Journal Of The American Medical Informatics Association[JOURNAL]
Sun
200 papers
AutoReporter: development of an artificial intelligence tool for automated assessment of research reporting guideline adherence.
Chen D, Li P, Khoshkish E … +6 more , Lee S, Ning T, Tahir U, Wong HCY, Lee MSF, Raman S
J Am Med Inform Assoc · 2026 Mar · PMID 41435508 · Full text
OBJECTIVES: To develop AutoReporter, a large language model (LLM) system that automates evaluation of adherence to research reporting guidelines. MATERIALS AND METHODS: Eight prompt-engineering and retrieval strategies c...
OBJECTIVES: To develop AutoReporter, a large language model (LLM) system that automates evaluation of adherence to research reporting guidelines. MATERIALS AND METHODS: Eight prompt-engineering and retrieval strategies coupled with reasoning and general-purpose LLMs were benchmarked on the SPIRIT-CONSORT-TM corpus. The top-performing approach, AutoReporter, was validated on BenchReport, a novel benchmark dataset of expert-rated reporting guideline assessments from 10 systematic reviews. RESULTS: AutoReporter, a zero-shot, no-retrieval prompt coupled with the o3-mini reasoning LLM, demonstrated strong accuracy (CONSORT 90.09%; SPIRIT: 92.07%), substantial agreement with humans (CONSORT Cohen's κ = 0.70, SPIRIT Cohen's κ = 0.77), runtime (CONSORT: 617.26 s; SPIRIT: 544.51 s), and cost (CONSORT: 0.68 USD; SPIRIT: 0.65 USD). AutoReporter achieved a mean accuracy of 91.8% and substantial agreement (Cohen's κ > 0.6) with expert ratings from the BenchReport benchmark. DISCUSSION: Structured prompting alone can match or exceed fine-tuned domain models while forgoing manually annotated corpora and computationally intensive training. CONCLUSION: Large language models can feasibly automate reporting guideline adherence assessments for scalable quality control in scientific research reporting. AutoReporter is publicly accessible at https://autoreporter.streamlit.app.
Identifying and supporting trafficked individuals: provider and community organization perspectives on existing sociotechnical approaches.
Gomez M, Clayton EW, Walsh CG … +1 more , Unertl KM
J Am Med Inform Assoc · 2026 Mar · PMID 41423834 · Full text
OBJECTIVES: Trafficked persons experience adverse health consequences and seek help, but many go unrecognized by health-care professionals. This study explored professionals' perspectives on current approaches toward ide...
OBJECTIVES: Trafficked persons experience adverse health consequences and seek help, but many go unrecognized by health-care professionals. This study explored professionals' perspectives on current approaches toward identifying and supporting trafficked persons in health-care settings, highlighting current technology roles, gaps, and future directions. MATERIALS AND METHODS: We developed an interview guide to investigate current human trafficking (HT) approaches, safety procedures, and HT education. Semistructured interviews were conducted via Zoom, iteratively coded in Dedoose, and analyzed using a thematic analysis approach. RESULTS: We interviewed 19 health-care and community group professionals and identified 3 themes: (1) participants described a responsibility to build trust with patients through compassionate communication, rapport, and trauma-informed approaches across different stages of care. (2) Technology played a dual role, as professionals navigated both benefits and challenges of tools such as Zoom, virtual interpreters, and cameras in trust building. (3) Safety and privacy concerns guided how participants documented patient encounters and shared community resources, ensuring confidentiality while supporting patient and community well-being. DISCUSSION: Technology can both support and hinder trust in health care, directly affecting trafficked patients and their safety. Informatics can improve care for trafficked persons, but further research is needed on technology-based interventions. We provide recommendations to strengthen trust, enhance safety, support trauma-informed care, and promote safe documentation practices. CONCLUSION: Effective sociotechnical approaches rely on trust, safety, and mindful documentation to support trafficked patients. Future research directions include refining the role of informatics in trauma-informed care to strengthen trust and mitigate unintended consequences.
Knowledge graph-augmented large language models for reconstructing life course risk pathways: a gestational diabetes mellitus-to-dementia case study.
