Eldenburg and Krishnan, 2003; McDermott and Stock, 2007; Hyer et al., 2009; Kc andTerwiesch, 2009), occupancy rate (Kim et al., 2000; Goldstein et al., 2002), or financialstatus measures, such as revenue per patient day (Eldenburg and Krishnan, 2003;Krishnan, 2005). One reason for this may be that data on quality are often not available instandardized form, since hospitals are usually not required to collect and report suchdata. In this study, we analyze operational performance and measure it in terms of lengthof stay (LOS). Consistent with the above cited prior studies, we define length of stay(LOS) as the number of days a patient spends in the hospital.

SpecializationIn a highly regulated public hospital sector such as that of Austria, hospitals usually facestructural constraints which make an exclusive focus on a small number of servicesdifficult or impossible. In Austria, public hospitals are subject to a Versorgungsauftrag(mandate for ambulant and inpatient care), according to which they have to accept allpatients that require treatment that is offered by the hospital. To be sure, not every hospitalprovides a comprehensive range of treatments. Rather, each provincial governmentdefines a plan which stipulates, among others, the department structure and maximumnumber of beds that each hospital is allowed to carry. Size and department structure thusconstitute external constraints for public hospitals. Within these structures, hospitals havesome possibilities to specialize, however. Specialization thereby takes place not so much onthe level of specific treatments, but rather on the level of broader groups of treatments(such as rheumatology)[5]. This is well captured with our definition of services asdepartment and ICD-code combinations. Consequently, we define Service Specialization(SPEC) as the percentage that the service has in the departments total number of patients.Our measure is based on the Specialization Ratio used in Capon et al. (1988)[6]. However,instead of measuring specialization of the department by the percentage of the biggestservice only, we look at each service and its degree of specialization separately. Ourapproach is therefore also different from the one adopted by Farley and Hogan (1990) whoexplore changes in case-mix specialization and their effects on costs. Farley and Hogan(1990, p. 759) define specialization as the extent to which [a hospitals] case-mixproportions deviate from what might be considered normal (Eastaugh, 2001)[7].

Volume effectWe follow Huckman and Zinner (2008) in separating the effect of specialization onperformance from that of volume and learning. Prior literature analyzes the effect

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of volume on performance in hospitals (Luft et al., 1990; Hannan, 1999). Predominantly,this literature finds positive performance effects of volume[8]. Since the volume effectcan be correlated with specialization, given the limited capacity of departments andhospitals, we control for it in our analysis. We use three proxies to account for thevolume effect. Department volume (Volume.D) is computed as the natural log ofthe number of patients in the hospital department (Huckman and Zinner, 2008). Servicevolume (Volume.S) is computed as the natural log of the number of patients in the sameservice. Patient volume (Volume.P) is computed as the natural log of the number ofpatients in the same patient group.

Learning effectPisano et al. (2001) and Huckman and Zinner (2008) argue that there is a positive learningeffect on operational performance. Consistent with Pisano et al. (2001) we define learningas the (over time) cumulative volume of patients. As with volume, we compute threeproxies for the learning effect, i.e. cumulative department volume (Learning.D),cumulative service volume (Learning.S) and cumulative patient volume (Learning.P).

Case characteristicsBoth length of stay of a patient and specialization might be affected by casecharacteristics. As pointed out by Li and Benton (1996, p. 453), measurement of healthcare production efficiency is usually based on the case mix and length of stay. Kc andTerwiesch (2009) find a correlation between patient characteristics and length of stayin an empirical study of cardiothoracic surgery patients. Specialization may also beaffected by patient characteristics, as hospitals are prone to favorable patient selection(Huckman and Zinner, 2008) and changes in the service mix (Eldenburg and Kallapur,1997). Under the case-mix approach patients are classified by their demographiccharacteristics, diagnosed illness and case acuity (Li and Benton, 1996). We thuscontrol in our models for patient age, origin and the service group (type oftreatment)[9]. Patient age is given in our database in the form of 21 age categories infive-year intervals. We use the variable Age as an ordinal variable ranging from one(lowest age group) to 21 (highest age group). In terms of patient origin, we distinguishbetween patients from the hospitals home province and other patients. To this end, wecreate two binary variables. Other Province is a binary variable equal to one if thepatient comes from a province other than the one in which the hospital is located in,and zero otherwise. Foreign is a binary variable that equals one if the patient comesfrom a foreign country and zero otherwise.

Hospital and department characteristicsPrior research has found that various institutional characteristics impact the performanceof hospitals (Goldstein et al., 2002; Li et al., 2002; Douglas and Ryman, 2003; Eldenburg andKrishnan, 2003; Eldenburg et al., 2004; Huckman and Pisano, 2006; McDermott and Stock,2007). These cross-sectional differences between hospitals and hospital departments canalso have an impact on the level of specialization in a particular service. Li et al. (2002)classify these characteristics into long term facility and service choices and intermediateoperations decisions. Long term facility and service choices include bed size, location,outpatient service and service network and equipment/technology. Intermediateoperations decisions include demand management, workforce management,

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and continuous improvement. We control for a majority of these characteristics byintroducing a series of control variables covering a range of department and hospitalcharacteristics identified in prior literature.

