IT support for administrative planning of clinical practice.

The administration of clinical practice placements for nursing students is a highly complex and information driven task. This demonstration is intended to give insight into the web based system KliPP (a Swedish acronym for Clinical Practice Planning) and to discuss the possibilities for further development and use.

Depicting the interplay between organisational tiers in the use of a national quality registry to develop quality of care in Sweden

BACKGROUND: With a pending need to identify potential means to improved quality of care, national quality registries (NQRs) are identified as a promising route. Yet, there is limited evidence with regards to what hinders and facilitates the NQR innovation, what signifies the contexts in which NQRs are applied and drive quality improvement. Supposedly, barriers and facilitators to NQR-driven quality improvement may be found in the healthcare context, in the politico-administrative context, as well as with an NQR itself. In this study, we investigated the potential variation with regards to if and how an NQR was applied by decision-makers and users in regions and clinical settings. The aim was to depict the interplay between the clinical and the politico-administrative tiers in the use of NQRs to develop quality of care, examining an established registry on stroke care as a case study. METHODS: We interviewed 44 individuals representing the clinical and the politico-administrative settings of 4 out of 21 regions strategically chosen for including stroke units representing a variety of outcomes in the NQR on stroke (Riksstroke) and a variety of settings. The transcribed interviews were analysed by applying The Consolidated Framework for Implementation Research (CFIR). RESULTS: In two regions, decision-makers and/or administrators had initiated healthcare process projects for stroke, engaging the health professionals in the local stroke units who contributed with, for example, local data from Riksstroke. The Riksstroke data was used for identifying improvement issues, for setting goals, and asserting that the stroke units achieved an equivalent standard of care and a certain level of quality of stroke care. Meanwhile, one region had more recently initiated such a project and the fourth region had no similar collaboration across tiers. Apart from these projects, there was limited joint communication across tiers and none that included all individuals and functions engaged in quality improvement with regards to stroke care. CONCLUSIONS: If NQRs are to provide for quality improvement and learning opportunities, advances must be made in the links between the structures and processes across all organisational tiers, including decision-makers, administrators and health professionals engaged in a particular healthcare process.

Modeling Hospital Readmissions in Dalarna County, Sweden

Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.