Wang S, Zhang Y, Gao Y … +3 more , He X, Deng G, Du J
J Am Med Inform Assoc · 2026 Mar · PMID 41411018 · Full text
OBJECTIVES: To develop and evaluate a knowledge graph-augmented large language model (LLM) framework that synthesizes epidemiological evidence to infer life-course exposure-outcome pathways, using gestational diabetes me...
OBJECTIVES: To develop and evaluate a knowledge graph-augmented large language model (LLM) framework that synthesizes epidemiological evidence to infer life-course exposure-outcome pathways, using gestational diabetes mellitus (GDM) and dementia as a case study. MATERIALS AND METHODS: We constructed a causal knowledge graph by extracting empirical epidemiological associations from scientific literature, excluding hypothetical assertions. The graph was integrated with GPT-4 through four graph retrieval-augmented generation (GRAG) strategies to infer bridging variables between early-life exposure (GDM) and later-life outcome (dementia). Semantic triples served as structured inputs to support LLM reasoning. Each GRAG strategy was evaluated by human clinical experts and three LLM-based reviewers (GPT-4o, Llama 3-70B, and Gemini Advanced), assessing scientific reliability, novelty, and clinical relevance. RESULTS: The GRAG strategy using a minimal set of abstracts specifically related to GDM-dementia bridging variables performed comparably to the strategy using broader sub-community abstracts, and both significantly outperformed approaches using the full GDM- or dementia-related corpus or baseline GPT-4 without external augmentation. The knowledge graph-augmented LLM identified 108 maternal candidate mediators, including validated risk factors such as chronic kidney disease and physical inactivity. The structured approach improved accuracy and reduced confabulation compared to standard LLM outputs. DISCUSSION: Our findings suggest that augmenting LLMs with epidemiological knowledge graphs enables effective reasoning over fragmented literature and supports the reconstruction of progressive risk pathways. Expert assessments revealed that LLMs may overestimate clinical relevance, highlighting the need for human-AI collaboration in interpretation and application. CONCLUSION: Integrating semantic epidemiological knowledge with LLMs via GRAG strategies provides a promising framework for life-course epidemiology, enabling early detection of modifiable risk factors and guiding variable selection in cohort study design.
Patient perspectives on gender identity and anatomy data collection in electronic health records: a qualitative study.
Dubin S, Mayer G, Pradhan N … +2 more , Xin M, Greene R
J Am Med Inform Assoc · 2026 Mar · PMID 41379022 · Full text
OBJECTIVES: Documentation of gender identity (GI) and anatomy data in the electronic health record (EHR) is a proposed standard of care for transgender populations. However, there is limited research on implementation of...
OBJECTIVES: Documentation of gender identity (GI) and anatomy data in the electronic health record (EHR) is a proposed standard of care for transgender populations. However, there is limited research on implementation of proposed best practices, particularly anatomy data collection. This study aims to characterize factors that influence patient preferences and comfort around the collection and documentation of GI and anatomy in EHRs. MATERIALS AND METHODS: From November 2023 to January 2024, 17 one-on-one, semi-structured virtual interviews were conducted with transgender adults residing in the Metropolitan New York area. Transcriptions were analyzed using inductive thematic analysis. RESULTS: Themes clustered around comfort and preferences for data collection processes and outcomes. Factors that influenced preferences and comfort around anatomy data were distinct from those impacting GI documentation preferences and comfort. The tension between the categories of GI and sex assigned at birth impacted anatomy data documentation preferences. Clinical context emerged as a consistent factor that impacts both preferences and comfort of GI and anatomy data documentation. DISCUSSION AND CONCLUSION: GI data collection efforts in clinical settings must consider the implication of anatomy data collection when determining data collection best practice methodologies. Anticipated and experienced stigma remain significant hurdles to patient comfort and willingness to collect GI and anatomy data, and their impact on actual data collection should be further elucidated among diverse gender identities. Clinical data collection methods, tools, and education warrant ongoing research investment to further elucidate best practices.
Patient attitudes toward ambient artificial intelligence scribes in clinical care: insights from a cross-sectional study.
Chandrasekaran R, Moustakas E
J Am Med Inform Assoc · 2026 Feb · PMID 41350107 · Full text
OBJECTIVE: To assess patient attitudes towards ambient artificial intelligence (AI) scribes, including comfort, trust, perceived impact on provider interactions, and willingness for future use, and to examine how sociode...
