Produktivitätsmotor Gesundheitswirtschaft: Finanziert sich der medizinisch-technische Fortschritt selbst?

Karmann, Alexander; Rösel, Felix; Schneider, Markus

doi:10.1515/pwp-2016-0004

Perspektiven Der Wirtschaftspolitik

2016

DOI: 10.1515/pwp-2016-0004

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Produktivitätsmotor Gesundheitswirtschaft: Finanziert sich der medizinisch-technische Fortschritt selbst?

Alexander Karmann

Felix Rösel

Markus Schneider³

Abstract: Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in… Show more

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2023

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Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals – a longitudinal national study based on official hospital statistics

Schoffer,

Schriefer,

Werblow

et al. 2023

BMC Health Serv Res

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Background Effects of demographic change, such as declining birth rates and increasing individual life expectancy, require health system adjustments offering age- and needs-based care. In addition, healthcare factors can also influence health services demand. Methods The official German hospital statistics database with odd-numbered years between 1995 and 2011 was analysed. This is a national comprehensive database of all general hospital inpatient services delivered. Official data from hospital statistics were linked at the district level with demographic and socio-economic data as well as population figures from the official regional statistics. Panel data regression, modelling case numbers per hospital, was performed for 13 diagnosis groups that characterised the patient structure. Socio-demographic variables included age, sex, household income, and healthcare factors included bed capacity, personnel and hospital characteristics. Results The median number of annual treatments per hospital increased from 6 015 (5th and 95th percentile [670; 24 812]) in 1995 to 7 817 in 2011 (5th and 95th percentile [301; 33 651]). We developed models characterising the patient structure of health care in Germany, considering both socio-demographic and hospital factors. Demographic factors influenced case numbers across all major diagnosis groups. For example, the age groups 65–74 and 75 + influenced cerebrovascular disease case numbers (p < 0.001). Other important factors included human and material resources of hospitals or the household income of patients. Distinct differences between the models for the individual diagnosis groups were observed. Conclusions Hospital planning should not only consider demographic change but also hospital infrastructure and socio-economic factors.

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Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals – a longitudinal national study based on official hospital statistics

Schoffer,

Schriefer,

Werblow

et al. 2023

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

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Produktivitätsmotor Gesundheitswirtschaft: Finanziert sich der medizinisch-technische Fortschritt selbst?

Cited by 1 publication

References 15 publications

Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals – a longitudinal national study based on official hospital statistics

Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals – a longitudinal national study based on official hospital statistics

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