Meaning and Practice of Palliative Care for Hospitalized Older Adults with Life Limiting Illnesses

Objective. To illustrate distinctions and intersections of palliative care (PC) and end-of-life (EOL) services through examples from case-centered data of older adults cared for during a four-year ethnographic study of an acute care hospital palliative care consultation service. Methods. Qualitative narrative and thematic analysis. Results. Description of four practice paradigms (EOL transitions, prognostic uncertainty, discharge planning, and patient/family values and preferences) and identification of the underlying structure and communication patterns of PC consultation services common to them. Conclusions. Consistent with reports by other researchers, study data support the need to move beyond equating PC with hospice or EOL care and the notion that EOL is a well-demarcated period of time before death. If professional health care providers assume that PC services are limited to assisting with and helping patients and families prepare for dying, they miss opportunities to provide care considered important to older individuals confronting life-limiting illnesses

A multi-response multilevel model with application in nurse care coordination

<p>Due to the aging of our society, patient care needs to be well coordinated within the health care team in order to effectively manage the overall health of each patient. Staff nurses, as the patient's “ever-present” health care team members, play a vital role in the care coordination. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables quantitative data to be collected to measure various aspects of nurse care coordination. Driven by this new development, we propose a multi-response multilevel model with joint fixed effect selection and joint random effect selection across multiple responses. This model is particularly suitable for modeling the unique data structure of the NCCI due to its ability of jointly modeling of multilevel predictors, including demographic and workload variables at the individual/nurse level and characteristics of the practice environment at the unit level and multiple response variables that measure the key components of nurse care coordination. We develop a Block Coordinate Descent algorithm integrated with an Expectation-Maximization framework for model estimation. Asymptotic properties are derived. Finally, we present an application to a data set collected across four U.S. hospitals using the NCCI and discuss implications of the findings.</p

Multiple data sources fusion for enterprise quality improvement by a multilevel latent response model

<div><p>Quality improvement of an enterprise needs a model to link multiple data sources, including the independent and interdependent activities of individuals in the enterprise, enterprise infrastructure, climate, and administration strategies, as well as the quality outcomes of the enterprise. This is a challenging problem because the data are at two levels—i.e., the individual and enterprise levels—and each individual's contribution to the enterprise quality outcome is usually not explicitly known. These challenges make general regression analysis and conventional multilevel models non-applicable to the problem. This article a new multilevel model that treats each individual's contribution to the enterprise quality outcome as a latent variable. Under this new formulation, an algorithm is developed to estimate the model parameters, which integrates the Fisher scoring algorithm and generalized least squares estimation. Extensive simulation studies are performed that demonstrate the superiority of the proposed model over the competing approach in terms of the statistical properties in parameter estimation. The proposed model is applied to a real-world application of nursing quality improvement and helps identify key nursing activities and unit (a hospital unit is an enterprise in this context) quality-improving measures that help reduce patient falls.</p> </div