OBJECTIVE: To assess patient attitudes towards ambient artificial intelligence (AI) scribes, including comfort, trust, perceived impact on provider interactions, and willingness for future use, and to examine how sociodemographic, health factors, digital literacy, and privacy concerns shape attitudes. MATERIALS AND METHODS: We analyzed cross-sectional data from an online survey of 12 153 adults (52.4% female; 23.1% aged ≥ 65; 41.2% with chronic conditions) in Canada conducted between February 6 and March 10, 2025. Survey-adjusted ordinal and binary logistic regression models assessed predictors, reporting adjusted odds ratios (aORs), 95% confidence intervals (CIs), and P-values. RESULTS: Most respondents (61.8%) were reluctant to future AI scribe use despite mixed attitudes: 39.3% reported some/very high comfort, 57.4% trusted documentation with human oversight, and 49.5% anticipated positive effects on patient-provider interactions. Awareness of AI scribe use was low (28.3%). Males showed higher odds of favorable comfort (aOR = 1.13, 95% CI, 1.05-1.22, P = .001), trust (aOR = 1.21, 95% CI, 1.10-1.32, P < .001), and future use (aOR = 1.38, 95% CI, 1.27-1.51, P < .001). Chronic conditions showed higher odds of future use (aOR = 1.19, 95% CI, 1.08-1.32, P < .001), whereas poorer general health was associated with lower odds across all outcomes. Fewer emergency room/urgent care visits, lower education, and income levels were associated with less favorable attitudes across outcomes. Higher digital health literacy (aOR = 1.03-1.04, all P < .001) and AI knowledge (aOR = 1.28-1.37, all P < .001) showed associations with higher odds across outcomes; privacy concerns were linked to lower odds (eg, future use: aOR = 0.65, 95% CI, 0.63-0.68, P < .001). DISCUSSION: Findings reveal a paradox-patients expressed conditional trust and comfort yet remained reluctant to adopt AI scribes, with privacy concerns and low awareness as key barriers. CONCLUSION: Targeted interventions addressing digital literacy, privacy safeguards, and clinician-patient communication about AI scribes are needed before widespread adoption.
Listening to the note: clinician perspectives on ambient artificial intelligence scribes in medical documentation.
Van Tiem J, Cramer E, Iverson C … +7 more , Kennelty K, Andrys N, Lee J, Knake L, Misurac J, Blum J, Reisinger HS
J Am Med Inform Assoc · 2026 Feb · PMID 41340524 · Full text
OBJECTIVES: To qualitatively characterize barriers and facilitators to implementing and using an ambient scribe across a large academic medical center, as well as how ambient transcription reshapes clinicians' perception...
OBJECTIVES: To qualitatively characterize barriers and facilitators to implementing and using an ambient scribe across a large academic medical center, as well as how ambient transcription reshapes clinicians' perceptions of their work. MATERIALS AND METHODS: We conducted semistructured interviews with clinicians who participated in an ambient scribe pilot (n = 8) and the initial enterprise rollout (n = 16). We sought heterogeneity by specialty, note volume, burnout, and prior time-in-notes. Interviews (26-60 min) were recorded, transcribed, and analyzed thematically using a naturalistic, ethnographic approach informed by broad implementation considerations, and an analytic lens treating note sections as documentation "genres." RESULTS: Clinicians described feeling more present with patients and greater satisfaction during visits. Fictions included overlong or underspecified sections (eg, History of Present Illness vs Assessment & Plan), unfamiliar formatting, and a perceived loss of "voice." Participants discussed how they used documentation to personalize practice, demonstrate expertise, manage impressions with colleagues and supervisors, and communicate sensitive findings-activities not fully captured by efficiency metrics. Inpatient and procedure-heavy contexts reported limited benefit where documentation was already highly standardized. DISCUSSION: Early ambient scribe implementation produced recognizable benefits, but introduced new work to reconcile AI-drafted text with local documentation genres and audience-specific communication. Tailored prompts, onboarding, and peer support may reduce the need to revise artificial intelligence (AI)-generated text. CONCLUSION: Ambient scribe adoption can enhance patient interactions and perceived efficiency while reshaping how clinicians express voice and expertise in notes. Implementation strategies attentive to documentation genre and audience may help align ambient scribe outputs with clinical communication needs.
