Bisiani Marisa A, Jurgens Corinne Y
Marisa A. Bisiani, DNP, RN, ANP-BC, has been in the field nursing for more than 20 years. As a young nurse who began her career as a nurse's aide, she pursued education as a licensed practical nurse and then an associate prepared registered nurse to obtaining a bachelor of science. She received her master of science from Stony Brook, leading her to become a licensed ANCC Board Certified Adult Nurse Practitioner. She recently graduated from Stony Brook Universities Doctorate of Nursing Practice (DNP) program. She works as a nurse executive and she has directed many departments such as care management, allied health professionals, employee health services, and hemodialysis. Her specialty is inclusive of focusing on care transitions, as we move one patient to each level of care. She is exceptionally interested in the work of preventable readmissions and population health. Corrine Y. Jurgens, PhD, RN, ANP-BC, FAHA, is a nurse scientist. Her program of research focuses on patients with heart failure and self-care. In particular, she studies symptom perception, recognition, and response in this population. Her prior work indicates patients lack a context for determining whether their symptoms are heart-related or associated with less threatening illnesses. She conducted a randomized trial testing an intervention based on my prior studies and theory that provides patients with a way to monitor and interpret their symptoms in a meaningful way. Other interests include investigating a phenotype for mild cognitive impairment in patients with heart failure, exploring self-care interventions for frail elders, implementing guidelines for heart failure management in skilled nursing facilities, and examining factors related to symptom blunting in this population.
Prof Case Manag. 2015 Jul-Aug;20(4):188-96. doi: 10.1097/NCM.0000000000000098.
Case management provides a process and structure in health care systems that influence and control quality of care while reducing costs. A quality indicator of widespread concern is 30-day readmission of patients. There is significant initiative to drive down hospital readmission rates through development and/or redesign of case management models.
To examine the relationship of a collaborative case management model on hospital readmission rates among patients aged 65 years and older.
A retrospective chart review of patients discharged alive (n = 978) was conducted to evaluate and compare 2 care management models on hospital readmission rates. Demographic data, diagnosis, insurance carrier, admission source, discharge disposition, and incidence of readmission were collected using a structured data extraction tool. Logistic regression was used to identify predictors of readmission within 30 days of hospital discharge.
The sample was elderly (mean age = 79.5 years), White (88.8%), and primarily female (60%). Mean length of stay between pre- and postmodel groups was not statistically different (p = .2). The model contained 6 independent variables (gender, payer, admission source, discharge disposition, diagnosis, and length of stay) and none were statistically significant, χ2 (1, n = 978) = 1.97, p = .58. The analysis indicates that group characteristics did not distinguish who would get readmitted on the basis of independent variables measured.
Age, gender, admit source, diagnosis, length of stay, and discharge disposition are not significant predictors of readmissions. Hospital case management programs may want to consider structuring processes to support patient adherence. Additional research is needed in this area.
病例管理在医疗保健系统中提供了一个流程和架构，既能影响和控制医疗质量，又能降低成本。一个广受关注的质量指标是患者30天再入院率。通过开发和/或重新设计病例管理模式，人们为降低医院再入院率付出了巨大努力。
研究协作式病例管理模式与65岁及以上患者医院再入院率之间的关系。
对978例存活出院患者进行回顾性病历审查，以评估和比较两种护理管理模式的医院再入院率。使用结构化数据提取工具收集人口统计学数据、诊断结果、保险公司、入院来源、出院处置情况和再入院发生率。采用逻辑回归分析确定出院后30天内再入院的预测因素。
样本为老年人（平均年龄 = 79.5岁），白人（88.8%），且以女性为主（60%）。模型前组和模型后组的平均住院时间无统计学差异（p = 0.2）。该模型包含6个自变量（性别、付款人、入院来源、出院处置情况、诊断结果和住院时间），且均无统计学意义，χ2（1，n = 978）= 1.97，p = 0.58。分析表明组间特征并不能根据所测量的自变量区分谁会再次入院。
年龄、性别、入院来源、诊断结果、住院时间和出院处置情况并非再入院的显著预测因素。医院病例管理项目可能需要考虑构建相关流程以支持患者坚持治疗。该领域还需要更多研究。
