The relationship between context, structure, and processes with outcomes of 6 regional diabetes networks in Europe

Abstract

BackgroundWhile health service provisioning for the chronic condition Type 2 Diabetes (T2D) often involves a network of organisations and professionals, most evidence on the relationships between the structures and processes of service provisioning and the outcomes considers single organisations or solo practitioners. Extending Donabedian’s Structure-Process-Outcome (SPO) model, we investigate how differences in quality of life, effective coverage of diabetes, and service satisfaction are associated with differences in the structures, processes, and context of T2D services in six regions in Finland, Germany, Greece, Netherlands, Spain, and UK.MethodsData collection consisted of: a) systematic modelling of provider network’s structures and processes, and b) a cross-sectional survey of patient reported outcomes and other information. The survey resulted in data from 1459 T2D patients, during 2011–2012. Stepwise linear regression models were used to identify how independent cumulative proportion of variance in quality of life and service satisfaction are related to differences in context, structure and process. The selected context, structure and process variables are based on Donabedian’s SPO model, a service quality research instrument (SERVQUAL), and previous organization and professional level evidence. Additional analysis deepens the possible bidirectional relation between outcomes and processes.ResultsThe regression models explain 44% of variance in service satisfaction, mostly by structure and process variables (such as human resource use and the SERVQUAL dimensions). The models explained 23% of variance in quality of life between the networks, much of which is related to contextual variables. Our results suggest that effectiveness of A1c control is negatively correlated with process variables such as total hours of care provided per year and cost of services per year.ConclusionsWhile the selected structure and process variables explain much of the variance in service satisfaction, this is less the case for quality of life. Moreover, it appears that the effect of the clinical outcome A1c control on processes is stronger than the other way around, as poorer control seems to relate to more service use, and higher cost. The standardized operational models used in this research prove to form a basis for expanding the network level evidence base for effective T2D service provisioning.

Insight into blood pressure targets for universal coverage of hypertension services in Iran: the 2017 ACC/AHA versus JNC 8 hypertension guidelines

“…We, however, call future research to examine other dimensions of effective structures and processes e.g. evidence-based care plan [39][40][41] and continuity of care [42,43] to improve hypertension control.…”

Introducing an Efficient Sampling Method for National Surveys with Limited Sample Sizes: Application to a National Study to Determine Quality and Cost of Healthcare.

“…Relying on the contemporary theories of quality of healthcare [15,16], an array of input indicators from demands, structures, and health outcomes were used by clustering methods to define optimum homogenous strata, which cover the spectrum of quality and cost in national studies as aimed by IQCAMP. The more homogeneous the strata, the more efficient the stratified sampling design [23].…”

“…We relied on the Donabedian's Structure-Process-Outcome (SPO) model for selecting the indicators of HCQC [15]. Furthermore, we considered indicators of healthcare delivery context in order to comply with the contemporary healthcare evaluation methods [16]. The selected indicators consisted of patient demands (as a part of context), service structures, and health outcomes.…”

The use of information in online healthcare provider choice

“…For another example, the success rate (outcome) of a particular operation in the hospital may be high because of appropriately highly trained staff (structure explanation) and these staff following appropriate high-quality operation procedures (process explanation). Other factors (social and environmental conditions and patient risk factors) also influence healthcare outcomes, but normally these cannot be controlled by the healthcare system (Larson & Muller, 2002;Mahdavi et al, 2018).…”

“…The third component is outcome (the effect of care delivered on a patient's health status; e.g., recovery, restoration of function and survival). Donabedian's model has previously been applied not only to managing (Larson & Muller, 2002) and modelling healthcare quality (Mahdavi et al, 2018), and the creation of healthcare quality measures for quality assessment and improvement modelling healthcare quality (Jacobs et al, 2012); it has also been applied to patients' evaluation of hospital care (Aboshaiqah et al, 2016), specifically to compare two hospital types on structure, process and outcome, and, crucially, to analyse provider characteristics in the study of patients' offline information use. According to this model the outcomes of care (as ultimate quality indicators) are the end goal for patients and the result of care processes (means towards the end goal); these, in turn, are influenced and constrained by structural factors (as further means towards the end goal), thereby indirectly influencing outcomes (Larson & Muller, 2002).…”