A Medical Student-Led Multi-Pronged Initiative to Close the Digital Divide in Outpatient Primary Care.

BACKGROUND: The COVID-19 pandemic accelerated the use of telehealth. However, this also exacerbated healthcare disparities for vulnerable populations. OBJECTIVE: To explore the feasibility and effectiveness of a medical student-led initiative to identify and address gaps in patient access to digital health resources in adult primary care clinics at a safety-net academic center. METHODS: Medical students used an online HIPAA-compliant resource directory to screen for digital needs, connect patients with resources, and track outcome metrics. Through a series of Plan-Do-Study-Act (PDSA) cycles, the program grew to offer services such as information and registration for subsidized internet and phone services via the Affordable Connectivity Program (ACP) and Lifeline, assistance setting up and utilizing MyChart (an online patient portal for access to electronic health records), orientation to telehealth applications, and connection to community-based digital literacy training. RESULTS: Between November 2021 and March 2023, the program received 608 assistance requests. The most successful intervention was MyChart help, resulting in 83% of those seeking assistance successfully signing up for MyChart accounts and 79% feeling comfortable navigating the portal. However, subsidized internet support, digital literacy training, and telehealth orientation had less favorable outcomes. The PDSA cycles highlighted numerous challenges such as inadequate patient outreach, time-consuming training, limited in-person support, and unequal language assistance. To overcome these barriers, the program evolved to utilize clinic space for outreach, increase flier distribution, standardize training, and enhance integration of multilingual resources. CONCLUSION: This study is, to the best of our knowledge, the first time a medical student-led initiative addresses the digital divide with a multi-pronged approach. We outline a system that can be implemented in other outpatient settings to increase patients' digital literacy and promote health equity, while also engaging students in important aspects of non-clinical patient care.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

The effect of frenectomy for tongue-tie, lip-tie, or cheek-tie on breastfeeding outcomes: A systematic review of articles over time and suggestions for management.

OBJECTIVE: Tongue-tie, or ankyloglossia, is a common condition characterized by an abnormally short or tight lingual frenulum and is known to cause breastfeeding difficulties, leading to damage to the nipple, early discontinuation of breastfeeding, and delayed infant growth. In addition to tongue-tie, abnormal frenulums such as the labial frenulum and buccal frenulum can cause lip-tie and cheek-tie, respectively. While both of these conditions have been reported to potentially cause similar issues related to breastfeeding as tongue-tie, limited research has been conducted to understand their effects and how we should treat these conditions. METHODS: In this systematic review, we conducted a comprehensive search of MEDLINE to analyze the trend in publications of all three of these conditions and their impact on breastfeeding for the past 36 years. Keywords included, "tongue-tie", "lip-tie", "cheek-tie", and "breastfeeding outcomes". RESULTS: We found that publications describing the effect of only tongue-ties on breastfeeding have increased exponentially over time while less focus has been on other oral ties. It was also discovered that the majority of studies describing only lip-tie or tongue-tie were editorials, commentary, perspectives, or consensus statements. Finally, we found that articles describing more than one abnormal frenulum were more likely to be cited and articles describing tongue-tie only were published in the highest impact factor journals. CONCLUSION: This study revealed a significant increase in publications discussing tongue-tie and a lack of research on lip-tie and cheek-tie in relation to breastfeeding. The findings highlight the need for more comprehensive research and attention to lip-tie and cheek-tie, as well as standardized diagnostic criteria. Ongoing debate surrounding management of these conditions stem from the lack of investigations on the impact of these abnormal frenulums and outcomes post-frenectomy. Future high-quality studies, specifically prospective cohort studies and randomized controlled trials, are necessary to provide more robust evidence and guide clinical practice.

Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data.

Importance: Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of eligible Americans are screened. CXR-LC, an open-source deep learning tool that estimates lung cancer risk from existing chest radiograph images and commonly available electronic medical record (EMR) data, may enable automated identification of high-risk patients as a step toward improving lung cancer screening participation. Objective: To validate CXR-LC using EMR data to identify individuals at high-risk for lung cancer to complement 2022 US Centers for Medicare & Medicaid Services (CMS) lung cancer screening eligibility guidelines. Design, Setting, and Participants: This prognostic study compared CXR-LC estimates with CMS screening guidelines using patient data from a large US hospital system. Included participants were persons who currently or formerly smoked cigarettes with an outpatient posterior-anterior chest radiograph between January 1, 2013, and December 31, 2014, with no history of lung cancer or screening CT. Data analysis was performed between May 2021 and June 2022. Exposures: CXR-LC lung cancer screening eligibility (previously defined as having a 3.297% or greater 12-year risk) based on inputs (chest radiograph image, age, sex, and whether currently smoking) extracted from the EMR. Main Outcomes and Measures: 6-year incident lung cancer. Results: A total of 14 737 persons were included in the study population (mean [SD] age, 62.6 [6.8] years; 7154 [48.5%] male; 204 [1.4%] Asian, 1051 [7.3%] Black, 432 [2.9%] Hispanic, 12 330 [85.2%] White) with a 2.4% rate of incident lung cancer over 6 years (361 patients with cancer). CMS eligibility could be determined in 6277 patients (42.6%) using smoking pack-year and quit-date from the EMR. Patients eligible by both CXR-LC and 2022 CMS criteria had a high rate of lung cancer (83 of 974 patients [8.5%]), higher than those eligible by 2022 CMS criteria alone (5 of 177 patients [2.8%]; P < .001). Patients eligible by CXR-LC but not 2022 CMS criteria also had a high 6-year incidence of lung cancer (121 of 3703 [3.3%]). In the 8460 cases (57.4%) where CMS eligibility was unknown, CXR-LC eligible patients had a 5-fold higher rate of lung cancer than ineligible (127 of 5177 [2.5%] vs 18 of 2283 [0.5%]; P < .001). Similar results were found in subgroups, including female patients and Black persons. Conclusions and Relevance: Using routine chest radiographs and other data automatically extracted from the EMR, CXR-LC identified high-risk individuals who may benefit from lung cancer screening CT.