Scalable confounding adjustment in real-world evidence: benchmarking data-adaptive and investigator-specified strategies in a large-scale trial emulation study.
Weckstein AR, Wang SV, Wyss R … +1 more , Schneeweiss S
J Am Med Inform Assoc · 2026 Mar · PMID 41338229 · Full text
OBJECTIVES: Real-world evidence (RWE) increasingly informs clinical decisions, yet manual adjustment for confounding limits scalability. Data-adaptive (DA) algorithms for high-dimensional proxy adjustment show promise bu...
OBJECTIVES: Real-world evidence (RWE) increasingly informs clinical decisions, yet manual adjustment for confounding limits scalability. Data-adaptive (DA) algorithms for high-dimensional proxy adjustment show promise but have not been systematically compared to investigator-specified (IS) approaches across diverse treatment scenarios. We evaluated whether DA strategies perform comparably to manually curated IS models using claims-based emulations of 15 randomized trials from the RCT-DUPLICATE initiative. MATERIALS AND METHODS: We identified new-user cohorts for 15 trial emulations in Optum's de-identified Clinformatics Data Mart Database (2004-2023). Treatment effects were estimated using 3 adjustment strategies: (1) IS models with manually tailored covariates; (2) full-DA strategies using empirical features from semiautomated pipelines; and (3) hybrid-DA models incorporating both empirical and investigator-defined covariates. Agreement with RCT benchmarks was assessed via binary metrics and difference-in-differences. RESULTS: Outcome-adaptive LASSO achieved better RWE-RCT agreement than IS adjustment in 73% of full-DA and 87% of hybrid-DA emulations. Other DA methods considering feature associations with both treatment and outcome performed similarly well, while models tuned solely for treatment prediction performed poorly. Performance of IS vs DA strategies differed across emulated trials. DISCUSSION: Top DA algorithms matched manual IS models on average, but impact varied by emulation. Case studies illustrate the continued importance of subject-matter knowledge, particularly for complex treatment strategies. CONCLUSION: Data-adaptive algorithms show promise for scalable confounding adjustment in large-scale evidence systems and as augmentation tools for investigator-specified designs. Hybrid strategies combining algorithmic methods with investigator expertise offer the most reliable approach for individual causal questions.
Are asynchronous or synchronous clinical decision support more likely to change provider behavior? A case study in dementia.
Puster EM, Grout RW, Dexter PR … +3 more , Ben-Miled Z, Owora A, Boustani MA
J Am Med Inform Assoc · 2026 Feb · PMID 41317042 · Full text
OBJECTIVE: Describe the impact of synchronous vs asynchronous clinical decision support (CDS) on clinician behavior in a single-site randomized, controlled environment. MATERIALS AND METHODS: Mixed effects binomial logis...
OBJECTIVE: Describe the impact of synchronous vs asynchronous clinical decision support (CDS) on clinician behavior in a single-site randomized, controlled environment. MATERIALS AND METHODS: Mixed effects binomial logistic regression was used to compare the impact of synchronous against asynchronous messaging on neurology orders in a three-arm study. RESULTS: Asynchronous messaging resulted in a significant increase in patient neurology orders for evaluation (Odds ratio, alert-only arm: 1.88; 95% CI: 1.39, 2.55; alert and questionnaire arm: 1.99; 95% CI: 1.52, 2.62). DISCUSSION: Alerts sometimes generate little action on the part of clinicians. In this case, asynchronous inbox messaging significantly increased neurology orders. CONCLUSION: Depending on context, asynchronous messaging may be superior to synchronous messaging when recommending a referral in an outpatient setting.
Determining optimal strategies for personalized atrial fibrillation treatment in intensive care unit patients using a deep learning-based causal inference approach: rhythm and/or rate control.
Kang MW, Ahn SY, Kang Y
J Am Med Inform Assoc · 2026 Mar · PMID 41317034 · Full text
OBJECTIVES: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients. Effective management of AF in this setting remains a subject of debate, with current guidelines often derived from outpatient studi...
