Objectives To determine nurse-sensitive outcomes in district nursing care for community-living older people. Nurse-sensitive outcomes are defined as patient outcomes that are relevant based on nurses’ scope and domain of practice and that are influenced by nursing inputs and interventions. Design A Delphi study following the RAND/UCLA Appropriateness Method with two rounds of data collection. Setting District nursing care in the community care setting in the Netherlands. Participants Experts with current or recent clinical experience as district nurses as well as expertise in research, teaching, practice, or policy in the area of district nursing. Main outcome measures Experts assessed potential nurse-sensitive outcomes for their sensitivity to nursing care by scoring the relevance of each outcome and the ability of the outcome to be influenced by nursing care (influenceability). The relevance and influenceability of each outcome were scored on a nine-point Likert scale. A group median of 7 to 9 indicated that the outcome was assessed as relevant and/or influenceable. To measure agreement among experts, the disagreement index was used, with a score of <1 indicating agreement. Results In Delphi round two, 11 experts assessed 46 outcomes. In total, 26 outcomes (56.5%) were assessed as nurse-sensitive. The nurse-sensitive outcomes with the highest median scores for both relevance and influenceability were the patient’s autonomy, the patient’s ability to make decisions regarding the provision of care, the patient’s satisfaction with delivered district nursing care, the quality of dying and death, and the compliance of the patient with needed care. Conclusions This study determined 26 nurse-sensitive outcomes for district nursing care for community-living older people based on the collective opinion of experts in district nursing care. This insight could guide the development of quality indicators for district nursing care. Further research is needed to operationalise the outcomes and to determine which outcomes are relevant for specific subgroups.
This report describes the results and recommendations of the assessment of a potential business model for the orchard of Mr. W. Hekkert, Terwolde, focusing on drying fruits for organic muesli.
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Research, advisory companies, consultants and system integrators all predict that a lot of money will be earned with decision management (business rules, algorithms and analytics). But how can you actually make money with decision management or in other words: Which business models are exactly available? In this article, we present seven business models for decision management.
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Thermal batteries, which store and release energy by hydrating and dehydrating salt crystals, hold great promise for domestic heating. Such batteries can be charged from waste heat from industrial processes, and discharged to provide neighbourhood heating. Unlike hot water storage systems, the energy is stored at room temperature, so the thermal losses are very low, making a salt battery highly efficient. However, the electrochemical change of the salt due to hydration and dehydration is very small, making it difficult to measure how much energy is stored in a battery. One promising technique is to measure the absolute humidity of the inlet and outlet air flow. The difference in humidity, combined with a rate equation model allows the total mass of water stored in the battery to be calculated, which can then be used to calculate the energy storage and battery power flow. However, there are several uncertainties in this approach. Commercially available sensors age over time, sometimes quite suddenly. It is not yet known if software can be used to compensate for sensor aging, or if a different sensor type is required. In addition to aging, each measurement is subject to random noise, which will be integrated into the model used to calculate the charge of the battery. It is not yet known how the noise will influence charge estimates. On the other hand, the sensor system must be as durable as domestic heating systems (decades). Hence, it is required to understand sensor aging in order to validate the sensor system for its intended use.
