Marco Artico1, Michela Piredda2, Daniela D'Angelo3, Marco Di Nitto4, Diana Giannarelli5, Anna Marchetti6, Gabriella Facchinetti7, Cosimo De Chirico8, Maria Grazia De Marinis9. 1. Palliative Care Unit, Azienda ULSS4 Veneto Orientale, Piazza De Gasperi, 5, San Donà di Piave, Venezia 30027, Italy. 2. Research Unit Nursing Science, Campus Bio-Medico di Roma University, Via Alvaro del Portillo, 21, Rome 00128, Italy. Electronic address: m.piredda@unicampus.it. 3. Center for Clinical Excellence and Quality of Care (CNEC), Istituto Superiore di Sanità (ISS), Via Regina Elena, 299, Rome 00161, Italy. Electronic address: daniela.dangelo@iss.it. 4. Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier, 1, Rome 00133, Italy. Electronic address: marco.dinitto@uniroma2.it. 5. Biostatistical Unit, National Cancer Institute "Regina Elena" - IRCCS, Via Chianesi, 53, Rome 00144, Italy. Electronic address: diana.giannarelli@ifo.gov.it. 6. Palliative Care Center "Insieme per la cura", Via Alvaro del Portillo, 15, Rome 00128, Italy. Electronic address: a.marchetti@unicampus.it. 7. Palliative Care Center "Insieme per la cura", Via Alvaro del Portillo, 15, Rome 00128, Italy. 8. Palliative Care Unit, Azienda ULSS4 Veneto Orientale, Piazza De Gasperi, 5, San Donà di Piave, Venezia 30027, Italy. Electronic address: cosimo.dechirico@aulss4.veneto.it. 9. Research Unit Nursing Science, Campus Bio-Medico di Roma University, Via Alvaro del Portillo, 21, Rome 00128, Italy. Electronic address: m.demarinis@unicampus.it.
Abstract
BACKGROUND: The number of patients using palliative care services, particularly residential hospices, is increasing. Policymakers are urging these services to reflect on the most effective organizational strategies for meeting patients' complex care needs. AIM: To analyze the predictive power of staffing, structure and process indicators towards optimal control of patients' clinically significant symptoms over time. DESIGN: Secondary analysis of data from a multicentre prospective longitudinal observational study (PRELUdiHO) collected between November 2017 and September 2018. SETTING/PARTICIPANTS: Adult patients (n = 992) enrolled in 13 Italian residential hospices. METHODS: Two generalized estimating equations logistic models were built, both with number of hospice beds and length of stay as independent variables as well as, in one case, patient-to-healthcare worker ratios, and, in the other, health professionals' qualification levels. Dependent variables were six not clinically significant (score<4) symptoms: pain, nausea, shortness of breath, feeling sad, feeling nervous, and 'how you feel overall', according to the Edmonton Symptom Assessment System revised (ESAS-r) scale. RESULTS: The generalized estimating equations indicators on staff revealed the following 'optimal' model: Patient-to-Physician ratio (5.5:1-6.5:1); Patient-to-Nurse ratio (1.5:1-2.7:1); Patient-to-Nurse-Assistant ratio (4.1:1-6.3:1); with the most balanced staff composition including 19% physicians, 23% nurse assistants, and 58% registered nurses; hospice beds (12-25); length of stay (median = 12 days). This model predicted an up to four times greater likelihood of controlling all six ESAS-r symptoms over time. The generalized estimating equations model on the educational level of physicians and registered nurses showed that it was significantly associated with optimal patients' symptom control during the entire hospice stay. CONCLUSIONS: This study showed the exact skill-mix composition and proportions of palliative care team able to ensure optimal control of patients' symptoms. The added value of physicians and nurses with a qualification in palliative care in terms of better patient outcomes reaffirmed the importance of education in guaranteeing quality care. Hospices with 12-25 beds, and recruitment methods guaranteeing at least 12-day stay ensured the most propitious organizational environment for optimal management of clinically significant symptoms. The transferability of these results mainly depends on whether the skills of health professionals in our `ideal' model are present in other contexts. Our results provide policymakers and hospice managers with specific, evidence-based information to support decision-making processes regarding hospice staffing and organization. Further prospective studies are needed to confirm the positive impact of this 'optimal' organizational framework on patient outcomes.
BACKGROUND: The number of patients using palliative care services, particularly residential hospices, is increasing. Policymakers are urging these services to reflect on the most effective organizational strategies for meeting patients' complex care needs. AIM: To analyze the predictive power of staffing, structure and process indicators towards optimal control of patients' clinically significant symptoms over time. DESIGN: Secondary analysis of data from a multicentre prospective longitudinal observational study (PRELUdiHO) collected between November 2017 and September 2018. SETTING/PARTICIPANTS: Adult patients (n = 992) enrolled in 13 Italian residential hospices. METHODS: Two generalized estimating equations logistic models were built, both with number of hospice beds and length of stay as independent variables as well as, in one case, patient-to-healthcare worker ratios, and, in the other, health professionals' qualification levels. Dependent variables were six not clinically significant (score<4) symptoms: pain, nausea, shortness of breath, feeling sad, feeling nervous, and 'how you feel overall', according to the Edmonton Symptom Assessment System revised (ESAS-r) scale. RESULTS: The generalized estimating equations indicators on staff revealed the following 'optimal' model: Patient-to-Physician ratio (5.5:1-6.5:1); Patient-to-Nurse ratio (1.5:1-2.7:1); Patient-to-Nurse-Assistant ratio (4.1:1-6.3:1); with the most balanced staff composition including 19% physicians, 23% nurse assistants, and 58% registered nurses; hospice beds (12-25); length of stay (median = 12 days). This model predicted an up to four times greater likelihood of controlling all six ESAS-r symptoms over time. The generalized estimating equations model on the educational level of physicians and registered nurses showed that it was significantly associated with optimal patients' symptom control during the entire hospice stay. CONCLUSIONS: This study showed the exact skill-mix composition and proportions of palliative care team able to ensure optimal control of patients' symptoms. The added value of physicians and nurses with a qualification in palliative care in terms of better patient outcomes reaffirmed the importance of education in guaranteeing quality care. Hospices with 12-25 beds, and recruitment methods guaranteeing at least 12-day stay ensured the most propitious organizational environment for optimal management of clinically significant symptoms. The transferability of these results mainly depends on whether the skills of health professionals in our `ideal' model are present in other contexts. Our results provide policymakers and hospice managers with specific, evidence-based information to support decision-making processes regarding hospice staffing and organization. Further prospective studies are needed to confirm the positive impact of this 'optimal' organizational framework on patient outcomes.
Authors: Marco Di Nitto; Marco Artico; Michela Piredda; Maddalena De Maria; Caterina Magnani; Anna Marchetti; Chiara Mastroianni; Roberto Latina; Maria Grazia De Marinis; Daniela D'Angelo Journal: Acta Biomed Date: 2022-05-12