OBJECTIVES: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients. Effective management of AF in this setting remains a subject of debate, with current guidelines often derived from outpatient studies. This study aims to evaluate the effectiveness of different AF management strategies-both, rhythm, rate, or no control-in reducing mortality in ICU patients using a deep learning-based causal inference model. MATERIALS AND METHODS: Data from the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV were utilized, encompassing ICU admissions with documented AF. Exposures included both rhythm and rate, only rhythm, and only rate, or no control. A deep learning-based causal inference model analyzed treatment effects. Additionally, the characteristics of patients who benefited more from rhythm control compared to rate control were identified using treatment effect sizes and multivariable logistic regression. RESULTS: The study population comprised 13 583 patients. Both rhythm and rate control, rhythm control-only, and rate control-only strategies significantly reduced in-hospital mortality compared to no control, with average treatment effects of -1.23% (-1.43% to -1.03%), -2.32% (-2.48% to -2.15%), and -9.11% (-9.29% to -8.93%), respectively. Rhythm control proved more effective than rate control in specific subgroups: older age, higher maximum heart rate, presence of new-onset AF, absence of hypertension, absence of diabetes, chronic liver disease, not having undergone heart surgery, and the use of vasopressor agents. CONCLUSION: Using a deep learning-based causal inference model, we quantified mortality reduction for each treatment strategy and identified the patient characteristics associated with the most favorable outcomes for each strategy.
Ensemble transfer learning for classifying physical examinations in GP consultation: a multi-model approach to human-object and human-to-human activity recognition.
Waheed M, Xiong H, Tong K … +1 more , Lau AYS
J Am Med Inform Assoc · 2026 Feb · PMID 41299889 · Full text
OBJECTIVES: This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep learning fusion model. The model distinguishes between two interaction typ...
OBJECTIVES: This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep learning fusion model. The model distinguishes between two interaction types: Human-Object Activities (HOA), such as blood pressure measurement, and Human-Human Activities (HHA), such as gland palpation. MATERIAL AND METHOD: A multi-component ensemble transfer learning framework was developed that integrates spatial and temporal feature analysis. The model comprises: (1) a CNN-LSTM module for spatial feature extraction and sequential modelling, (2) an ensemble of EfficientNet-B7, DenseNet-121, and Inception-v3 to capture diverse spatial representations, and (3) a fusion module that concatenates outputs from both streams, refined by an attention mechanism to prioritise salient features. Transfer learning was applied to fine-tune pre-trained networks on GP consultation video data. Model performance was evaluated using five-fold stratified video-level cross-validation, reporting mean ± SD for precision, recall, F1-score, specificity, Cohen's κ, and PR-AUC. RESULTS: The fusion model achieved robust overall performance, with a precision of 92.1 ± 1.4%, recall of 89.9 ± 1.8%, F1-score of 90.9 ± 1.5%, specificity of 93.1 ± 1.3%, Cohen's κ of 0.90 ± 0.02, and PR-AUC of 0.935 ± 0.02. It consistently outperformed ten state-of-the-art baselines, while ablation analysis showed F1-score improvements of 17% over CNN-LSTM and 16% over the ensemble model, confirming the benefit of combining spatial and temporal analysis. CONCLUSION: The proposed fusion framework accurately recognises physical examinations in GP consultations and supports future telehealth and diagnostic research.
Evaluation of trajectory analysis for disease risk assessment: a scoping review.
Pollington F, Denaxas SC, Li K … +3 more , Thygesen JH, Lyratzopoulos G, White B
J Am Med Inform Assoc · 2026 Feb · PMID 41293989 · Full text
OBJECTIVES: Increasingly, structured longitudinal electronic health records (EHRs) are being harnessed to predict risk of having present but as yet undetected disease by analyzing "patient trajectories." Trajectory studi...
