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Background: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. At the moment, such monitoring is done by means of regular sentiment analysis with a poor performance on detecting negative stance towards vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms.Results: We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, considerably outperforming the currently used sentiment analysis that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. We also show that the recall of our system could be optimized to 0.60 at little loss of precision.Conclusion: The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Nonetheless, the model showed sufficient recall on identifying negative tweets so as to reduce the manual effort of reviewing messages. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions. [ABSTRACT FROM AUTHOR]

Background: As the implementation of Electronic Medical Records (EMRs) in hospitals may be challenged by different responses of different user groups, this paper examines the differences between doctors and nurses in their response to the implementation and use of EMRs in their hospital and how this affects the perceived quality of the data in EMRs.Methods: Questionnaire data of 402 doctors and 512 nurses who had experience with the implementation and the use of EMRs in hospitals was analysed with Multi group Structural equation modelling (SEM). The models included measures of organisational factors, results of the implementation (ease of use and alignment of EMR with daily routine), perceived added value, timeliness of use and perceived quality of patient data.Results: Doctors and nurses differ in their response to the organisational factors (support of IT, HR and administrative departments) considering the success of the implementation. Nurses respond to culture while doctors do not. Doctors and nurses agree that an EMR that is easier to work with and better aligned with their work has more added value, but for the doctors this is more pronounced. The doctors and nurses perceive that the quality of the patient data is better when EMRs are easier to use and better aligned with their daily routine.Conclusions: The result of the implementation, in terms of ease of use and alignment with work, seems to affect the perceived quality of patient data more strongly than timeliness of entering patient data. Doctors and nurses value bottom-up communication and support of the IT department for the result of the implementation, and nurses respond to an open and innovative organisational culture. [ABSTRACT FROM AUTHOR]

Background: Patient empowerment is crucial in the successful self-management of people with chronic diseases. In this study, we investigated whether discussions about medicine use taking place on online message boards contribute to patient empowerment and could subsequently result in the more effective use of medicines. We discuss the extent to which patient empowerment processes occur in discussions on online message boards, focusing on patients with three disorders with different characteristics: diabetes, Amyotrophic Lateral Sclerosis (ALS) and Attention Deficit / Hyperactivity Disorder (ADHD). Because information is an important factor in both patient empowerment and self-management, we also evaluate the quality of the information being exchanged. Methods: We used a deductive thematic analysis method based on pre-existing categories. We gathered and analysed 5532 posts related to the conditions ADHD, ALS and diabetes from seven message boards (three for ADHD, three for diabetes, and one for ALS). We coded the posts for empowerment processes and the quality of the information exchanged. Results: We identified patient empowerment processes in posts related to all three disorders. There is some variation in the frequency of these processes, but they show a similar order in the results: patients used the online message boards to exchange information, share personal experiences and for empathy or support. The type of information shared in these processes could contribute to the patient's self-efficacy when it comes to medicine use. The exchanged information was either correct or largely harmless. We also observed a tendency whereby participants correct previously posted incorrect information, and refer people to a healthcare professional following a request for medical advice, e.g. concerning the choice of medicines or dosage. Conclusions: Our findings show that patient empowerment processes occur in posts related to all three disorders. The type of information shared in these processes can contribute to the patient's self-efficacy when it comes to medicine use. The tendency to refer people to a healthcare professional shows that patients still reserve an important role for healthcare professionals in the care process, despite the development towards more self-management. [ABSTRACT FROM AUTHOR]

Background: Decisions to adopt a particular innovation may vary between stakeholders because individual stakeholders may disagree on the costs and benefits involved. This may translate to disagreement between stakeholders on priorities in the implementation process, possibly explaining the slow diffusion of innovations in health care. In this study, we explore the differences in stakeholder preferences for innovations, and quantify the difference in stakeholder priorities regarding costs and benefits.Methods: The decision support technique called the analytic hierarchy process was used to quantify the preferences of stakeholders for nine information technology (IT) innovations in hospital care. The selection of the innovations was based on a literature review and expert judgments. Decision criteria related to the costs and benefits of the innovations were defined. These criteria were improvement in efficiency, health gains, satisfaction with care process, and investments required. Stakeholders judged the importance of the decision criteria and subsequently prioritized the selected IT innovations according to their expectations of how well the innovations would perform for these decision criteria.Results: The stakeholder groups (patients, nurses, physicians, managers, health care insurers, and policy makers) had different preference structures for the innovations selected. For instance, self-tests were one of the innovations most preferred by health care insurers and managers, owing to their expected positive impacts on efficiency and health gains. However, physicians, nurses and patients strongly doubted the health gains of self-tests, and accordingly ranked self-tests as the least-preferred innovation.Conclusions: The various stakeholder groups had different expectations of the value of the nine IT innovations. The differences are likely due to perceived stakeholder benefits of each innovation, and less to the costs to individual stakeholder groups. This study provides a first exploratory quantitative insight into stakeholder positions concerning innovation in health care, and presents a novel way to study differences in stakeholder preferences. The results may be taken into account by decision makers involved in the implementation of innovations. [ABSTRACT FROM AUTHOR]