Improvement in Adherance to Anesthesia Preoperative Appointment With Telemedicine: A Retrospective Analysis.

BACKGROUND: Amidst the coronavirus disease 2019 (COVID-19) pandemic, the sudden demand for virtual medical visits drove the expansion of telemedicine across all medical specialties. Current literature demonstrates limited knowledge of the impact of telehealth on appointment adherence, particularly in preoperative anesthesia evaluations. This study aims to describe the impact of telemedicine-based anesthesia evaluation and its effects on appointment completion.  Methods: This was a retrospective, non-randomized, cohort study of adult patients at the University of California, Los Angeles, United States, who received preoperative anesthesia evaluations by telemedicine or in-person in an academic medical center. From January to September 2021, we evaluated telemedicine and in-person appointment completion in patients scheduled for surgery. The primary outcome was the incidence of appointment completion. The secondary outcomes included appointment no-shows and cancellations.  Results: Of 1332 patients included in this study, 956 patients received telehealth visits while 376 patients received in-person preoperative anesthesia evaluations. Compared to the in-person group, the telemedicine group had more appointment completions (81.38% vs 76.60%), fewer cancellations (12.55% vs 19.41%), and no statistical difference in appointment no-shows (6.07% vs 3.99%). Compared to the in-person group, patients who received telemedicine evaluations were younger (55.81 ± 18.38 vs 65.97 ± 15.19), less likely Native American and Alaska Native (0.31% vs 1.60%), more likely of Hispanic or Latino ethnicity (16.63% vs 12.23%), required less interpreter services (4.18% vs 9.31%), had more private insurance coverage (53.45% vs 37.50%) and less Medicare coverage (37.03% vs 50.53%). CONCLUSIONS: This study demonstrates that telemedicine can improve preoperative anesthesia appointment completion and decrease appointment cancellations. We also demonstrate potential shortcomings of telemedicine in serving patients who are older, require interpreter services, or are non-privately insured. These inequities highlight potential avenues to increase equity and access to telemedicine.

Human-Centered Design and Development of a Fall Prevention Exercise App For Older Adults in Primary Care Settings.

BACKGROUND: Falls in older adults are a serious public health problem that can lead to reduced quality of life or death. Patients often do not receive fall prevention guidance from primary care providers, despite evidence that falls can be prevented. Mobile health technologies may help to address this disparity and promote evidence-based fall prevention. OBJECTIVE: Our main objective was to use Human-Centered Design (HCD) to develop a user-friendly, fall prevention exercise app using validated user requirements. The app features evidence-based behavior change strategies and exercise content to support older people initiating and adhering to a progressive fall prevention exercise program. METHODS: We organized our multi-stage, iterative design process into three phases: Gathering User Requirements, Usability Evaluation, and Refining App Features. Our methods include focus groups, usability testing, and subject matter expert meetings. RESULTS: Focus groups (Total n=6), usability testing (n=30) including a post-test questionnaire [Health-ITUES score: mean (SD)= 4.2 (1.1)], and subject matter expert meetings demonstrate participant satisfaction with the app concept and design. Overall, participants saw value in receiving exercise prescriptions from the app that would be recommended by their PCP and reported satisfaction with the content of the app, but several participants felt that they were not the right user for the app. CONCLUSIONS: This study demonstrates the development, refinement and usability testing of a fall prevention exercise app and corresponding tools that primary care providers may use to prescribe tailored exercise recommendations to their older patients as an evidence-based fall prevention strategy accessible in the context of busy clinical workflows.

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires. METHODS: Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors. RESULTS: Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models. CONCLUSIONS: The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.

Addressing Racism in Medical Education: A Longitudinal Antiracism Discussion Curriculum for Medical Students.

Although recent efforts have been engaged to combat bias in medical education, minimal attention has been dedicated to developing antiracism curricula for medical students. We developed a year-long discussion curriculum for 175 first-year medical students centered around Ibram X. Kendi's How to be an Antiracist. The discussion curriculum consisted of six, 2 hour seminars. We evaluated students' perceptions regarding discussing and actively addressing racism. Students reported an improved ability and comfort to discuss and address racism within healthcare settings. These data suggest that antiracism discussion curricula may be effective for training medical students to address racism in their future careers.

Development and Usability Testing of an Exercise-Based Primary Care Fall Prevention Clinical Decision Support Tool.

For older patients, falls are the leading cause offatal and nonfatal injuries. Guidelines recommend that at-risk older adults are referred to appropriate fall-prevention exercise programs, but many do not receive support for fall-risk management in the primary care setting. Advances in health information technology may be able to address this gap. This article describes the development and usability testing of a clinical decision support (CDS) tool for fall prevention exercise. Using rapid qualitative analysis and human-centered design, our team developed and tested the usability of our CDS prototype with primary care team members. Across 31 Health-Information Technology Usability Evaluation Scale surveys, our CDS prototype received a median score of 5.0, mean (SD) of 4.5 (0.8), and a range of 4.1-4.9. This study highlights the features and usability offall prevention CDS for helping primary care providers deliver patient-centeredfall prevention care.