OBJECTIVES: Increasingly, structured longitudinal electronic health records (EHRs) are being harnessed to predict risk of having present but as yet undetected disease by analyzing "patient trajectories." Trajectory studies explore clinical event associations, characterize disease trajectories, and enhance risk prediction. This scoping review assesses study characteristics and objectives, identifies model types, and appraises model performance and reporting. MATERIALS AND METHODS: We conducted a scoping review, focused on a PubMed and Web of Science search for studies using temporal EHR sequences to identify disease signatures or predict disease presence. RESULTS: We identified 62 studies. Statistical methods, such as testing temporal associations were primarily used for clustering, while deep learning models focused on outcome prediction. Sixty-five percent of studies used secondary care data, with the most common outcomes being disease agnostic (39%) and cardiovascular disease (20%). Forty-eight studies aimed at risk prediction, with 50% comparing trajectory-based models to static baselines. Among 31 studies reporting area under the curve (AUC), temporal models showed moderate performance gains (relative/absolute AUC: median 5.7%/4.2%, range -2.6% to 58.9%/-2.3% to 33.0%). DISCUSSION: Trajectory studies are increasing in volume, but lacking in application to primary care datasets, a diverse set of diseases, external validation, and consideration of clinical applicability. CONCLUSION: While the field's nascency hinders firm conclusions, there are promising results across a range of model types and objectives. Continued research from diverse perspectives will help determine whether this growing field can deliver meaningful clinical benefits.
Comparison of clinical outcomes of sepsis patients in two county emergency departments using systemic inflammatory response syndrome versus Epic's proprietary severe sepsis alert.
Ostermayer DG, Braunheim B, Mehta A … +3 more , Ward J, Andrabi S, Sirajuddin AM
J Am Med Inform Assoc · 2026 Feb · PMID 41293980 · Full text
OBJECTIVES: To compare the clinical outcomes of sepsis patients when an augmented systemic inflammatory response syndrome (SIRS+) and the Epic sepsis predictive model version 1 (ESPMv1) alert were active in the emergency...
OBJECTIVES: To compare the clinical outcomes of sepsis patients when an augmented systemic inflammatory response syndrome (SIRS+) and the Epic sepsis predictive model version 1 (ESPMv1) alert were active in the emergency department at two county hospitals. MATERIALS AND METHODS: This retrospective study from January 2018 to January 2024, evaluated the clinical outcomes of 881 201 emergency department patients of which 29 852 patients were septic. From January 2018 to June 2022 sepsis notices were presented to physicians based on a SIRS plus organ dysfunction criteria and from December 2022 to January 2024 using the ESPMv1 alert. Sepsis was defined according to the Sepsis-3 definition with the onset of sepsis defined as two or more points on the Sequential Organ Function Assessment (SOFA) score in patients where physicians ordered at least one blood culture and antibiotic. SIRS+ alerting occurred when 2 of 4 criteria was reached plus one organ dysfunction measurement. The ESPMv1 alerting occurred at the Epic recommended threshold of six. We evaluated the times to blood cultures, antibiotics and ICU admission requests, and in-hospital death rates. RESULTS: SIRS+ alerts had a sensitivity of 14.25%, specificity 96.1%, positive predicative value (PPV) of 7.8% and negative predative value (NPV) of 98%. The ESPMv1 had a sensitivity of 15.6%, specificity 95.4%, positive predictive value of 8.1%, and negative predictive value of 98% for diagnosing sepsis. No statistical differences in time to antibiotics (5.1 vs 5.9 h), time to blood culture draws (3.6 vs 3.5 h) or time to ICU admission (10.4 vs 9.6 h) were observed. We did observe a difference in hospital death rates between the two time periods (11% vs 8%) but no statistical difference when adjusting for unvaccinated covid-19 (OR 0.95 [0.87-1.03]). DISCUSSION AND CONCLUSION: No statistically significant clinical differences or performance metrics were observed between SIRS+ based alerting and ESPMv1 alerts in an undifferentiated emergency department population. Both alerting systems had similarly poor diagnostic characteristics.
Frameworks and methods.
Bakken S
J Am Med Inform Assoc · 2025 Dec · PMID 41289523 · Full text
Abstract loading — click title to view on PubMed.
Mapping social media analytics in firearm injury exposure research: a scoping review.
Flynch M, He L, Badurak M … +1 more , Bakken S
J Am Med Inform Assoc · 2026 Feb · PMID 41285155 · Full text
OBJECTIVE: To examine how social media analytics have been applied in research on firearm injury exposure, with a focus on informatics approaches, analytical methodologies, and public health surveillance applications. MA...
