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2013 15: 157Journal of Health ManagementFotis Papathanassopoulos, Kostas Kounetas and Dimitris Skuras

Medical Equipment Adoption in Greek Hospitals: The Case of CT Scanners  

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Impact on Stock Price by the Inclusion to and Exclusion from CNX Nifty Index 157Article

Medical Equipment Adoption in Greek Hospitals: The Case of CT Scanners

Fotis Papathanassopoulos Kostas Kounetas Dimitris Skuras

AbstractThe paper aims to unravel the elements which constitute the decision-making process concerning new medical technologies in the context of the Greek Health System, where there are more than one deci-sion makers. Computerized tomography is used as a case study. Using a unique data setting that refers to the total number of the Greek Public Hospitals, the pattern of adoption is outlined. At the second stage, data is associated with regional and geographical characteristics as well as information related to the hospital efficiency. A probit model is used for the factor analysis and a survival function hazard model for time to adopt. Results indicate that the models used are suitable for examining the factors influenc-ing the adoption of medical technologies as well as the time that such technologies are adopted. It was found that the size of the hospital and its plenitude positively influence not only the probability of adop-tion but also the time of adoption of computerized tomography. Findings are encouraging; they support the use of the model in studying the adoption of other medical technologies too and can be used also as a tool by policy makers to assist the process of investment in new health technologies.

Keywordscomputerized tomography, adoption of medical technology, decision making, health technology assessment

Introduction

Although the literature has been dealing with the adoption of new technologies for almost 30 years, the main volume of knowledge that has been gathered concerns mostly the adoption procedures followed by firms of the Private Sector (Geroski 2000). In this direction and in order to examine the reasons that drive firms to adopt these new technologies, researchers have employed a variety of modelling approaches, namely the epidemic, rank, order, stock and supply-side effects models (Karshenas and Stoneman 1993). One could argue that the epidemic models have more to do with the adoption of technologies that are

Journal of Health Management 15(2) 157–167

© 2013 Indian Institute of Health Management Research

SAGE PublicationsLos Angeles, London,

New Delhi, Singapore, Washington DC

DOI: 10.1177/0972063413489002http://jhm.sagepub.com

Fotis Papathanassopoulos, Department of Medical Physics, Biomedical Engineering Unit, University of Patras, Greece 26504. Email: fpap@upatras.grKostas Kounetas, Department of Economics, University of Patras, Greece. Email: kounetas@upatras.grDimitris Skuras, Department of Economics, University of Patras, Greece. Email: skuras@econ.upatras.gr

158 Fotis Papathanassopoulos, Kostas Kounetas and Dimitris Skuras

Journal of Health Management, 15, 2 (2013): 157–167

rather mature or that are not in their infancy, in the sense that they have already been adopted by some other agents (Geroski 2000). The rest of the model types form a group that focuses more on the adoption of fairly new or emerging technologies (Morgenstern and Al-Jurf 1999), which we will call emerging technology models for the sake of convenience.

The main reason why alternative frameworks for the analysis of the decision making procedures con-cerning the adoption of new technologies by the Public Sector have been developed is that the basic hypothesis for private sector is related to profit maximization (Baker 2001; Baker and Phibbs 2002; Luft, Garnick and Maerki 1986; Rapoport 1978). The situation becomes even more complicated in case of the adoption of new medical technologies by Public Hospitals, as it involves parameters related, on one hand, to financial efficiency and the restraint of expenses that often characterize public finances and, on the other hand, to social factors as well as factors of expanding and improving the health care services offered to the population (Howie and Erickson 2002; McCue 1997; Segesten, Lundgren and Lindström 1998). Things get even more crucial under the Greek crisis due to hospital deficit and management inef-ficiency, forcing the Ministry of Health to budget reengineering and cutting.

In cases of National Health Systems structured in multiple levels, the decision-making procedures concerning the adoption of new medical technologies remain unexplored. In such cases, the objectives of the decision makers may differ from level to level and it is not known which one prevails or what the different criteria are. The Greek National Health System is such a case with two level points. The first one is at a regional level where the demand of the Public Hospital is first evaluated. The second one operates at a central/national level, where the demand of the Public Hospital for adopting new medical technology is evaluated yet again, the decision of the Regional Administration is taken, the priorities at a national level are set and the outcome of the procedure is finally produced.

