RESUMO
Better education around the recognition of transfusion-associated adverse events is warranted. It is unknown if checklist use improves recognition by student nurses. This study examined whether using a checklist could improve transfusion-associated adverse event recognition behaviors. There was an increased frequency of transfusion-associated adverse event management behaviors in the checklist group, but overall recognition was no greater than other groups. A transfusion-associated adverse event checklist may increase patient safety by promoting identification behaviors.
Assuntos
Transfusão de Sangue/métodos , Lista de Checagem/métodos , Estudantes de Enfermagem/psicologia , Adulto , Transfusão de Sangue/estatística & dados numéricos , Lista de Checagem/tendências , Distribuição de Qui-Quadrado , Feminino , Humanos , Masculino , Erros Médicos/prevenção & controle , Segurança do Paciente/normas , Segurança do Paciente/estatística & dados numéricos , Estudantes de Enfermagem/estatística & dados numéricos , Reação Transfusional/prevenção & controle , Reação Transfusional/terapiaRESUMO
OBJECTIVES: High medication adherence is important for HIV suppression (antiretroviral therapy) and pre-exposure prophylaxis efficacy. We are developing sensor-based technologies to detect pill-taking gestures, trigger reminders, and generate adherence reports. MATERIALS AND METHODS: We collected interview, observation, and questionnaire data from individuals with and at-risk for HIV (N = 17). We assessed their medication-taking practices and physical actions, and feedback on our initial design. RESULTS: While participants displayed diverse medication taking practices and physical actions, most (67%) wanted to use the system to receive real-time and summative feedback, and most (69%) wanted to share data with their physicians. Participants preferred reminders via the wrist-worn device or mobile app, and summative feedback via mobile app or email. DISCUSSION: Adoption of these systems is promising if designs accommodate diverse behaviors and preferences. CONCLUSION: Our findings may help improve the accuracy and adoption of the system by accounting for user behaviors, physical actions, and preferences.