OBJECTIVE: To examine how social media analytics have been applied in research on firearm injury exposure, with a focus on informatics approaches, analytical methodologies, and public health surveillance applications. MATERIALS AND METHODS: Following the PRISMA-ScR framework for scoping reviews, we systematically searched 5 databases (Web of Science, Scopus, PubMed, IEEE Xplore, ACM Digital Library) for studies published 2014-2025 that used social media analytics to investigate firearm injury exposure. The most recent search was conducted on February 18, 2025. Two independent reviewers screened studies using standardized criteria and extracted study characteristics via Covidence. Inter-rater reliability was found to be (Cohen's κ = 0.63). Of 742 initial records, 16 studies met the inclusion criteria. RESULTS: All included studies (16/16; 100%) used X (formerly Twitter) as the data source. Analytical approaches were natural language processing (n = 12), topic modeling (n = 8), and sentiment analysis (n = 6). Most studies were US-based (n = 12) and examined direct exposure (eg, witnessing shootings) and indirect exposure (eg, media coverage). Key informatics applications included sentiment detection, temporal discourse pattern analysis, and computational methods for evaluating community-level impacts. DISCUSSION: Findings revealed significant methodological homogeneity, with overreliance on X and limited use of longitudinal designs. Implementations of analytics methods varied considerably. Lack of platform diversity and standardization limits generalizability and integration with public health surveillance systems, impeding translation into policy and intervention. CONCLUSION: Social media analytics represent a promising tool for advancing public health informatics related to firearm injuries. Future research should employ platform diversity, longitudinal approaches, and computational metrics to enhance integration with health information systems.
Gaps in artificial intelligence research for rural health in the United States: a scoping review.
Brown KE, Davis SE
J Am Med Inform Assoc · 2026 Feb · PMID 41285144 · Full text
OBJECTIVE: Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers in the US. There are concerns, however, that the promise of AI may not be realized in rural communities. This scoping...
OBJECTIVE: Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers in the US. There are concerns, however, that the promise of AI may not be realized in rural communities. This scoping review aims to determine the extent of AI research in the rural US. MATERIALS AND METHODS: We conducted a scoping review following the PRISMA guidelines. We included peer-reviewed, original research studies indexed in PubMed, Embase, and WebOfScience after January 1, 2010 and through April 29, 2025. Studies were required to discuss the development, implementation, or evaluation of AI tools in rural US healthcare, including frameworks that help facilitate AI development (eg, data warehouses). RESULTS: Our search strategy found 26 studies meeting inclusion criteria after full text screening with 14 papers discussing predictive AI models and 12 papers discussing data or research infrastructure. AI models most often targeted resource allocation and distribution. Few studies explored model deployment and impact. Half noted the lack of data and analytic resources as a limitation. None of the studies discussed examples of generative AI being trained, evaluated, or deployed in a rural setting. DISCUSSION: Practical limitations may be influencing and limiting the types of AI models evaluated in the rural US. Validation of tools in the rural US was underwhelming. CONCLUSION: With few studies moving beyond AI model design and development stages, there are clear gaps in our understanding of how to reliably validate, deploy, and sustain AI models in rural settings to advance health in all communities.
A lossless one-shot distributed algorithm for addressing heterogeneity in multi-site generalized linear models.
Zhang B, Wu Q, Reps JM … +11 more , Li L, Tong J, Lu Y, Zhang D, Ramirez-Anguita JM, Bian J, Brand MT, Falconer T, Mayer MA, Williams RD, Chen Y
J Am Med Inform Assoc · 2026 Mar · PMID 41259033 · Full text
OBJECTIVE: We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous...
OBJECTIVE: We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous multi-institutional data while relying solely on instituion-level summary information rather than patient-level data. MATERIALS AND METHODS: Generalized Linear Models (GLMs) are widely used in medical research for analyzing diverse outcome types. In multi-institution settings, we demonstrated that the global likelihood can be reconstructed using only institution-level summary statistics, enabling lossless estimation without accessing individual records. We validated COLA-GLM-H in two real-world studies: (1) an emulated U.S. pediatric centralized network (719,383 patients) evaluating long-term cardiovascular risks following COVID-19, and (2) an internationally decentralized network of 120,429 hospitalized patients from seven databases across three countries assessing risk factors for COVID-19 mortality. RESULTS: In the centralized network, COLA-GLM-H produced estimates identical to those from pooled analyses. In the decentralized setting, the algorithm effectively integrated heterogeneous data across multiple clinical institutions using a single communication round. CONCLUSIONS: COLA-GLM-H provides a lossless, communication-efficient, and computation-efficient solution for multi-institutional research using only institution-level summary data. It accounts for between-institution heterogeneity and supports all outcome types within the exponential family, enabling secure, scalable, and accurate analysis in collaborative clinical research.