Taking into account the above, the main objective of the present paper is to unravel the elements which constitute the decision making process concerning new medical technologies in the context of the Greek health system, where there is more than one decision maker. For that reason, we use the case of Computerized Topography (CT) scanners, which is analyzed through, firstly, probit models in order to examine the components of the decision making procedure regarding the adoption, and, secondly, sur-vival models that allow us to identify the reasons for which hospitals cluster in groups of early or late adopters. The rest of the paper is structured as follows: The main elements of the relevant research litera-ture are also presented in this section. The next section describes briefly the Greek National Health System, the data and the methodology used, while in the third section we present the results regarding decision-making process, technology adoption and its time. Finally, in the last section we conclude the paper discussing the results and highlighting some policy implications.

Health Technology Adoption

Governments face the urgent need to limit rise in healthcare costs without compromising quality, equity and access, and also to investigate new ways of organizing and delivering health services (Howie and Erickson 2002; McCue 1997; Segesten, Lundgren and Lindström 1998). Effective technology adoption requires adaptation of work practices, reorientation, and organizational change far beyond what was initially believed, especially in the knowledge-intensive sector of medical practice (Mayo-Smith and Agrawal 2007).

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The adoption of health technologies has been investigated by many researchers since Romeo, Wagner and Lee (1984) and factors such as the hospital size, the number of medical specialties and the type of hospital (public or private) were found to have a positive effect on technology adoption. Most of the researchers have focused mostly on adoption of healthcare information technology (HIT) (Bhattacherjee et al. 2007; Lin and Roan 2011).

The diffusion of medical technology and in particular the history of the diffusion of CT scanners was examined by Perry (1984), who illustrates the problems that can result from the lack of a coherent strategy in the diffusion of major medical technologies. On the other hand, government policies toward: (a) research and development, (b) evaluation, (c) safety and efficacy regulation, and (d) investment in medical tech-nologies clarify how these work in practice (Banta & Russell 1981). Factors that influence the diffusion of computed tomography were also examined by Oh, Imanaka and Evans (2005) in a review of the literature on the diffusion of health technologies where a logical model with multiple variables of the mechanism governing technology diffusion was presented, resulting in variation across countries in the diffusion of medical technology.

Decision Making and Health Technology

Introduction of new technologies has brought remarkable improvements to the health system; there is, however, widespread variation in how effective and efficient technologies are used. The discipline of Health Technology Assessment (HTA) has received increasing attention from Health Policy scholars as an instrument to support decision making at each level in healthcare (Buse and Walt 1996). Decision making should be evidence-informed (McKee and Figueras 1996) and oriented towards selecting health care programs or technologies that are ‘value for money’ (López-Bastida et al. 2010; Sendi, Al, Gafni and Birtch 2003). Decision makers should consider different dimensions for technology assessment and acquisition at hospital level (Anderson and Steinberg 1994).

Health Policy and HTA have paid more attention to decision making at a national/regional level than at the hospital level. Reeleder, Goel, singer and Martin(2006) claimed that the priority setting for tech-nology adoption at hospital level is largely overlooked and wide research efforts should be made con-cerning this emerging issue. Other authors stated that HTA guidelines should be extended to the hospital level, in order to facilitate the creation of a spirit of responsibility and accountability (Gagnon, Sánchez and Pons 2006; Madden, Martin, Downey and Singer 2005; Martin, Walton and Singer 2003). Moreover, technology adoption in healthcare is often regulated by law, thus making changes more laborious (Faulkner and Kent 2001).

As far as market structure—and especially competition between hospitals—and technology adoption are concerned, it seems that hospitals in more competitive markets tend to adopt innovations faster (Rapoport 1978), while Luft et al. (1986) found that a hospital is more likely to offer a particular special-ized service if other hospitals in the market also offer a similar service. Economists and policy analysts consider innovation in technology—along with weak cost-containment measures—to be a major driver in rising healthcare costs (Lubitz 2005). The diffusion of new technology was investigated by Hamilton and McManus (2005), who reported that hospitals in competitive markets offer new technology earlier than in monopolies. Areas of assessment, adoption and utilization of medical technology were defined since 1981 by Greer (1981).

