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. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy. METHODS: This qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified. RESULTS: We received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information. CONCLUSION: This study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.

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. The design for this study was a descriptive, longitudinal, observational cohort design using EHR audit log and administrative data. The study was set at a large, urban medical center in the United States and included 132 new graduate nurses who worked on adult, inpatient units. Research variables included human and environmental factors. Data analysis included descriptive and inferential analyses. This study found that participants continued with administration of a medication in 90.75% of alert encounters. When considering the response to an alert, residency cohort, alert category, and previous exposure variables were associated with the decision to proceed with administration. It is important to continue to study factors that influence nurses' decision-making, particularly during the process of medication administration, to improve patient safety and outcomes.

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. METHODS: Using a randomized, multisite, nonblinded controlled trial, 332 cancer family caregivers were enrolled and analyzed (159 SCH vs. 173 UC). Caregivers were primarily White (92%), female (69%), and spouse caregivers (53%). Caregivers provided daily reports on severity levels (0-10 scale) for their anxiety, depressed mood, fatigue, disturbed sleep, and caregiving interference with normal activities. These scores combined constituted the Caregiver Burden primary outcome. Based on reported symptoms, SCH caregivers received automated, tailored coaching about improving their well-being. Reports of moderate-to-severe caregiving symptoms also triggered hospice nurse notification. Secondary outcomes of Mood and Vitality were subcomponents of the Caregiver Burden score. A combined bereavement adjustment tool captured sixth month bereavement. RESULTS: The SCH intervention reduced overall Caregiver Burden compared to UC (p < .001), with a 38% reduction at 8 weeks and a medium-to-large effect size (d = .61). SCH caregivers experienced less (p < .001) disruption in both Mood and Vitality. There were higher levels of moderate-to-severe caregiving symptoms overtime in UC (OR, 2.722). All SCH caregivers benefited regardless of caregiver: sex, caregiver relationship, age, patient diagnosis and family income. SCH spouse/partner caregivers achieved better sixth month bereavement adjustment than UC (p < .007). CONCLUSIONS: The SCH intervention significantly decreased caregiving burden over UC and supports the maintenance of family caregiver mood and vitality throughout caregiving with extended benefit into bereavement.

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. Ongoing feedback informed design decisions, and directed qualitative content analysis of interview transcripts was used to evaluate usability and identify enhancement requirements. RESULTS: Design processes resulted in an interface with 7 sections, each addressing user-focused questions, supporting oncologists to "tell a story" as they discuss prognosis during a clinical encounter. The iteratively enhanced interface both triggered and reflected design decisions relevant when attempting to communicate ML-based prognosis, and exposed misassumptions. Clinicians requested enhancements that emphasized interpretability over explainability. Qualitative findings confirmed that previously identified issues were resolved and clarified necessary enhancements (eg, use months not days) and concerns about usability and trust (eg, address LoT received elsewhere). Appropriate use should be in the context of a conversation with an oncologist. CONCLUSION: User-centered design, ongoing clinical input, and a visualization to communicate ML-related outcomes are important elements for designing any decision support tool enabled by artificial intelligence, particularly when communicating prognosis risk.

External Validation of a Machine Learning Model to Predict 6-Month Mortality for Patients With Advanced Solid Tumors.

This prognostic study performed external validation of a machine learning model to predict 6-month mortality among patients with advanced solid tumors.

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. Secondary outcomes compared individual symptom severity. METHODS: Caregivers (n = 298) were randomly assigned to the Symptom Care at Home (SCH) intervention (n = 144) or usual hospice care (UC) (n = 154). All caregivers placed daily calls to the automated system that assessed the presence and severity of 11 end-of-life patient physical and psychosocial symptoms. SCH caregivers received automated coaching on symptom care based on reported patient symptoms and their severity. Moderate-to-severe symptoms were also relayed to the hospice nurse. RESULTS: The SCH intervention produced a mean overall symptom reduction benefit, over UC, of 4.89 severity points (95% CI 2.86-6.92) (P < 0.001), with a moderate effect size (d = 0.55). The SCH benefit also occurred at each timepoint (P < 0.001- 0.020). There was a 38% reduction in days reporting moderate-to-severe patient symptoms compared to UC (P < 0.001) with 10/11 symptoms significantly reduced in SCH compared to UC. CONCLUSION: Automated mHealth symptom reporting by caregivers, paired with tailored caregiver coaching on symptom management and nurse notifications, reduces cancer patients' physical and psychosocial symptoms during home hospice, providing a novel and efficient approach to improving end-of-life care.

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. METHODS: A systematic review of the literature using PRISMA guidelines was conducted using CINAHL, PubMed, EMBASE, and Ovid Medline databases. RESULTS: After screening for inclusion and exclusion criteria, a total of 8 articles were identified and included in the review. Alert types included patient mismatch, wrong medication, and wrong dose, though other alert types were also reported. The frequency of alert generation varied across studies, from 0.18% to 42%, and not all alerts were clinically meaningful. CONCLUSIONS: This systematic review synthesized literature related to alert type and frequency during BCMA. However, further studies are needed to better describe alert generation patterns as well as factors that influence alert generation.