Following prior research, we control for size in our models by using the number ofbeds in each department. Eldenburg and Krishnan (2003) and Krishnan (2005) showthat size has a positive impact on operating margin and revenue per patient day,respectively. McDermott and Stock (2007) find size to be positively correlated withlength of stay. We compute the variable D.Beds as the natural log of the number ofbeds in the given department.

In addition to the number of beds we control for the fraction of intensive care beds inthe department. We expect services in departments with higher fraction of intensivecare beds to have a longer length of stay due to the type of patients they are able toadmit. To this end, we use the variable IntBeds which equals one if the department hasintensive care beds and zero otherwise[10].

Organizational characteristics and compensation of staff in hospital departmentsimpact their operational and financial performance (Becker and Gerhart, 1996; Li et al.,2002; Brown et al., 2003; Ittner et al., 2007). To control for these effects we construct twovariables. We use the ratio of medical staff to total staff in the department (MStaff) andthe ratio of medical doctors to medical staff (MDs) as control variables. We expect bothMStaff and MDs to be negatively related to the length of stay, as we expect the morequalified staff to contribute to better performance.

Higher occupancy rate can signify better ability of management to attract patients,but at the same time it can signify unfavorable patient selection. Krishnan (2005) doesnot hypothesize about the sign of the correlation between occupancy rate and revenueper patient day, but finds a negative correlation between occupancy rate and revenueper patient day. We control for occupancy rate (OcRate), the department use ofcapacity, but make no prediction on whether this coefficient should be negatively orpositively associated with length of stay.

We use a measure of complexity as a control variable in our regression models.Number of services (N.Services) is the number of services in the department the servicebelongs to. We control for the number of services to exclude the possibility thatspecialization is driven by the number of services.

To take into account the effect that institutional characteristics can have on length of stayand specialization, we control for a number of available hospital characteristics.Goldstein et al. (2002) and Li et al. (2002) find hospital location has a significant impact on itsperformance (McDermott and Stock, 2007). Eldenburg and Krishnan (2003) argue thatprivate hospitals perform better than their public counterparts (Eldenburg etal., 2004). In ourmodels we therefore control for the province in which the hospital is located by introducingprovince fixed effects. We also introduce ownership fixed effects to control for crosssectional ownership differences. We thereby use ownership dummies representing fourtypes of ownership: religious institutions, municipalities, provinces and private owners. Atthe hospital level, like at the level of departments, we control for size and complexity. We usethe natural log of hospital number of beds as a proxy for the hospital size (H.Size) and thenumber of departments as a proxy for the hospital complexity (N.Departments).

Huckman and Pisano (2006), however, find there are other unobserved hospitalcharacteristics that improve their performance. As a robustness check, we control forhospital fixed effects.

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Determinants of specializationAll of the above discussed variables can have an impact on the level of specialization in aparticular service. However, in addition to those variables, we identify three variablesthat should be correlated with specialization, but should not be correlated with length ofstay: competition intensity, introduction of new services, and change in regulation.Competition intensity in a particular service in a given province should limit the extent towhich a hospital can specialize in that service. Competition intensity, however, shouldnot have a direct impact on patient length of stay. Similar to Douglas and Ryman (2003)and Krishnan (2005) we proxy for competition intensity by using theHerfindahl-Hirschman Index (HHI) as a sum of the squared market shares of hospitalsin the market (Hirschman, 1964). To compute market shares we use the number ofpatients by hospital and the total number of patients in the province. We compute theHHI for every service, separately for every province. For easier interpretation we use(1-HHI) where higher values represent higher levels of competition in a particularservice. Introducing a new service should arguably limit the extent to which a hospitalcan specialize in that service. To proxy for the introduction of new services indepartments, we create a dummy variable NewServ that is equal to one if the service wasintroduced during our 2002-2006 sample period and zero otherwise. Finally, to proxy forregulatory change we use the change in the reimbursement policy of the province ofCarinthia. As of January 1, 2005, Carinthia started to discourage the provision of certaintypes of services within Carinthian hospitals. This concerns especially those serviceswhich can also be consumed in private practices. The new regulation stipulates a list ofservices and a benchmark level for the DRG points that a hospital accumulates with eachof these services. Hospitals are then penalized for surpassing this benchmark levelinsofar as all DRG points that they generate above the level are reimbursed at a reducedrate. To control for this change in regulation, we introduce a variable called RegChangethat is equal to one if the observation is from the province of Carinthia and if year equals2005 or 2006, and zero otherwise. We argue that a change in specialization will beexogenously determined since it is driven by a change in reimbursement policy.