Retraction and replacement of: Electronic connectivity between hospital pairs: impact on emergency department-related utilization.
J Am Med Inform Assoc · 2025 Nov · PMID 41252586 · Full text
Abstract loading — click title to view on PubMed.
Bias, artificial intelligence, and humans.
Bakken S
J Am Med Inform Assoc · 2025 Nov · PMID 41252585 · Full text
Abstract loading — click title to view on PubMed.
Electronic connectivity between hospital pairs: impact on emergency department-related utilization.
Adler-Milstein J, Linden A, Hsia RY … +1 more , Everson J
J Am Med Inform Assoc · 2025 Nov · PMID 41252584 · Full text
OBJECTIVE: To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. MATER...
OBJECTIVE: To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. MATERIALS AND METHODS: We combined 2018 Medicare fee-for-service claims to identify beneficiaries with 2 ED encounters within 30 days, and Definitive Healthcare and AHA IT Supplement data to identify hospital participation in HIE networks (HIOs and EHR vendor networks). We determined whether the 2 encounters for the same beneficiary occurred at: the same organization, different organizations connected by HIE, or different organizations not connected by HIE. Outcomes were: (1) whether any repeat imaging occurred during the second ED visit; (2) for beneficiaries with a treat-and-release ED visit followed by a second ED visit, whether they were admitted to the hospital after the second visit; (3) for beneficiaries discharged from the hospital followed by an ED visit, whether they were admitted to the hospital. RESULTS: In adjusted mixed effects models, for the first two outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations; for the third outcome, those returning had higher utilization. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging or likelihood of admission following hospital discharge. HIE was associated with lower likelihood of hospital admission following a treat-and-release ED visit (1.83 percentage points [-3.44 to 0.21]). DISCUSSION: Differing utilization for beneficiaries returning to the same organization could reflect better access to information or other factors such as aligned incentives. CONCLUSION: HIE is not consistently associated with utilization outcomes reflecting more coordinated care in the ED setting.
Does it save me money? The economic impact of mobile health interventions on medical expenditure of diabetic patients.
Liu X, Varshney U, Li P
J Am Med Inform Assoc · 2026 Feb · PMID 41252182 · Full text
OBJECTIVE: This study evaluates the impact of self-management and support of m-health applications on medication adherence (MA) and the corresponding long-term medical expenditures among patients with Type 2 Diabetes (T2...
OBJECTIVE: This study evaluates the impact of self-management and support of m-health applications on medication adherence (MA) and the corresponding long-term medical expenditures among patients with Type 2 Diabetes (T2D), using an analytic framework generalizable to other chronic conditions. MATERIALS AND METHODS: A systematic review and meta-analysis of randomized controlled trials were conducted to estimate the synthesized effect of m-health interventions on MA. These results were integrated into a Markov state-transition model to simulate patient transitions among three adherence levels over a 10-year horizon. Medical expenditure data by adherence level were derived from the Medical Expenditure Panel Survey (MEPS). Monte Carlo simulation was applied to assess uncertainty and estimate individual- and population-level cost outcomes under baseline and intervention scenarios. RESULTS: The meta-analysis showed a significant positive effect of m-health on MA (standardized mean difference = 0.21, 95% CI: 0.14-0.28). Patients in the intervention scenario experienced an average cost reduction of $4400 over 10 years. At the population level, a cohort of 10 000 patients using m-health tools would yield projected direct medical cost savings of $44 million. DISCUSSION: This study demonstrates the potential of m-health interventions to improve patient behavior and generate substantial long-term cost savings. By linking behavioral health data to downstream cost outcomes, the study adds to the growing evidence base for informatics-driven population health strategies. CONCLUSION: Our study underscores the importance of integrating digital support tools into chronic disease care and informs policy decisions aimed at integrating health informatics innovations.
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