160 Fotis Papathanassopoulos, Kostas Kounetas and Dimitris Skuras

Journal of Health Management, 15, 2 (2013): 157–167

Material and Method

The Greek NHS – Definition of Data and Variables

The Greek Health Care System (NHS), which was established in 1983, is a ‘mixed’ system, with ele-ments of both the Bismarck (financed mainly by social insurance) and the Beveridge (financed mainly by the state taxes) models, where funding is open-ended and mainly demand-led (Tountas, Karnaki, Pavi and Souliotis 2005).

The new reform of the NHS, which was announced in 2001, had the decentralization of the system and its regional organization as main objectives, introducing the establishment of Regional Health Systems (RHS). The country was divided into 17 health regions, which were later reduced to seven in June 2006. Each region has an autonomous regional health system and all regional health services are under the jurisdiction of the RHS. However, the system is still characterized by centralization, fragmen-tation of coverage, problematic access to health services and heavy reliance on relatively expensive inputs (Mossialos, Allin and Davaki 2005), as the Ministry of Health is responsible for the organization and provision of health services in a highly centralized system. Moreover, the insurance system has its own administrative, organizational, financial, and control structures with serious bureaucratic and deci-sion-making restrictions. The extent and the form of public intervention vary significantly from one region to another, as political appointees with no training in healthcare management often head RHS (Liaropoulos and Kaitelidou 2000).

In order to outline the pattern of adoption for computerized tomography in Greece, as well as to inves-tigate the time of the adoption, the medical device inventory and profile of 125 hospitals were examined. The inventory process was initiated by the Department of Medical Physics of the University of Patras as the basis for a national inventory of medical instrumentation, and included 125 public hospitals of all types (Regional, Prefectural and University Hospitals) and sizes. Groups of specialized biomedical engi-neers performed a room-by-room and item-by-item inventory with the assistance of senior hospital per-sonnel. All data collected was stored in a database following the coding and classification of device groups in compliance with the Universal Medical Device Nomenclature System (UMDNS), developed by Emergency Care Research Institute (ECRI 2000). For the purposes of the present paper, additional data sources were used and in particular annual hospital reports, data from the Ministry of Health as well as data collected through face-to-face and phone interviews with hospital and regional health managers. The present effort was part of a PhD thesis which was recently completed.

The required data regarding the CT scanners and the hospitals that operate them refer to: a dummy variable that reflects the adoption of CTs (CT) that takes the value of 1 if the hospital uses the CT tech-nology and 0 otherwise, the year of installation (YEAR), the number of beds (BEDS), the number of beds per doctor (DOCBEDS), the number of doctors and nursing personnel (DOCNUR). The specific variables reflect the hospital’s capacity to host patients and its ability to serve the local community, rep-resenting the medical size of the hospital. On the other hand, we used the plenitude of hospital (PLEN) and the number of visitors in the emergency department per doctor (VISDOC) that reveal the workload and the pressure each hospital has, as well as the type of hospital (university or prefectural) (HTYPE), which is an important variable for the adoption of CT scanner technology. Finally, we have used the percentage of the population in the prefecture over 65 years old (POPUL). This data was associated with the regional and geographical characteristics of the prefecture in which each hospital operates in addition

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to information related to the hospital efficiency.1 The descriptive statistical elements of the sample of hospitals are presented in Table 1.

Table 1. Descriptive Statistics of the Used Variables

Variables Mean* Std. Dev.

Dependent VariableCT 0: 44.00%

1: 66.00%0.50

Explanatory Variables—Hospital SpecificBEDS 246.29 208.05PLEN 65.44 16.52DOCBEDS 1.71 1.78DOCNUR 737.14 1155.55VISDOC 927.13 811.67HTYPE 0:0.20

1:0.80 0.402

Explanatory Variables—Region SpecificPOPUL 19.22 3.314N = 125

Note: *Percentages are reported for dummy variables.

