Mobile App-Facilitated Collaborative Palliative Care Intervention for Critically Ill Older Adults: A Randomized Clinical Trial.

Importance: Few person-centered, scalable models of collaborative intensive care unit (ICU) clinician-palliative care specialist care exist.

Objective: To evaluate the effect of a collaborative palliative care intervention compared to usual care among family members of patients in the ICU.

Design, Setting, And Participants: This parallel-group randomized clinical trial with patient-level randomization was conducted between April 2021 and September 2023.

Use of Cannabis-Based Medical Products for Pediatric Health Conditions: A Systematic Review of the Recent Literature.

Introduction: Cannabis policy is rapidly changing in the USA and across the globe, with 24 states legalizing cannabis for adult use and 38 states making medical cannabis available for those with qualified conditions. Building on prior evidence, we reviewed the recently published literature (from the past 5 years) focused on the treatment effects of naturally derived medical cannabis products within the pediatric population.

Methods: We conducted a systematic literature review of three electronic databases using MeSH terms and free-text.

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Objectives: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

Materials And Methods: We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52).

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study.

Context: Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).

Objectives: Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care.

Methods: We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital.

Factors Associated With Hospital Admission in the Last Month: A Retrospective Single Center Analysis.

Context: Driven by concerns about care quality, patient experience, and national metrics, health systems are increasingly focusing on identifying risk factors for patients who are hospitalized in the last month of life.

Objective: To evaluate patient factors associated with hospital admission in the last month (30 days).

Methods: We analyzed a retrospective cohort of 8488 patients with a primary care visit in a tertiary health system in the last year of life using a linked electronic health record and decedent dataset.