OBJECTIVE: Prior studies often examine single telehealth encounter types or aggregate all digital care, overlooking how patients combine multiple digital and in-person modalities in hybrid care. To address this gap, we derived hybrid care engagement phenotypes and assessed sociodemographic differences and associations with glycemic control among adults with type 2 diabetes (T2DM). METHODS: We conducted a retrospective cohort study of 10 671 adults with T2DM receiving primary care at an academic (UCSF) or safety-net system (SFHN) from April 2021 to March 2023. K-medoids clustering was applied to five encounter modalities (in-person, video, telephone visits; portal messages; unscheduled telephone calls) to derive four engagement phenotypes. We assessed sociodemographic differences using chi-square and Kruskal-Wallis tests and evaluated associations between phenotype and follow-up HbA1c control using logistic regression. We tested interactions with baseline HbA1c and estimated predicted probabilities using Tukey-adjusted contrasts. RESULTS: Four phenotypes emerged per system: Digitally Engaged Multimodal, Traditional High Utilizers, Digitally Leaning (UCSF), Telephone Reliant (SFHN), and Low Digital. UCSF patients belonged to digitally forward phenotypes, whereas SFHN patients concentrated in traditional, lower-tech phenotypes. Among patients with uncontrolled diabetes, digitally forward phenotypes had 13-20 percentage points higher predicted probability of achieving control (UCSF: 56% Digitally Leaning vs 36% Traditional; SFHN: 53% Multimodal vs 40% Telephone). DISCUSSION: Phenotypes varied by health system and sociodemographic factors, with modest, system-specific associations between digitally forward phenotypes and glycemic control among patients with uncontrolled diabetes. Findings underscore structural and sociodemographic inequities in hybrid care engagement and the need for proactive, tailored strategies to promote equitable hybrid care.

Marine Carpuat, Omri Asscher, Kalika Bali, Luisa Bentivogli, Fred Blain, Lynne Bowker, Monojit Choudhury, Hal Daumé Iii, Kevin Duh, Ge Gao, Alvin C Grissom II, Marzena Karpinska, Elaine C Khoong, William D. Lewis, Andre Martins, Mary Nurminen, Douglas W. Oard, Maja Popovic, Michel Simard, François Yvon. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

OBJECTIVE: To characterize patient and clinician perceived barriers and facilitators to using electronic patient-generated data (PGD) in safety-net systems. MATERIALS AND METHODS: We conducted 43 semi-structured interviews (18 clinicians and 25 patients) and observed 15 patient-clinician interactions. Clinical observations were conducted in an integrated urban safety-net system. Patients who spoke English, Spanish, or Cantonese were recruited from this system. Pharmacists, nurses, and clinicians treating chronic diseases were sampled from multiple California safety-net systems. Interview guides were developed based on the Consolidated Framework for Implementation Research (CFIR) and the Behavior Change Wheel (BCW). We conducted thematic analysis through a combination of inductive and deductive coding. RESULTS: Themes most frequently identified by both clinicians and patients as impacting electronic PGD use were capability-related (knowledge about collecting and using PGD), motivation-related (preference for data sharing; attitude toward digital tools and learning how to use them; the importance of measuring the outcome for health; privacy; and patient-clinician relationship), and opportunity-related (social support). Non-English speakers expressed concerns about inconveniencing others. Clinicians also identified additional opportunity-related themes (resource availability; implementation process; external incentives). DISCUSSION: Despite the growth in electronic PGD and its potential to improve chronic disease care and outcomes, implementation in safety-net systems would benefit from consideration of capability, motivation, and opportunity-related barriers. CONCLUSION: PGD is an increasingly vital part of clinical care. If implementation is pursued without concurrently addressing factors at the patient, clinician, health system, and policy levels, barriers to adoption will persist, especially in under-resourced settings.

OBJECTIVE: To identify a brief scale to accurately assess digital skills among older adults for use in identifying need for support to use digital health tools. MATERIALS AND METHODS: Patients age ≥50 speaking English, Spanish, or Cantonese completed surveys (n = 186) assessing digital health access, use, and skills. A subsample (n = 101) completed observational task assessments gauging competency on 4 tasks essential to digital health skills: (1) launch a video visit from an email/text message hyperlink, (2) visit a specific health website, (3) sign up for a patient portal, and (4) log in to a patient portal. We used exploratory factor analysis, receiver operator characteristic, logistic regression, and dominance analysis methods to identify and evaluate a scale measuring digital skills essential to using digital health tools. RESULTS: We found that a 9-item scale demonstrated unidimensionality and reliability (Cronbach's alpha 0.93) in measuring digital skills. Mean score was 19.3 out of 36. For each task, handout/video support was inadequate in facilitating completion for one-quarter of participants. We found high accuracy of the scale in predicting digital health competency (area under the curve 0.77-0.88). DISCUSSION: The Skills Measurement and Readiness Training for Digital Health (SMART Digital Health) scale is a measure of digital skills with evidence of reliability and validity to be used as a diagnostic tool to identify patients requiring support to use digital health tools. CONCLUSION: This early work supports the identification of patients with digital literacy needs who may require interventions to effectively engage in digital health communication and management.

A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs.Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have primarily been evaluated in vitro by comparison against human judgments outside of a specific context of use.This paper evaluates quality estimation feedback in vivo with a human study simulating decision-making in high-stakes medical settings.Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient.We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses.

OBJECTIVE: To determine interest in and barriers to video visits in safety-net patients with diverse age, racial/ethnic, or linguistic background. MATERIALS AND METHODS: We surveyed patients in an urban safety-net system to assess: interest in video visits; ability to successfully complete test video visits; and barriers to successful completion of test video visits. RESULTS: Among 202 participants, of which 177 (87.6%) were persons of color and 113 (55.9%) preferred non-English languages, 132 (65.3%) were interested in and 109 (54.0%) successfully completed a test video visit. Younger age, non-English preference, and prior smartphone application use were associated with interest. Over half (n = 112) reported barriers to video visits; Internet/data access was the most common barrier (n = 50, 24.8%). CONCLUSION: Safety-net patients are interested in video visits and able to successfully complete test visits. Internet or mobile data access is a common barrier in even urban safety-net settings and may impact equitable telemedicine access.

OBJECTIVE: The study sought to evaluate if peer input on outpatient cases impacted diagnostic confidence. MATERIALS AND METHODS: This randomized trial of a peer input intervention occurred among 28 clinicians with case-level randomization. Encounters with diagnostic uncertainty were entered onto a digital platform to collect input from ≥5 clinicians. The primary outcome was diagnostic confidence. We used mixed-effects logistic regression analyses to assess for intervention impact on diagnostic confidence. RESULTS: Among the 509 cases (255 control; 254 intervention), the intervention did not impact confidence (odds ratio [OR], 1.46; 95% confidence interval [CI], 0.999-2.12), but after adjusting for clinician and case traits, the intervention was associated with higher confidence (OR, 1.53; 95% CI, 1.01-2.32). The intervention impact was greater in cases with high uncertainty (OR, 3.23; 95% CI, 1.09- 9.52). CONCLUSIONS: Peer input increased diagnostic confidence primarily in high-uncertainty cases, consistent with findings that clinicians desire input primarily in cases with continued uncertainty.