Econometric Approach

The Decision to Adopt CT Technology

A probit model was used to investigate in further detail the factors that lead to CT adoption implying an underlying expected utility from adoption since the variable of adoption is binary. A formal specification on the non-observed utility is given:

* 'x = β +y (1)

where x is a vector of characteristics of each hospital and region (prefecture) in which the hospital oper-ates, b is a vector of factors to estimate and ε the error. Thus, in this formulation b'x is the function indicator. We determine the probability that a hospital has adopted the CT technology as:

*Pr( 0) Pr( 'x 0) Pr( 'x) Pr( 'x) ( 'x)> = β + > = > −β = < β = Φ βy (2)

1 Note that the inclusion of the used variables that explain CT adoption satisfies two criteria. Relevant literature and data availability. On the other hand the inclusion of other variables (i.e., the maintenance cost for CT equipment) didn’t improve the econometric performance of our model.

162 Fotis Papathanassopoulos, Kostas Kounetas and Dimitris Skuras

Journal of Health Management, 15, 2 (2013): 157–167

In order to test the hypotheses regarding the effects of independent variable characteristics on changes in the probability of CT adoption, we calculate the marginal effects defined by:

[ | x] ( 'x)x

∂

= β β∂

E y

(3)

where (.) is the standard normal probability density function evaluated at variables’ means. In order to fit the best model, variables that in theory appeared to influence the adoption of medical technology have been included along with their transformations.

Time to Adopt—Non-parametric and Parametric Models

At a second stage, survival function and hazard rate models allow us to relate the probability of time to adopt since CT was available, with a set of explanatory variables including time. In this study we con-sider both a non-parametric and a parametric approach in a comparative way. For the non-parametric investigation we use the Kaplan- Meier survivor function estimation. In the parametric model, the param-eters of the baseline survival function and the hazard models need to be estimated in addition to the parameters of the covariates. Several statistical and visual tests guided our decision to use the Weibull parametric model versus other alternative parameterizations. The Weibull model is both an Accelerated Failure Time (AFT) model and a Proportional Hazards (PH) model. The survival function is the proba-bility that a spell (time to adopt CT) will last up to or exceed time t, for the Weibull specification being:

( )( )

−= tS t e (4)

where = e–bx with x representing the vector of covariates, b a vector of unknown parameters to be estimated, and p = 1/ where is the scale parameter of the Weibull distribution.

The hazard function is the pace with which a period without CT adoption will finish in time t after 1984, when the CT scanners start to be available for adoption by hospitals in Greece. For the Weibull model lasting up to t, time is given as:

1( ) ( ) −=h t t (5)

The estimation of the survival or hazard functions is facilitated by the transformation (ln 'x)/b = −w t , and the log linear model to be estimated becomes:

ln 'xb = +t w (6)

where w has the standard extreme value distribution. The estimation of the median of the survival distribution is obtained by solving the equation S(t) = 0.5 which for the Weibull model yields

1/1 (ln 2)

=M .

Results

Factors that influence the adoption of medical technology, and especially computerized tomography scanners (CTs), in Greek public hospitals have been examined. Using the econometric software LIMDEP 7.0, our results appear in Table 2 including marginal effects, where the effect of the independent variable

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in the change of probability is shown (negative or positive sign). The model shows the positive effect that hospital size (number of beds) has on the probability of the adoption of the CT. The plenitude of the hospital as well as the percentage of the population over 65 years in the prefecture also positively affects the probability of a hospital adopting a CT. On the other hand, the number of beds per doctor, an indica-tor of work pressure of the medical personnel, was found to have a negative effect on the adoption of technology. Moreover, by observing the marginal effects we realized that an increase of the ratio of beds to doctors by one unit decreases the probability of adoption by 15.3 per cent.

Moreover, the time of CT technology adoption by public hospitals was investigated with the use of survival models. The first CT scanner was available for purchase and adoption from the Greek hospitals since 1976. However, in the current study we consider the year 1984 as the reference point concerning adoption, given that the first recorded CT in our database was purchased by a Greek hospital during that year. Consequently, the time of adoption for the hospitals that have a CT scanner is the difference between the actual year of adoption and 1984, while the hospitals that have not adopted CTs are presented in 2004 (year of the inventory). Hence the variable time of adoption gets a value between 1 and 20, while hospitals that have not adopted get the value 20. As it can be seen from the underlying hazard function (Table 3) based on the observed data, the probability of adopting CT was almost zero until the end of 2004 and only 9 per cent at the end of 2000. On the other hand, the survival function estimated for the parametric model indicated that for the first five years the probability of remaining in the original state remains closely to 100. Within 15 years it drops to almost 60 per cent. Table 4 shows that, when a para-metric Weibull model is employed, the time to adopt is influenced only by two factors, the number of beds, and the hospital plenitude. The negative signs of the coefficients show that the larger the hospital and the higher its plenitude, the faster the time to adopt. No region specific characteristics were found to influence the time to adopt.

