The relationship between hospital adoption and use of high technology medical imaging and in-patient mortality and length of stay

Utilisation and time to performance of diagnostic imaging in patients admitted to Danish emergency departments: a nationwide register-based study from 2007 to 2017

OBJECTIVES

To describe the development of diagnostic imaging utilisation in Denmark from 2007 to 2017, coinciding with a major national reform of the emergency healthcare system.

DESIGN

Nationwide descriptive register-based study.

SETTING

All public hospitals in Denmark.

PARTICIPANTS

All unplanned hospital contacts ≥18 years old at somatic hospitals in Denmark from 1 January 2007 to 31 December 2017.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was the probability of having CT, X-ray, MRI or ultrasound performed during hospitalisation in 2017 compared with 2007. The secondary outcome measure was receiving diagnostic imaging within 4 hours of hospitalisation.

RESULTS

The probability of having a radiological examination during unplanned hospital admission increased from 2007 to 2017 (CT: 3.5%-10.3%; MRI: 0.2%-0.8%; ultrasound: 2.3%-4.5%; X-ray: 23.8%-26.8%). For CT scan, the adjusted OR was 3.09 (95% CI: 2.73, 3.51); for MRI, the adjusted OR was 3.39 (95% CI: 1.87, 6.12) and for ultrasound, the adjusted OR was 1.93 (95% CI: 1.56, 2.38). The likelihood of having the examination within the first 4 hours in the hospital increased from 2007 to 2017. For X-ray, the adjusted OR was 1.39 (95% CI: 1.07, 1.56); for CT scan, the adjusted OR was 1.35 (95% CI: 1.16, 1.59); for MRI, the adjusted OR was 1.34 (95% CI: 1.09, 1.66) and for ultrasound, the adjusted OR was 1.38 (95% CI: 1.16, 1.64).

CONCLUSION

This nationwide study describes the development of diagnostic imaging utilisation in Denmark from 2007 to 2017. The probability of receiving radiological examinations during unplanned hospitalisation increased over this period, and the time from hospital contact to performance decreased. This supports the notion that enhancement in radiological equipment will also lead to more frequent and faster utilisation.

Explaining the adoption and use of computed tomography and magnetic resonance image technologies in public hospitals

Objective

This article examines what the adoption and use of advanced medical technologies – computed tomography (CT) and magnetic resonance imaging (MRI) – by public hospitals depend on and to what extent.

Methods

From a sample of panel data for all public hospitals in the health service of Galicia (a subregion of the Galicia-North of Portugal Euroregion) for the 2010–2017 period, we grouped explanatory variables into inputs (resources), outputs (activities) and socio-demographic variables. Factor analysis was used to reduce as much as possible the number of analysed variables, discriminant analysis to examine the technologies adoption decision, and multiple regression analysis to investigate their use.

Results

Factor analysis identified motivators on adoption and use of CT and MRI medical technologies as follows: hospital inputs/outputs (Factor 1); radiology studies and adoption of CT by public hospitals (Factor 2); research/teaching role and big-ticket diagnostic and therapeutic (lithotripsy) technologies (Factor 3); number of transplants (Factor 4); cancer diagnosis/treatment (Factor 5); and catchment area geographical dispersion (Factor 6). Cronbach’s alpha of 0.881 indicated an acceptable degree of reliability of the factor variables. Regarding adoption of these technologies, Factor 1 is the most influential, explaining 37% of the variance and showing adequate global internal consistency, whereas Factor 2 is limited to 13% of the variance. In the discriminant analysis, values for Box’s M test and canonical correlations such as Wilks’s lambda for the two technologies underpin the reliability and predictive capacity of the discriminant equations. Finally, and according to the regression analysis, the factor with the greatest influence on CT and MRI use is Factor 2, followed by Factors 1 and 3 in the case of CT use, and Factors 3 and 5 in the case of MRI use.

Conclusion

CT and MRI adoption by public hospitals is mainly determined by hospital inputs and outputs. However, the use of both medical technologies is mainly influenced by conventional radiology studies and CT adoption. These results suggest that both choices – adoption and use of advanced medical technology – may be separate decisions as they are taken possibly by different people (the former by managers and policymakers and the latter by physicians).