Regression modelsIn order to test our hypothesis that specialization yields positive performance effectswe first estimate the following OLS regression model, with the dependent variable(LOS) being measured on the level of patient groups within each service:

LOS b0 b1Specialization b2Volume b3Learning b4Age b5Other Province b6Foreign diServices Type Dummies b7Beds:D b8IntBeds b9MStaff b10MDs b11OcRate b12N:Services b13Beds:H b14N:Departments diOwner Dummies dkProvince Dummies dlYear Dummies

1

To correct for the presence of serial correlation and heteroskedasticity, we computecluster robust t-statistics (Arellano, 1987; Kezdi, 2004; Stock and Watson, 2008). SinceLOS is computed for each patient group (within a service), while our Specializationvariable is computed at the level of services, we cluster standard errors by clustersdefined by hospital and service type[11]. We introduce fixed effects by service type,ownership type, province, and year. Fixed effects and other control variables account for

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differences between patient characteristics and treatment types (Age, Other Province,Foreign, and Service Type Dummies), department characteristics (Beds.D IntBeds,MStaff, MDs, OcRate, and N.Services), hospital characteristics (Beds.H, N.Department,Owner Dummies, Province Dummies) and different time periods (Year Dummies).

As measures of Volume and Learning, we use department volume and cumulativedepartment volume (Volume.D and Learning.D). As we explain in more detail in theResults section, we run two robustness checks where we replace Volume.D andLearning.D first with Volume.S and Learning.S and then with Volume.P and Learning.P.These two robustness checks yield qualitatively unchanged results. In the remainder ofthe paper, we refer to Volume.D and Learning.D simply as Volume and Learning,respectively.

Based on our hypothesis that specialization has a positive impact on operationalperformance (H4), we expect the coefficient associated with specialization(Specialization) to be negative and statistically significant. In line with H1 and H2,we expect the coefficients associated with Volume and Learning to be negative andstatistically significant, consistent with the results of prior studies. Based on H3, weanticipate that favorable patient selection in terms of age and patient origin has anegative impact on length of stay. We therefore expect our variables Age, OtherProvince, and Foreign to have negative and significant coefficients.

McDermott and Stock (2007) find size to be positively correlated with length of stay.Accordingly, we expect the coefficient associated with the Beds.D variable to be positiveand statistically significant. We further expect the coefficient associated with theIntBeds variable to be positive and statistically significant given that departmentswhich have intensive care beds can be expected to admit patients with longer length ofstay. We expect both the coefficients associated with MStaff and MDs to be negative andstatistically significant, as we expect the more qualified staff to contribute to betterperformance. Higher occupancy rate (OcRate) can signify better ability of managementto attract patients, but at the same time it can signify unfavorable patient selection.Krishnan (2005) does not hypothesize about the sign of the correlation betweenoccupancy rate and revenue per patient day, but finds a negative correlation betweenoccupancy rate and revenue per patient day. We control for occupancy rate, thedepartment use of capacity including outpatients, but make no prediction on whetherthis coefficient should be negatively or positively associated with our LOS variable.Finally, we control for the number of services in the department (N.Services) as ameasure of complexity, but make no prediction on the sign of the coefficient associatedwith this variable.

Consistent with the above cited prior literature, we control for the following hospitalcharacteristics: size (Beds.H), complexity (N.Departments), ownership (OwnershipDummies), and location (Province Dummies). Table I summarises all variables ofinterest and the control variables that can be expected to impact the operationalperformance of a service.

Direction of causality between specialization and length of stayIn our regression model (1), we assume the Specialization variable to be exogenouslygiven. However, it is possible that a decrease in length of stay, as a proxy for hospitalefficiency, attracts more patients to the hospital, which in return allows the hospital tobecome more specialized in the service in question. To account for this potential

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reverse causality, we estimate a two stage generalized method of moments(GMM)[12-14]. In the first stage, we estimate the following regression models:

Specialization b0 b1Volume b2Learning b3Age b4Other Province b5Foreign diServices Type Dummies b6Beds:D b7IntBeds b8MStaff b9MDs b10OcRate b11N:Services b12Beds:H b13N:Departments diOwner Dummies dkProvince Dummies b14NewServ b15RegChg dlYear Dummies

2

Specialization b0 b1Volume b2Learning b3Age b4Other Province b5Foreign diServices Type Dummies b6Beds:D b7IntBeds b8MStaff b9MDs b10OcRate b11N:Services b12Beds:H b13N:Departments diOwner Dummies dkProvince Dummies b14L:Specialization b15RegChg dlYear Dummies

3

In model (2), NewServ and RegChg serve as additional instruments in the regression onthe full sample of observations. In model (3), we replace the NewServ variable by alagged Specialization (L.Specialization) variable as an instrument[15]. This reduces thenumber of observations to 669,016. While including all three instruments into the sameregression, or estimating the regression model with the any combination of instruments,yields qualitatively unchanged results, we choose to use two instruments at a time.Bound et al. (1996) argue that the problem of choice among multiple valid instrumentspersists in large samples (Donald and Newey, 2001). Roodman (2009, p. 135) argues that

Variable Expected sign

Main variables of interestSpecialization Volume Learning (cumulated volume) Patient age Patient origin (other province) Patient origin (foreign) Controls for department characteristicsSize (number of beds) Share of intensive care beds Medical staff in total staff Doctors in medical staff Occupancy rate ?Complexity (number of services) ?Controls for hospital characteristicsProvince ?Ownership ?Size (number of beds) ?Complexity (number of departments) ?

