BACKGROUND: Enteral tubes are prevalent among children with medical complexity (CMC), and complications can lead to costly health care use. Our objective was to design and test the usability of a mobile application (app) to support family-delivered enteral tube care. METHODS:Human-centered design methods (affinity diagramming, persona development, and software development) were applied with family caregivers of CMC to develop a prototype. During 3 waves of usability testing with design refinement between waves, screen capture software collected user-app interactions and inductive content analysis of narrative feedback identified areas for design improvement. The National Aeronautics and Space Administration Task Load Index and the System Usability Scale quantified mental workload and ease of use. RESULTS:Design participants identified core app functions, including displaying care routines, reminders, tracking inventory and health data, caregiver communication, and troubleshooting. Usability testing participants were 80% non-Hispanic white, 28% lived in rural settings, and 20% had not completed high school. Median years providing enteral care was 2 (range 1-14). Design iterations improved app function, simplification, and user experience. The mean System Usability Scale score was 76, indicating above-average usability. National Aeronautics and Space Administration Task Load Index revealed low mental demand, frustration, and effort. All 14 participants reported that they would recommend the app, and that the app would help with organization, communication, and caregiver transitions. CONCLUSIONS:Using a human-centered codesign process, we created a highly usable mobile application to support enteral tube caregiving at home. Future work involves evaluating the feasibility of longitudinal use and effectiveness in improving self-efficacy and reduce device complications.
OBJECTIVES: Children with medical complexity (CMC) are commonly assisted by medical devices, which family caregivers are responsible for managing and troubleshooting in the home. Optimizing device use by maximizing the benefits and minimizing the complications is a critical goal for CMC but is relatively unexplored. In this study, we sought to identify and describe workarounds families have developed to optimize medical device use for their needs. METHODS: We conducted 30 contextual inquiry interviews with families of CMC in homes. Interviews were recorded, transcribed, and analyzed for barriers and workarounds specific to medical device usage through a directed content analysis. We used observation notes and photographs to confirm and elaborate on interview findings. RESULTS: We identified 4 barriers to using medical devices in the home: (1) the quantity and type of devices allotted do not meet family needs, (2) the device is not designed to be used in locations families require, (3) device use is physically or organizationally disruptive to the home, and (4) the device is not designed to fit the user. We also identified 11 categories of workarounds to the barriers. CONCLUSIONS: Families face many barriers in using medical devices to care for CMC. Our findings offer rich narrative and photographic data revealing the ways in which caregivers work around these barriers. Future researchers should explore the downstream effects of these ubiquitous, necessary workarounds on CMC outcomes toward developing interventions that optimize device use for families.
Background Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. Objective The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. Methods To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. Results The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. Conclusions This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. Trial Registration ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064 International Registered Report Identifier (IRRID) DERR1-10.2196/48128
Objective To describe older adult patients’ and care partners’ knowledge broker roles during emergency department (ED) visits. Background Older adult patients are vulnerable to communication and coordination challenges during an ED visit, which can be exacerbated by the time and resource constrained ED environment. Yet, as a constant throughout the patient journey, patients and care partners can act as an information conduit, or knowledge broker, between fragmented care systems to attain high-quality, safe care. Methods Participants included 14 older adult patients ([Formula: see text] 65 years old) and their care partners (e.g., spouse, adult child) who presented to the ED after having experienced a fall. Human factors researchers collected observation data from patients, care partners and clinician interactions during the patient’s ED visit. We used an inductive content analysis to determine the role of patients and care partners as knowledge brokers. Results We found that patients and care partners act as knowledge brokers by providing information about diagnostic testing, medications, the patient’s health history, and care accommodations at the disposition location. Patients and care partners filled the role of knowledge broker proactively (i.e. offer information) and reactively (i.e. are asked to provide information by clinicians or staff), within-ED work system and across work systems (e.g., between the ED and hospital), and in anticipation of future knowledge brokering. Conclusion Patients and care partners, acting as knowledge brokers, often fill gaps in communication and participate in care coordination that assists in mitigating health care fragmentation.
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