Abstract
Our aim was to estimate the diagnostic performance of institutions and healthcare regions from a nationwide hospitalisation database. The Shannon diversity index was used as an indicator of diagnostic performance based on the International Classification of Disease, 10th revision, German Modification (ICD-10-GM codes). The dataset included a total of 9,325,326 hospitalisation cases from 2009 to 2015 and was provided by the Swiss Federal Office for Statistics. A total of 16,435 diagnostic items from the ICD-10-GM codes were taken as the basis for the calculation of the diagnostic diversity index (DDI). Numerical simulations were performed to evaluate the effect of misdiagnoses in the DDI. We arbitrarily defined the minimum clinically important difference (MCID) as 10% misdiagnoses. The R statistical software was used for all analyses. Diagnostic performance of institutions and healthcare regions as measured by the DDI were strongly associated with caseload and number of inhabitants, respectively. A caseload of >7217 hospitalisations per year for institutions and a population size >363,522 for healthcare regions were indicators of an acceptable diagnostic performance. Among hospitals, there was notable heterogeneity of diagnostic diversity, which was strongly associated with caseload. Application of misdiagnosis-thresholds within each ICD-10-GM category allowed classification of hospitals in four distinct groups: high-volume hospitals with an all-over comprehensive diagnostic performance; high- to mid-volume hospitals with extensive to relevant basic diagnostic performance in most categories; low-volume specialised hospitals with a high diagnostic performance in a single category; and low-volume hospitals with inadequate diagnostic performance in all categories. The diagnostic diversity observed in the 26 Swiss healthcare regions showed relevant heterogeneity, an association with ICD-10-GM code utilisation, and was strongly associated with the size of the healthcare region. The limited diagnostic performance in small healthcare regions was partially, but not fully, compensated for by consumption of health services outside of their own healthcare region. Calculation of the DDI from ICD-10 codes is easy and complements the information derived from other quality indicators as it sheds a light on the fitness of the institutionalised interplay between primary and specialised medical inpatient care.  .
Highlights
Measuring healthcare quality is essential to optimise the effectiveness of healthcare delivery in a rapidly changing healthcare environment, but is challenging and at times controversial
The Shannon diversity index was used as an indicator of diagnostic performance based on the International Classification of Disease, 10th revision, German Modification (ICD-10-GM codes)
Diagnostic performance of institutions and healthcare regions as measured by the diagnostic diversity index (DDI) were strongly associated with caseload and number of inhabitants, respectively
Summary
Measuring healthcare quality is essential to optimise the effectiveness of healthcare delivery in a rapidly changing healthcare environment, but is challenging and at times controversial. There is a plethora of quality indicators [1] from self-reported health status [2] to mortality rates in different age groups and conditions that are applied to evaluate care across regions [3] and institutions [4]. These measures are affected by a complex interplay between availability of resources, and socioeconomic and healthcare factors [5]. The publication of annual institutional mortality rates and other quality indicators is imposed on institutions in different industrial countries [9]. Such publications rather become public relations and market factors of a nonvalidated nature in the hands of nonprofessionals [11]
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