Christopher E Cox1, Maren K Olsen2, David Casarett3, Krista Haines4, Mashael Al-Hegelan5, Raquel R Bartz6, Jason N Katz7, Colleen Naglee8, Deepshikha Ashana9, Daniel Gilstrap10, Jessie Gu11, Alice Parish12, Allie Frear13, Deepthi Krishnamaneni14, Andrew Corcoran15, Sharron L Docherty16. 1. Department of Medicine, Division of Pulmonary & Critical Care Medicine and the Program to Support People and Enhance Recovery (ProSPER), Duke University, Durham, NC, United States of America. Electronic address: christopher.cox@duke.edu. 2. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America; Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States of America. Electronic address: maren.olsen@duke.edu. 3. Department of Medicine, Section of Palliative Care and Hospice Medicine, Duke University, Durham, NC, United States of America. Electronic address: David.casarett@duke.edu. 4. Department of Surgery, Division of Trauma and Critical Care and Acute Care Surgery, Duke University, Durham, North, Carolina;, United States of America. Electronic address: krista.haines@duke.edu. 5. Department of Medicine, Division of Pulmonary & Critical Care Medicine and the Program to Support People and Enhance Recovery (ProSPER), Duke University, Durham, NC, United States of America. Electronic address: mashael.alhegelan@duke.edu. 6. Department of Anesthesia, Division of Critical Care Medicine, Duke University, Durham, NC, United States of America. Electronic address: raquel.bartz@duke.edu. 7. Department of Medicine, Division of Cardiology, Duke University, Durham, NC, United States of America. Electronic address: jason.katz@duke.edu. 8. Department of Anesthesia, Division of Neurology, Duke University, Durham, NC, United States of America. 9. Department of Medicine, Division of Pulmonary & Critical Care Medicine and the Program to Support People and Enhance Recovery (ProSPER), Duke University, Durham, NC, United States of America. Electronic address: deepshikha.ashana@duke.edu. 10. Department of Medicine, Division of Pulmonary & Critical Care Medicine and the Program to Support People and Enhance Recovery (ProSPER), Duke University, Durham, NC, United States of America. Electronic address: daniel.gilstrap@duke.edu. 11. Department of Medicine, Division of Pulmonary & Critical Care Medicine and the Program to Support People and Enhance Recovery (ProSPER), Duke University, Durham, NC, United States of America. Electronic address: jessie.gu@duke.edu. 12. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America. Electronic address: alice.parish@duke.edu. 13. Department of Medicine, Division of Pulmonary & Critical Care Medicine and the Program to Support People and Enhance Recovery (ProSPER), Duke University, Durham, NC, United States of America. Electronic address: allie.frear@duke.edu. 14. Duke Health Technology Solutions, Duke University, Durham, NC, United States of America. Electronic address: deepthi.krishnamaneni@duke.edu. 15. Office of Academic Solutions and Information Systems, Duke University, Durham, NC, United States of America. Electronic address: andrew.corcoran@duke.edu. 16. School of Nursing, Duke University, Durham, NC, United States of America. Electronic address: sharron.docherty@duke.edu.
Abstract
INTRODUCTION: The number of older adults who receive life support in an intensive care unit (ICU), now 2 million per year, is increasing while survival remains unchanged. Because the quality of ICU-based palliative care is highly variable, we developed a mobile app intervention that integrates into the electronic health records (EHR) system called PCplanner (Palliative Care planner) with the goal of improving collaborative primary and specialist palliative care delivery in ICU settings. OBJECTIVE: To describe the methods of a randomized clinical trial (RCT) being conducted to compare PCplanner vs. usual care. METHODS AND ANALYSIS: The goal of this two-arm, parallel group mixed methods RCT is to determine the clinical impact of the PCplanner intervention on outcomes of interest to patients, family members, clinicians, and policymakers over a 3-month follow up period. The primary outcome is change in unmet palliative care needs measured by the NEST instrument between baseline and 1 week post-randomization. Secondary outcomes include goal concordance of care, patient-centeredness of care, and quality of communication at 1 week post-randomization; length of stay; as well as symptoms of depression, anxiety, and post-traumatic stress disorder at 3 months post-randomization. We will use general linear models for repeated measures to compare outcomes across the main effects and interactions of the factors. We hypothesize that compared to usual care, PCplanner will have a greater impact on the quality of ICU-based palliative care delivery across domains of core palliative care needs, psychological distress, patient-centeredness, and healthcare resource utilization.
RCT Entities:
INTRODUCTION: The number of older adults who receive life support in an intensive care unit (ICU), now 2 million per year, is increasing while survival remains unchanged. Because the quality of ICU-based palliative care is highly variable, we developed a mobile app intervention that integrates into the electronic health records (EHR) system called PCplanner (Palliative Care planner) with the goal of improving collaborative primary and specialist palliative care delivery in ICU settings. OBJECTIVE: To describe the methods of a randomized clinical trial (RCT) being conducted to compare PCplanner vs. usual care. METHODS AND ANALYSIS: The goal of this two-arm, parallel group mixed methods RCT is to determine the clinical impact of the PCplanner intervention on outcomes of interest to patients, family members, clinicians, and policymakers over a 3-month follow up period. The primary outcome is change in unmet palliative care needs measured by the NEST instrument between baseline and 1 week post-randomization. Secondary outcomes include goal concordance of care, patient-centeredness of care, and quality of communication at 1 week post-randomization; length of stay; as well as symptoms of depression, anxiety, and post-traumatic stress disorder at 3 months post-randomization. We will use general linear models for repeated measures to compare outcomes across the main effects and interactions of the factors. We hypothesize that compared to usual care, PCplanner will have a greater impact on the quality of ICU-based palliative care delivery across domains of core palliative care needs, psychological distress, patient-centeredness, and healthcare resource utilization.
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