Refining the implementation research logic model: a citation analysis, user survey, and scoping review protocol.

Introduction: The Implementation Research Logic Model (IRLM) aids users in combining, organizing, and specifying the relationships between important constructs in implementation research. The goal of the IRLM is to improve the rigor, reproducibility, and transparency of implementation research projects. The article describing the IRLM was published September 25, 2020 (, Vol 15); it has since been highly cited and included as a required element in multiple funding opportunity announcements from federal agencies.

Essential Components of an Electronic Patient-Reported Symptom Monitoring and Management System: A Randomized Clinical Trial.

Importance: Multicomponent electronic patient-reported outcome cancer symptom management systems reduce symptom burden. Whether all components contribute to symptom reduction is unknown.

Objective: To deconstruct intervention components of the Symptom Care at Home (SCH) system, a digital symptom monitoring and management intervention that has demonstrated efficacy, to determine which component or combination of components results in the lowest symptom burden.

Assessing Patient Perspectives and the Health Equity of a Digital Cancer Symptom Remote Monitoring and Management System.

Purpose: People with cancer experience poorly controlled symptoms that persist between treatment visits. Automated digital technology can remotely monitor and facilitate symptom management at home. Essential to digital interventions is patient engagement, user satisfaction, and intervention benefits that are distributed across patient populations so as not to perpetuate inequities.

Toward Alleviating Clinician Documentation Burden: A Scoping Review of Burden Reduction Efforts.

Background:  Studies have shown that documentation burden experienced by clinicians may lead to less direct patient care, increased errors, and job dissatisfaction. Implementing effective strategies within health care systems to mitigate documentation burden can result in improved clinician satisfaction and more time spent with patients. However, there is a gap in the literature regarding evidence-based interventions to reduce documentation burden.

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

Purpose: Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers.

Factors Influencing Medication Administration Outcomes Among New Graduate Nurses Using Bar Code-Assisted Medication Administration.

Paramount to patient safety is the ability for nurses to make clinical decisions free from human error. Yet, the dynamic clinical environment in which nurses work is characterized by uncertainty, urgency, and high consequence, necessitating that nurses make quick and critical decisions. The aim of this study was to examine the influence of human and environmental factors on the decision to administer among new graduate nurses in response to alert generation during bar code-assisted medication administration.

Describing Medication Administration and Alert Patterns Experienced by New Graduate Nurses During the First Year of Practice.

The aim of this study was to describe medication administration and alert patterns among a cohort of new graduate nurses over the first year of practice. Medical errors related to clinical decision-making, including medication administration errors, may occur more frequently among new graduate nurses. To better understand nursing workflow and documentation workload in today's clinical environment, it is important to understand patterns of medication administration and alert generation during barcode-assisted medication administration.

Impact of an automated, remote monitoring and coaching intervention in reducing hospice cancer family caregiving burden: A multisite randomized controlled trial.

Background: Care for those with life-limiting cancer heavily involves family caregivers who may experience significant physical and emotional burden. The purpose of this study was to test the impact of Symptom Care at Home (SCH), an automated digital family caregiver coaching intervention, during home hospice, when compared to usual hospice care (UC) on the primary outcome of overall caregiver burden. Secondary outcomes included Caregiver Burden at weeks 1 and 8, Mood and Vitality subscales, overall moderate-to-severe caregiving symptoms, and sixth month spouse/partner bereavement outcomes.

Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.

Objectives: To design an interface to support communication of machine learning (ML)-based prognosis for patients with advanced solid tumors, incorporating oncologists' needs and feedback throughout design.

Materials And Methods: Using an interdisciplinary user-centered design approach, we performed 5 rounds of iterative design to refine an interface, involving expert review based on usability heuristics, input from a color-blind adult, and 13 individual semi-structured interviews with oncologists. Individual interviews included patient vignettes and a series of interfaces populated with representative patient data and predicted survival for each treatment decision point when a new line of therapy (LoT) was being considered.

Technology-Assisted mHealth Caregiver Support to Manage Cancer Patient Symptoms: A Randomized Controlled Trial.

Context: Caregivers managing symptoms of family members with cancer during home hospice care, often feel ill-prepared and need patient care coaching.

Objectives: This study tested the efficacy of an automated mHealth platform that included caregiver coaching on patient symptom care and nurse notifications of poorly controlled symptoms. The primary outcome was caregiver perception of patients' overall symptom severity throughout hospice care and at weeks one, two, four, and eight.

Nurse Cognition, Decision Support, and Barcode Medication Administration: A Conceptual Framework for Research, Practice, and Education.

This article synthesizes theoretical perspectives related to nurse cognition. We present a conceptual model that can be used by multiple stakeholders to study and contemplate how nurses use clinical decision support systems, and specifically, Barcode-Assisted Medication Administration, to make decisions during the delivery of care. Theoretical perspectives integrated into the model include dual process theory, the Cognitive Continuum Theory, human factors engineering, and the Recognition-Primed Decision model.

Alert Types and Frequencies During Bar Code-Assisted Medication Administration: A Systematic Review.

Background: Existing literature explores the effectiveness of bar code-assisted medication administration (BCMA) on the reduction of medication administration error as well as on nurse workarounds during BCMA. However, there is no review that comprehensively explores types and frequencies of alerts generated by nurses during BCMA.

Purpose: The purpose was to describe alert generation type and frequency during BCMA.