RESUMO
A screening tool utilized by nurses at a critical point in the discharge planning process has the potential to improve caregiver decisions and enhance communication. The Early Screen for Discharge Planning-Child version (ESDP-C) identifies pediatric patients early in their hospital stay who will benefit from early engagement of a discharge planner. This study used a quasi-experimental, non-equivalent comparison group design to evaluate the impact of the ESDP-C on important outcomes related to discharge planning. Findings from the study provide preliminary evidence that the integration of the ESDP-C into the pediatric discharge planning process may be clinically useful.
Assuntos
Continuidade da Assistência ao Paciente/organização & administração , Técnicas de Apoio para a Decisão , Alta do Paciente/tendências , Readmissão do Paciente/estatística & dados numéricos , Adolescente , Fatores Etários , Criança , Pré-Escolar , Estudos de Viabilidade , Feminino , Hospitalização/estatística & dados numéricos , Hospitais Pediátricos , Humanos , Tempo de Internação , Masculino , Seleção de Pacientes , Estudos Prospectivos , Melhoria de Qualidade , Medição de Risco , Fatores Sexuais , Fatores de TempoRESUMO
PURPOSE: To develop and test a decision support tool that identifies patients who would benefit from early consult with discharge planners. DESIGN AND METHODS: A predictive, correlational design was used with parents/guardians of children (1 month to 18 years; N = 197). Data were collected by interviews and record reviews. Expert consensus determined referral to discharge planning. RESULTS: Mean age was 8.7 years; mean length of stay was 7.5 days. Forty percent (n = 79) were identified for early referral. The variable "substantial post-acute care needs" had the strongest association with expert consensus (internally validated AUC = 0.79). PRACTICE IMPLICATIONS: Findings from this study provide preliminary evidence for a decision support tool to improve the discharge planning process by reducing individual decision-making variability through systematic matching of patient needs to service delivery.