Table I.Independent variablesand their expected impact(sign) on length of stay(LOS)

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a large instrument collection overfits endogenous variables even as it weakens theHansen (1982) test of the instruments joint validity. In the second stage, we estimate theregression model (1). In all regression models we cluster standard errors byhospital-service clusters (as in our main regression models).

Volume and learning as additional endogenous regressorsAs with Specialization, length of stay (LOS), consistent with our potential reversecausality argument, could have an impact on the Volume and Learning variables. In orderto account for all three variables (Specialization, Volume, and Learning) as endogenousregressors, we estimate a two-stage GMM with three first stage regressions as follows:

Specialization; Volume; Learning b3Age b4Other Province b5Foreign diServices Type Dummies b6Beds:D b7IntBeds b8MStaff b9MDs b10OcRate b11N:Services b12Beds:H b13N:Departments diOwner Dummies dkProvince Dummies b14L:Specialization b15L:Volume b16L:Learning b17RegChg dlYear Dummies

4

The regressions are estimated separately for Specialization, Volume, and Learning.L.Specialization, L.Volume, L.Learning, and RegChg are (additional) instrumentalvariables. L.Volume and L.Learning are lagged Volume and lagged Learning variables,respectively. In the second stage, we estimate the regression model (1). For the reasonscited above, we keep the number of instruments to the minimum (number of endogenousregressors plus one).

4. Data analysis and resultsIn order to estimate our regression models, we first impose several restrictions on ourdataset. We exclude observations where all required data are not available. We also excludehospitals and departments that started their operations after 2002 since their characteristicsand service mix is not comparable to existing hospitals and their departments[16]. Our finalsample thus contains data on 142 hospitals (666 hospital-year observations), their876 departments (4,133 department-year observations), their 14,786 services(60,248 service-year observations) and finally their 322,193 patient groups(841,687 patient group-year observations). For each patient group observation, thedatabase contains the number of patients in the group and length of stay (total for group).

We provide descriptive statistics in Table II.Panel A shows the distribution of hospitals and observations by Austrian

provinces. Austrian hospitals operate in nine Austrian provinces with the number ofhospitals ranging from five (Burgenland) to 26 (Steiermark), with a mean of 16 and amedian of 12 hospitals. Panel B of Table II shows the distribution of hospitals andobservations by ownership type. More than half of the Austrian hospitals are owned

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N hospitals % N observations %Panel A distribution of observations by provinceBurgenland 5 3.52 33,646 4.00Carinthia 11 7.75 62,146 7.38Lower Austria 24 16.90 121,568 14.44Upper Austria 24 16.90 148,876 17.69Salzburg 10 7.04 75,499 8.97Steiermark 26 18.31 109,753 13.04Tirol 12 8.45 91,109 10.82Vorarlberg 6 4.23 35,896 4.26Vienna 24 16.90 163,194 19.39Total 142 100.00 841,687 100.00Panel B distribution of observations by ownershipReligious order 31 21.83 174,712 20.76Municipal 19 13.38 133,677 15.88Provincial 89 62.68 524,953 62.37Private 3 2.11 8,345 0.99Total 142 100.00 841,687 100.00Panel C distribution of observations by year

Year N %2002 172,661 20.512003 171,084 20.332004 168,157 19.982005 167,971 19.962006 161,814 19.22Total 841,687 100.00Panel D descriptive statisticsVariable N Mean SD Min. p25 Mdn p75 Max.LOS 841,687 7.05 6.52 1.00 3.00 5.00 8.85 39.50Specialization 841,687 0.15 0.20 0.00 0.02 0.06 0.21 0.71Volume 841,687 7.96 0.91 0.00 7.59 8.01 8.46 10.26Learning 841,687 8.88 1.09 0.00 8.31 8.99 9.56 11.75Age 841,687 10.70 4.84 1.00 7.00 11.00 15.00 21.00Other province 841,687 0.33 0.47 0.00 0.00 0.00 1.00 1.00Foreign 841,687 0.07 0.26 0.00 0.00 0.00 0.00 1.00Beds.D 841,687 4.07 0.83 0.00 3.56 4.11 4.55 6.46IntBeds 841,687 0.31 0.46 0.00 0.00 0.00 1.00 1.00MStaff 841,687 0.91 0.06 0.40 0.87 0.91 0.96 1.00MDs 841,687 0.22 0.06 0.00 0.19 0.21 0.26 0.73OcRate 841,687 8.97 0.17 8.35 8.89 9.01 9.08 9.26N.Services 841,687 16.44 2.66 1.00 15.00 17.00 18.00 20.00Beds.H 841,687 6.07 0.84 3.37 5.40 5.93 6.86 7.61N.Departments 841,687 10.25 6.16 1.00 5.00 9.00 15.00 24.00HHI 841,687 0.70 0.27 0.00 0.63 0.81 0.89 0.95NewServ 841,687 0.02 0.13 0.00 0.00 0.00 0.00 1.00RegChange 841,687 0.03 0.17 0.00 0.00 0.00 0.00 1.00

