Arif H Kamal1,2, Devon K Check1,3, Janet Bull4, Steven Wolf5, Jesse Troy6, Greg Samsa5, Jonathan M Nicolla1, Matthew Harker7, Donald H Taylor7,8. 1. Department of Medicine, Duke Cancer Institute, Duke University, Durham, North Carolina, USA. 2. Duke Fuqua School of Business, Durham, North Carolina, USA. 3. Department of Population Health Sciences, Duke School of Medicine, Durham, North Carolina, USA. 4. Four Seasons, Hendersonville, North Carolina, USA. 5. Department of Biostatistics, Duke School of Medicine, Durham, North Carolina, USA. 6. Department of Pediatrics, Duke School of Medicine, Durham, North Carolina, USA. 7. Duke Clinical Research Institute, Duke Margolis Center for Health Policy, Durham, North Carolina, USA. 8. Department of Family Medicine and Community Health, Duke Sanford School of Public Policy, Durham, North Carolina, USA.
Abstract
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.
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.
Entities:
Keywords:
complexity; consultation; palliative care; service delivery; service planning
Authors: Arif H Kamal; David C Currow; Christine Ritchie; Janet Bull; Jane L Wheeler; Amy P Abernethy Journal: Curr Oncol Rep Date: 2011-08 Impact factor: 5.075
Authors: Jaclyn Yoong; Elyse R Park; Joseph A Greer; Vicki A Jackson; Emily R Gallagher; William F Pirl; Anthony L Back; Jennifer S Temel Journal: JAMA Intern Med Date: 2013-02-25 Impact factor: 21.873
Authors: Arif H Kamal; Donald H Taylor; Benjamin Neely; Matthew Harker; Parampal Bhullar; John Morris; Lindsay Bonsignore; Janet Bull Journal: J Pain Symptom Manage Date: 2017-07-25 Impact factor: 3.612
Authors: Arif H Kamal; Janet Bull; Dio Kavalieratos; Jonathan M Nicolla; Laura Roe; Martha Adams; Amy P Abernethy Journal: J Palliat Med Date: 2016-06-27 Impact factor: 2.947