Discussion

In the present article the factors that influence the adoption of medical technology in Greek public hos-pitals were examined. A cross-sectional data set has been used in order to identify the above-mentioned factors. Subsequently, maximum likelihood estimates from a probit model have been used to examine

Table 2. Maximum Likelihood Estimates and Marginal Effects of the Decision to Adopt CTs

Independent Variables Coefficient Estimates t-ratios Marginal Effects t-ratios

Constant –4,531 –3.626* – –BEDS 0.006 4.765* 0.002 4.663*PLEN 0.027 2.379 * 0.011 2.394*DOCBEDS –0.385 –3.009* –0.153 –2.994*POPUL 0.099 2.074* 0.039 2.076*X2 = 67.4 (d.f = 4)McFadden r2 0.39% Correct Predictions 82.4%

Note: Asterisk (*) denotes statistical significance at 5 per cent.

164 Fotis Papathanassopoulos, Kostas Kounetas and Dimitris Skuras

Journal of Health Management, 15, 2 (2013): 157–167

the influence of a set of environmental variables, whereas the use of survival models grouped the hospi-tals in early and late adopters. Finally, the decision-making process concerning the adoption of new medical technologies was examined.

As it has already been mentioned, the Greek National Health System belongs to the Public Sector and healthcare services are provided to all citizens free of charge. Consequently, there is no competition involved to boost the adoption of health technology, as reported by Hamilton and McManus (2005). Furthermore, decision making in Greece is also evidence-informed; as McKee and Figueras (1996) have proposed, healthcare programmes or technologies that are ‘value for money’ (Sendi et al. 2003) are selected because the adoption of technology in the Greek Health Care System during the last few years is mostly supported by financial resources drawn from the second and third European Union Support Frameworks (Greek Ministry of Health 2000) and therefore healthcare technology acquisition is regu-lated by Health Technology Assessment (HTA), through feasibility studies supporting the purchase pro-cedure. However, although policy-makers appeared to be positive towards HTA, the processes of policy making in Greece do not seem to be based on a full understanding of HTA, as it appears that market

Table 3. Survival Rate for the Time to Adopt CTs

Survival Enter Censored At Risk Exited Survival Rate Hazard

0.0–2.0 125 0 125 1 1.0000 (.000) 0.0040 2.0–4.0 124 0 124 1 0.9920 (.008) 0.0040 4.0–6.0 123 0 123 1 0.9840 (.011) 0.0041 6.0–8.0 122 0 122 6 0.9760 (.014) 0.0252 8.0–10.0 116 0 116 4 0.9280 (.023) 0.0175 10.0–12.0 112 0 112 3 0.8960 (.027) 0.0136 12.0–14.0 109 0 109 11 0.8720 (.030) 0.0531 14.0–16.0 98 0 98 11 0.7840 (.037) 0.0595 16.0–18.0 87 0 87 9 0.6960 (.041) 0.0545 18.0–20.0 78 70 43 8 0.6240 (.043) 0.1026

Notes: Number of observations in stratum = 125.Number of observations exiting = 55.Number of observations censored = 70.

Table 4. Estimates of A Weibull Model for the Time to Adopt CTs

Independent Variables Coefficient Estimates t-ratios

Constant 4.350 11.511*BEDS –.0012 –5.170*PLEN –.0121 –2.344*Maximum Likelihood –78.231l .0386

r 2.622

Note: Asterisk(*) denotes statistical significance at 5 per cent.

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forces, such as advertising, physician and consumer demands, etc., appear to have strong influence on the diffusion and use of the HTA.