Notes: The sample of patient groups comes from the Austrian Ministry of Health database; the samplecontains information on patients in 142 Austrian hospitals for the five-year period between 2002 and 2006; thisincludes detailed information on, hospitals, departments and 841,687 patient group-year observations; hospitallevel data include information on hospital location by Austrian federal state and ownership information;department level data include information on the department type, number of beds, number of intensive carebeds, use of capacity, and information on number of employees by employee category; for each service, thedatabase contains the number of patients in the group, length of stay (total for group); panel A shows thedistribution of hospitals and observations by Austrian provinces; panel B shows the distribution of hospitalsand observation by the type of ownership; panel C shows the distribution of observations by year; panel Dshows descriptive statistics on variables used in our study; variables are defined in the Appendix

Table II.Sample characteristics

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by provinces (89 out of 142 hospitals), while the rest is repartitioned between religiousorders (31 hospitals), municipalities (19 hospitals) and private owners (three hospitals).Panel C shows the distribution of observations by year.

The number of hospital departments ranges from one to 24 (a mean of six and amedian of five departments). The number of services per hospital department rangesfrom one to 20, with a mean of 17 and a median of 18 while the number of services perhospital ranges from one to 424, with a mean of 104 and a median of 84 (untabulated).Big differences in hospital characteristics are the result of our sample containing allpublicly funded, non-profit hospitals in Austria, including the highly focused ones(e.g. psychiatric hospitals with only one department). Excluding these specializedhospitals from our sample does not alter our findings. We included them since thesehospitals can easily be compared at the department level to other hospitals, whichprovide the same types of service in equivalent departments.

In Panel D of Table II we show descriptive statistics of the variables used in ouranalysis. For brevity, we do not show descriptive statistics for the Volume.P,Learning.P, Volume.S, and Learning.S variables as we do not tabulate results includingthese four variables. Length of stay (LOS) ranges from one day to 39 and half days,with a mean of seven and a median of five days. Across all observations, specializationranges from less than one to 71 percent of department number of patients, with a meanof 20 percent and a median of 6 percent.

Table III shows Spearman pairwise correlations between our variables. There is ahigh and statistically significant correlation between Volume and Learning regardlessif we measure these variables at the department, service or patient group level (allcorrelations exceed 0.85). There is also a high and statistically significant correlationbetween department number of beds (Beds.D) and department volume and learning(0.86 with Volume.D and 0.72 with Learning.D).

We present our main results in Table IV.Table IV shows estimates of our regression model (1). The results are consistent with

our hypothesis that specializing in a service yields shorter length of stay. The result isstatistically and economically significant. For a change in Specialization by 10 percent,LOS would decrease by one-third of a day (0.31 fraction of a day), which compared to themedian length of stay of five days represents a significant improvement in performance.

Coefficients associated with the Volume and Learning variables are negative andstatistically significant, consistent with our predictions. They imply that positiveperformance effects of volume and learning exist. Patient characteristics all have asignificant impact on length of stay. The results are shown for Volume.D and Learning.Dvariables. In spite of high and statistically significant correlation between Volume.D andLearning.D (0.8547), postestimation tests indicate no presence of multicollinearity in ourregression models. Individual variance inflation factors (VIFs) remain below ten, while themean VIF remains below six (see, e.g. Kutner et al., 2004 for interpretation of VIFs). We runtwo robustness checks. First, we replace Volume.D and Learning.D with patient groupvolume (Volume.P) and cumulative volume (Learning.P), respectively, (untabulated). Ourresults remain qualitatively unchanged. Second, we replace Volume.D and Learning.Dwith service volume (Volume.S) and cumulative volume (Learning.S). Correlation betweenSpecialization and Volume.S and Learning.S variables equals 0.6700 and 0.6365,respectively, while the correlation between Volume.S. and Learning.S equals 0.9504.In this case, Volume.S and Learning.S VIFs exceed ten (10.42 and 12.99, respectively)

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Table III.Correlation matrix

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indicating a potential multicollinearity problem in our regression model. To circumventthis problem, we use a modified Gram-Schmidt procedure Golub and Van Loan, 1996) toorthogonalize Specialization, Volume.S and Learning.S variables. This proceduregenerates a new set of orthogonal variables, eliminating the problem of multicollinearityin our regression model. Estimating our regression model with orthogonalized variablesyields results qualitatively unchanged compare to our main results (untabulated).However, as orthogonalizing variables is a procedure that depends on the sequence ofvariables orthogonalized in the equation, we rather use Volume.D and Learning.D ascontrol variables in our main results.

Age is naturally positively correlated with LOS, while patients from other Austrianprovinces (Other Provinces) and foreign patients (Foreign) spend less time in hospitals,all else being equal. Huckman and Zinner (2008) argue that even patients destined for thesame treatment, if they are capable of travelling larger distances, should be healthier andmore mobile than other patients. Our results are consistent with this argument.

