RESUMO
Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clinical notes from patients discharged home when a clinical decision support system recommended post-acute care. We annotated B2PAC sentences from discharge planning notes and developed an NLP classifier to identify the highest-value B2PAC class (negative patient preferences). Thirteen machine learning models were compared with Amazon's AutoGluon deep learning model. The study included 594 acute care notes from 100 patient encounters (1156 sentences contained 11 B2PAC) in a large academic health system. The most frequent and modifiable B2PAC class was negative patient preferences (18.3%). The best supervised model was Extreme Gradient Boosting (F1: 0.859), but the deep learning model performed better (F1: 0.916). Alerting clinicians of negative patient preferences early in the hospitalization can prompt interventions such as patient education to ensure patients receive the right level of care and avoid negative outcomes.
Assuntos
Processamento de Linguagem Natural , Preferência do Paciente , Humanos , Idoso , Cuidados Semi-Intensivos , Aprendizado de Máquina , Encaminhamento e Consulta , Registros Eletrônicos de SaúdeRESUMO
Objective: Review transitions in care clinical decision support system (CDSS) implementation studies and describe human factors considerations in users, design, alert types, intervention timing, and implementation outcomes. Methods: Literature review in PubMed guided by subject matter experts. Results: Twelve articles were included. Targeted users included physicians, nurses, pharmacists, or interdisciplinary teams. Alerts were deployed via email, cloud-based software, or the EHR in inpatient and/or outpatient settings. Outcome measures varied across articles, with mixed performance. There were six readmissions-focused, two prescribing, one laboratory, two prescribing and laboratory, and one discharge disposition CDSS. Few articles reported statistically significant differences in outcomes, and many reported alert fatigue. Discussion and Conclusion: Despite the increasing prevalence of CDSS for transitions in care, few articles describe implementation processes and outcomes, and evidence of clinical practice improvement is mixed. Future studies should utilize implementation science frameworks and incorporate appropriate implementation outcomes in addition to traditional clinical outcomes like readmission rates.