The decision-making process including health planning is centralized and subject to political consid-erations. Consequently, law regulation is a complex process and often not considered as a high priority. This makes Health Technology Assessment difficult to be applied, mainly because of the following char-acteristics of the health system: (a) the variety of social insurance organizations with their own selection criteria, validation procedures, etc., (b) the variety of providers of health services and differences in rela-tion with their social security organizations, and (c) the large variation in insurance coverage across different types and classes of members. However, the main obstacle to rational decision-making and policy-based health technology assessment is the reduced reliability of statistics on the health sector. The data is often unreliable or incomplete and not collected in a form useful for the user. Moreover there are no records of diseases and the elements are oriented mainly to resource consumption and therefore researchers should periodically check the raw data in order to make the analysis.

HTA has been pointed out as a form of research to support the process of policy-making in health-care by providing reliable information, which can be utilized in formulating and addressing problems (Gilson and McIntyre 2008). Thus, it is important to understand why evidence is not directly trans-ferred into practice in the policy-making process. Lack of coordination at the policy-making level of the healthcare system also seems to play an important role, given that coordination is recognized as an important factor for increasing organizational effectiveness, defined here as a measure of the extent to which a programme or a sector is successful in achieving its predetermined goals and objectives (Battista 2006).

Finally, policy makers should put effort and resources in decision-making processes in the public sec-tor and any investment in new technology should be carefully examined. A thoughtful strategic plan, determined by the highest authorities of the Ministry of Health and based on real needs assessment and priority setting in relation to HTA, is recommended. Establishment of a coordination body could assist the development, implementation, monitoring and evaluation of HTA policies and action plans. Future research should be focused on produced output and data from different countries and periods.

ReferencesAnderson, G.F. & E.P. Steinberg (1994). Role of the hospital in the acquisition of technology. In A.C. Gelins and H.V.

Dawkins ED: (eds), Adopting new medical technology? (pp. 61–70), Washington DC: National Academy Press.Banta, H.D. & L.B. Russell (1981). Policies toward medical technology: An international review. International

Journal of Health Services, 11(4), 631–652. Baker, L.C. (2001). Managed care and technology adoption in health care: evidence from magnetic resonance

imaging. Journal of Health Economics, 20(3), 395–421. Baker, L.C. & C.S. Phibbs (2002). Managed care, technology adoption, and health care: The adoption of neonatal

intensive care. RAND Journal of Economics, 33(3), 524–548.Battista, R.N. (2006). Expanding the scientific basis of health technology assessment: A research agenda for the next

decade. International Journal of Technology Assessment in Health Care, 22(3), 275–280. Bhattacherjee, A., N. Hikmet, N. Menachemi,V.O. Kayhan & R.G. Brooks (2007). The differential performance

effects of healthcare information technology adoption. Information Systems Management, 24(1), 5–14. Buse K. & G. Walt (1996). Aid coordination for health sector reform: A conceptual framework for analysis and

assessment. Health Policy, 38, 173–187.

166 Fotis Papathanassopoulos, Kostas Kounetas and Dimitris Skuras

Journal of Health Management, 15, 2 (2013): 157–167

Emergency Care Research Institute (ECRI) (2000). Health Technology Forecast, 1999/2000. PA: ECRI. Faulkner, A. & J. Kent (2001). Innovation and regulation in human implant technologies: Developing comparative

approaches. Social Science and Medicine, 53(7), 895–913. Gagnon, M.-P., E. Sánchez & J.M.V. Pons (2006). From recommendation to action: Psychosocial factors influencing

physician intention to use. Health Technology Assessment (HTA) recommendations. Implementation Science, 1(1), Article No. 8. DOI Number: 10.1186/1748-5908-1-8. Available at http://www.implementationscience.com/content/1/1/8

Geroski, P.A. (2000). Models of technology diffusion. Research Policy, 29(4–5), 603–625. Gilson L. & D. McIntyre (2008). The interface between research and policy: Experience from South Africa. Social

Science and Medicine, 67, 748–759.Greek Ministry of Health (2000). Operational Program for Health and Care 2000–2006 Strategy, Objectives and

Priorities. Athens: Greek Ministry of Health.Greer A.L. (1981), Medical Technology: Assessment, Adoption and Utilization. Journal of Medical Systems, 5(1–2),