Similar to McDermott and Stock (2007) we find size (Beds.D) to be positivelycorrelated with length of stay. We additionally find that the presence of intensive care

LOS

Specialization 23.104 * * * (6.840)Volume 23.979 * * *(14.739)Learning 20.600 * *(2.318)Age 0.417 * * *(99.726)Other province 20.076 * *(2.376)Foreign 20.929 * * *(19.262)Beds.D 5.007 * * *(28.208)IntBeds 20.490 * * *(6.365)MStaff 21.159 * *(2.244)MDs 20.813 (1.524)OcRate 4.456 * * *(20.500)N.Services 20.062 * *(2.551)Beds.H 20.086 (0.522)N.Departments 0.034 * *(1.976)Owner municipalities 20.249 * * *(3.158)Owner state 20.040 (0.572)Owner private 1.268 * * *(3.468)Constant 218.335 * * *(13.006)Service type FE IncludedYear FE IncludedProvince FE IncludedNumber of observations 841,687F 501.185Prob . F 0.000Adjusted R 2 0.316

Notes: Significant at: *10, * *5 and * * *1 percent levels, respectively; determinants of length of stay(LOS); the sample contains data on 841,687 patient group-year observations from 142 Austrian non-profit hospitals financed from public sources in the 2002-2006 period; the regression model is estimatedas OLS regression models; the regression includes fixed effect as stated in the table; standard errorsare clustered at the hospital-service level; t-statistics are in parentheses; all data are annual; all datacome from the Austrian Ministry of Health Database; variables are defined in the Appendix

Table IV.Specialization and

length of stay

Servicespecialization

in hospitals

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beds (IntBeds) reduces the number of days patients spend in the department,inconsistent with our prediction. The coefficients associated with MStaff and MDsvariables have the predicted signs indicating that more medical staff and more medicaldoctors lower length of stay. The coefficient associated with occupancy rate (OcRate) ispositive and statistically significant providing support for the unfavorable patientselection argument. We find no correlation between hospital size (Beds.H) and length ofstay (LOS). While length of stay (LOS) is negatively correlated with number of serviceswithin a department (N.Services), it seems to be unrelated to the number ofdepartments in a hospital (N.Department). Finally, our analysis indicates thatcompared to hospitals owned by religious orders (our base case), hospitals owned bymunicipalities are more efficient, while private hospitals are less efficient. Provincialhospitals do not differ significantly from those owned by religious orders.

Table V shows estimates of two-stage GMM LOS regressions with models (2) and(3) in the first stage. For brevity we do not tabulate the first stage regression estimates.Our results are consistent with the results for the OLS regression model (1). Thecoefficient associated with the Specialization variable is negative and statisticallysignificant in both regressions. With the exception of Learning and Other Province, allvariables in the LOS regression with model (2) in the second stage are statisticallysignificant and have the same sign as the coefficient in the OLS regression. Coefficientsin the LOS regression with model (3) in the second stage are statistically significantand have the same sign as those from the OLS regression. For both regression models,post-estimation, we test for the validity of our instruments. We use the Sargan-Hansentest of over-identifying restrictions. In both cases, the test does not reject the nullhypothesis that our instruments are valid (Hansens J statistic p-value is .0.10). Ourtests also reject under-identification, implying that our instruments are correlated withthe endogenous regressor (Kleibergen-Paap rk statistic p-value , 0.01)[17]. Thep-values of the F-statistic of the first stage regressions and F-statistic of excludedinstruments are all ,0.01. The endogeneity test of the endogenous regressor (as thedifference between Sargan-Hansen statistics for the model with the endogenousregressor and the model with the variable being treated as exogenous) supports ourpreference for a two-stage GMM as compared to an OLS regression model.

Table VI shows the estimates of the two-stage GMM model with three models (4) inthe first stage. As in Table V we do not tabulate the first stage regression estimates. Thecoefficient associated with the Specialization variable is negative and statisticallysignificant, in line with our expectations and in line with our prior tests. Compared to theestimates presented in Tables IV and V, the coefficient associated with the Learningvariable is not statistically significant, while its sign remains negative. As with LOSregression models (2) and (3) in the first stage, our post-estimation tests confirm thevalidity of the chosen regressors. The p-values of the F-statistic of the first stageregressions and F-statistic of additional instruments are all,0.01. The endogeneity testof endogenous regressors supports our choice of the two-stage GMM over the OLSregression model.

5. Discussion and conclusionIn this paper we analyze the impact of specialization within hospital departments onoperational performance. More precisely, we examine the impact of the relative weight ofa service in a department on patient length of stay. The empirical analysis is conducted

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LOS with model(2) in the first stage

LOS with model(3) in the first stage

Specialization 212.482 * *(2.542) 22.896 * * *(5.914)Volume 24.178 * * *(14.587) 24.024 * * *(15.024)Learning 20.446(1.617) 20.572 * *(2.249)Age 0.415 * * *(95.804) 0.412 * * *(96.695)Other province 0.086(0.964) 20.084 * *(2.534)Foreign 20.786 * * *(8.666) 20.889 * * *(17.977)Beds.D 5.034 * * *(27.524) 5.047 * * *(28.612)IntBeds 20.494 * * *(5.784) 20.460 * * *(5.705)MStaff 21.332 * *(2.423) 21.242 * *(2.380)MDs 20.827(1.463) 20.915 *(1.710)OcRate 4.482 * * *(18.734) 4.439 * * *(19.996)N.Services 20.104 * * *(3.577) 20.071 * * *(2.824)Beds.H 20.075(0.441) 20.126(0.765)N.Departments 0.032 *(1.843) 0.035 * *(2.057)Owner municipalities 20.254 * * *(2.912) 20.194 * *(2.412)Owner state 20.042(0.544) 20.013(0.186)Owner private 1.340 * * *(3.331) 1.254 * * *(3.425)Constant 20.002(0.060) 20.001(0.029)Service Type FE Included IncludedYear FE Included IncludedProvince FE Included IncludedNumber of observations 841,687 669,026F 471.298 482.587Prob . F 0.000 0.000R 2 0.164 0.176p-value (F test of excluded instruments) 0.0000 0.0000p-value of Hansens J statistic 0.2814 0.3733p-value of Kleibergen-Paap rk LM statistic 0.0000 0.0000

