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.

Impact of a hospital service for adults with chronic childhood-onset disease: A propensity weighted analysis.

Background: Young adults with chronic childhood-onset diseases (CCOD) transitioning care from pediatrics to adult care are at high risk for readmission after hospital discharge. At our institution, we have implemented an inpatient service, the Med-Peds (MP) line, to improve transitions to adult care and reduce hospital utilization by young adults with CCOD.

Objective: This study aimed to assess the effect of the MP line on length of stay (LOS) and 30-day readmission rates compared to other inpatient services.

The Use of Templates for Documenting Advance Care Planning Conversations: A Descriptive Analysis.

Context: While professional societies and expert panels have recommended quality indicators related to advance care planning (ACP) documentation, including using structured documentation templates, it is unclear how clinicians document these conversations.

Objective: To explore how clinicians document ACP, specifically, which components of these conversations are documented.

Methods: A codebook was developed based on existing frameworks for ACP conversations and documentation.

A Customized Electronic Health Record-Based Tool Highlights and Addresses Gaps in Patient Safety.

The authors present a tool to improve gaps in patient safety using the electronic health record. The tool integrates gap identification, passive alerts, and actions into a single interface embedded within clinicians' workflow. The tool was developed to address venous thromboembolism prophylaxis, prevention of hypo- and hyperglycemia, code status documentation, bowel movement frequency, and skilled nursing facility transitions.

Implementation of a comprehensive hospitalist-led initiative to improve care for patients with opioid use disorder.

Background: As opioid-related hospitalizations rise, hospitals must be prepared to evaluate and treat patients with opioid use disorder (OUD). We implemented a hospitalist-led program, Project Caring for patients with Opioid Misuse through Evidence-based Treatment (COMET) to address gaps in care for hospitalized patients with OUD.

Objective: Implement evidence-based treatment for inpatients with OUD and refer to postdischarge care.

Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.

Importance: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable. Similarly, few have been implemented, and none have been evaluated prospectively and externally validated.

Assessing the Impact of an Order Panel Utilizing Weight-Based Insulin and Standardized Monitoring of Blood Glucose for Patients With Hyperkalemia.

Intravenous insulin with glucose is used in urgent treatment for hyperkalemia but has a significant risk of hypoglycemia. The authors developed an order panel within the electronic health record system that utilizes weight-based insulin dosing and standardized blood glucose monitoring to reduce hypoglycemia. As initial evaluation of this protocol, the authors retrospectively compared potassium and blood glucose lowering in patients treated with the weight-based (0.

The highest utilizers of care: individualized care plans to coordinate care, improve healthcare service utilization, and reduce costs at an academic tertiary care center.

Background: High utilizers are medically and psychosocially complex, have high rates of emergency department (ED) visits and hospital admissions, and contribute to rising healthcare costs.

Objective: Develop individualized care plans to reduce unnecessary healthcare service utilization and hospital costs for complex, high utilizers of inpatient and ED care.

Design: Quality-improvement intervention with a retrospective pre/post intervention analysis.