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.

Hospice Use Among Patients With Cancer: Trends, Barriers, and Future Directions.

Patients with advanced cancer and their families frequently encounter clinical and logistical challenges related to end-of-life care. Hospice provides interdisciplinary and holistic care to meet patients' biomedical, psychosocial, and spiritual needs in the last phases of life. Despite increasing general acceptance and use among patients with cancer, hospice remains underused. Underuse stems from ongoing misconceptions regarding hospice and its purpose, coupled with the rapid development of novel anticancer treatments, such as immunotherapies and targeted therapies, that have changed the landscape of possibilities. Furthermore, rapid evolutions in how end-of-life care is structured and reimbursed for will affect how oncology patients will intersect with hospice care. In this review, we explore the current and future challenges to greater integration of hospice care in the care of patients with advanced cancer and propose five recommendations as part of the path forward.

Usability of PCforMe in Patients With Advanced Cancer Referred to Outpatient Palliative Care: Results of a Randomized, Active-Controlled Pilot Trial.

CONTEXT: Low utilization of palliative care services warrant testing of new solutions to educate and engage patients around the benefits of palliative care. OBJECTIVES: We sought out to develop and test a novel, mobile health solution to prepare patients for an upcoming outpatient palliative care appointment. METHODS: After developing a web-based tool called PCforMe (Palliative Care for Me), we conducted a randomized, active-controlled, trial of PCforMe. The primary outcome was the score on the System Usability Scale (SUS). Secondary outcomes were patient self-efficacy and change in knowledge. We compared PCforMe to three common online resources for patients seeking information about palliative care. RESULTS: A total of 80 patients were randomized. There were no significant demographic differences. Mean SUS score for PCforMe was 78.2, significantly above the normative average SUS score of 68 (P-value < 0.0001). Mean change in Perceived Efficacy in Patient-Physician Interactions score was -2.2 for PCforMe and -1.7 for control group (P-value = 0.72). Preparedness for an upcoming palliative care visit increased 50% in the intervention group and 13.3% in the control group. Difference in the number of patients with improved knowledge regarding palliative care approached significance (P = 0.06). Lastly, we found that the no-show rate was lower during Q1 2017 (during trial) and Q1 2016 (before trial), at 11.7% and 21%, respectively (P < 0.05). Comparing the full calendar year (CY) 2016 with 2017, we did not find a statistical difference (CY 2016 of 18.8% and 15% in CY 2017; P = 0.22). CONCLUSION: PCforMe is a usable mobile health tool to prepare patients for an upcoming palliative care appointment. Further research is needed to test effectiveness.

Quality Improvement Pearls for the Palliative Care and Hospice Professional.

Rapid changes in how palliative care clinicians are evaluated and paid present an imperative for clinicians to adeptly and routinely perform quality improvement in usual practice. Like empathic communication and facilitating goals of care discussions, quality improvement skills must be learned, honed, and practiced, so identifying problems and brainstorming solutions becomes a natural component of delivering serious illness care. Using our experience in both failures and successes in performing quality improvement, here we provide a prioritized list of 10 pearls specifically aimed to palliative care and hospice professionals. We aim to demystify quality improvement, highlight areas where rigor and a systematic approach are needed for success, and offer our own lessons learned and mistakes made to promote success for our colleagues and our field.

Development of the Quality Data Collection Tool for Prospective Quality Assessment and Reporting in Palliative Care.

BACKGROUND: Assessing and reporting the quality of care provided are increasingly important in palliative care, but we currently lack practical, efficient approaches for collection and reporting. OBJECTIVE: In response, the Global Palliative Care Quality Alliance ("Alliance") sought to create a Quality Data Collection Tool for Palliative Care (QDACT-PC). METHODS: We collaboratively and iteratively developed QDACT-PC, an electronic, point-of-care quality monitoring system for palliative care that supports prospective quality assessment and reporting in any clinical setting. QDACT-PC is the web-based data collection and reporting interface. Quality measures selected to be used in QDACT-PC were derived from a systematic review summarizing all published palliative care quality measure sets; Alliance clinical providers prioritized measures to be included in QDACT-PC to ensure maximal clinical relevance. Data elements and variables required to ascertain conformance to all selected quality measures were included in the QDACT-PC data dictionary. Whenever possible, variables collected in QDACT-PC align with validated surveys and/or nationally recognized common data elements. QDACT-PC data elements and software programmed business rules inform real-time assessments of conformance to selected quality measures. Data are deposited into a centralized registry for future analyses. RESULTS: QDACT-PC can be used to report on >80% of all published palliative care quality measures and 100% of high-priority measure. CONCLUSION: Electronic methods for collecting point-of-care quality monitoring data can be developed using collaborative partnerships between community and academic palliative care providers. Feasibility testing and creation of feedback reports are ongoing.

Quality of palliative care for patients with advanced cancer in a community consortium.

BACKGROUND: Measuring quality of care delivery is essential to palliative care program growth and sustainability. We formed the Carolinas Consortium for Palliative Care and collected a quality data registry to monitor our practice and inform quality improvement efforts. MEASURES: We analyzed all palliative care consultations in patients with cancer in our quality registry from March 2008 through October 2011 using 18 palliative care quality measures. Descriptive metric adherence was calculated after analyzing the relevant population for measurement. INTERVENTION: We used a paper-based, prospective method to monitor adherence for quality measures in a community-based palliative care consortium. OUTCOMES: We demonstrate that measures evaluating process assessment (range 63%-100%), as opposed to interventions (range 3%-17%), are better documented. CONCLUSIONS/LESSONS LEARNED: Analyzing data on quality is feasible and valuable in community-based palliative care. Overall, processes to collect data on quality using nontechnology methods may underestimate true adherence to quality measures.