Notes: Significant at: *10, * *5 and * * *1 percent levels, respectively; determinants of length of stay(LOS); the sample contains data on 841,687 patient group-year observations from 142 Austrian non-profithospitals financed from public sources in the 2002-2006 period; the regression models are estimated as atwo-stage GMM regression models; first stage regressions are estimated from the following models:

Specialization b0 b1Volume b2Learning b3Age b4Other Province b5Foreign diServices Type Dummies b6Beds:D b7IntBeds b8MStaff b9MDs b10OcRate b11N:Services b12Beds:H b13N:Departments diOwner Dummies dkProvince Dummies b14NewServ b15RegChg dlYear Dummies

2

Specialization b0b1Volumeb2Learningb3Ageb4OtherProvinceb5ForeigndiServicesTypeDummiesb6Beds:Db7IntBedsb8MStaffb9MDsb10OcRateb11N:Servicesb12Beds:Hb13N:DepartmentsdiOwnerDummiesdkProvinceDummiesb14L:Specializationb15RegChgdlYearDummies

3

The regression includes fixed effect as stated in the table. Standard errors are clustered at the hospital-service level. t-statistics are in parentheses. All data are annual. All data come from the Austrian Ministryof Health Database. Variables are defined in the Appendix

Table V.Two-stage GMM

estimates withspecialization as

endogenous regressor

Servicespecialization

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on a sample of 142 Austrian non-profit hospitals for the 2002-2006 period. The Austrianmarket is characterized by standardized services, fixed service prices, fixed capacity anda limited ability of managers to make major investment decisions. In this environment,hospitals have to treat a patient if they have the capabilities and capacity to do so. While,therefore, an exclusive focus on a limited number of services will usually not be

LOS with models (4) in the first stage

Specialization 22.899 * * *(5.921)Volume 24.383 * * *(9.692)Learning 20.368(0.878)Age 0.411 * * *(96.555)Other province 20.081 * *(2.440)Foreign 20.885 * * *(17.906)Beds.D 5.168 * * *(28.762)IntBeds 20.467 * * *(5.769)MStaff 21.235 * *(2.367)MDs 20.738(1.371)OcRate 4.563 * * *(20.324)N.Services 20.060 * *(2.274)Beds.H 20.104(0.631)N.Departments 0.034 * *(1.967)Owner Municipalities 20.196 * *(2.440)Owner State 20.017(0.237)Owner Private 1.292 * * *(3.546)Constant 20.000(0.016)Service Type FE IncludedYear FE IncludedProvince FE IncludedNumber of observations 669,016F 478.393Prob . F 0.000R 2 0.176p-values (three F-tests of excluded instruments) 0.0000p-value of Hansens J statistic 0.4885p-value of Kleibergen-Paap rk LM statistic 0.0000

Notes: Significant at: *10, * *5 and * * *1 percent levels, respectively; determinants of length of stay(LOS); the sample contains data on 841,687 patient group-year observations from 142 Austrian non-profit hospitals financed from public sources in the 2002-2006 period; the regression model is estimatedas a two-stage GMM regression models; first stage regressions are estimated from the following models:

Specialization;Volume;Learningb3Ageb4OtherProvinceb5ForeigndiServicesTypeDummiesb6Beds:Db7IntBedsb8MStaffb9MDsb10OcRateb11N:Servicesb12Beds:Hb13N:DepartmentsdiOwnerDummiesdkProvinceDummiesb14L:Specializationb15L:Volumeb16L:Learningb17RegChgdlYearDummies

4

The regression includes fixed effect as stated in the table. Standard errors are clustered at the hospital-service level. t-statistics are in parentheses. All data are annual. All data come from the Austrian Ministryof Health Database. Variables are defined in the Appendix

Table VI.Two-stage GMMestimates withspecialization, volumeand learning asendogenous regressors

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a feasible strategy, hospital managers still have possibilities to emphasize certainservices in which their hospitals have a particularly strong expertise or interest.

We find that specialization results in shorter patient length of stay. We find thiseffect to be not only statistically, but also economically significant. On average, anincrease in specialization by 10 percent would yield an approximately one-third of aday shorter length of stay for that service. Our results indicate that this effect existsover and above related effects of volume, learning and favorable patient selection. Inaddition to our main results, we find that some organizational characteristics, such asthe number of medical doctors, have an impact on operational performance. Our resultsare in line with the findings from prior research on economies of scale, learning effects,and operational focus in hospitals (Pisano et al., 2001; Huckman and Zinner, 2008;Hyer et al., 2009).