129–145. Hamilton Â.H. & B. McManus (2005). Technology diffusion and market structure: Evidence from infertility

treatment markets. Working paper, Washington University Olin School of Business. Howie, J.N. & M. Erickson (2002). Acute care nurse practitioners: Creating and implementing a model of care for

an inpatient general medical service. American Journal of Critical Care, 11(5), 448–458. Karshenas, M. & P. Stoneman (1993). Rank, stock and order effects in the diffusion of new process technologies: An

empirical model of adoption duration. RAND Journal of Economics, 24(4), 503–528.Liaropoulos, L. & D. Kaitelidou (2000). Health technology assessment in Greece. International Journal of

Technology Assessment in Health Care, 16(2), 429–448. Lin C, I-C. Lin & J. Roan (2011). Barriers to physicians’ adoption of healthcare Information Technology: An

empirical study on multiple hospitals. Journal of Medical Systems, 19 February.López-Bastida, J., J. Oliva, F. Antoñanzas, A. García-Altés, R. Gisbert, J. Mar & J. Puig-Junoy (2010). Spanish

recommendations on economic evaluation of health technologies. European Journal of Health Economics, 11(5), 513–520.

Lubitz James (2005). Health technology, and medical care spending. Health Affairs, 24(2), 81–85. Luft H.S., J.C. Robinson, D.W. Garnick & S.C. Maerki (1986). The role of specialized clinical services in competition

among hospitals. Inquiry, 23(1), 83–94.Madden, S., D.K. Martin, S. Downey & P.A. Singer (2005). Hospital priority setting with an appeals process: A

qualitative case study and evaluation. Health Policy, 73(1), 10–20. Martin, D.K., N. Walton & P.A. Singer (2003). Priority setting in surgery: Improve the process and share the learning.

World Journal of Surgery, 27(8), 962–966. Mayo-Smith, M.F. & A. Agrawal (2007). Factors associated with improved completion of computerized clinical

reminders across a large healthcare system. International Journal of Medical Informatics, 76(10), 710–716.McCue, M.J. (1997). Small hospitals with positive cash flow: Why are they winners? Medical Care Research and

Review, 54(1), 32–60. McKee, M. & J. Figueras (1996). Setting priorities: Can Britain learn from Sweden? British Medical Journal,

312(7032), 691–694.Morgenstern, R.D. & S. Al-Jurf (1999). Can free information really accelerate technology diffusion? Technological

Forecasting and Social Change, 61(1), 13–24. Mossialos E, S. Allin & K. Davaki (2005). Analyzing the Greek health system: A tale of fragmentation and inertia.

Health Economics, 14, S151–S168.Oh, E.H., Y. Imanaka, & E. Evans (2005). Determinants of the diffusion of computed tomography and magnetic

resonance imaging. International Journal of Technology Assessment in Health Care, 21(1), 73–80.Perry, S. (1984). Diffusion of new technologies: Rational and irrational. Journal of Health Care Technology. 1(2),

73–88.

Medical Equipment Adoption in Greek Hospitals 167

Journal of Health Management, 15, 2 (2013): 157–167

Rapoport J. (1978). Diffusion of technological innovation among nonprofit firms: A case study of radioisotopes in the U.S. hospitals. Journal of Economics and Business, 30, 108–118.

Reeleder, D., V. Goel, P.A. Singer & D.K. Martin (2006). Leadership and priority setting: The perspective of hospital CEOs. Health Policy, 79(1), 24–34.

Romeo, A.A., J.L. Wagner & R.H. Lee (1984). Prospective reimbursement and the diffusion of new technologies in hospitals. Journal of Health Economics, 3(1), 1–24.

Segesten, K., Lundgren, S. & I. Lindström (1998). Versatility consequence of changing from mixed to all registered nurse staffing on a surgical ward. Journal of Nursing Management, 6(4), 223–230.

Sendi, P., M.J. Al, A. Gafni & S. Birch (2003). Optimizing a portfolio of health care programs in the presence of uncertainty and constrained resources. Social Science and Medicine, 57(11), 2207–2215.

Tountas Y., P. Karnaki, E. Pavi & K. Souliotis (2005). The ‘unexpected’ growth of the private health sector in Greece. Health Policy, 74(2), 167–180.

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