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BackgroundIneffective communication of infection control requirements during transitions of care is a potential cause of non-compliance with infection control precautions by healthcare personnel. In this study, interventions to enhance communication during inpatient transfers between wards and radiology were implemented, in the attempt to improve adherence to precautions during transfers.MethodsTwo interventions were implemented, comprising (i) a pre-transfer checklist used by radiology porters to confirm a patient’s infectious status; (ii) a coloured cue to highlight written infectious status information in the transfer form. The effectiveness of the interventions in promoting adherence to standard precautions by radiology porters when transporting infectious patients was evaluated using a randomised crossover trial at a teaching hospital in Australia.Results300 transfers were observed over a period of 4 months. Compliance with infection control precautions in the intervention groups was significantly improved relative to the control group (p < 0.01). Adherence rate in the control group was 38%. Applying the coloured cue resulted in a compliance rate of 73%. The pre-transfer checklist intervention achieved a comparable compliance rate of 71%. When both interventions were applied, a compliance rate of 74% was attained. Acceptability of the coloured cue was high, but adherence to the checklist was low (40%).ConclusionsSimple measures to enhance communication through the provision of a checklist and the use a coloured cue brought about significant improvement in compliance with infection control precautions by transport personnel during inpatient transfers. The study underscores the importance of effective communication in ensuring compliance with infection control precautions during transitions of care.

Abstract-Predicting people who other people may like has recently become an important task in many online social networks. Traditional collaborative filtering (CF) approaches are popular in recommender systems to effectively predict user preferences for items. One major problem in CF is computing similarity between users or items. Traditional CF methods often use heuristic methods to combine the ratings given to an item by similar users, which may not reflect the characteristics of the active user and can give unsatisfactory performance. In contrast to heuristic approaches we have developed CollabNet, a novel algorithm that uses gradient descent to learn the relative contributions of similar users or items to the ranking of recommendations produced by a recommender system, using weights to represent the contributions of similar users for each active user. We have applied CollabNet to the challenging problem of people to people recommendation in social networks, where people have a dual role as both "users" and "items", e.g., both initiating and receiving communications, to recommend other users to a given user, based on user similarity in terms of both taste (whom they like) and attractiveness (who likes them). Evaluation of CollabNet recommendations on datasets from a commercial online social network shows improved performance over standard CF.