The reduction of patients length of stay should be interpreted as an improvement ofoperational efficiency. Increasing the relative importance of a particular servicerelative to other services allows a hospital department to become more efficient inperforming the service in question. We would argue that the main reason behind thiseffect is that a service with high weight in the department is likely to receive particularadministrative attention, because performance improvements (or deteriorations) in thisservice will have a relatively high impact on the overall performance of the department.

The fact that we find an impact of specialization on operational performance suggeststhat this is something for hospital managers to pay attention to when makingservice-mix decisions. Existing research has shown that hospital managers actively seekto emphasize services which are highly attractive for their hospital (Krishnan et al.,2004). Our findings add an interesting nuance to this. It is not only important to knowwhere one is good at, but also by how much one can improve in any of the activitiescarried out. Hence, whether a service is attractive for a hospital is not only a question ofthe current operational performance of that service, as measured, for example, byaverage length of stay. It is also important to know by how much operationalperformance in each service can be improved over time through stronger specializationin that service. As our paper shows, this marginal effect is likely to differ betweenservices. Specializing more strongly in those services where the marginal improvementis particularly high would thus seem like a good strategy in order to improve a hospitalsoverall operational performance.

Our results are based on data collected and provided to us by the Austrian Ministry ofHealth. While the data we obtained are very rich, they do not contain all variables thatwould have been of interest for our research question. The database does not containdata on the severity of cases, which limits the interpretation of our results, as the severityof cases is correlated with patient length of stay, and as it may be correlated withspecialization. A hospital may decide to specialize in less severe cases within an ICD,which will have a direct impact on length of stay. While we control for age and patientorigin (Austrian province), these are arguably only imperfect proxies for the severity ofcases within each ICD.

Moreover, data on the quality of service, for example, would add a differentdimension of service performance, but are not available in our database. Past researchhas examined the importance of quality as an outcome of hospital operations. Li andBenton (1996) discuss measures of external and internal quality, while Li (1997) linkshospital quality management to service quality performance. Future research could

Servicespecialization

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build upon this earlier research and examine to which extent the quality of services isinfluenced by specialization, and how it mediates the relationship to economicperformance on the service level.

Notes

1. For each inpatient, a hospital has to record administrative data (e.g. age of the patient, residence,admission date, receiving hospital department, etc.) and medical data (main diagnosis,additional diagnoses, and specific medical treatments). Main and additional diagnoses are basedon the international ICD classification, whereas specific medical treatments are classifiedaccording to a classification established by the Ministry of Health.

2. Name and responsibilities of the Ministry have changed over the years. As of 2008, it iscalled the Ministry of Health, Family and Youth. To simplify matters, we will use theshortcut Ministry of Health throughout the paper.

3. The calculation of DRG points per inpatient-case depends on the type of diagnosis and/orspecific medical treatment as well as on the length of stay of the patient. Since DRG pointsincrease on a diminishing scale with length of stay, the system motivates hospitals to reducelength of stay (Theurl and Winner, 2007).

4. During the observed five-year period, some of the hospitals discontinued their operations orchanged the way they are financed. For 2006, our database contains 133 hospitals. These arenon-profit hospitals that are fully integrated into the DRG-based funding system. While allof these are financed by public means, they are not all in public ownership. About 78 of themare owned by provincial agencies, 31 are owned by religious orders and congregations, 18 bymunicipalities, three by private institutions, and three by other owners. Ownership isimportant insofar as it is the owner of a hospital who has to cover any deficit that remainsafter costs have been reimbursed through the Austrian DRG system.

5. We conducted interviews with administrative directors of ten Austrian hospitals thatindicate specialization occurs at the level of treatment groups that we refer to as Services.

6. Brush and Karnani (1996) use a similar specialization ratio but at the industry aggregate level.

7. In our analysis we control for fixed effects by service type, effectively demeaning ourspecialization variable by service type. However, we run a robustness check where wecompute the deviation of specialization from the average specialization in Austria for thatservice. Our results are qualitatively the same.

8. MacKenzie et al. (1996) report that hospitals with higher volumes also have longer patientlength of stay.

9. While Kc and Terwiesch (2009) find other patient characteristics have impact on length ofstay, data availability limits our choice to only these three variables.

10. Using a fraction of intensive care beds in the total number of beds in a departments yieldqualitatively unchanged results.

11. Clustering standard levels at the observation level or using Huber White Sandwich robuststandard errors does not change our results qualitatively. However, this deflates standarderrors and inflates t-statistics. According to Petersen (2009) double clustering corrects forheteroskedasticity and serial correlation more efficiently. We thus also cluster by twodimensions (e.g. observations and year). This yields qualitatively unchanged results.

12. See Baum et al. (2003, 2007) and Baum (2006) for methodology and application in Statasoftware package.

13. Using limited information maximum likelihood (LIML) or two stage least square (2SLS) insteaddoes not change our results qualitatively. We use GMM as it is a more efficient estimator.

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14. For practical (computational) reasons we demean our