Associations of Patient Characteristics and Care Setting with Complexity of Specialty Palliative Care Visits.

Background: Information routinely collected during a palliative care consultation request may help predict the level of complexity of that patient encounter. Objectives: We examined whether patient and consultation characteristics, as captured in consultation requests, are associated with the number of unmet palliative care needs that emerge during consultation, as an indicator of complexity. Design: We performed a retrospective cohort analysis of palliative care consultations. Setting: We analyzed quality-of-care data from specialty palliative care consultations contained in the Quality Data Collection Tool of the Global Palliative Care Quality Alliance from 2012 to 2017. Measurements: Using 13 point-of-care assessments of quality of life, symptoms, advance care planning, and prognosis, we created a complexity score ranging from 0 (not complex) to 13 (highest complexity). Using multivariable linear regression, we examined the relationships of consultation setting and patient characteristics with complexity score. Results: Patients in our cohort (N = 3121) had an average complexity score of 6.7 (standard deviation = 3.7). Female gender, nonwhite race, and neurological (e.g., dementia) and noncancer primary diagnosis were associated with increased complexity score. The hospital intensive care unit, compared with the general floor, was associated with higher complexity scores. In contrast, outpatient and residence, compared with the general floor, were associated with lower complexity scores. Conclusion: Patient, disease, and care setting factors known at the time of specialty palliative care consultation request are associated with level of complexity, and they may inform teams about the right service provisions, including time and expertise, required to meet patient needs.

Duke Scholars

Author Devon Karnes Check Population Health Sciences

Author Arif H Kamal Medicine, Medical Oncology

Author Jesse David Troy Biostatistics & Bioinformatics, Division of Biostatistics

Author Donald H. Taylor Jr. Sanford School of Public Policy